Merge branch 'master' of https://github.com/Microsoft/CNTK into amitaga/cntkv2Library
This commit is contained in:
Amit Agarwal
Коммит
37b6897e94
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@ -11,6 +11,13 @@
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#define __UNIX__
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#endif
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#ifdef _MSC_VER
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// TODO: thread_local is supported in VS2015. Remove this macro when we uprade to VS2015
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#define THREAD_LOCAL __declspec(thread)
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#else
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#define THREAD_LOCAL thread_local
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#endif
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// ===========================================================================
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// compiler differences
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// ===========================================================================
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@ -11,6 +11,7 @@
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#include <stdio.h>
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#include <vector>
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#include <algorithm>
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#include <random>
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namespace Microsoft { namespace MSR { namespace CNTK {
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@ -24,6 +25,31 @@ static inline size_t rand(const size_t begin, const size_t end)
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return begin + randno % (end - begin);
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}
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// Rand based on Mersenne Twister.
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// We use our own distribution in order to match baselines between different operating systems,
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// because uniform_distribution is not guranteed to provide the same numbers on different platforms.
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// TODO: Switching to Boost would eliminate this problem.
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static inline size_t RandMT(const size_t begin, const size_t end, std::mt19937_64& rng)
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{
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const size_t randomNumber = rng();
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return begin + randomNumber % (end - begin);
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}
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// Rand based on Mersenne Twister.
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// We use our own distribution in order to match baselines between different operating systems,
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// instead of using std::shuffle which uses unitform_distribution internally.
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// TODO: Switching to Boost would eliminate this problem.
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template <typename TVector>
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inline void RandomShuffleMT(TVector& v, std::mt19937_64& rng)
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{
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foreach_index(currentLocation, v)
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{
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// Pick a random location a location and swap with current
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const size_t randomLocation = RandMT(0, v.size(), rng);
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std::swap(v[currentLocation], v[randomLocation]);
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}
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}
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class RandomOrdering // note: NOT thread-safe at all
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{
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// constants for randomization
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@ -237,7 +237,7 @@ std::pair<size_t, size_t> TracingGPUMemoryAllocator::GetFreeAndTotalMemoryInMBs(
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// deviceId - the device on which the operation will take place
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void PrepareDevice(DEVICEID_TYPE deviceId)
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{
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static DEVICEID_TYPE currentDevice = DEVICEID_NOTYETDETERMINED;
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THREAD_LOCAL static DEVICEID_TYPE currentDevice = DEVICEID_NOTYETDETERMINED;
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// and if we last set the device to be this device we are good
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if (deviceId == currentDevice)
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return;
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@ -517,11 +517,11 @@ void HTKMLFReader<ElemType>::PrepareForTrainingOrTesting(const ConfigRecordType&
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m_lattices->setverbosity(m_verbosity);
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// now get the frame source. This has better randomization and doesn't create temp files
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bool minimizeReaderMemoryFootprint = readerConfig(L"minimizeReaderMemoryFootprint", true);
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m_frameSource.reset(new msra::dbn::minibatchutterancesourcemulti(infilesmulti, labelsmulti, m_featDims, m_labelDims,
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bool useMersenneTwisterRand = readerConfig(L"useMersenneTwisterRand", false);
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m_frameSource.reset(new msra::dbn::minibatchutterancesourcemulti(useMersenneTwisterRand, infilesmulti, labelsmulti, m_featDims, m_labelDims,
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numContextLeft, numContextRight, randomize,
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*m_lattices, m_latticeMap, m_frameMode,
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minimizeReaderMemoryFootprint, m_expandToUtt));
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m_expandToUtt));
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m_frameSource->setverbosity(m_verbosity);
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}
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else if (EqualCI(readMethod, L"rollingWindow"))
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@ -12,7 +12,8 @@
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#include "latticearchive.h" // for reading HTK phoneme lattices (MMI training)
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#include "minibatchsourcehelpers.h"
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#include "minibatchiterator.h"
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#include "unordered_set"
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#include <unordered_set>
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#include <random>
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namespace msra { namespace dbn {
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@ -38,6 +39,10 @@ class minibatchutterancesourcemulti : public minibatchsource
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// const std::vector<unique_ptr<latticesource>> &lattices;
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const latticesource &lattices;
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// Flag indicating whether to use Mersenne Twister random generator.
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bool m_useMersenneTwister;
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std::mt19937_64 m_rng;
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// std::vector<latticesource> lattices;
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// word-level transcripts (for MMI mode when adding best path to lattices)
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const map<wstring, msra::lattices::lattice::htkmlfwordsequence> &allwordtranscripts; // (used for getting word-level transcripts)
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@ -413,6 +418,7 @@ class minibatchutterancesourcemulti : public minibatchsource
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// When true we use a rolling window of randomized framerefs to minimize memory
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// footprint, instead of using a large vector listing all frames in the training corpus
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// Functionally, the 2 methods are identical.
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// When it is true, we also use Mersenne Twister random generator for randomization.
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const bool m_minimizeMemoryFootprint;
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// [globalt-sweepts] -> (chunk, utt, frame) lookup table for randomized frames --this can be REALLY big!
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@ -429,6 +435,10 @@ class minibatchutterancesourcemulti : public minibatchsource
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size_t m_currentRangeEndChunkIdx;
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size_t m_nextFramePosNotYetRandomized;
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// If m_minimizeMemoryFootprint is true, Mersenne Twister is used for randomization
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// because rand has problems in distributed case.
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std::mt19937_64 m_rng;
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public:
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framerandomizer(const std::vector<std::vector<chunk>>& randomizedChunks, bool minimizeMemoryFootprint)
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: m_randomizedChunks(randomizedChunks), m_minimizeMemoryFootprint(minimizeMemoryFootprint), m_currentRangeBeginChunkIdx(0), m_currentRangeEndChunkIdx(0), m_nextFramePosNotYetRandomized(0)
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@ -496,7 +506,9 @@ class minibatchutterancesourcemulti : public minibatchsource
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for (;;) // (randomization retry loop)
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{
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size_t tswap = Microsoft::MSR::CNTK::rand(postbegin, postend); // random frame position within allowed range
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size_t tswap = m_minimizeMemoryFootprint ?
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Microsoft::MSR::CNTK::RandMT(postbegin, postend, m_rng) :
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Microsoft::MSR::CNTK::rand(postbegin, postend); // random frame position within allowed range
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// We want to swap 't' to 'tswap' and 'tswap' to 't'.
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// - Both may have been swapped before.
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// - Both must stay within the randomization window of their respective position.
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@ -542,11 +554,11 @@ class minibatchutterancesourcemulti : public minibatchsource
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void reset(unsigned int randSeed)
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{
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srand(randSeed);
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size_t sweepts = m_randomizedChunks[0][0].globalts;
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size_t totalFrames = m_randomizedChunks[0].back().globalte() - sweepts;
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if (m_minimizeMemoryFootprint)
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{
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m_rng.seed(randSeed);
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m_randomizedframerefsWindow.clear();
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m_currentRangeBeginChunkIdx = m_randomizedChunks[0][0].windowbegin;
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m_currentRangeEndChunkIdx = m_currentRangeBeginChunkIdx;
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@ -554,6 +566,7 @@ class minibatchutterancesourcemulti : public minibatchsource
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}
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else
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{
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srand(randSeed + 1);
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if (m_randomizedframerefs.size() != totalFrames)
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m_randomizedframerefs.resize(totalFrames);
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@ -866,10 +879,11 @@ public:
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// constructor
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// Pass empty labels to denote unsupervised training (so getbatch() will not return uids).
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// This mode requires utterances with time stamps.
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minibatchutterancesourcemulti(const std::vector<std::vector<wstring>> &infiles, const std::vector<map<wstring, std::vector<msra::asr::htkmlfentry>>> &labels,
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minibatchutterancesourcemulti(bool useMersenneTwister, const std::vector<std::vector<wstring>> &infiles, const std::vector<map<wstring, std::vector<msra::asr::htkmlfentry>>> &labels,
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std::vector<size_t> vdim, std::vector<size_t> udim, std::vector<size_t> leftcontext, std::vector<size_t> rightcontext, size_t randomizationrange,
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const latticesource &lattices, const map<wstring, msra::lattices::lattice::htkmlfwordsequence> &allwordtranscripts, const bool framemode, bool minimizeMemoryFootprint, std::vector<bool> expandToUtt)
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: vdim(vdim), leftcontext(leftcontext), rightcontext(rightcontext), sampperiod(0), featdim(0), randomizationrange(randomizationrange), currentsweep(SIZE_MAX), lattices(lattices), allwordtranscripts(allwordtranscripts), framemode(framemode), chunksinram(0), timegetbatch(0), verbosity(2), m_generatePhoneBoundaries(!lattices.empty()), m_frameRandomizer(randomizedchunks, minimizeMemoryFootprint), expandToUtt(expandToUtt)
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const latticesource &lattices, const map<wstring, msra::lattices::lattice::htkmlfwordsequence> &allwordtranscripts, const bool framemode, std::vector<bool> expandToUtt)
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: vdim(vdim), leftcontext(leftcontext), rightcontext(rightcontext), sampperiod(0), featdim(0), randomizationrange(randomizationrange), currentsweep(SIZE_MAX), lattices(lattices), allwordtranscripts(allwordtranscripts), framemode(framemode), chunksinram(0), timegetbatch(0), verbosity(2), m_generatePhoneBoundaries(!lattices.empty()), m_frameRandomizer(randomizedchunks, useMersenneTwister), expandToUtt(expandToUtt),
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m_useMersenneTwister(useMersenneTwister)
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// [v-hansu] change framemode (lattices.empty()) into framemode (false) to run utterance mode without lattice
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// you also need to change another line, search : [v-hansu] comment out to run utterance mode without lattice
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{
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@ -1251,8 +1265,16 @@ private:
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randomizedchunkrefs[i].push_back(allchunks[i].begin() + j);
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assert(randomizedchunkrefs[i].size() == allchunks[i].size());
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// note that sincew randomshuffle() uses sweep as seed, this will keep the randomization common across all feature streams
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randomshuffle(randomizedchunkrefs[i], sweep); // bring into random order (with random seed depending on sweep)
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if (m_useMersenneTwister)
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{
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m_rng.seed((unsigned long)sweep);
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Microsoft::MSR::CNTK::RandomShuffleMT(randomizedchunkrefs[i], m_rng); // bring into random order (with random seed depending on sweep)
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}
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else
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{
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// note that sincew randomshuffle() uses sweep as seed, this will keep the randomization common across all feature streams
|
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randomshuffle(randomizedchunkrefs[i], sweep); // bring into random order (with random seed depending on sweep)
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}
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}
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// place them onto the global timeline -> randomizedchunks[]
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@ -1348,7 +1370,7 @@ private:
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// check we got those setup right
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|
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// we now randomly shuffle randomizedutterancerefs[pos], while considering the constraints of what chunk range needs to be in memory
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srand((unsigned int) sweep + 1);
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m_useMersenneTwister ? m_rng.seed((unsigned long)sweep) : srand((unsigned int)sweep + 1);
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for (size_t i = 0; i < randomizedutterancerefs.size(); i++)
|
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{
|
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// get valid randomization range, expressed in chunks
|
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@ -1364,7 +1386,9 @@ private:
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for (;;)
|
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{
|
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// pick a random location
|
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const size_t j = Microsoft::MSR::CNTK::rand(posbegin, posend); // a random number within the window
|
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const size_t j = m_useMersenneTwister ?
|
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Microsoft::MSR::CNTK::RandMT(posbegin, posend, m_rng) :
|
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Microsoft::MSR::CNTK::rand(posbegin, posend); // a random number within the window
|
||||
if (i == j)
|
||||
break; // the random gods say "this one points to its original position"... nothing wrong about that, but better not try to swap
|
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|
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|
@ -1416,7 +1440,7 @@ private:
|
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}
|
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else // frame mode
|
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{
|
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m_frameRandomizer.reset((unsigned int)sweep + 1);
|
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m_frameRandomizer.reset((unsigned int)sweep);
|
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}
|
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|
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return sweep;
|
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|
|
|
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|
|
@ -105,7 +105,7 @@ void BlockRandomizer::PrepareNewSweepIfNeeded(size_t samplePosition)
|
|||
m_chunkRandomizer->Randomize((unsigned int)m_sweep);
|
||||
|
||||
// Resetting sequence randomizer.
|
||||
m_sequenceRandomizer->Reset(m_sweep + 1);
|
||||
m_sequenceRandomizer->Reset(m_sweep);
|
||||
m_lastSeenChunkId = CHUNKID_MAX;
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -10,25 +10,6 @@
|
|||
|
||||
namespace Microsoft { namespace MSR { namespace CNTK {
|
||||
|
||||
// NOTE: This is an old code, used for legacy randomization to make sure we preserve the same behavior for the tests.
|
||||
// TODO: Deprecate when the new randomizer is in place.
|
||||
template <typename TVector>
|
||||
void RandomShuffle(TVector& v, size_t randomSeed)
|
||||
{
|
||||
if (v.size() > RAND_MAX * static_cast<size_t>(RAND_MAX))
|
||||
{
|
||||
RuntimeError("RandomShuffle: too large set: need to change to different random generator!");
|
||||
}
|
||||
|
||||
srand(static_cast<unsigned int>(randomSeed));
|
||||
foreach_index(currentLocation, v)
|
||||
{
|
||||
// Pick a random location a location and swap with current
|
||||
const size_t randomLocation = rand(0, v.size());
|
||||
std::swap(v[currentLocation], v[randomLocation]);
|
||||
}
|
||||
}
|
||||
|
||||
ChunkRandomizer::ChunkRandomizer(IDataDeserializerPtr deserializer, size_t randomizationRangeInSamples, bool legacy) :
|
||||
m_deserializer(deserializer), m_legacy(legacy), m_randomizationRangeInSamples(randomizationRangeInSamples)
|
||||
{
|
||||
|
|
@ -52,15 +33,8 @@ namespace Microsoft { namespace MSR { namespace CNTK {
|
|||
randomizedChunkIndices.push_back(i);
|
||||
}
|
||||
|
||||
if (m_legacy)
|
||||
{
|
||||
RandomShuffle(randomizedChunkIndices, seed);
|
||||
}
|
||||
else
|
||||
{
|
||||
std::mt19937 m_rng(static_cast<int>(seed));
|
||||
std::shuffle(randomizedChunkIndices.begin(), randomizedChunkIndices.end(), m_rng);
|
||||
}
|
||||
m_rng.seed(seed);
|
||||
RandomShuffleMT(randomizedChunkIndices, m_rng);
|
||||
|
||||
// Place randomized chunks on the timeline
|
||||
m_randomizedChunks.clear();
|
||||
|
|
|
|||
|
|
@ -7,6 +7,7 @@
|
|||
|
||||
#include <vector>
|
||||
#include "DataDeserializer.h"
|
||||
#include <random>
|
||||
|
||||
namespace Microsoft { namespace MSR { namespace CNTK {
|
||||
|
||||
|
|
@ -68,6 +69,8 @@ namespace Microsoft { namespace MSR { namespace CNTK {
|
|||
bool m_legacy;
|
||||
// Randomization range in samples.
|
||||
size_t m_randomizationRangeInSamples;
|
||||
|
||||
std::mt19937_64 m_rng;
|
||||
};
|
||||
|
||||
typedef std::shared_ptr<ChunkRandomizer> ChunkRandomizerPtr;
|
||||
|
|
|
|||
|
|
@ -45,7 +45,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
|
|||
// Resets the current sweep according to the randomization seed provided.
|
||||
void SequenceRandomizer::Reset(size_t randSeed)
|
||||
{
|
||||
srand((unsigned int)randSeed);
|
||||
m_rng.seed((unsigned long)randSeed);
|
||||
|
||||
m_sequenceWindow.clear();
|
||||
m_chunkWindow.clear();
|
||||
|
|
@ -197,7 +197,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
|
|||
for (;;)
|
||||
{
|
||||
// Pick a sequence position from [posBegin, posEnd)
|
||||
const size_t j = rand(posBegin, posEnd);
|
||||
const size_t j = RandMT(posBegin, posEnd, m_rng);
|
||||
|
||||
// Pick up j sequence.
|
||||
ChunkIdType jChunkIndex = GetChunkIndexForSequencePosition(j);
|
||||
|
|
|
|||
|
|
@ -11,6 +11,7 @@
|
|||
#include "DataDeserializer.h"
|
||||
#include "ChunkRandomizer.h"
|
||||
#include <deque>
|
||||
#include <random>
|
||||
|
||||
namespace Microsoft { namespace MSR { namespace CNTK {
|
||||
|
||||
|
|
@ -164,6 +165,8 @@ private:
|
|||
|
||||
// General configuration
|
||||
int m_verbosity;
|
||||
|
||||
std::mt19937_64 m_rng;
|
||||
};
|
||||
|
||||
typedef std::shared_ptr<SequenceRandomizer> SequenceRandomizerPtr;
|
||||
|
|
|
|||
|
|
@ -1,71 +1,103 @@
|
|||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/01_OneHidden/../../../../../../../Examples/Image/MNIST/Config/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/Config/01_OneHidden.cntk currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/01_OneHidden/../../../../../../../Examples/Image/MNIST/Config OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 264172964 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/01_OneHidden/../../../../../../Examples/Image/MNIST/Config/01_OneHidden.cntk currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/01_OneHidden/../../../../../../Examples/Image/MNIST/Config OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 13 2016 14:50:25
|
||||
Last modified date: Thu May 12 14:00:37 2016
|
||||
Built time: Aug 16 2016 09:41:56
|
||||
Last modified date: Fri Aug 12 07:32:43 2016
|
||||
Build type: release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: no
|
||||
Math lib: acml
|
||||
Math lib: mkl
|
||||
CUDA_PATH: /usr/local/cuda-7.5
|
||||
CUB_PATH: /usr/local/cub-1.4.1
|
||||
CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
Built by philly on d8dc82703b0f
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by philly on f67b30a647de
|
||||
Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to /tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData
|
||||
05/13/2016 15:10:02: -------------------------------------------------------------------
|
||||
05/13/2016 15:10:02: Build info:
|
||||
Changed current directory to /tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData
|
||||
08/16/2016 10:49:43: -------------------------------------------------------------------
|
||||
08/16/2016 10:49:43: Build info:
|
||||
|
||||
05/13/2016 15:10:02: Built time: May 13 2016 14:50:25
|
||||
05/13/2016 15:10:02: Last modified date: Thu May 12 14:00:37 2016
|
||||
05/13/2016 15:10:02: Build type: release
|
||||
05/13/2016 15:10:02: Build target: GPU
|
||||
05/13/2016 15:10:02: With 1bit-SGD: no
|
||||
05/13/2016 15:10:02: Math lib: acml
|
||||
05/13/2016 15:10:02: CUDA_PATH: /usr/local/cuda-7.5
|
||||
05/13/2016 15:10:02: CUB_PATH: /usr/local/cub-1.4.1
|
||||
05/13/2016 15:10:02: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
05/13/2016 15:10:02: Build Branch: HEAD
|
||||
05/13/2016 15:10:02: Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
05/13/2016 15:10:02: Built by philly on d8dc82703b0f
|
||||
05/13/2016 15:10:02: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
05/13/2016 15:10:02: -------------------------------------------------------------------
|
||||
08/16/2016 10:49:43: Built time: Aug 16 2016 09:41:56
|
||||
08/16/2016 10:49:43: Last modified date: Fri Aug 12 07:32:43 2016
|
||||
08/16/2016 10:49:43: Build type: release
|
||||
08/16/2016 10:49:43: Build target: GPU
|
||||
08/16/2016 10:49:43: With 1bit-SGD: no
|
||||
08/16/2016 10:49:43: Math lib: mkl
|
||||
08/16/2016 10:49:43: CUDA_PATH: /usr/local/cuda-7.5
|
||||
08/16/2016 10:49:43: CUB_PATH: /usr/local/cub-1.4.1
|
||||
08/16/2016 10:49:43: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
08/16/2016 10:49:43: Build Branch: HEAD
|
||||
08/16/2016 10:49:43: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 10:49:43: Built by philly on f67b30a647de
|
||||
08/16/2016 10:49:43: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
08/16/2016 10:49:43: -------------------------------------------------------------------
|
||||
08/16/2016 10:49:43: -------------------------------------------------------------------
|
||||
08/16/2016 10:49:43: GPU info:
|
||||
|
||||
05/13/2016 15:10:02: Running on localhost at 2016/05/13 15:10:02
|
||||
05/13/2016 15:10:02: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/01_OneHidden/../../../../../../../Examples/Image/MNIST/Config/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/Config/01_OneHidden.cntk currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/01_OneHidden/../../../../../../../Examples/Image/MNIST/Config OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
08/16/2016 10:49:43: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:49:43: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:49:43: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:49:43: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:49:43: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 10:49:43: Running on localhost at 2016/08/16 10:49:43
|
||||
08/16/2016 10:49:43: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/01_OneHidden/../../../../../../Examples/Image/MNIST/Config/01_OneHidden.cntk currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/01_OneHidden/../../../../../../Examples/Image/MNIST/Config OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
|
||||
|
||||
|
||||
05/13/2016 15:10:02: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 15:10:02: RootDir = ".."
|
||||
ConfigDir = "$RootDir$/Config"
|
||||
DataDir = "$RootDir$/Data"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
ModelDir = "$OutputDir$/Models"
|
||||
08/16/2016 10:49:43: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:49:43: rootDir = ".."
|
||||
configDir = "$rootDir$/Config"
|
||||
dataDir = "$rootDir$/Data"
|
||||
outputDir = "$rootDir$/Output"
|
||||
modelDir = "$outputDir$/Models"
|
||||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
command = train:test
|
||||
precision = "float"
|
||||
modelPath = "$ModelDir$/01_OneHidden"
|
||||
ndlMacros = "$ConfigDir$/Macros.ndl"
|
||||
traceLevel=1
|
||||
numMBsToShowResult=500
|
||||
initOnCPUOnly=true
|
||||
modelPath = "$modelDir$/01_OneHidden"
|
||||
traceLevel = 1
|
||||
numMBsToShowResult = 500
|
||||
train = [
|
||||
action = "train"
|
||||
BrainScriptNetworkBuilder_disabled = [
|
||||
include "Shared.bs"
|
||||
featDim = 28 * 28
|
||||
labelDim = 10
|
||||
features = Input (featDim)
|
||||
featScaled = Constant (1.0 / 256.0) .* features
|
||||
labels = Input (labelDim)
|
||||
hiddenDim = 200
|
||||
h1 = DNNSigmoidLayer (featDim, hiddenDim, featScaled, 1)
|
||||
z = DNNLayer (hiddenDim, labelDim, h1, 1)
|
||||
ce = CrossEntropyWithSoftmax (labels, z)
|
||||
errs = ErrorPrediction (labels, z)
|
||||
top5Errs = ErrorPrediction (labels, z, topN=5)
|
||||
featureNodes = (features)
|
||||
labelNodes = (labels)
|
||||
criterionNodes = (ce)
|
||||
evaluationNodes = (errs)
|
||||
outputNodes = (z)
|
||||
]
|
||||
NDLNetworkBuilder = [
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly = true
|
||||
ndlMacros = "$configDir$/Macros.ndl"
|
||||
networkDescription = "$ConfigDir$/01_OneHidden.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 60000
|
||||
minibatchSize = 32
|
||||
learningRatesPerMB = 0.1
|
||||
momentumPerMB = 0
|
||||
learningRatesPerSample = 0.003125
|
||||
momentumAsTimeConstant = 0
|
||||
maxEpochs = 30
|
||||
]
|
||||
reader = [
|
||||
|
|
@ -85,7 +117,8 @@ train = [
|
|||
]
|
||||
test = [
|
||||
action = "test"
|
||||
minibatchSize = 16
|
||||
minibatchSize = 1024
|
||||
evalNodeNames = ce:errs:top5Errs
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "$DataDir$/Test-28x28_cntk_text.txt"
|
||||
|
|
@ -101,48 +134,67 @@ test = [
|
|||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/01_OneHidden/../../../../../../../Examples/Image/MNIST/Config
|
||||
OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu
|
||||
currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/01_OneHidden/../../../../../../Examples/Image/MNIST/Config
|
||||
OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
train=[SGD=[maxEpochs=3]]
|
||||
imageLayout="cudnn"
|
||||
|
||||
05/13/2016 15:10:02: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:49:43: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 15:10:02: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 15:10:02: RootDir = ".."
|
||||
ConfigDir = "../Config"
|
||||
DataDir = "../Data"
|
||||
OutputDir = "../Output"
|
||||
ModelDir = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/Models"
|
||||
08/16/2016 10:49:43: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:49:43: rootDir = ".."
|
||||
configDir = "../Config"
|
||||
dataDir = "../Data"
|
||||
outputDir = "../Output"
|
||||
modelDir = "/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/Models"
|
||||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
command = train:test
|
||||
precision = "float"
|
||||
modelPath = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/Models/01_OneHidden"
|
||||
ndlMacros = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/01_OneHidden/../../../../../../../Examples/Image/MNIST/Config/Macros.ndl"
|
||||
traceLevel=1
|
||||
numMBsToShowResult=500
|
||||
initOnCPUOnly=true
|
||||
modelPath = "/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/Models/01_OneHidden"
|
||||
traceLevel = 1
|
||||
numMBsToShowResult = 500
|
||||
train = [
|
||||
action = "train"
|
||||
BrainScriptNetworkBuilder_disabled = [
|
||||
include "Shared.bs"
|
||||
featDim = 28 * 28
|
||||
labelDim = 10
|
||||
features = Input (featDim)
|
||||
featScaled = Constant (1.0 / 256.0) .* features
|
||||
labels = Input (labelDim)
|
||||
hiddenDim = 200
|
||||
h1 = DNNSigmoidLayer (featDim, hiddenDim, featScaled, 1)
|
||||
z = DNNLayer (hiddenDim, labelDim, h1, 1)
|
||||
ce = CrossEntropyWithSoftmax (labels, z)
|
||||
errs = ErrorPrediction (labels, z)
|
||||
top5Errs = ErrorPrediction (labels, z, topN=5)
|
||||
featureNodes = (features)
|
||||
labelNodes = (labels)
|
||||
criterionNodes = (ce)
|
||||
evaluationNodes = (errs)
|
||||
outputNodes = (z)
|
||||
]
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/01_OneHidden/../../../../../../../Examples/Image/MNIST/Config/01_OneHidden.ndl"
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly = true
|
||||
ndlMacros = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/01_OneHidden/../../../../../../Examples/Image/MNIST/Config/Macros.ndl"
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/01_OneHidden/../../../../../../Examples/Image/MNIST/Config/01_OneHidden.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 60000
|
||||
minibatchSize = 32
|
||||
learningRatesPerMB = 0.1
|
||||
momentumPerMB = 0
|
||||
learningRatesPerSample = 0.003125
|
||||
momentumAsTimeConstant = 0
|
||||
maxEpochs = 30
|
||||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData/Train-28x28_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData/Train-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
|
|
@ -157,10 +209,11 @@ train = [
|
|||
]
|
||||
test = [
|
||||
action = "test"
|
||||
minibatchSize = 16
|
||||
minibatchSize = 1024
|
||||
evalNodeNames = ce:errs:top5Errs
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData/Test-28x28_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData/Test-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
|
|
@ -173,40 +226,39 @@ test = [
|
|||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/01_OneHidden/../../../../../../../Examples/Image/MNIST/Config
|
||||
OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu
|
||||
currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/01_OneHidden/../../../../../../Examples/Image/MNIST/Config
|
||||
OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
train=[SGD=[maxEpochs=3]]
|
||||
imageLayout="cudnn"
|
||||
|
||||
05/13/2016 15:10:02: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:49:43: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 15:10:02: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:49:43: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: 01_OneHidden.cntk:command=train:test
|
||||
configparameters: 01_OneHidden.cntk:ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/01_OneHidden/../../../../../../../Examples/Image/MNIST/Config
|
||||
configparameters: 01_OneHidden.cntk:currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData
|
||||
configparameters: 01_OneHidden.cntk:DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData
|
||||
configparameters: 01_OneHidden.cntk:configDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/01_OneHidden/../../../../../../Examples/Image/MNIST/Config
|
||||
configparameters: 01_OneHidden.cntk:currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData
|
||||
configparameters: 01_OneHidden.cntk:dataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData
|
||||
configparameters: 01_OneHidden.cntk:deviceId=0
|
||||
configparameters: 01_OneHidden.cntk:imageLayout=cudnn
|
||||
configparameters: 01_OneHidden.cntk:initOnCPUOnly=true
|
||||
configparameters: 01_OneHidden.cntk:ModelDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/Models
|
||||
configparameters: 01_OneHidden.cntk:modelPath=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/Models/01_OneHidden
|
||||
configparameters: 01_OneHidden.cntk:ndlMacros=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/01_OneHidden/../../../../../../../Examples/Image/MNIST/Config/Macros.ndl
|
||||
configparameters: 01_OneHidden.cntk:modelDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/Models
|
||||
configparameters: 01_OneHidden.cntk:modelPath=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/Models/01_OneHidden
|
||||
configparameters: 01_OneHidden.cntk:numMBsToShowResult=500
|
||||
configparameters: 01_OneHidden.cntk:OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu
|
||||
configparameters: 01_OneHidden.cntk:outputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu
|
||||
configparameters: 01_OneHidden.cntk:precision=float
|
||||
configparameters: 01_OneHidden.cntk:RootDir=..
|
||||
configparameters: 01_OneHidden.cntk:RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu
|
||||
configparameters: 01_OneHidden.cntk:rootDir=..
|
||||
configparameters: 01_OneHidden.cntk:RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu
|
||||
configparameters: 01_OneHidden.cntk:test=[
|
||||
action = "test"
|
||||
minibatchSize = 16
|
||||
minibatchSize = 1024
|
||||
evalNodeNames = ce:errs:top5Errs
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData/Test-28x28_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData/Test-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
|
|
@ -224,19 +276,41 @@ configparameters: 01_OneHidden.cntk:timestamping=true
|
|||
configparameters: 01_OneHidden.cntk:traceLevel=1
|
||||
configparameters: 01_OneHidden.cntk:train=[
|
||||
action = "train"
|
||||
BrainScriptNetworkBuilder_disabled = [
|
||||
include "Shared.bs"
|
||||
featDim = 28 * 28
|
||||
labelDim = 10
|
||||
features = Input (featDim)
|
||||
featScaled = Constant (1.0 / 256.0) .* features
|
||||
labels = Input (labelDim)
|
||||
hiddenDim = 200
|
||||
h1 = DNNSigmoidLayer (featDim, hiddenDim, featScaled, 1)
|
||||
z = DNNLayer (hiddenDim, labelDim, h1, 1)
|
||||
ce = CrossEntropyWithSoftmax (labels, z)
|
||||
errs = ErrorPrediction (labels, z)
|
||||
top5Errs = ErrorPrediction (labels, z, topN=5)
|
||||
featureNodes = (features)
|
||||
labelNodes = (labels)
|
||||
criterionNodes = (ce)
|
||||
evaluationNodes = (errs)
|
||||
outputNodes = (z)
|
||||
]
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/01_OneHidden/../../../../../../../Examples/Image/MNIST/Config/01_OneHidden.ndl"
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly = true
|
||||
ndlMacros = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/01_OneHidden/../../../../../../Examples/Image/MNIST/Config/Macros.ndl"
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/01_OneHidden/../../../../../../Examples/Image/MNIST/Config/01_OneHidden.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 60000
|
||||
minibatchSize = 32
|
||||
learningRatesPerMB = 0.1
|
||||
momentumPerMB = 0
|
||||
learningRatesPerSample = 0.003125
|
||||
momentumAsTimeConstant = 0
|
||||
maxEpochs = 30
|
||||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData/Train-28x28_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/TestData/Train-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
|
|
@ -250,31 +324,45 @@ configparameters: 01_OneHidden.cntk:train=[
|
|||
]
|
||||
] [SGD=[maxEpochs=3]]
|
||||
|
||||
05/13/2016 15:10:02: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/13/2016 15:10:02: Commands: train test
|
||||
05/13/2016 15:10:02: Precision = "float"
|
||||
05/13/2016 15:10:02: CNTKModelPath: /tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/Models/01_OneHidden
|
||||
05/13/2016 15:10:02: CNTKCommandTrainInfo: train : 3
|
||||
05/13/2016 15:10:02: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
08/16/2016 10:49:43: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:49:43: Commands: train test
|
||||
08/16/2016 10:49:43: Precision = "float"
|
||||
08/16/2016 10:49:43: CNTKModelPath: /tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/Models/01_OneHidden
|
||||
08/16/2016 10:49:43: CNTKCommandTrainInfo: train : 3
|
||||
08/16/2016 10:49:43: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
|
||||
05/13/2016 15:10:02: ##############################################################################
|
||||
05/13/2016 15:10:02: # #
|
||||
05/13/2016 15:10:02: # Action "train" #
|
||||
05/13/2016 15:10:02: # #
|
||||
05/13/2016 15:10:02: ##############################################################################
|
||||
08/16/2016 10:49:43: ##############################################################################
|
||||
08/16/2016 10:49:43: # #
|
||||
08/16/2016 10:49:43: # Action "train" #
|
||||
08/16/2016 10:49:43: # #
|
||||
08/16/2016 10:49:43: ##############################################################################
|
||||
|
||||
05/13/2016 15:10:02: CNTKCommandTrainBegin: train
|
||||
08/16/2016 10:49:43: CNTKCommandTrainBegin: train
|
||||
NDLBuilder Using GPU 0
|
||||
|
||||
05/13/2016 15:10:02: Creating virgin network.
|
||||
08/16/2016 10:49:44: Creating virgin network.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[200 x 784] <- 0.000000.
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[200 x 1] <- 0.000000.
|
||||
Node 'ol.W' (LearnableParameter operation): Initializing Parameter[10 x 200] <- 0.000000.
|
||||
Node 'ol.b' (LearnableParameter operation): Initializing Parameter[10 x 1] <- 0.000000.
|
||||
Node 'unnamed89' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.000000.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.003906.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.003906.
|
||||
Node 'unnamed89' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 5.000000.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.003906.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[200 x 784] <- uniform(seed=1, range=0.050000*1.000000, onCPU=true).
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[200 x 1] <- uniform(seed=2, range=0.050000*1.000000, onCPU=true).
|
||||
Node 'ol.W' (LearnableParameter operation): Initializing Parameter[10 x 200] <- uniform(seed=3, range=0.050000*1.000000, onCPU=true).
|
||||
Node 'ol.b' (LearnableParameter operation): Initializing Parameter[10 x 1] <- uniform(seed=4, range=0.050000*1.000000, onCPU=true).
|
||||
|
||||
Post-processing network...
|
||||
|
||||
4 roots:
|
||||
ce = CrossEntropyWithSoftmax()
|
||||
err = ErrorPrediction()
|
||||
errTop1 = ErrorPrediction()
|
||||
errs = ErrorPrediction()
|
||||
ol.z = Plus()
|
||||
top5Errs = ErrorPrediction()
|
||||
|
||||
Validating network. 17 nodes to process in pass 1.
|
||||
|
||||
|
|
@ -292,9 +380,9 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 200], [200 x 1 x *] -> [10 x 1
|
|||
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
|
||||
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x 1 x *], [10 x 1] -> [10 x 1 x *]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
|
||||
Validating --> unnamed81 = LearnableParameter() : -> [1 x 1]
|
||||
Validating --> errTop1 = ErrorPrediction (labels, ol.z, unnamed81) : [10 x *], [10 x 1 x *], [1 x 1] -> [1]
|
||||
Validating --> errs = ErrorPrediction (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
|
||||
Validating --> unnamed89 = LearnableParameter() : -> [1 x 1]
|
||||
Validating --> top5Errs = ErrorPrediction (labels, ol.z, unnamed89) : [10 x *], [10 x 1 x *], [1 x 1] -> [1]
|
||||
|
||||
Validating network. 9 nodes to process in pass 2.
|
||||
|
||||
|
|
@ -307,92 +395,88 @@ Validating network, final pass.
|
|||
|
||||
Post-processing network complete.
|
||||
|
||||
05/13/2016 15:10:02: Created model with 17 nodes on GPU 0.
|
||||
08/16/2016 10:49:44: Created model with 17 nodes on GPU 0.
|
||||
|
||||
05/13/2016 15:10:02: Training criterion node(s):
|
||||
05/13/2016 15:10:02: ce = CrossEntropyWithSoftmax
|
||||
08/16/2016 10:49:44: Training criterion node(s):
|
||||
08/16/2016 10:49:44: ce = CrossEntropyWithSoftmax
|
||||
|
||||
05/13/2016 15:10:02: Evaluation criterion node(s):
|
||||
|
||||
05/13/2016 15:10:02: errTop1 = ErrorPrediction
|
||||
05/13/2016 15:10:02: err = ErrorPrediction
|
||||
08/16/2016 10:49:44: Evaluation criterion node(s):
|
||||
08/16/2016 10:49:44: top5Errs = ErrorPrediction
|
||||
08/16/2016 10:49:44: errs = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 27 matrices, 10 are shared as 5, and 17 are not shared.
|
||||
|
||||
(nil): {[err Gradient[1]] [errTop1 Gradient[1]] [featScale Gradient[1 x 1]] [featScaled Gradient[784 x 1 x *]] [features Gradient[784 x *]] [labels Gradient[10 x *]] [unnamed81 Gradient[1 x 1]] }
|
||||
0x1bde9d8: {[errTop1 Value[1]] }
|
||||
0x1bdeb98: {[err Value[1]] }
|
||||
0x1be1e38: {[features Value[784 x *]] }
|
||||
0x2447ab8: {[featScale Value[1 x 1]] }
|
||||
0x2448c28: {[labels Value[10 x *]] }
|
||||
0x2449368: {[h1.W Value[200 x 784]] }
|
||||
0x29577e8: {[h1.b Value[200 x 1]] }
|
||||
0x2958938: {[ol.W Value[10 x 200]] }
|
||||
0x2959808: {[ol.b Value[10 x 1]] }
|
||||
0x295b198: {[unnamed81 Value[1 x 1]] }
|
||||
0x295ece8: {[featScaled Value[784 x 1 x *]] }
|
||||
0x295ef48: {[ol.z Value[10 x 1 x *]] }
|
||||
0x295f108: {[ce Value[1]] }
|
||||
0x29609d8: {[h1.t Value[200 x 1 x *]] }
|
||||
0x2960d88: {[h1.W Gradient[200 x 784]] [h1.z Value[200 x 1 x *]] }
|
||||
0x2960ee8: {[h1.t Gradient[200 x 1 x *]] [h1.y Value[200 x 1 x *]] }
|
||||
0x2961048: {[h1.z Gradient[200 x 1 x *]] [ol.t Value[10 x 1 x *]] }
|
||||
0x2961fa8: {[ce Gradient[1]] }
|
||||
0x2962168: {[ol.W Gradient[10 x 200]] [ol.z Gradient[10 x 1 x *]] }
|
||||
0x2962328: {[ol.t Gradient[10 x 1 x *]] }
|
||||
0x29624e8: {[ol.b Gradient[10 x 1]] }
|
||||
0x29626a8: {[h1.b Gradient[200 x 1]] [h1.y Gradient[200 x 1 x *]] }
|
||||
|
||||
05/13/2016 15:10:02: No PreCompute nodes found, skipping PreCompute step.
|
||||
|
||||
05/13/2016 15:10:02: Starting Epoch 1: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
|
||||
05/13/2016 15:10:02: Starting minibatch loop.
|
||||
05/13/2016 15:10:03: Epoch[ 1 of 3]-Minibatch[1-500, 26.67%]: ce = 1.30072449 * 16000; errs = 38.4688% * 16000; err = 0.38468750 * 16000; time = 1.2825s; samplesPerSecond = 12475.2
|
||||
05/13/2016 15:10:04: Epoch[ 1 of 3]-Minibatch[501-1000, 53.33%]: ce = 0.49017273 * 16000; errs = 13.0375% * 16000; err = 0.13037500 * 16000; time = 0.2861s; samplesPerSecond = 55923.1
|
||||
05/13/2016 15:10:04: Epoch[ 1 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.39744922 * 16000; errs = 11.1687% * 16000; err = 0.11168750 * 16000; time = 0.2889s; samplesPerSecond = 55389.2
|
||||
05/13/2016 15:10:04: Finished Epoch[ 1 of 3]: [Training] ce = 0.65501042 * 60000; errs = 18.685% * 60000; err = 0.18685000 * 60000; totalSamplesSeen = 60000; learningRatePerSample = 0.003125; epochTime=2.09125s
|
||||
05/13/2016 15:10:04: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/Models/01_OneHidden.1'
|
||||
|
||||
05/13/2016 15:10:04: Starting Epoch 2: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
|
||||
05/13/2016 15:10:04: Starting minibatch loop.
|
||||
05/13/2016 15:10:04: Epoch[ 2 of 3]-Minibatch[1-500, 26.67%]: ce = 0.32870679 * 16000; errs = 9.53125% * 16000; err = 0.09531250 * 16000; time = 0.2809s; samplesPerSecond = 56955.1
|
||||
05/13/2016 15:10:05: Epoch[ 2 of 3]-Minibatch[501-1000, 53.33%]: ce = 0.31809930 * 16000; errs = 9.20625% * 16000; err = 0.09206250 * 16000; time = 0.2862s; samplesPerSecond = 55905.9
|
||||
05/13/2016 15:10:05: Epoch[ 2 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.31002502 * 16000; errs = 8.7625% * 16000; err = 0.08762500 * 16000; time = 0.2946s; samplesPerSecond = 54305.4
|
||||
05/13/2016 15:10:05: Finished Epoch[ 2 of 3]: [Training] ce = 0.31494245 * 60000; errs = 9.09% * 60000; err = 0.09090000 * 60000; totalSamplesSeen = 120000; learningRatePerSample = 0.003125; epochTime=1.08973s
|
||||
05/13/2016 15:10:05: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/Models/01_OneHidden.2'
|
||||
|
||||
05/13/2016 15:10:06: Starting Epoch 3: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
|
||||
05/13/2016 15:10:06: Starting minibatch loop.
|
||||
05/13/2016 15:10:06: Epoch[ 3 of 3]-Minibatch[1-500, 26.67%]: ce = 0.28016867 * 16000; errs = 8.1875% * 16000; err = 0.08187500 * 16000; time = 0.2894s; samplesPerSecond = 55283.2
|
||||
05/13/2016 15:10:06: Epoch[ 3 of 3]-Minibatch[501-1000, 53.33%]: ce = 0.28037985 * 16000; errs = 8.09375% * 16000; err = 0.08093750 * 16000; time = 0.2860s; samplesPerSecond = 55935.8
|
||||
05/13/2016 15:10:06: Epoch[ 3 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.27621069 * 16000; errs = 8.2375% * 16000; err = 0.08237500 * 16000; time = 0.2791s; samplesPerSecond = 57323.8
|
||||
05/13/2016 15:10:07: Finished Epoch[ 3 of 3]: [Training] ce = 0.27476087 * 60000; errs = 8.01167% * 60000; err = 0.08011667 * 60000; totalSamplesSeen = 180000; learningRatePerSample = 0.003125; epochTime=1.07334s
|
||||
05/13/2016 15:10:07: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_01_OneHidden@release_gpu/Models/01_OneHidden'
|
||||
05/13/2016 15:10:07: CNTKCommandTrainEnd: train
|
||||
|
||||
05/13/2016 15:10:07: Action "train" complete.
|
||||
{ h1.W : [200 x 784] (gradient)
|
||||
h1.z : [200 x 1 x *] }
|
||||
{ h1.t : [200 x 1 x *] (gradient)
|
||||
h1.y : [200 x 1 x *] }
|
||||
{ h1.z : [200 x 1 x *] (gradient)
|
||||
ol.t : [10 x 1 x *] }
|
||||
{ ol.W : [10 x 200] (gradient)
|
||||
ol.z : [10 x 1 x *] (gradient) }
|
||||
{ h1.b : [200 x 1] (gradient)
|
||||
h1.y : [200 x 1 x *] (gradient) }
|
||||
|
||||
|
||||
05/13/2016 15:10:07: ##############################################################################
|
||||
05/13/2016 15:10:07: # #
|
||||
05/13/2016 15:10:07: # Action "test" #
|
||||
05/13/2016 15:10:07: # #
|
||||
05/13/2016 15:10:07: ##############################################################################
|
||||
08/16/2016 10:49:44: Training 159010 parameters in 4 out of 4 parameter tensors and 10 nodes with gradient:
|
||||
|
||||
08/16/2016 10:49:44: Node 'h1.W' (LearnableParameter operation) : [200 x 784]
|
||||
08/16/2016 10:49:44: Node 'h1.b' (LearnableParameter operation) : [200 x 1]
|
||||
08/16/2016 10:49:44: Node 'ol.W' (LearnableParameter operation) : [10 x 200]
|
||||
08/16/2016 10:49:44: Node 'ol.b' (LearnableParameter operation) : [10 x 1]
|
||||
|
||||
08/16/2016 10:49:44: No PreCompute nodes found, or all already computed. Skipping pre-computation step.
|
||||
|
||||
08/16/2016 10:49:44: Starting Epoch 1: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..60000] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:49:44: Starting minibatch loop.
|
||||
08/16/2016 10:49:45: Epoch[ 1 of 3]-Minibatch[ 1- 500, 26.67%]: ce = 1.30363245 * 16000; top5Errs = 9.406% * 16000; errs = 38.738% * 16000; time = 1.3753s; samplesPerSecond = 11634.0
|
||||
08/16/2016 10:49:45: Epoch[ 1 of 3]-Minibatch[ 501-1000, 53.33%]: ce = 0.50894336 * 16000; top5Errs = 1.012% * 16000; errs = 13.812% * 16000; time = 0.3462s; samplesPerSecond = 46222.2
|
||||
08/16/2016 10:49:46: Epoch[ 1 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.38341980 * 16000; top5Errs = 0.831% * 16000; errs = 10.675% * 16000; time = 0.3682s; samplesPerSecond = 43448.9
|
||||
08/16/2016 10:49:46: Finished Epoch[ 1 of 3]: [Training] ce = 0.65623809 * 60000; top5Errs = 3.097% * 60000; errs = 18.925% * 60000; totalSamplesSeen = 60000; learningRatePerSample = 0.003125; epochTime=2.3719s
|
||||
08/16/2016 10:49:46: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/Models/01_OneHidden.1'
|
||||
|
||||
08/16/2016 10:49:46: Starting Epoch 2: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [60000..120000] (first sequence at sample 60000), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:49:46: Starting minibatch loop.
|
||||
08/16/2016 10:49:46: Epoch[ 2 of 3]-Minibatch[ 1- 500, 26.67%]: ce = 0.33441489 * 16000; top5Errs = 0.544% * 16000; errs = 9.863% * 16000; time = 0.3524s; samplesPerSecond = 45406.7
|
||||
08/16/2016 10:49:47: Epoch[ 2 of 3]-Minibatch[ 501-1000, 53.33%]: ce = 0.30540207 * 16000; top5Errs = 0.494% * 16000; errs = 8.906% * 16000; time = 0.3500s; samplesPerSecond = 45712.5
|
||||
08/16/2016 10:49:47: Epoch[ 2 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.30959253 * 16000; top5Errs = 0.619% * 16000; errs = 9.137% * 16000; time = 0.3429s; samplesPerSecond = 46654.7
|
||||
08/16/2016 10:49:47: Finished Epoch[ 2 of 3]: [Training] ce = 0.31571312 * 60000; top5Errs = 0.568% * 60000; errs = 9.238% * 60000; totalSamplesSeen = 120000; learningRatePerSample = 0.003125; epochTime=1.31573s
|
||||
08/16/2016 10:49:47: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/Models/01_OneHidden.2'
|
||||
|
||||
08/16/2016 10:49:47: Starting Epoch 3: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [120000..180000] (first sequence at sample 120000), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:49:47: Starting minibatch loop.
|
||||
08/16/2016 10:49:48: Epoch[ 3 of 3]-Minibatch[ 1- 500, 26.67%]: ce = 0.29071878 * 16000; top5Errs = 0.513% * 16000; errs = 8.588% * 16000; time = 0.3318s; samplesPerSecond = 48219.3
|
||||
08/16/2016 10:49:48: Epoch[ 3 of 3]-Minibatch[ 501-1000, 53.33%]: ce = 0.27951419 * 16000; top5Errs = 0.494% * 16000; errs = 8.162% * 16000; time = 0.3306s; samplesPerSecond = 48394.7
|
||||
08/16/2016 10:49:48: Epoch[ 3 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.27461359 * 16000; top5Errs = 0.500% * 16000; errs = 7.906% * 16000; time = 0.3387s; samplesPerSecond = 47232.8
|
||||
08/16/2016 10:49:49: Finished Epoch[ 3 of 3]: [Training] ce = 0.27566595 * 60000; top5Errs = 0.467% * 60000; errs = 8.047% * 60000; totalSamplesSeen = 180000; learningRatePerSample = 0.003125; epochTime=1.26453s
|
||||
08/16/2016 10:49:49: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_01_OneHidden@release_gpu/Models/01_OneHidden'
|
||||
08/16/2016 10:49:49: CNTKCommandTrainEnd: train
|
||||
|
||||
08/16/2016 10:49:49: Action "train" complete.
|
||||
|
||||
|
||||
08/16/2016 10:49:49: ##############################################################################
|
||||
08/16/2016 10:49:49: # #
|
||||
08/16/2016 10:49:49: # Action "test" #
|
||||
08/16/2016 10:49:49: # #
|
||||
08/16/2016 10:49:49: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
||||
4 roots:
|
||||
3 roots:
|
||||
ce = CrossEntropyWithSoftmax()
|
||||
err = ErrorPrediction()
|
||||
errTop1 = ErrorPrediction()
|
||||
ol.z = Plus()
|
||||
errs = ErrorPrediction()
|
||||
top5Errs = ErrorPrediction()
|
||||
|
||||
Validating network. 17 nodes to process in pass 1.
|
||||
|
||||
|
|
@ -410,9 +494,9 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 200], [200 x 1 x *1] -> [10 x 1
|
|||
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
|
||||
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x 1 x *1], [10 x 1] -> [10 x 1 x *1]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
|
||||
Validating --> unnamed81 = LearnableParameter() : -> [1 x 1]
|
||||
Validating --> errTop1 = ErrorPrediction (labels, ol.z, unnamed81) : [10 x *1], [10 x 1 x *1], [1 x 1] -> [1]
|
||||
Validating --> errs = ErrorPrediction (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
|
||||
Validating --> unnamed89 = LearnableParameter() : -> [1 x 1]
|
||||
Validating --> top5Errs = ErrorPrediction (labels, ol.z, unnamed89) : [10 x *1], [10 x 1 x *1], [1 x 1] -> [1]
|
||||
|
||||
Validating network. 9 nodes to process in pass 2.
|
||||
|
||||
|
|
@ -425,34 +509,17 @@ Validating network, final pass.
|
|||
|
||||
Post-processing network complete.
|
||||
|
||||
evalNodeNames are not specified, using all the default evalnodes and training criterion nodes.
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 17 matrices, 0 are shared as 0, and 17 are not shared.
|
||||
|
||||
(nil): {[ce Gradient[1]] [err Gradient[1]] [errTop1 Gradient[1]] [featScale Gradient[1 x 1]] [featScaled Gradient[784 x 1 x *1]] [features Gradient[784 x *1]] [h1.W Gradient[200 x 784]] [h1.b Gradient[200 x 1]] [h1.t Gradient[200 x 1 x *1]] [h1.y Gradient[200 x 1 x *1]] [h1.z Gradient[200 x 1 x *1]] [labels Gradient[10 x *1]] [ol.W Gradient[10 x 200]] [ol.b Gradient[10 x 1]] [ol.t Gradient[10 x 1 x *1]] [ol.z Gradient[10 x 1 x *1]] [unnamed81 Gradient[1 x 1]] }
|
||||
0x7f0d2f269e18: {[labels Value[10 x *1]] }
|
||||
0x7f0d2f26a4c8: {[ol.b Value[10 x 1]] }
|
||||
0x7f0d2f26b578: {[ol.W Value[10 x 200]] }
|
||||
0x7f0d2f26bd18: {[unnamed81 Value[1 x 1]] }
|
||||
0x7f0d2f270658: {[errTop1 Value[1]] }
|
||||
0x7f0d2f270818: {[err Value[1]] }
|
||||
0x7f0d2f2709d8: {[ce Value[1]] }
|
||||
0x7f0d2f270f28: {[h1.t Value[200 x 1 x *1]] }
|
||||
0x7f0d2f2720b8: {[featScaled Value[784 x 1 x *1]] }
|
||||
0x7f0d2f272588: {[h1.z Value[200 x 1 x *1]] }
|
||||
0x7f0d2f272748: {[h1.y Value[200 x 1 x *1]] }
|
||||
0x7f0d2f272908: {[ol.t Value[10 x 1 x *1]] }
|
||||
0x7f0d2f272ac8: {[ol.z Value[10 x 1 x *1]] }
|
||||
0x7f0d35693b68: {[featScale Value[1 x 1]] }
|
||||
0x7f0d4bd02258: {[h1.b Value[200 x 1]] }
|
||||
0x7f0d4bd02f98: {[features Value[784 x *1]] }
|
||||
0x7f0d4bd03c78: {[h1.W Value[200 x 784]] }
|
||||
|
||||
05/13/2016 15:10:10: Final Results: Minibatch[1-10]: errs = 7.140% * 10000; top5Errs = 0.420% * 10000; ce = 0.25287636 * 10000; perplexity = 1.28772405
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
08/16/2016 10:49:49: Minibatch[1-10]: ce = 0.24784377 * 10000; errs = 7.110% * 10000; top5Errs = 0.420% * 10000
|
||||
08/16/2016 10:49:49: Final Results: Minibatch[1-10]: ce = 0.24784377 * 10000; perplexity = 1.28125974; errs = 7.110% * 10000; top5Errs = 0.420% * 10000
|
||||
|
||||
05/13/2016 15:10:10: Action "test" complete.
|
||||
08/16/2016 10:49:49: Action "test" complete.
|
||||
|
||||
05/13/2016 15:10:10: __COMPLETED__
|
||||
08/16/2016 10:49:49: __COMPLETED__
|
||||
|
|
@ -1,69 +1,103 @@
|
|||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/Config/01_OneHidden.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 268381192 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/01_OneHidden.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 13 2016 08:06:01
|
||||
Last modified date: Thu May 12 07:31:50 2016
|
||||
Built time: Aug 16 2016 02:54:53
|
||||
Last modified date: Fri Aug 12 05:31:21 2016
|
||||
Build type: Release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: no
|
||||
Math lib: mkl
|
||||
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
CUB_PATH: c:\src\cub-1.4.1
|
||||
CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by svcphil on Philly-Pool3
|
||||
Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData
|
||||
05/13/2016 08:15:51: -------------------------------------------------------------------
|
||||
05/13/2016 08:15:51: Build info:
|
||||
Changed current directory to C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData
|
||||
08/16/2016 03:00:44: -------------------------------------------------------------------
|
||||
08/16/2016 03:00:44: Build info:
|
||||
|
||||
05/13/2016 08:15:51: Built time: May 13 2016 08:06:01
|
||||
05/13/2016 08:15:51: Last modified date: Thu May 12 07:31:50 2016
|
||||
05/13/2016 08:15:51: Build type: Release
|
||||
05/13/2016 08:15:51: Build target: GPU
|
||||
05/13/2016 08:15:51: With 1bit-SGD: no
|
||||
05/13/2016 08:15:51: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
05/13/2016 08:15:51: CUB_PATH: c:\src\cub-1.4.1
|
||||
05/13/2016 08:15:51: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
05/13/2016 08:15:51: Build Branch: HEAD
|
||||
05/13/2016 08:15:51: Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
05/13/2016 08:15:51: Built by svcphil on Philly-Pool3
|
||||
05/13/2016 08:15:51: Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
05/13/2016 08:15:51: -------------------------------------------------------------------
|
||||
08/16/2016 03:00:44: Built time: Aug 16 2016 02:54:53
|
||||
08/16/2016 03:00:44: Last modified date: Fri Aug 12 05:31:21 2016
|
||||
08/16/2016 03:00:44: Build type: Release
|
||||
08/16/2016 03:00:44: Build target: GPU
|
||||
08/16/2016 03:00:44: With 1bit-SGD: no
|
||||
08/16/2016 03:00:44: Math lib: mkl
|
||||
08/16/2016 03:00:44: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
08/16/2016 03:00:44: CUB_PATH: c:\src\cub-1.4.1
|
||||
08/16/2016 03:00:44: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
08/16/2016 03:00:44: Build Branch: HEAD
|
||||
08/16/2016 03:00:44: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 03:00:44: Built by svcphil on Philly-Pool3
|
||||
08/16/2016 03:00:44: Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
08/16/2016 03:00:44: -------------------------------------------------------------------
|
||||
08/16/2016 03:00:46: -------------------------------------------------------------------
|
||||
08/16/2016 03:00:46: GPU info:
|
||||
|
||||
05/13/2016 08:15:51: Running on Philly-Pool2 at 2016/05/13 08:15:51
|
||||
05/13/2016 08:15:51: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/Config/01_OneHidden.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
08/16/2016 03:00:46: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:00:46: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:00:46: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:00:46: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:00:46: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 03:00:46: Running on DPHAIM-24 at 2016/08/16 03:00:46
|
||||
08/16/2016 03:00:46: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/01_OneHidden.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
|
||||
|
||||
|
||||
05/13/2016 08:15:51: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 08:15:51: RootDir = ".."
|
||||
ConfigDir = "$RootDir$/Config"
|
||||
DataDir = "$RootDir$/Data"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
ModelDir = "$OutputDir$/Models"
|
||||
08/16/2016 03:00:46: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:00:46: rootDir = ".."
|
||||
configDir = "$rootDir$/Config"
|
||||
dataDir = "$rootDir$/Data"
|
||||
outputDir = "$rootDir$/Output"
|
||||
modelDir = "$outputDir$/Models"
|
||||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
command = train:test
|
||||
precision = "float"
|
||||
modelPath = "$ModelDir$/01_OneHidden"
|
||||
ndlMacros = "$ConfigDir$/Macros.ndl"
|
||||
traceLevel=1
|
||||
numMBsToShowResult=500
|
||||
initOnCPUOnly=true
|
||||
modelPath = "$modelDir$/01_OneHidden"
|
||||
traceLevel = 1
|
||||
numMBsToShowResult = 500
|
||||
train = [
|
||||
action = "train"
|
||||
BrainScriptNetworkBuilder_disabled = [
|
||||
include "Shared.bs"
|
||||
featDim = 28 * 28
|
||||
labelDim = 10
|
||||
features = Input (featDim)
|
||||
featScaled = Constant (1.0 / 256.0) .* features
|
||||
labels = Input (labelDim)
|
||||
hiddenDim = 200
|
||||
h1 = DNNSigmoidLayer (featDim, hiddenDim, featScaled, 1)
|
||||
z = DNNLayer (hiddenDim, labelDim, h1, 1)
|
||||
ce = CrossEntropyWithSoftmax (labels, z)
|
||||
errs = ErrorPrediction (labels, z)
|
||||
top5Errs = ErrorPrediction (labels, z, topN=5)
|
||||
featureNodes = (features)
|
||||
labelNodes = (labels)
|
||||
criterionNodes = (ce)
|
||||
evaluationNodes = (errs)
|
||||
outputNodes = (z)
|
||||
]
|
||||
NDLNetworkBuilder = [
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly = true
|
||||
ndlMacros = "$configDir$/Macros.ndl"
|
||||
networkDescription = "$ConfigDir$/01_OneHidden.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 60000
|
||||
minibatchSize = 32
|
||||
learningRatesPerMB = 0.1
|
||||
momentumPerMB = 0
|
||||
learningRatesPerSample = 0.003125
|
||||
momentumAsTimeConstant = 0
|
||||
maxEpochs = 30
|
||||
]
|
||||
reader = [
|
||||
|
|
@ -83,7 +117,8 @@ train = [
|
|||
]
|
||||
test = [
|
||||
action = "test"
|
||||
minibatchSize = 16
|
||||
minibatchSize = 1024
|
||||
evalNodeNames = ce:errs:top5Errs
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "$DataDir$/Test-28x28_cntk_text.txt"
|
||||
|
|
@ -99,48 +134,67 @@ test = [
|
|||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
train=[SGD=[maxEpochs=3]]
|
||||
imageLayout="cudnn"
|
||||
|
||||
05/13/2016 08:15:51: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:00:46: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 08:15:51: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 08:15:51: RootDir = ".."
|
||||
ConfigDir = "../Config"
|
||||
DataDir = "../Data"
|
||||
OutputDir = "../Output"
|
||||
ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu/Models"
|
||||
08/16/2016 03:00:46: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:00:46: rootDir = ".."
|
||||
configDir = "../Config"
|
||||
dataDir = "../Data"
|
||||
outputDir = "../Output"
|
||||
modelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu/Models"
|
||||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
command = train:test
|
||||
precision = "float"
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu/Models/01_OneHidden"
|
||||
ndlMacros = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/Macros.ndl"
|
||||
traceLevel=1
|
||||
numMBsToShowResult=500
|
||||
initOnCPUOnly=true
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu/Models/01_OneHidden"
|
||||
traceLevel = 1
|
||||
numMBsToShowResult = 500
|
||||
train = [
|
||||
action = "train"
|
||||
BrainScriptNetworkBuilder_disabled = [
|
||||
include "Shared.bs"
|
||||
featDim = 28 * 28
|
||||
labelDim = 10
|
||||
features = Input (featDim)
|
||||
featScaled = Constant (1.0 / 256.0) .* features
|
||||
labels = Input (labelDim)
|
||||
hiddenDim = 200
|
||||
h1 = DNNSigmoidLayer (featDim, hiddenDim, featScaled, 1)
|
||||
z = DNNLayer (hiddenDim, labelDim, h1, 1)
|
||||
ce = CrossEntropyWithSoftmax (labels, z)
|
||||
errs = ErrorPrediction (labels, z)
|
||||
top5Errs = ErrorPrediction (labels, z, topN=5)
|
||||
featureNodes = (features)
|
||||
labelNodes = (labels)
|
||||
criterionNodes = (ce)
|
||||
evaluationNodes = (errs)
|
||||
outputNodes = (z)
|
||||
]
|
||||
NDLNetworkBuilder = [
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly = true
|
||||
ndlMacros = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/Macros.ndl"
|
||||
networkDescription = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/01_OneHidden.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 60000
|
||||
minibatchSize = 32
|
||||
learningRatesPerMB = 0.1
|
||||
momentumPerMB = 0
|
||||
learningRatesPerSample = 0.003125
|
||||
momentumAsTimeConstant = 0
|
||||
maxEpochs = 30
|
||||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData/Train-28x28_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData/Train-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
|
|
@ -155,10 +209,11 @@ train = [
|
|||
]
|
||||
test = [
|
||||
action = "test"
|
||||
minibatchSize = 16
|
||||
minibatchSize = 1024
|
||||
evalNodeNames = ce:errs:top5Errs
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData/Test-28x28_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData/Test-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
|
|
@ -171,40 +226,39 @@ test = [
|
|||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
train=[SGD=[maxEpochs=3]]
|
||||
imageLayout="cudnn"
|
||||
|
||||
05/13/2016 08:15:51: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:00:46: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 08:15:51: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:00:46: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: 01_OneHidden.cntk:command=train:test
|
||||
configparameters: 01_OneHidden.cntk:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config
|
||||
configparameters: 01_OneHidden.cntk:currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData
|
||||
configparameters: 01_OneHidden.cntk:DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData
|
||||
configparameters: 01_OneHidden.cntk:configDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config
|
||||
configparameters: 01_OneHidden.cntk:currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData
|
||||
configparameters: 01_OneHidden.cntk:dataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData
|
||||
configparameters: 01_OneHidden.cntk:deviceId=0
|
||||
configparameters: 01_OneHidden.cntk:imageLayout=cudnn
|
||||
configparameters: 01_OneHidden.cntk:initOnCPUOnly=true
|
||||
configparameters: 01_OneHidden.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu/Models
|
||||
configparameters: 01_OneHidden.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu/Models/01_OneHidden
|
||||
configparameters: 01_OneHidden.cntk:ndlMacros=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/Macros.ndl
|
||||
configparameters: 01_OneHidden.cntk:modelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu/Models
|
||||
configparameters: 01_OneHidden.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu/Models/01_OneHidden
|
||||
configparameters: 01_OneHidden.cntk:numMBsToShowResult=500
|
||||
configparameters: 01_OneHidden.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu
|
||||
configparameters: 01_OneHidden.cntk:outputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu
|
||||
configparameters: 01_OneHidden.cntk:precision=float
|
||||
configparameters: 01_OneHidden.cntk:RootDir=..
|
||||
configparameters: 01_OneHidden.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu
|
||||
configparameters: 01_OneHidden.cntk:rootDir=..
|
||||
configparameters: 01_OneHidden.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu
|
||||
configparameters: 01_OneHidden.cntk:test=[
|
||||
action = "test"
|
||||
minibatchSize = 16
|
||||
minibatchSize = 1024
|
||||
evalNodeNames = ce:errs:top5Errs
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData/Test-28x28_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData/Test-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
|
|
@ -222,19 +276,41 @@ configparameters: 01_OneHidden.cntk:timestamping=true
|
|||
configparameters: 01_OneHidden.cntk:traceLevel=1
|
||||
configparameters: 01_OneHidden.cntk:train=[
|
||||
action = "train"
|
||||
BrainScriptNetworkBuilder_disabled = [
|
||||
include "Shared.bs"
|
||||
featDim = 28 * 28
|
||||
labelDim = 10
|
||||
features = Input (featDim)
|
||||
featScaled = Constant (1.0 / 256.0) .* features
|
||||
labels = Input (labelDim)
|
||||
hiddenDim = 200
|
||||
h1 = DNNSigmoidLayer (featDim, hiddenDim, featScaled, 1)
|
||||
z = DNNLayer (hiddenDim, labelDim, h1, 1)
|
||||
ce = CrossEntropyWithSoftmax (labels, z)
|
||||
errs = ErrorPrediction (labels, z)
|
||||
top5Errs = ErrorPrediction (labels, z, topN=5)
|
||||
featureNodes = (features)
|
||||
labelNodes = (labels)
|
||||
criterionNodes = (ce)
|
||||
evaluationNodes = (errs)
|
||||
outputNodes = (z)
|
||||
]
|
||||
NDLNetworkBuilder = [
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly = true
|
||||
ndlMacros = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/Macros.ndl"
|
||||
networkDescription = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/01_OneHidden.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 60000
|
||||
minibatchSize = 32
|
||||
learningRatesPerMB = 0.1
|
||||
momentumPerMB = 0
|
||||
learningRatesPerSample = 0.003125
|
||||
momentumAsTimeConstant = 0
|
||||
maxEpochs = 30
|
||||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData/Train-28x28_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu\TestData/Train-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
|
|
@ -248,31 +324,45 @@ configparameters: 01_OneHidden.cntk:train=[
|
|||
]
|
||||
] [SGD=[maxEpochs=3]]
|
||||
|
||||
05/13/2016 08:15:51: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/13/2016 08:15:51: Commands: train test
|
||||
05/13/2016 08:15:51: Precision = "float"
|
||||
05/13/2016 08:15:51: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu/Models/01_OneHidden
|
||||
05/13/2016 08:15:51: CNTKCommandTrainInfo: train : 3
|
||||
05/13/2016 08:15:51: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
08/16/2016 03:00:46: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:00:46: Commands: train test
|
||||
08/16/2016 03:00:46: Precision = "float"
|
||||
08/16/2016 03:00:46: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu/Models/01_OneHidden
|
||||
08/16/2016 03:00:46: CNTKCommandTrainInfo: train : 3
|
||||
08/16/2016 03:00:46: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
|
||||
05/13/2016 08:15:51: ##############################################################################
|
||||
05/13/2016 08:15:51: # #
|
||||
05/13/2016 08:15:51: # Action "train" #
|
||||
05/13/2016 08:15:51: # #
|
||||
05/13/2016 08:15:51: ##############################################################################
|
||||
08/16/2016 03:00:46: ##############################################################################
|
||||
08/16/2016 03:00:46: # #
|
||||
08/16/2016 03:00:46: # Action "train" #
|
||||
08/16/2016 03:00:46: # #
|
||||
08/16/2016 03:00:46: ##############################################################################
|
||||
|
||||
05/13/2016 08:15:51: CNTKCommandTrainBegin: train
|
||||
08/16/2016 03:00:46: CNTKCommandTrainBegin: train
|
||||
NDLBuilder Using GPU 0
|
||||
|
||||
05/13/2016 08:15:52: Creating virgin network.
|
||||
08/16/2016 03:00:47: Creating virgin network.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[200 x 784] <- 0.000000.
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[200 x 1] <- 0.000000.
|
||||
Node 'ol.W' (LearnableParameter operation): Initializing Parameter[10 x 200] <- 0.000000.
|
||||
Node 'ol.b' (LearnableParameter operation): Initializing Parameter[10 x 1] <- 0.000000.
|
||||
Node 'unnamed89' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.000000.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.003906.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.003906.
|
||||
Node 'unnamed89' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 5.000000.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.003906.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[200 x 784] <- uniform(seed=1, range=0.050000*1.000000, onCPU=true).
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[200 x 1] <- uniform(seed=2, range=0.050000*1.000000, onCPU=true).
|
||||
Node 'ol.W' (LearnableParameter operation): Initializing Parameter[10 x 200] <- uniform(seed=3, range=0.050000*1.000000, onCPU=true).
|
||||
Node 'ol.b' (LearnableParameter operation): Initializing Parameter[10 x 1] <- uniform(seed=4, range=0.050000*1.000000, onCPU=true).
|
||||
|
||||
Post-processing network...
|
||||
|
||||
4 roots:
|
||||
ce = CrossEntropyWithSoftmax()
|
||||
err = ErrorPrediction()
|
||||
errTop1 = ErrorPrediction()
|
||||
errs = ErrorPrediction()
|
||||
ol.z = Plus()
|
||||
top5Errs = ErrorPrediction()
|
||||
|
||||
Validating network. 17 nodes to process in pass 1.
|
||||
|
||||
|
|
@ -290,9 +380,9 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 200], [200 x 1 x *] -> [10 x 1
|
|||
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
|
||||
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x 1 x *], [10 x 1] -> [10 x 1 x *]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
|
||||
Validating --> unnamed81 = LearnableParameter() : -> [1 x 1]
|
||||
Validating --> errTop1 = ErrorPrediction (labels, ol.z, unnamed81) : [10 x *], [10 x 1 x *], [1 x 1] -> [1]
|
||||
Validating --> errs = ErrorPrediction (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
|
||||
Validating --> unnamed89 = LearnableParameter() : -> [1 x 1]
|
||||
Validating --> top5Errs = ErrorPrediction (labels, ol.z, unnamed89) : [10 x *], [10 x 1 x *], [1 x 1] -> [1]
|
||||
|
||||
Validating network. 9 nodes to process in pass 2.
|
||||
|
||||
|
|
@ -305,92 +395,88 @@ Validating network, final pass.
|
|||
|
||||
Post-processing network complete.
|
||||
|
||||
05/13/2016 08:15:53: Created model with 17 nodes on GPU 0.
|
||||
08/16/2016 03:00:47: Created model with 17 nodes on GPU 0.
|
||||
|
||||
05/13/2016 08:15:53: Training criterion node(s):
|
||||
05/13/2016 08:15:53: ce = CrossEntropyWithSoftmax
|
||||
08/16/2016 03:00:47: Training criterion node(s):
|
||||
08/16/2016 03:00:47: ce = CrossEntropyWithSoftmax
|
||||
|
||||
05/13/2016 08:15:53: Evaluation criterion node(s):
|
||||
|
||||
05/13/2016 08:15:53: errTop1 = ErrorPrediction
|
||||
05/13/2016 08:15:53: err = ErrorPrediction
|
||||
08/16/2016 03:00:47: Evaluation criterion node(s):
|
||||
08/16/2016 03:00:47: top5Errs = ErrorPrediction
|
||||
08/16/2016 03:00:47: errs = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 27 matrices, 10 are shared as 5, and 17 are not shared.
|
||||
|
||||
0000000000000000: {[err Gradient[1]] [errTop1 Gradient[1]] [featScale Gradient[1 x 1]] [featScaled Gradient[784 x 1 x *]] [features Gradient[784 x *]] [labels Gradient[10 x *]] [unnamed81 Gradient[1 x 1]] }
|
||||
000000780D2D93A0: {[unnamed81 Value[1 x 1]] }
|
||||
000000780D2D9440: {[featScaled Value[784 x 1 x *]] }
|
||||
000000780D2D94E0: {[h1.W Gradient[200 x 784]] [h1.z Value[200 x 1 x *]] }
|
||||
000000780D2D9580: {[h1.t Gradient[200 x 1 x *]] [h1.y Value[200 x 1 x *]] }
|
||||
000000780D2D9620: {[h1.z Gradient[200 x 1 x *]] [ol.t Value[10 x 1 x *]] }
|
||||
000000780D2D96C0: {[ol.W Value[10 x 200]] }
|
||||
000000780D2D9760: {[ol.b Value[10 x 1]] }
|
||||
000000780D2D99E0: {[errTop1 Value[1]] }
|
||||
000000780D2D9EE0: {[err Value[1]] }
|
||||
000000780D2DA0C0: {[ol.z Value[10 x 1 x *]] }
|
||||
000000780D2DA160: {[ce Value[1]] }
|
||||
000000780D2DA2A0: {[h1.t Value[200 x 1 x *]] }
|
||||
000000780D33AB50: {[ce Gradient[1]] }
|
||||
000000780D33ABF0: {[ol.t Gradient[10 x 1 x *]] }
|
||||
000000780D33AFB0: {[ol.b Gradient[10 x 1]] }
|
||||
000000780D33C270: {[h1.b Gradient[200 x 1]] [h1.y Gradient[200 x 1 x *]] }
|
||||
000000780D33C9F0: {[ol.W Gradient[10 x 200]] [ol.z Gradient[10 x 1 x *]] }
|
||||
00000078767789E0: {[featScale Value[1 x 1]] }
|
||||
0000007876778A80: {[labels Value[10 x *]] }
|
||||
0000007876778B20: {[h1.W Value[200 x 784]] }
|
||||
0000007876778BC0: {[h1.b Value[200 x 1]] }
|
||||
000000787677A1A0: {[features Value[784 x *]] }
|
||||
|
||||
05/13/2016 08:15:53: No PreCompute nodes found, skipping PreCompute step.
|
||||
|
||||
05/13/2016 08:15:53: Starting Epoch 1: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
|
||||
05/13/2016 08:15:53: Starting minibatch loop.
|
||||
05/13/2016 08:15:56: Epoch[ 1 of 3]-Minibatch[1-500, 26.67%]: ce = 1.29023352 * 16000; errs = 37.9813% * 16000; err = 0.37981250 * 16000; time = 3.1210s; samplesPerSecond = 5126.5
|
||||
05/13/2016 08:15:57: Epoch[ 1 of 3]-Minibatch[501-1000, 53.33%]: ce = 0.50742346 * 16000; errs = 13.9% * 16000; err = 0.13900000 * 16000; time = 0.6202s; samplesPerSecond = 25796.5
|
||||
05/13/2016 08:15:57: Epoch[ 1 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.39415479 * 16000; errs = 11.0812% * 16000; err = 0.11081250 * 16000; time = 0.6195s; samplesPerSecond = 25828.0
|
||||
05/13/2016 08:15:58: Finished Epoch[ 1 of 3]: [Training] ce = 0.65521146 * 60000; errs = 18.8467% * 60000; err = 0.18846667 * 60000; totalSamplesSeen = 60000; learningRatePerSample = 0.003125; epochTime=4.86409s
|
||||
05/13/2016 08:15:58: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu/Models/01_OneHidden.1'
|
||||
|
||||
05/13/2016 08:15:58: Starting Epoch 2: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
|
||||
05/13/2016 08:15:58: Starting minibatch loop.
|
||||
05/13/2016 08:15:59: Epoch[ 2 of 3]-Minibatch[1-500, 26.67%]: ce = 0.33321408 * 16000; errs = 9.58125% * 16000; err = 0.09581250 * 16000; time = 0.6590s; samplesPerSecond = 24277.8
|
||||
05/13/2016 08:15:59: Epoch[ 2 of 3]-Minibatch[501-1000, 53.33%]: ce = 0.31547781 * 16000; errs = 9.2875% * 16000; err = 0.09287500 * 16000; time = 0.6704s; samplesPerSecond = 23866.0
|
||||
05/13/2016 08:16:00: Epoch[ 2 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.31882251 * 16000; errs = 9.21875% * 16000; err = 0.09218750 * 16000; time = 0.6720s; samplesPerSecond = 23808.7
|
||||
05/13/2016 08:16:00: Finished Epoch[ 2 of 3]: [Training] ce = 0.31533239 * 60000; errs = 9.15833% * 60000; err = 0.09158333 * 60000; totalSamplesSeen = 120000; learningRatePerSample = 0.003125; epochTime=2.52448s
|
||||
05/13/2016 08:16:00: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu/Models/01_OneHidden.2'
|
||||
|
||||
05/13/2016 08:16:00: Starting Epoch 3: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
|
||||
05/13/2016 08:16:00: Starting minibatch loop.
|
||||
05/13/2016 08:16:01: Epoch[ 3 of 3]-Minibatch[1-500, 26.67%]: ce = 0.28751190 * 16000; errs = 8.39375% * 16000; err = 0.08393750 * 16000; time = 0.6195s; samplesPerSecond = 25825.2
|
||||
05/13/2016 08:16:02: Epoch[ 3 of 3]-Minibatch[501-1000, 53.33%]: ce = 0.27455951 * 16000; errs = 7.95% * 16000; err = 0.07950000 * 16000; time = 0.6193s; samplesPerSecond = 25834.3
|
||||
05/13/2016 08:16:02: Epoch[ 3 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.27693610 * 16000; errs = 7.9875% * 16000; err = 0.07987500 * 16000; time = 0.6192s; samplesPerSecond = 25839.8
|
||||
05/13/2016 08:16:03: Finished Epoch[ 3 of 3]: [Training] ce = 0.27493141 * 60000; errs = 7.98333% * 60000; err = 0.07983333 * 60000; totalSamplesSeen = 180000; learningRatePerSample = 0.003125; epochTime=2.34147s
|
||||
05/13/2016 08:16:03: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_01_OneHidden@release_gpu/Models/01_OneHidden'
|
||||
05/13/2016 08:16:03: CNTKCommandTrainEnd: train
|
||||
|
||||
05/13/2016 08:16:03: Action "train" complete.
|
||||
{ h1.W : [200 x 784] (gradient)
|
||||
h1.z : [200 x 1 x *] }
|
||||
{ ol.W : [10 x 200] (gradient)
|
||||
ol.z : [10 x 1 x *] (gradient) }
|
||||
{ h1.z : [200 x 1 x *] (gradient)
|
||||
ol.t : [10 x 1 x *] }
|
||||
{ h1.t : [200 x 1 x *] (gradient)
|
||||
h1.y : [200 x 1 x *] }
|
||||
{ h1.b : [200 x 1] (gradient)
|
||||
h1.y : [200 x 1 x *] (gradient) }
|
||||
|
||||
|
||||
05/13/2016 08:16:03: ##############################################################################
|
||||
05/13/2016 08:16:03: # #
|
||||
05/13/2016 08:16:03: # Action "test" #
|
||||
05/13/2016 08:16:03: # #
|
||||
05/13/2016 08:16:03: ##############################################################################
|
||||
08/16/2016 03:00:47: Training 159010 parameters in 4 out of 4 parameter tensors and 10 nodes with gradient:
|
||||
|
||||
08/16/2016 03:00:47: Node 'h1.W' (LearnableParameter operation) : [200 x 784]
|
||||
08/16/2016 03:00:47: Node 'h1.b' (LearnableParameter operation) : [200 x 1]
|
||||
08/16/2016 03:00:47: Node 'ol.W' (LearnableParameter operation) : [10 x 200]
|
||||
08/16/2016 03:00:47: Node 'ol.b' (LearnableParameter operation) : [10 x 1]
|
||||
|
||||
08/16/2016 03:00:47: No PreCompute nodes found, or all already computed. Skipping pre-computation step.
|
||||
|
||||
08/16/2016 03:00:47: Starting Epoch 1: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..60000] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:00:47: Starting minibatch loop.
|
||||
08/16/2016 03:00:50: Epoch[ 1 of 3]-Minibatch[ 1- 500, 26.67%]: ce = 1.29666760 * 16000; top5Errs = 9.300% * 16000; errs = 38.350% * 16000; time = 2.6226s; samplesPerSecond = 6100.9
|
||||
08/16/2016 03:00:51: Epoch[ 1 of 3]-Minibatch[ 501-1000, 53.33%]: ce = 0.50958875 * 16000; top5Errs = 1.100% * 16000; errs = 13.856% * 16000; time = 0.9727s; samplesPerSecond = 16448.3
|
||||
08/16/2016 03:00:52: Epoch[ 1 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.38464209 * 16000; top5Errs = 0.831% * 16000; errs = 10.700% * 16000; time = 0.9697s; samplesPerSecond = 16500.2
|
||||
08/16/2016 03:00:53: Finished Epoch[ 1 of 3]: [Training] ce = 0.65508639 * 60000; top5Errs = 3.093% * 60000; errs = 18.835% * 60000; totalSamplesSeen = 60000; learningRatePerSample = 0.003125; epochTime=5.31129s
|
||||
08/16/2016 03:00:53: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu/Models/01_OneHidden.1'
|
||||
|
||||
08/16/2016 03:00:53: Starting Epoch 2: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [60000..120000] (first sequence at sample 60000), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:00:53: Starting minibatch loop.
|
||||
08/16/2016 03:00:54: Epoch[ 2 of 3]-Minibatch[ 1- 500, 26.67%]: ce = 0.33479074 * 16000; top5Errs = 0.563% * 16000; errs = 9.781% * 16000; time = 0.9692s; samplesPerSecond = 16508.2
|
||||
08/16/2016 03:00:55: Epoch[ 2 of 3]-Minibatch[ 501-1000, 53.33%]: ce = 0.30564261 * 16000; top5Errs = 0.469% * 16000; errs = 8.906% * 16000; time = 0.9679s; samplesPerSecond = 16530.7
|
||||
08/16/2016 03:00:55: Epoch[ 2 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.30993488 * 16000; top5Errs = 0.613% * 16000; errs = 9.063% * 16000; time = 0.9663s; samplesPerSecond = 16557.3
|
||||
08/16/2016 03:00:56: Finished Epoch[ 2 of 3]: [Training] ce = 0.31617907 * 60000; top5Errs = 0.563% * 60000; errs = 9.202% * 60000; totalSamplesSeen = 120000; learningRatePerSample = 0.003125; epochTime=3.64141s
|
||||
08/16/2016 03:00:56: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu/Models/01_OneHidden.2'
|
||||
|
||||
08/16/2016 03:00:56: Starting Epoch 3: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [120000..180000] (first sequence at sample 120000), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:00:56: Starting minibatch loop.
|
||||
08/16/2016 03:00:57: Epoch[ 3 of 3]-Minibatch[ 1- 500, 26.67%]: ce = 0.29109534 * 16000; top5Errs = 0.531% * 16000; errs = 8.563% * 16000; time = 0.9705s; samplesPerSecond = 16486.0
|
||||
08/16/2016 03:00:58: Epoch[ 3 of 3]-Minibatch[ 501-1000, 53.33%]: ce = 0.27885516 * 16000; top5Errs = 0.506% * 16000; errs = 8.194% * 16000; time = 0.9618s; samplesPerSecond = 16636.3
|
||||
08/16/2016 03:00:59: Epoch[ 3 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.27411078 * 16000; top5Errs = 0.513% * 16000; errs = 7.775% * 16000; time = 0.9625s; samplesPerSecond = 16622.6
|
||||
08/16/2016 03:01:00: Finished Epoch[ 3 of 3]: [Training] ce = 0.27539870 * 60000; top5Errs = 0.478% * 60000; errs = 8.005% * 60000; totalSamplesSeen = 180000; learningRatePerSample = 0.003125; epochTime=3.6287s
|
||||
08/16/2016 03:01:00: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_01_OneHidden@release_gpu/Models/01_OneHidden'
|
||||
08/16/2016 03:01:00: CNTKCommandTrainEnd: train
|
||||
|
||||
08/16/2016 03:01:00: Action "train" complete.
|
||||
|
||||
|
||||
08/16/2016 03:01:00: ##############################################################################
|
||||
08/16/2016 03:01:00: # #
|
||||
08/16/2016 03:01:00: # Action "test" #
|
||||
08/16/2016 03:01:00: # #
|
||||
08/16/2016 03:01:00: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
||||
4 roots:
|
||||
3 roots:
|
||||
ce = CrossEntropyWithSoftmax()
|
||||
err = ErrorPrediction()
|
||||
errTop1 = ErrorPrediction()
|
||||
ol.z = Plus()
|
||||
errs = ErrorPrediction()
|
||||
top5Errs = ErrorPrediction()
|
||||
|
||||
Validating network. 17 nodes to process in pass 1.
|
||||
|
||||
|
|
@ -408,9 +494,9 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 200], [200 x 1 x *1] -> [10 x 1
|
|||
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
|
||||
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x 1 x *1], [10 x 1] -> [10 x 1 x *1]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
|
||||
Validating --> unnamed81 = LearnableParameter() : -> [1 x 1]
|
||||
Validating --> errTop1 = ErrorPrediction (labels, ol.z, unnamed81) : [10 x *1], [10 x 1 x *1], [1 x 1] -> [1]
|
||||
Validating --> errs = ErrorPrediction (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
|
||||
Validating --> unnamed89 = LearnableParameter() : -> [1 x 1]
|
||||
Validating --> top5Errs = ErrorPrediction (labels, ol.z, unnamed89) : [10 x *1], [10 x 1 x *1], [1 x 1] -> [1]
|
||||
|
||||
Validating network. 9 nodes to process in pass 2.
|
||||
|
||||
|
|
@ -423,34 +509,17 @@ Validating network, final pass.
|
|||
|
||||
Post-processing network complete.
|
||||
|
||||
evalNodeNames are not specified, using all the default evalnodes and training criterion nodes.
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 17 matrices, 0 are shared as 0, and 17 are not shared.
|
||||
|
||||
0000000000000000: {[ce Gradient[1]] [err Gradient[1]] [errTop1 Gradient[1]] [featScale Gradient[1 x 1]] [featScaled Gradient[784 x 1 x *1]] [features Gradient[784 x *1]] [h1.W Gradient[200 x 784]] [h1.b Gradient[200 x 1]] [h1.t Gradient[200 x 1 x *1]] [h1.y Gradient[200 x 1 x *1]] [h1.z Gradient[200 x 1 x *1]] [labels Gradient[10 x *1]] [ol.W Gradient[10 x 200]] [ol.b Gradient[10 x 1]] [ol.t Gradient[10 x 1 x *1]] [ol.z Gradient[10 x 1 x *1]] [unnamed81 Gradient[1 x 1]] }
|
||||
000000780D33B230: {[labels Value[10 x *1]] }
|
||||
000000780D33BA50: {[ol.b Value[10 x 1]] }
|
||||
000000780D33BD70: {[featScale Value[1 x 1]] }
|
||||
000000780D33BF50: {[h1.b Value[200 x 1]] }
|
||||
000000780D33C6D0: {[features Value[784 x *1]] }
|
||||
000000780D33C770: {[h1.W Value[200 x 784]] }
|
||||
000000787673E350: {[ol.z Value[10 x 1 x *1]] }
|
||||
000000787673E850: {[ol.t Value[10 x 1 x *1]] }
|
||||
00000078767789E0: {[ol.W Value[10 x 200]] }
|
||||
0000007876778A80: {[unnamed81 Value[1 x 1]] }
|
||||
0000007876779020: {[errTop1 Value[1]] }
|
||||
00000078767790C0: {[err Value[1]] }
|
||||
0000007876779160: {[ce Value[1]] }
|
||||
00000078767792A0: {[h1.t Value[200 x 1 x *1]] }
|
||||
00000078767793E0: {[h1.z Value[200 x 1 x *1]] }
|
||||
00000078767795C0: {[h1.y Value[200 x 1 x *1]] }
|
||||
00000078767797A0: {[featScaled Value[784 x 1 x *1]] }
|
||||
|
||||
05/13/2016 08:16:11: Final Results: Minibatch[1-10]: errs = 7.460% * 10000; top5Errs = 0.440% * 10000; ce = 0.26425332 * 10000; perplexity = 1.30245809
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
08/16/2016 03:01:00: Minibatch[1-10]: ce = 0.24924074 * 10000; errs = 7.140% * 10000; top5Errs = 0.420% * 10000
|
||||
08/16/2016 03:01:00: Final Results: Minibatch[1-10]: ce = 0.24924074 * 10000; perplexity = 1.28305088; errs = 7.140% * 10000; top5Errs = 0.420% * 10000
|
||||
|
||||
05/13/2016 08:16:11: Action "test" complete.
|
||||
08/16/2016 03:01:00: Action "test" complete.
|
||||
|
||||
05/13/2016 08:16:11: __COMPLETED__
|
||||
08/16/2016 03:01:00: __COMPLETED__
|
||||
|
|
@ -1,65 +1,77 @@
|
|||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/02_Convolution/../../../../../../../Examples/Image/MNIST/Config/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/Config/02_Convolution.cntk currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/TestData RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/02_Convolution/../../../../../../../Examples/Image/MNIST/Config OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 264172964 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/02_Convolution/../../../../../../Examples/Image/MNIST/Config/02_Convolution.cntk currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/TestData RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/02_Convolution/../../../../../../Examples/Image/MNIST/Config OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 13 2016 14:50:25
|
||||
Last modified date: Thu May 12 14:00:37 2016
|
||||
Built time: Aug 16 2016 09:41:56
|
||||
Last modified date: Fri Aug 12 07:32:43 2016
|
||||
Build type: release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: no
|
||||
Math lib: acml
|
||||
Math lib: mkl
|
||||
CUDA_PATH: /usr/local/cuda-7.5
|
||||
CUB_PATH: /usr/local/cub-1.4.1
|
||||
CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
Built by philly on d8dc82703b0f
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by philly on f67b30a647de
|
||||
Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to /tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/TestData
|
||||
05/13/2016 15:10:11: -------------------------------------------------------------------
|
||||
05/13/2016 15:10:11: Build info:
|
||||
Changed current directory to /tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/TestData
|
||||
08/16/2016 10:49:50: -------------------------------------------------------------------
|
||||
08/16/2016 10:49:50: Build info:
|
||||
|
||||
05/13/2016 15:10:11: Built time: May 13 2016 14:50:25
|
||||
05/13/2016 15:10:11: Last modified date: Thu May 12 14:00:37 2016
|
||||
05/13/2016 15:10:11: Build type: release
|
||||
05/13/2016 15:10:11: Build target: GPU
|
||||
05/13/2016 15:10:11: With 1bit-SGD: no
|
||||
05/13/2016 15:10:11: Math lib: acml
|
||||
05/13/2016 15:10:11: CUDA_PATH: /usr/local/cuda-7.5
|
||||
05/13/2016 15:10:11: CUB_PATH: /usr/local/cub-1.4.1
|
||||
05/13/2016 15:10:11: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
05/13/2016 15:10:11: Build Branch: HEAD
|
||||
05/13/2016 15:10:11: Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
05/13/2016 15:10:11: Built by philly on d8dc82703b0f
|
||||
05/13/2016 15:10:11: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
05/13/2016 15:10:11: -------------------------------------------------------------------
|
||||
08/16/2016 10:49:50: Built time: Aug 16 2016 09:41:56
|
||||
08/16/2016 10:49:50: Last modified date: Fri Aug 12 07:32:43 2016
|
||||
08/16/2016 10:49:50: Build type: release
|
||||
08/16/2016 10:49:50: Build target: GPU
|
||||
08/16/2016 10:49:50: With 1bit-SGD: no
|
||||
08/16/2016 10:49:50: Math lib: mkl
|
||||
08/16/2016 10:49:50: CUDA_PATH: /usr/local/cuda-7.5
|
||||
08/16/2016 10:49:50: CUB_PATH: /usr/local/cub-1.4.1
|
||||
08/16/2016 10:49:50: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
08/16/2016 10:49:50: Build Branch: HEAD
|
||||
08/16/2016 10:49:50: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 10:49:50: Built by philly on f67b30a647de
|
||||
08/16/2016 10:49:50: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
08/16/2016 10:49:50: -------------------------------------------------------------------
|
||||
08/16/2016 10:49:51: -------------------------------------------------------------------
|
||||
08/16/2016 10:49:51: GPU info:
|
||||
|
||||
05/13/2016 15:10:11: Running on localhost at 2016/05/13 15:10:11
|
||||
05/13/2016 15:10:11: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/02_Convolution/../../../../../../../Examples/Image/MNIST/Config/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/Config/02_Convolution.cntk currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/TestData RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/02_Convolution/../../../../../../../Examples/Image/MNIST/Config OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
08/16/2016 10:49:51: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:49:51: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:49:51: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:49:51: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:49:51: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 10:49:51: Running on localhost at 2016/08/16 10:49:51
|
||||
08/16/2016 10:49:51: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/02_Convolution/../../../../../../Examples/Image/MNIST/Config/02_Convolution.cntk currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/TestData RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/02_Convolution/../../../../../../Examples/Image/MNIST/Config OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
|
||||
|
||||
|
||||
05/13/2016 15:10:11: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 15:10:11: RootDir = ".."
|
||||
ConfigDir = "$RootDir$/Config"
|
||||
DataDir = "$RootDir$/Data"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
ModelDir = "$OutputDir$/Models"
|
||||
08/16/2016 10:49:51: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:49:51: rootDir = ".."
|
||||
configDir = "$rootDir$/Config"
|
||||
dataDir = "$rootDir$/Data"
|
||||
outputDir = "$rootDir$/Output"
|
||||
modelDir = "$outputDir$/Models"
|
||||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
command = train:test
|
||||
precision = "float"
|
||||
modelPath = "$ModelDir$/02_Convolution"
|
||||
ndlMacros = "$ConfigDir$/Macros.ndl"
|
||||
traceLevel=1
|
||||
numMBsToShowResult=500
|
||||
prefetch=true
|
||||
initOnCPUOnly=true
|
||||
modelPath = "$modelDir$/02_Convolution"
|
||||
traceLevel = 1
|
||||
numMBsToShowResult = 500
|
||||
train = [
|
||||
action = "train"
|
||||
NDLNetworkBuilder = [
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly = true
|
||||
ndlMacros = "$configDir$/Macros.ndl"
|
||||
networkDescription = "$ConfigDir$/02_Convolution.ndl"
|
||||
]
|
||||
SGD = [
|
||||
|
|
@ -82,21 +94,18 @@ train = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
test = [
|
||||
action = test
|
||||
minibatchSize = 16
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "$ConfigDir$/02_Convolution.ndl"
|
||||
]
|
||||
minibatchSize = 1024
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "$DataDir$/Test-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
format = "dense"
|
||||
dim = 784
|
||||
format = "dense"
|
||||
]
|
||||
labels = [
|
||||
dim = 10
|
||||
|
|
@ -105,38 +114,37 @@ test = [
|
|||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/02_Convolution/../../../../../../../Examples/Image/MNIST/Config
|
||||
OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu
|
||||
currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/02_Convolution/../../../../../../Examples/Image/MNIST/Config
|
||||
OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
train=[SGD=[maxEpochs=3]]
|
||||
imageLayout="cudnn"
|
||||
|
||||
05/13/2016 15:10:11: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:49:51: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 15:10:11: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 15:10:11: RootDir = ".."
|
||||
ConfigDir = "../Config"
|
||||
DataDir = "../Data"
|
||||
OutputDir = "../Output"
|
||||
ModelDir = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/Models"
|
||||
08/16/2016 10:49:51: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:49:51: rootDir = ".."
|
||||
configDir = "../Config"
|
||||
dataDir = "../Data"
|
||||
outputDir = "../Output"
|
||||
modelDir = "/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/Models"
|
||||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
command = train:test
|
||||
precision = "float"
|
||||
modelPath = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/Models/02_Convolution"
|
||||
ndlMacros = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/02_Convolution/../../../../../../../Examples/Image/MNIST/Config/Macros.ndl"
|
||||
traceLevel=1
|
||||
numMBsToShowResult=500
|
||||
prefetch=true
|
||||
initOnCPUOnly=true
|
||||
modelPath = "/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/Models/02_Convolution"
|
||||
traceLevel = 1
|
||||
numMBsToShowResult = 500
|
||||
train = [
|
||||
action = "train"
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/02_Convolution/../../../../../../../Examples/Image/MNIST/Config/02_Convolution.ndl"
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly = true
|
||||
ndlMacros = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/02_Convolution/../../../../../../Examples/Image/MNIST/Config/Macros.ndl"
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/02_Convolution/../../../../../../Examples/Image/MNIST/Config/02_Convolution.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 60000
|
||||
|
|
@ -147,7 +155,7 @@ train = [
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/TestData/Train-28x28_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/TestData/Train-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
|
|
@ -158,21 +166,18 @@ train = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
test = [
|
||||
action = test
|
||||
minibatchSize = 16
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/02_Convolution/../../../../../../../Examples/Image/MNIST/Config/02_Convolution.ndl"
|
||||
]
|
||||
minibatchSize = 1024
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/TestData/Test-28x28_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/TestData/Test-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
format = "dense"
|
||||
dim = 784
|
||||
format = "dense"
|
||||
]
|
||||
labels = [
|
||||
dim = 10
|
||||
|
|
@ -181,48 +186,42 @@ test = [
|
|||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/02_Convolution/../../../../../../../Examples/Image/MNIST/Config
|
||||
OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu
|
||||
currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/02_Convolution/../../../../../../Examples/Image/MNIST/Config
|
||||
OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
train=[SGD=[maxEpochs=3]]
|
||||
imageLayout="cudnn"
|
||||
|
||||
05/13/2016 15:10:11: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:49:51: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 15:10:11: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:49:51: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: 02_Convolution.cntk:command=train:test
|
||||
configparameters: 02_Convolution.cntk:ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/02_Convolution/../../../../../../../Examples/Image/MNIST/Config
|
||||
configparameters: 02_Convolution.cntk:currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/TestData
|
||||
configparameters: 02_Convolution.cntk:DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/TestData
|
||||
configparameters: 02_Convolution.cntk:configDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/02_Convolution/../../../../../../Examples/Image/MNIST/Config
|
||||
configparameters: 02_Convolution.cntk:currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/TestData
|
||||
configparameters: 02_Convolution.cntk:dataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/TestData
|
||||
configparameters: 02_Convolution.cntk:deviceId=0
|
||||
configparameters: 02_Convolution.cntk:imageLayout=cudnn
|
||||
configparameters: 02_Convolution.cntk:initOnCPUOnly=true
|
||||
configparameters: 02_Convolution.cntk:ModelDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/Models
|
||||
configparameters: 02_Convolution.cntk:modelPath=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/Models/02_Convolution
|
||||
configparameters: 02_Convolution.cntk:ndlMacros=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/02_Convolution/../../../../../../../Examples/Image/MNIST/Config/Macros.ndl
|
||||
configparameters: 02_Convolution.cntk:modelDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/Models
|
||||
configparameters: 02_Convolution.cntk:modelPath=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/Models/02_Convolution
|
||||
configparameters: 02_Convolution.cntk:numMBsToShowResult=500
|
||||
configparameters: 02_Convolution.cntk:OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu
|
||||
configparameters: 02_Convolution.cntk:outputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu
|
||||
configparameters: 02_Convolution.cntk:precision=float
|
||||
configparameters: 02_Convolution.cntk:prefetch=true
|
||||
configparameters: 02_Convolution.cntk:RootDir=..
|
||||
configparameters: 02_Convolution.cntk:RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu
|
||||
configparameters: 02_Convolution.cntk:rootDir=..
|
||||
configparameters: 02_Convolution.cntk:RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu
|
||||
configparameters: 02_Convolution.cntk:test=[
|
||||
action = test
|
||||
minibatchSize = 16
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/02_Convolution/../../../../../../../Examples/Image/MNIST/Config/02_Convolution.ndl"
|
||||
]
|
||||
minibatchSize = 1024
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/TestData/Test-28x28_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/TestData/Test-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
format = "dense"
|
||||
dim = 784
|
||||
format = "dense"
|
||||
]
|
||||
labels = [
|
||||
dim = 10
|
||||
|
|
@ -237,7 +236,10 @@ configparameters: 02_Convolution.cntk:traceLevel=1
|
|||
configparameters: 02_Convolution.cntk:train=[
|
||||
action = "train"
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/02_Convolution/../../../../../../../Examples/Image/MNIST/Config/02_Convolution.ndl"
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly = true
|
||||
ndlMacros = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/02_Convolution/../../../../../../Examples/Image/MNIST/Config/Macros.ndl"
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/02_Convolution/../../../../../../Examples/Image/MNIST/Config/02_Convolution.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 60000
|
||||
|
|
@ -248,7 +250,7 @@ configparameters: 02_Convolution.cntk:train=[
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/TestData/Train-28x28_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/TestData/Train-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
|
|
@ -259,32 +261,52 @@ configparameters: 02_Convolution.cntk:train=[
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
] [SGD=[maxEpochs=3]]
|
||||
|
||||
05/13/2016 15:10:11: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/13/2016 15:10:11: Commands: train test
|
||||
05/13/2016 15:10:11: Precision = "float"
|
||||
05/13/2016 15:10:11: CNTKModelPath: /tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/Models/02_Convolution
|
||||
05/13/2016 15:10:11: CNTKCommandTrainInfo: train : 3
|
||||
05/13/2016 15:10:11: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
08/16/2016 10:49:51: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:49:51: Commands: train test
|
||||
08/16/2016 10:49:51: Precision = "float"
|
||||
08/16/2016 10:49:51: CNTKModelPath: /tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/Models/02_Convolution
|
||||
08/16/2016 10:49:51: CNTKCommandTrainInfo: train : 3
|
||||
08/16/2016 10:49:51: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
|
||||
05/13/2016 15:10:11: ##############################################################################
|
||||
05/13/2016 15:10:11: # #
|
||||
05/13/2016 15:10:11: # Action "train" #
|
||||
05/13/2016 15:10:11: # #
|
||||
05/13/2016 15:10:11: ##############################################################################
|
||||
08/16/2016 10:49:51: ##############################################################################
|
||||
08/16/2016 10:49:51: # #
|
||||
08/16/2016 10:49:51: # Action "train" #
|
||||
08/16/2016 10:49:51: # #
|
||||
08/16/2016 10:49:51: ##############################################################################
|
||||
|
||||
05/13/2016 15:10:11: CNTKCommandTrainBegin: train
|
||||
08/16/2016 10:49:51: CNTKCommandTrainBegin: train
|
||||
NDLBuilder Using GPU 0
|
||||
|
||||
05/13/2016 15:10:11: Creating virgin network.
|
||||
08/16/2016 10:49:51: Creating virgin network.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.000000.
|
||||
Node 'conv1.w.W' (LearnableParameter operation): Initializing Parameter[16 x 25] <- 0.000000.
|
||||
Node 'conv1.b.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 16] <- 0.000000.
|
||||
Node 'conv2.w.W' (LearnableParameter operation): Initializing Parameter[32 x 400] <- 0.000000.
|
||||
Node 'conv2.b.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 32] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[128 x 7 x 7 x 32] <- 0.000000.
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[128 x 1] <- 0.000000.
|
||||
Node 'ol.W' (LearnableParameter operation): Initializing Parameter[10 x 128] <- 0.000000.
|
||||
Node 'ol.b' (LearnableParameter operation): Initializing Parameter[10 x 1] <- 0.000000.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.003906.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.003906.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.003906.
|
||||
Node 'conv1.w.W' (LearnableParameter operation): Initializing Parameter[16 x 25] <- uniform(seed=1, range=0.050000*10.000000, onCPU=true).
|
||||
Node 'conv1.b.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 16] <- 1.000000.
|
||||
Node 'conv2.w.W' (LearnableParameter operation): Initializing Parameter[32 x 400] <- uniform(seed=2, range=0.050000*10.000000, onCPU=true).
|
||||
Node 'conv2.b.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 32] <- 1.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[128 x 7 x 7 x 32] <- uniform(seed=3, range=0.050000*1.000000, onCPU=true).
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[128 x 1] <- uniform(seed=4, range=0.050000*1.000000, onCPU=true).
|
||||
Node 'ol.W' (LearnableParameter operation): Initializing Parameter[10 x 128] <- uniform(seed=5, range=0.050000*1.000000, onCPU=true).
|
||||
Node 'ol.b' (LearnableParameter operation): Initializing Parameter[10 x 1] <- uniform(seed=6, range=0.050000*1.000000, onCPU=true).
|
||||
|
||||
Post-processing network...
|
||||
|
||||
3 roots:
|
||||
ce = CrossEntropyWithSoftmax()
|
||||
err = ErrorPrediction()
|
||||
errs = ErrorPrediction()
|
||||
ol.z = Plus()
|
||||
|
||||
Validating network. 27 nodes to process in pass 1.
|
||||
|
|
@ -315,7 +337,7 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 128], [128 x 1 x *] -> [10 x 1
|
|||
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
|
||||
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x 1 x *], [10 x 1] -> [10 x 1 x *]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
|
||||
Validating --> errs = ErrorPrediction (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
|
||||
|
||||
Validating network. 16 nodes to process in pass 2.
|
||||
|
||||
|
|
@ -323,113 +345,122 @@ Validating network. 16 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 28 x 28 x 1, Output: 28 x 28 x 16, Kernel: 5 x 5 x 1, Map: 1 x 1 x 16, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c.c: using cuDNN convolution engine for geometry: Input: 28 x 28 x 1, Output: 28 x 28 x 16, Kernel: 5 x 5 x 1, Map: 1 x 1 x 16, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 28 x 28 x 16, Output: 14 x 14 x 16, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 28 x 28 x 16, Output: 14 x 14 x 16, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 14 x 14 x 16, Output: 14 x 14 x 32, Kernel: 5 x 5 x 16, Map: 32, Stride: 1 x 1 x 16, Sharing: (1, 1, 1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c.c: using cuDNN convolution engine for geometry: Input: 14 x 14 x 16, Output: 14 x 14 x 32, Kernel: 5 x 5 x 16, Map: 32, Stride: 1 x 1 x 16, Sharing: (1, 1, 1), AutoPad: (1, 1, 0), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 14 x 14 x 32, Output: 7 x 7 x 32, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
|
||||
pool2.p: using cuDNN convolution engine for geometry: Input: 14 x 14 x 32, Output: 7 x 7 x 32, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1, 1, 1), AutoPad: (1, 1, 0), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.
|
||||
|
||||
|
||||
11 out of 27 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
05/13/2016 15:10:11: Created model with 27 nodes on GPU 0.
|
||||
08/16/2016 10:49:52: Created model with 27 nodes on GPU 0.
|
||||
|
||||
05/13/2016 15:10:11: Training criterion node(s):
|
||||
05/13/2016 15:10:11: ce = CrossEntropyWithSoftmax
|
||||
08/16/2016 10:49:52: Training criterion node(s):
|
||||
08/16/2016 10:49:52: ce = CrossEntropyWithSoftmax
|
||||
|
||||
05/13/2016 15:10:11: Evaluation criterion node(s):
|
||||
|
||||
05/13/2016 15:10:11: err = ErrorPrediction
|
||||
08/16/2016 10:49:52: Evaluation criterion node(s):
|
||||
08/16/2016 10:49:52: errs = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 49 matrices, 29 are shared as 13, and 20 are not shared.
|
||||
|
||||
(nil): {[err Gradient[1]] [featScale Gradient[1 x 1]] [featScaled Gradient[28 x 28 x 1 x *]] [features Gradient[28 x 28 x 1 x *]] [labels Gradient[10 x *]] }
|
||||
0x132d628: {[features Value[28 x 28 x 1 x *]] }
|
||||
0x1854138: {[featScale Value[1 x 1]] }
|
||||
0x1ca8388: {[labels Value[10 x *]] }
|
||||
0x1ca8b18: {[conv1.w.W Value[16 x 25]] }
|
||||
0x1ca9778: {[conv1.b.b Value[1 x 1 x 16]] }
|
||||
0x1caaa88: {[conv2.w.W Value[32 x 400]] }
|
||||
0x1cac278: {[conv2.b.b Value[1 x 1 x 32]] }
|
||||
0x1cb04f8: {[h1.W Value[128 x 7 x 7 x 32]] }
|
||||
0x1cb1728: {[h1.b Value[128 x 1]] }
|
||||
0x1cb2318: {[ol.W Value[10 x 128]] }
|
||||
0x1cb3468: {[ol.b Value[10 x 1]] }
|
||||
0x7f427f204c08: {[conv1.c.c Value[28 x 28 x 16 x *]] }
|
||||
0x7f427f20bd48: {[h1.b Gradient[128 x 1]] [h1.y Gradient[128 x 1 x *]] }
|
||||
0x7f427f4d3118: {[err Value[1]] }
|
||||
0x7f427f4e3b08: {[featScaled Value[28 x 28 x 1 x *]] }
|
||||
0x7f427f4e3db8: {[conv1.cpb Value[28 x 28 x 16 x *]] [conv1.w.W Gradient[16 x 25]] }
|
||||
0x7f427f4e42d8: {[conv1.c.c Gradient[28 x 28 x 16 x *]] [conv1.out Value[28 x 28 x 16 x *]] }
|
||||
0x7f427f4e4498: {[conv1.cpb Gradient[28 x 28 x 16 x *]] [pool1 Value[14 x 14 x 16 x *]] }
|
||||
0x7f427f4e4658: {[conv2.c.c Value[14 x 14 x 32 x *]] }
|
||||
0x7f427f4e4818: {[conv1.b.b Gradient[1 x 1 x 16]] [conv1.out Gradient[28 x 28 x 16 x *]] }
|
||||
0x7f427f4e49d8: {[conv2.cpb Value[14 x 14 x 32 x *]] [conv2.w.W Gradient[32 x 400]] }
|
||||
0x7f427f4e4b98: {[conv2.c.c Gradient[14 x 14 x 32 x *]] [conv2.out Value[14 x 14 x 32 x *]] }
|
||||
0x7f427f4e4d58: {[conv2.cpb Gradient[14 x 14 x 32 x *]] [pool1 Gradient[14 x 14 x 16 x *]] [pool2.p Value[7 x 7 x 32 x *]] }
|
||||
0x7f427f4e4f18: {[conv2.b.b Gradient[1 x 1 x 32]] [conv2.out Gradient[14 x 14 x 32 x *]] [h1.t Value[128 x *]] }
|
||||
0x7f427f4e50d8: {[h1.W Gradient[128 x 7 x 7 x 32]] [h1.z Value[128 x 1 x *]] }
|
||||
0x7f427f4e5298: {[h1.t Gradient[128 x *]] [h1.y Value[128 x 1 x *]] }
|
||||
0x7f427f4e5458: {[h1.z Gradient[128 x 1 x *]] [ol.t Value[10 x 1 x *]] [pool2.p Gradient[7 x 7 x 32 x *]] }
|
||||
0x7f427f4e5f38: {[ce Gradient[1]] }
|
||||
0x7f427f4e60f8: {[ol.W Gradient[10 x 128]] [ol.z Gradient[10 x 1 x *]] }
|
||||
0x7f427f4e62b8: {[ol.t Gradient[10 x 1 x *]] }
|
||||
0x7f427f4e6478: {[ol.b Gradient[10 x 1]] }
|
||||
0x7f427f4ff658: {[ce Value[1]] }
|
||||
0x7f427f4ffea8: {[ol.z Value[10 x 1 x *]] }
|
||||
|
||||
05/13/2016 15:10:11: No PreCompute nodes found, skipping PreCompute step.
|
||||
|
||||
05/13/2016 15:10:11: Starting Epoch 1: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
|
||||
05/13/2016 15:10:11: Starting minibatch loop.
|
||||
05/13/2016 15:10:13: Epoch[ 1 of 3]-Minibatch[1-500, 26.67%]: ce = 1.05460791 * 16000; errs = 35.2563% * 16000; time = 2.0377s; samplesPerSecond = 7852.2
|
||||
05/13/2016 15:10:14: Epoch[ 1 of 3]-Minibatch[501-1000, 53.33%]: ce = 0.16176135 * 16000; errs = 4.425% * 16000; time = 0.9884s; samplesPerSecond = 16187.9
|
||||
05/13/2016 15:10:15: Epoch[ 1 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.10889783 * 16000; errs = 3.04375% * 16000; time = 0.9868s; samplesPerSecond = 16214.2
|
||||
05/13/2016 15:10:16: Finished Epoch[ 1 of 3]: [Training] ce = 0.37214827 * 60000; errs = 11.9817% * 60000; totalSamplesSeen = 60000; learningRatePerSample = 0.003125; epochTime=4.77593s
|
||||
05/13/2016 15:10:16: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/Models/02_Convolution.1'
|
||||
|
||||
05/13/2016 15:10:16: Starting Epoch 2: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
|
||||
05/13/2016 15:10:16: Starting minibatch loop.
|
||||
05/13/2016 15:10:17: Epoch[ 2 of 3]-Minibatch[1-500, 26.67%]: ce = 0.07178102 * 16000; errs = 2.20625% * 16000; time = 0.9982s; samplesPerSecond = 16029.6
|
||||
05/13/2016 15:10:18: Epoch[ 2 of 3]-Minibatch[501-1000, 53.33%]: ce = 0.06225394 * 16000; errs = 1.8% * 16000; time = 0.9949s; samplesPerSecond = 16082.6
|
||||
05/13/2016 15:10:19: Epoch[ 2 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.06624252 * 16000; errs = 2.025% * 16000; time = 0.9961s; samplesPerSecond = 16062.5
|
||||
05/13/2016 15:10:19: Finished Epoch[ 2 of 3]: [Training] ce = 0.06652122 * 60000; errs = 1.995% * 60000; totalSamplesSeen = 120000; learningRatePerSample = 0.003125; epochTime=3.74643s
|
||||
05/13/2016 15:10:20: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/Models/02_Convolution.2'
|
||||
|
||||
05/13/2016 15:10:20: Starting Epoch 3: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
|
||||
05/13/2016 15:10:20: Starting minibatch loop.
|
||||
05/13/2016 15:10:21: Epoch[ 3 of 3]-Minibatch[1-500, 26.67%]: ce = 0.04257084 * 16000; errs = 1.25625% * 16000; time = 0.9942s; samplesPerSecond = 16093.1
|
||||
05/13/2016 15:10:21: Epoch[ 3 of 3]-Minibatch[501-1000, 53.33%]: ce = 0.04675156 * 16000; errs = 1.41875% * 16000; time = 0.9927s; samplesPerSecond = 16118.2
|
||||
05/13/2016 15:10:22: Epoch[ 3 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.04904524 * 16000; errs = 1.475% * 16000; time = 0.9901s; samplesPerSecond = 16160.8
|
||||
05/13/2016 15:10:23: Finished Epoch[ 3 of 3]: [Training] ce = 0.04529028 * 60000; errs = 1.36667% * 60000; totalSamplesSeen = 180000; learningRatePerSample = 0.003125; epochTime=3.73418s
|
||||
05/13/2016 15:10:23: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_02_Convolution@release_gpu/Models/02_Convolution'
|
||||
05/13/2016 15:10:23: CNTKCommandTrainEnd: train
|
||||
|
||||
05/13/2016 15:10:23: Action "train" complete.
|
||||
{ conv1.cpb : [28 x 28 x 16 x *]
|
||||
conv1.w.W : [16 x 25] (gradient) }
|
||||
{ conv1.c.c : [28 x 28 x 16 x *] (gradient)
|
||||
conv1.out : [28 x 28 x 16 x *] }
|
||||
{ conv1.cpb : [28 x 28 x 16 x *] (gradient)
|
||||
pool1 : [14 x 14 x 16 x *] }
|
||||
{ conv1.b.b : [1 x 1 x 16] (gradient)
|
||||
conv1.out : [28 x 28 x 16 x *] (gradient) }
|
||||
{ conv2.cpb : [14 x 14 x 32 x *]
|
||||
conv2.w.W : [32 x 400] (gradient) }
|
||||
{ conv2.c.c : [14 x 14 x 32 x *] (gradient)
|
||||
conv2.out : [14 x 14 x 32 x *] }
|
||||
{ conv2.cpb : [14 x 14 x 32 x *] (gradient)
|
||||
pool1 : [14 x 14 x 16 x *] (gradient)
|
||||
pool2.p : [7 x 7 x 32 x *] }
|
||||
{ conv2.b.b : [1 x 1 x 32] (gradient)
|
||||
conv2.out : [14 x 14 x 32 x *] (gradient)
|
||||
h1.t : [128 x *] }
|
||||
{ h1.W : [128 x 7 x 7 x 32] (gradient)
|
||||
h1.z : [128 x 1 x *] }
|
||||
{ h1.t : [128 x *] (gradient)
|
||||
h1.y : [128 x 1 x *] }
|
||||
{ h1.z : [128 x 1 x *] (gradient)
|
||||
ol.t : [10 x 1 x *]
|
||||
pool2.p : [7 x 7 x 32 x *] (gradient) }
|
||||
{ ol.W : [10 x 128] (gradient)
|
||||
ol.z : [10 x 1 x *] (gradient) }
|
||||
{ h1.b : [128 x 1] (gradient)
|
||||
h1.y : [128 x 1 x *] (gradient) }
|
||||
|
||||
|
||||
05/13/2016 15:10:23: ##############################################################################
|
||||
05/13/2016 15:10:23: # #
|
||||
05/13/2016 15:10:23: # Action "test" #
|
||||
05/13/2016 15:10:23: # #
|
||||
05/13/2016 15:10:23: ##############################################################################
|
||||
08/16/2016 10:49:52: Training 215370 parameters in 8 out of 8 parameter tensors and 22 nodes with gradient:
|
||||
|
||||
08/16/2016 10:49:52: Node 'conv1.b.b' (LearnableParameter operation) : [1 x 1 x 16]
|
||||
08/16/2016 10:49:52: Node 'conv1.w.W' (LearnableParameter operation) : [16 x 25]
|
||||
08/16/2016 10:49:52: Node 'conv2.b.b' (LearnableParameter operation) : [1 x 1 x 32]
|
||||
08/16/2016 10:49:52: Node 'conv2.w.W' (LearnableParameter operation) : [32 x 400]
|
||||
08/16/2016 10:49:52: Node 'h1.W' (LearnableParameter operation) : [128 x 7 x 7 x 32]
|
||||
08/16/2016 10:49:52: Node 'h1.b' (LearnableParameter operation) : [128 x 1]
|
||||
08/16/2016 10:49:52: Node 'ol.W' (LearnableParameter operation) : [10 x 128]
|
||||
08/16/2016 10:49:52: Node 'ol.b' (LearnableParameter operation) : [10 x 1]
|
||||
|
||||
08/16/2016 10:49:52: No PreCompute nodes found, or all already computed. Skipping pre-computation step.
|
||||
|
||||
08/16/2016 10:49:52: Starting Epoch 1: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..60000] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:49:52: Starting minibatch loop.
|
||||
08/16/2016 10:49:54: Epoch[ 1 of 3]-Minibatch[ 1- 500, 26.67%]: ce = 1.04692261 * 16000; errs = 35.156% * 16000; time = 2.0092s; samplesPerSecond = 7963.4
|
||||
08/16/2016 10:49:55: Epoch[ 1 of 3]-Minibatch[ 501-1000, 53.33%]: ce = 0.17001367 * 16000; errs = 4.913% * 16000; time = 0.9653s; samplesPerSecond = 16575.7
|
||||
08/16/2016 10:49:56: Epoch[ 1 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.10910559 * 16000; errs = 3.169% * 16000; time = 0.9622s; samplesPerSecond = 16627.8
|
||||
08/16/2016 10:49:56: Finished Epoch[ 1 of 3]: [Training] ce = 0.37089482 * 60000; errs = 12.038% * 60000; totalSamplesSeen = 60000; learningRatePerSample = 0.003125; epochTime=4.67495s
|
||||
08/16/2016 10:49:56: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/Models/02_Convolution.1'
|
||||
|
||||
08/16/2016 10:49:56: Starting Epoch 2: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [60000..120000] (first sequence at sample 60000), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:49:56: Starting minibatch loop.
|
||||
08/16/2016 10:49:57: Epoch[ 2 of 3]-Minibatch[ 1- 500, 26.67%]: ce = 0.07433912 * 16000; errs = 2.369% * 16000; time = 0.9731s; samplesPerSecond = 16442.2
|
||||
08/16/2016 10:49:58: Epoch[ 2 of 3]-Minibatch[ 501-1000, 53.33%]: ce = 0.06223948 * 16000; errs = 1.875% * 16000; time = 0.9738s; samplesPerSecond = 16430.7
|
||||
08/16/2016 10:49:59: Epoch[ 2 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.06269952 * 16000; errs = 1.812% * 16000; time = 0.9724s; samplesPerSecond = 16453.8
|
||||
08/16/2016 10:50:00: Finished Epoch[ 2 of 3]: [Training] ce = 0.06625302 * 60000; errs = 2.018% * 60000; totalSamplesSeen = 120000; learningRatePerSample = 0.003125; epochTime=3.6549s
|
||||
08/16/2016 10:50:00: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/Models/02_Convolution.2'
|
||||
|
||||
08/16/2016 10:50:00: Starting Epoch 3: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [120000..180000] (first sequence at sample 120000), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:50:00: Starting minibatch loop.
|
||||
08/16/2016 10:50:01: Epoch[ 3 of 3]-Minibatch[ 1- 500, 26.67%]: ce = 0.04532548 * 16000; errs = 1.319% * 16000; time = 0.9745s; samplesPerSecond = 16419.1
|
||||
08/16/2016 10:50:02: Epoch[ 3 of 3]-Minibatch[ 501-1000, 53.33%]: ce = 0.04296139 * 16000; errs = 1.256% * 16000; time = 0.9719s; samplesPerSecond = 16463.3
|
||||
08/16/2016 10:50:03: Epoch[ 3 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.04916875 * 16000; errs = 1.456% * 16000; time = 0.9710s; samplesPerSecond = 16477.7
|
||||
08/16/2016 10:50:04: Finished Epoch[ 3 of 3]: [Training] ce = 0.04531107 * 60000; errs = 1.337% * 60000; totalSamplesSeen = 180000; learningRatePerSample = 0.003125; epochTime=3.65691s
|
||||
08/16/2016 10:50:04: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_02_Convolution@release_gpu/Models/02_Convolution'
|
||||
08/16/2016 10:50:04: CNTKCommandTrainEnd: train
|
||||
|
||||
08/16/2016 10:50:04: Action "train" complete.
|
||||
|
||||
|
||||
08/16/2016 10:50:04: ##############################################################################
|
||||
08/16/2016 10:50:04: # #
|
||||
08/16/2016 10:50:04: # Action "test" #
|
||||
08/16/2016 10:50:04: # #
|
||||
08/16/2016 10:50:04: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
||||
3 roots:
|
||||
ce = CrossEntropyWithSoftmax()
|
||||
err = ErrorPrediction()
|
||||
errs = ErrorPrediction()
|
||||
ol.z = Plus()
|
||||
|
||||
Validating network. 27 nodes to process in pass 1.
|
||||
|
|
@ -460,7 +491,7 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 128], [128 x 1 x *1] -> [10 x 1
|
|||
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
|
||||
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x 1 x *1], [10 x 1] -> [10 x 1 x *1]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
|
||||
Validating --> errs = ErrorPrediction (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
|
||||
|
||||
Validating network. 16 nodes to process in pass 2.
|
||||
|
||||
|
|
@ -468,13 +499,13 @@ Validating network. 16 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 28 x 28 x 1, Output: 28 x 28 x 16, Kernel: 5 x 5 x 1, Map: 1 x 1 x 16, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c.c: using cuDNN convolution engine for geometry: Input: 28 x 28 x 1, Output: 28 x 28 x 16, Kernel: 5 x 5 x 1, Map: 1 x 1 x 16, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 28 x 28 x 16, Output: 14 x 14 x 16, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 28 x 28 x 16, Output: 14 x 14 x 16, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 14 x 14 x 16, Output: 14 x 14 x 32, Kernel: 5 x 5 x 16, Map: 32, Stride: 1 x 1 x 16, Sharing: (1, 1, 1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c.c: using cuDNN convolution engine for geometry: Input: 14 x 14 x 16, Output: 14 x 14 x 32, Kernel: 5 x 5 x 16, Map: 32, Stride: 1 x 1 x 16, Sharing: (1, 1, 1), AutoPad: (1, 1, 0), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 14 x 14 x 32, Output: 7 x 7 x 32, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
|
||||
pool2.p: using cuDNN convolution engine for geometry: Input: 14 x 14 x 32, Output: 7 x 7 x 32, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1, 1, 1), AutoPad: (1, 1, 0), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.
|
||||
|
||||
|
||||
11 out of 27 nodes do not share the minibatch layout with the input data.
|
||||
|
|
@ -486,39 +517,13 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 27 matrices, 0 are shared as 0, and 27 are not shared.
|
||||
|
||||
(nil): {[ce Gradient[1]] [conv1.b.b Gradient[1 x 1 x 16]] [conv1.c.c Gradient[28 x 28 x 16 x *1]] [conv1.cpb Gradient[28 x 28 x 16 x *1]] [conv1.out Gradient[28 x 28 x 16 x *1]] [conv1.w.W Gradient[16 x 25]] [conv2.b.b Gradient[1 x 1 x 32]] [conv2.c.c Gradient[14 x 14 x 32 x *1]] [conv2.cpb Gradient[14 x 14 x 32 x *1]] [conv2.out Gradient[14 x 14 x 32 x *1]] [conv2.w.W Gradient[32 x 400]] [err Gradient[1]] [featScale Gradient[1 x 1]] [featScaled Gradient[28 x 28 x 1 x *1]] [features Gradient[28 x 28 x 1 x *1]] [h1.W Gradient[128 x 7 x 7 x 32]] [h1.b Gradient[128 x 1]] [h1.t Gradient[128 x *1]] [h1.y Gradient[128 x 1 x *1]] [h1.z Gradient[128 x 1 x *1]] [labels Gradient[10 x *1]] [ol.W Gradient[10 x 128]] [ol.b Gradient[10 x 1]] [ol.t Gradient[10 x 1 x *1]] [ol.z Gradient[10 x 1 x *1]] [pool1 Gradient[14 x 14 x 16 x *1]] [pool2.p Gradient[7 x 7 x 32 x *1]] }
|
||||
0x7f4274adf028: {[conv1.b.b Value[1 x 1 x 16]] }
|
||||
0x7f4274adfe98: {[h1.b Value[128 x 1]] }
|
||||
0x7f427ae42308: {[err Value[1]] }
|
||||
0x7f427ae62498: {[featScaled Value[28 x 28 x 1 x *1]] }
|
||||
0x7f427ae62748: {[conv1.c.c Value[28 x 28 x 16 x *1]] }
|
||||
0x7f427ae62c08: {[conv1.cpb Value[28 x 28 x 16 x *1]] }
|
||||
0x7f427ae62dc8: {[conv1.out Value[28 x 28 x 16 x *1]] }
|
||||
0x7f427ae62f88: {[pool1 Value[14 x 14 x 16 x *1]] }
|
||||
0x7f427ae63148: {[conv2.c.c Value[14 x 14 x 32 x *1]] }
|
||||
0x7f427ae634c8: {[conv2.cpb Value[14 x 14 x 32 x *1]] }
|
||||
0x7f427ae63688: {[conv2.out Value[14 x 14 x 32 x *1]] }
|
||||
0x7f427ae63848: {[pool2.p Value[7 x 7 x 32 x *1]] }
|
||||
0x7f427ae646a8: {[labels Value[10 x *1]] }
|
||||
0x7f427ae64b18: {[ol.b Value[10 x 1]] }
|
||||
0x7f427ae668a8: {[conv2.w.W Value[32 x 400]] }
|
||||
0x7f427ae72368: {[h1.W Value[128 x 7 x 7 x 32]] }
|
||||
0x7f427f20cb08: {[ol.W Value[10 x 128]] }
|
||||
0x7f427f20e888: {[featScale Value[1 x 1]] }
|
||||
0x7f427f20ea48: {[features Value[28 x 28 x 1 x *1]] }
|
||||
0x7f427f4d37a8: {[conv1.w.W Value[16 x 25]] }
|
||||
0x7f427f4d3968: {[conv2.b.b Value[1 x 1 x 32]] }
|
||||
0x7f427f4e2108: {[ce Value[1]] }
|
||||
0x7f427f4fcea8: {[h1.t Value[128 x *1]] }
|
||||
0x7f427f4fd068: {[h1.z Value[128 x 1 x *1]] }
|
||||
0x7f427f4fd228: {[h1.y Value[128 x 1 x *1]] }
|
||||
0x7f427f4fd3e8: {[ol.t Value[10 x 1 x *1]] }
|
||||
0x7f427f4fd5a8: {[ol.z Value[10 x 1 x *1]] }
|
||||
|
||||
05/13/2016 15:10:28: Final Results: Minibatch[1-10]: errs = 1.46% * 10000; ce = 0.04549626 * 10000; perplexity = 1.04654709
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
08/16/2016 10:50:04: Minibatch[1-10]: errs = 1.580% * 10000; ce = 0.05054137 * 10000
|
||||
08/16/2016 10:50:04: Final Results: Minibatch[1-10]: errs = 1.580% * 10000; ce = 0.05054137 * 10000; perplexity = 1.05184038
|
||||
|
||||
05/13/2016 15:10:28: Action "test" complete.
|
||||
08/16/2016 10:50:04: Action "test" complete.
|
||||
|
||||
05/13/2016 15:10:28: __COMPLETED__
|
||||
08/16/2016 10:50:04: __COMPLETED__
|
||||
|
|
@ -1,63 +1,77 @@
|
|||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/Config/02_Convolution.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 268381192 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/02_Convolution.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 13 2016 08:06:01
|
||||
Last modified date: Thu May 12 07:31:50 2016
|
||||
Built time: Aug 16 2016 02:54:53
|
||||
Last modified date: Fri Aug 12 05:31:21 2016
|
||||
Build type: Release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: no
|
||||
Math lib: mkl
|
||||
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
CUB_PATH: c:\src\cub-1.4.1
|
||||
CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by svcphil on Philly-Pool3
|
||||
Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu\TestData
|
||||
05/13/2016 08:16:16: -------------------------------------------------------------------
|
||||
05/13/2016 08:16:16: Build info:
|
||||
Changed current directory to C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu\TestData
|
||||
08/16/2016 03:01:04: -------------------------------------------------------------------
|
||||
08/16/2016 03:01:04: Build info:
|
||||
|
||||
05/13/2016 08:16:16: Built time: May 13 2016 08:06:01
|
||||
05/13/2016 08:16:16: Last modified date: Thu May 12 07:31:50 2016
|
||||
05/13/2016 08:16:16: Build type: Release
|
||||
05/13/2016 08:16:16: Build target: GPU
|
||||
05/13/2016 08:16:16: With 1bit-SGD: no
|
||||
05/13/2016 08:16:16: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
05/13/2016 08:16:16: CUB_PATH: c:\src\cub-1.4.1
|
||||
05/13/2016 08:16:16: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
05/13/2016 08:16:16: Build Branch: HEAD
|
||||
05/13/2016 08:16:16: Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
05/13/2016 08:16:16: Built by svcphil on Philly-Pool3
|
||||
05/13/2016 08:16:16: Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
05/13/2016 08:16:16: -------------------------------------------------------------------
|
||||
08/16/2016 03:01:04: Built time: Aug 16 2016 02:54:53
|
||||
08/16/2016 03:01:04: Last modified date: Fri Aug 12 05:31:21 2016
|
||||
08/16/2016 03:01:04: Build type: Release
|
||||
08/16/2016 03:01:04: Build target: GPU
|
||||
08/16/2016 03:01:04: With 1bit-SGD: no
|
||||
08/16/2016 03:01:04: Math lib: mkl
|
||||
08/16/2016 03:01:04: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
08/16/2016 03:01:04: CUB_PATH: c:\src\cub-1.4.1
|
||||
08/16/2016 03:01:04: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
08/16/2016 03:01:04: Build Branch: HEAD
|
||||
08/16/2016 03:01:04: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 03:01:04: Built by svcphil on Philly-Pool3
|
||||
08/16/2016 03:01:04: Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
08/16/2016 03:01:04: -------------------------------------------------------------------
|
||||
08/16/2016 03:01:07: -------------------------------------------------------------------
|
||||
08/16/2016 03:01:07: GPU info:
|
||||
|
||||
05/13/2016 08:16:16: Running on Philly-Pool2 at 2016/05/13 08:16:16
|
||||
05/13/2016 08:16:16: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/Config/02_Convolution.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
08/16/2016 03:01:07: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:01:07: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:01:07: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:01:07: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:01:07: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 03:01:07: Running on DPHAIM-24 at 2016/08/16 03:01:07
|
||||
08/16/2016 03:01:07: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/02_Convolution.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
|
||||
|
||||
|
||||
05/13/2016 08:16:16: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 08:16:16: RootDir = ".."
|
||||
ConfigDir = "$RootDir$/Config"
|
||||
DataDir = "$RootDir$/Data"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
ModelDir = "$OutputDir$/Models"
|
||||
08/16/2016 03:01:07: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:01:07: rootDir = ".."
|
||||
configDir = "$rootDir$/Config"
|
||||
dataDir = "$rootDir$/Data"
|
||||
outputDir = "$rootDir$/Output"
|
||||
modelDir = "$outputDir$/Models"
|
||||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
command = train:test
|
||||
precision = "float"
|
||||
modelPath = "$ModelDir$/02_Convolution"
|
||||
ndlMacros = "$ConfigDir$/Macros.ndl"
|
||||
traceLevel=1
|
||||
numMBsToShowResult=500
|
||||
prefetch=true
|
||||
initOnCPUOnly=true
|
||||
modelPath = "$modelDir$/02_Convolution"
|
||||
traceLevel = 1
|
||||
numMBsToShowResult = 500
|
||||
train = [
|
||||
action = "train"
|
||||
NDLNetworkBuilder = [
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly = true
|
||||
ndlMacros = "$configDir$/Macros.ndl"
|
||||
networkDescription = "$ConfigDir$/02_Convolution.ndl"
|
||||
]
|
||||
SGD = [
|
||||
|
|
@ -80,21 +94,18 @@ train = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
test = [
|
||||
action = test
|
||||
minibatchSize = 16
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "$ConfigDir$/02_Convolution.ndl"
|
||||
]
|
||||
minibatchSize = 1024
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "$DataDir$/Test-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
format = "dense"
|
||||
dim = 784
|
||||
format = "dense"
|
||||
]
|
||||
labels = [
|
||||
dim = 10
|
||||
|
|
@ -103,37 +114,36 @@ test = [
|
|||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu\TestData
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu\TestData
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
train=[SGD=[maxEpochs=3]]
|
||||
imageLayout="cudnn"
|
||||
|
||||
05/13/2016 08:16:16: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:01:07: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 08:16:16: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 08:16:16: RootDir = ".."
|
||||
ConfigDir = "../Config"
|
||||
DataDir = "../Data"
|
||||
OutputDir = "../Output"
|
||||
ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu/Models"
|
||||
08/16/2016 03:01:07: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:01:07: rootDir = ".."
|
||||
configDir = "../Config"
|
||||
dataDir = "../Data"
|
||||
outputDir = "../Output"
|
||||
modelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu/Models"
|
||||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
command = train:test
|
||||
precision = "float"
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu/Models/02_Convolution"
|
||||
ndlMacros = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/Macros.ndl"
|
||||
traceLevel=1
|
||||
numMBsToShowResult=500
|
||||
prefetch=true
|
||||
initOnCPUOnly=true
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu/Models/02_Convolution"
|
||||
traceLevel = 1
|
||||
numMBsToShowResult = 500
|
||||
train = [
|
||||
action = "train"
|
||||
NDLNetworkBuilder = [
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly = true
|
||||
ndlMacros = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/Macros.ndl"
|
||||
networkDescription = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/02_Convolution.ndl"
|
||||
]
|
||||
SGD = [
|
||||
|
|
@ -145,7 +155,7 @@ train = [
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu\TestData/Train-28x28_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu\TestData/Train-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
|
|
@ -156,21 +166,18 @@ train = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
test = [
|
||||
action = test
|
||||
minibatchSize = 16
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/02_Convolution.ndl"
|
||||
]
|
||||
minibatchSize = 1024
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu\TestData/Test-28x28_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu\TestData/Test-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
format = "dense"
|
||||
dim = 784
|
||||
format = "dense"
|
||||
]
|
||||
labels = [
|
||||
dim = 10
|
||||
|
|
@ -179,48 +186,42 @@ test = [
|
|||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu\TestData
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu\TestData
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
train=[SGD=[maxEpochs=3]]
|
||||
imageLayout="cudnn"
|
||||
|
||||
05/13/2016 08:16:16: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:01:07: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 08:16:16: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:01:07: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: 02_Convolution.cntk:command=train:test
|
||||
configparameters: 02_Convolution.cntk:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config
|
||||
configparameters: 02_Convolution.cntk:currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu\TestData
|
||||
configparameters: 02_Convolution.cntk:DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu\TestData
|
||||
configparameters: 02_Convolution.cntk:configDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config
|
||||
configparameters: 02_Convolution.cntk:currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu\TestData
|
||||
configparameters: 02_Convolution.cntk:dataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu\TestData
|
||||
configparameters: 02_Convolution.cntk:deviceId=0
|
||||
configparameters: 02_Convolution.cntk:imageLayout=cudnn
|
||||
configparameters: 02_Convolution.cntk:initOnCPUOnly=true
|
||||
configparameters: 02_Convolution.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu/Models
|
||||
configparameters: 02_Convolution.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu/Models/02_Convolution
|
||||
configparameters: 02_Convolution.cntk:ndlMacros=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/Macros.ndl
|
||||
configparameters: 02_Convolution.cntk:modelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu/Models
|
||||
configparameters: 02_Convolution.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu/Models/02_Convolution
|
||||
configparameters: 02_Convolution.cntk:numMBsToShowResult=500
|
||||
configparameters: 02_Convolution.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu
|
||||
configparameters: 02_Convolution.cntk:outputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu
|
||||
configparameters: 02_Convolution.cntk:precision=float
|
||||
configparameters: 02_Convolution.cntk:prefetch=true
|
||||
configparameters: 02_Convolution.cntk:RootDir=..
|
||||
configparameters: 02_Convolution.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu
|
||||
configparameters: 02_Convolution.cntk:rootDir=..
|
||||
configparameters: 02_Convolution.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu
|
||||
configparameters: 02_Convolution.cntk:test=[
|
||||
action = test
|
||||
minibatchSize = 16
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/02_Convolution.ndl"
|
||||
]
|
||||
minibatchSize = 1024
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu\TestData/Test-28x28_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu\TestData/Test-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
format = "dense"
|
||||
dim = 784
|
||||
format = "dense"
|
||||
]
|
||||
labels = [
|
||||
dim = 10
|
||||
|
|
@ -235,6 +236,9 @@ configparameters: 02_Convolution.cntk:traceLevel=1
|
|||
configparameters: 02_Convolution.cntk:train=[
|
||||
action = "train"
|
||||
NDLNetworkBuilder = [
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly = true
|
||||
ndlMacros = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/Macros.ndl"
|
||||
networkDescription = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/02_Convolution.ndl"
|
||||
]
|
||||
SGD = [
|
||||
|
|
@ -246,7 +250,7 @@ configparameters: 02_Convolution.cntk:train=[
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu\TestData/Train-28x28_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu\TestData/Train-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
|
|
@ -257,32 +261,52 @@ configparameters: 02_Convolution.cntk:train=[
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
] [SGD=[maxEpochs=3]]
|
||||
|
||||
05/13/2016 08:16:16: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/13/2016 08:16:16: Commands: train test
|
||||
05/13/2016 08:16:16: Precision = "float"
|
||||
05/13/2016 08:16:16: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu/Models/02_Convolution
|
||||
05/13/2016 08:16:16: CNTKCommandTrainInfo: train : 3
|
||||
05/13/2016 08:16:16: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
08/16/2016 03:01:07: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:01:07: Commands: train test
|
||||
08/16/2016 03:01:07: Precision = "float"
|
||||
08/16/2016 03:01:07: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu/Models/02_Convolution
|
||||
08/16/2016 03:01:07: CNTKCommandTrainInfo: train : 3
|
||||
08/16/2016 03:01:07: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
|
||||
05/13/2016 08:16:16: ##############################################################################
|
||||
05/13/2016 08:16:16: # #
|
||||
05/13/2016 08:16:16: # Action "train" #
|
||||
05/13/2016 08:16:16: # #
|
||||
05/13/2016 08:16:16: ##############################################################################
|
||||
08/16/2016 03:01:07: ##############################################################################
|
||||
08/16/2016 03:01:07: # #
|
||||
08/16/2016 03:01:07: # Action "train" #
|
||||
08/16/2016 03:01:07: # #
|
||||
08/16/2016 03:01:07: ##############################################################################
|
||||
|
||||
05/13/2016 08:16:16: CNTKCommandTrainBegin: train
|
||||
08/16/2016 03:01:07: CNTKCommandTrainBegin: train
|
||||
NDLBuilder Using GPU 0
|
||||
|
||||
05/13/2016 08:16:16: Creating virgin network.
|
||||
08/16/2016 03:01:07: Creating virgin network.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.000000.
|
||||
Node 'conv1.w.W' (LearnableParameter operation): Initializing Parameter[16 x 25] <- 0.000000.
|
||||
Node 'conv1.b.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 16] <- 0.000000.
|
||||
Node 'conv2.w.W' (LearnableParameter operation): Initializing Parameter[32 x 400] <- 0.000000.
|
||||
Node 'conv2.b.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 32] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[128 x 7 x 7 x 32] <- 0.000000.
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[128 x 1] <- 0.000000.
|
||||
Node 'ol.W' (LearnableParameter operation): Initializing Parameter[10 x 128] <- 0.000000.
|
||||
Node 'ol.b' (LearnableParameter operation): Initializing Parameter[10 x 1] <- 0.000000.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.003906.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.003906.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.003906.
|
||||
Node 'conv1.w.W' (LearnableParameter operation): Initializing Parameter[16 x 25] <- uniform(seed=1, range=0.050000*10.000000, onCPU=true).
|
||||
Node 'conv1.b.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 16] <- 1.000000.
|
||||
Node 'conv2.w.W' (LearnableParameter operation): Initializing Parameter[32 x 400] <- uniform(seed=2, range=0.050000*10.000000, onCPU=true).
|
||||
Node 'conv2.b.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 32] <- 1.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[128 x 7 x 7 x 32] <- uniform(seed=3, range=0.050000*1.000000, onCPU=true).
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[128 x 1] <- uniform(seed=4, range=0.050000*1.000000, onCPU=true).
|
||||
Node 'ol.W' (LearnableParameter operation): Initializing Parameter[10 x 128] <- uniform(seed=5, range=0.050000*1.000000, onCPU=true).
|
||||
Node 'ol.b' (LearnableParameter operation): Initializing Parameter[10 x 1] <- uniform(seed=6, range=0.050000*1.000000, onCPU=true).
|
||||
|
||||
Post-processing network...
|
||||
|
||||
3 roots:
|
||||
ce = CrossEntropyWithSoftmax()
|
||||
err = ErrorPrediction()
|
||||
errs = ErrorPrediction()
|
||||
ol.z = Plus()
|
||||
|
||||
Validating network. 27 nodes to process in pass 1.
|
||||
|
|
@ -313,7 +337,7 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 128], [128 x 1 x *] -> [10 x 1
|
|||
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
|
||||
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x 1 x *], [10 x 1] -> [10 x 1 x *]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
|
||||
Validating --> errs = ErrorPrediction (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
|
||||
|
||||
Validating network. 16 nodes to process in pass 2.
|
||||
|
||||
|
|
@ -321,113 +345,122 @@ Validating network. 16 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 28 x 28 x 1, Output: 28 x 28 x 16, Kernel: 5 x 5 x 1, Map: 1 x 1 x 16, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c.c: using cuDNN convolution engine for geometry: Input: 28 x 28 x 1, Output: 28 x 28 x 16, Kernel: 5 x 5 x 1, Map: 1 x 1 x 16, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 28 x 28 x 16, Output: 14 x 14 x 16, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 28 x 28 x 16, Output: 14 x 14 x 16, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 14 x 14 x 16, Output: 14 x 14 x 32, Kernel: 5 x 5 x 16, Map: 32, Stride: 1 x 1 x 16, Sharing: (1, 1, 1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c.c: using cuDNN convolution engine for geometry: Input: 14 x 14 x 16, Output: 14 x 14 x 32, Kernel: 5 x 5 x 16, Map: 32, Stride: 1 x 1 x 16, Sharing: (1, 1, 1), AutoPad: (1, 1, 0), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 14 x 14 x 32, Output: 7 x 7 x 32, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
|
||||
pool2.p: using cuDNN convolution engine for geometry: Input: 14 x 14 x 32, Output: 7 x 7 x 32, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1, 1, 1), AutoPad: (1, 1, 0), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.
|
||||
|
||||
|
||||
11 out of 27 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
05/13/2016 08:16:18: Created model with 27 nodes on GPU 0.
|
||||
08/16/2016 03:01:08: Created model with 27 nodes on GPU 0.
|
||||
|
||||
05/13/2016 08:16:18: Training criterion node(s):
|
||||
05/13/2016 08:16:18: ce = CrossEntropyWithSoftmax
|
||||
08/16/2016 03:01:08: Training criterion node(s):
|
||||
08/16/2016 03:01:08: ce = CrossEntropyWithSoftmax
|
||||
|
||||
05/13/2016 08:16:18: Evaluation criterion node(s):
|
||||
|
||||
05/13/2016 08:16:18: err = ErrorPrediction
|
||||
08/16/2016 03:01:08: Evaluation criterion node(s):
|
||||
08/16/2016 03:01:08: errs = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 49 matrices, 29 are shared as 13, and 20 are not shared.
|
||||
|
||||
0000000000000000: {[err Gradient[1]] [featScale Gradient[1 x 1]] [featScaled Gradient[28 x 28 x 1 x *]] [features Gradient[28 x 28 x 1 x *]] [labels Gradient[10 x *]] }
|
||||
000000CB919F83E0: {[features Value[28 x 28 x 1 x *]] }
|
||||
000000CBAA188380: {[h1.W Value[128 x 7 x 7 x 32]] }
|
||||
000000CBAA188560: {[ol.W Value[10 x 128]] }
|
||||
000000CBAA1887E0: {[ol.b Value[10 x 1]] }
|
||||
000000CBAA188A60: {[featScale Value[1 x 1]] }
|
||||
000000CBAA188CE0: {[conv1.w.W Value[16 x 25]] }
|
||||
000000CBAA1890A0: {[labels Value[10 x *]] }
|
||||
000000CBAA189320: {[conv2.b.b Value[1 x 1 x 32]] }
|
||||
000000CBAA1893C0: {[conv2.w.W Value[32 x 400]] }
|
||||
000000CBAA189C80: {[conv1.b.b Value[1 x 1 x 16]] }
|
||||
000000CBAA189DC0: {[h1.b Value[128 x 1]] }
|
||||
000000CBB0834910: {[ol.z Value[10 x 1 x *]] }
|
||||
000000CBB0834AF0: {[err Value[1]] }
|
||||
000000CBB0834B90: {[ol.t Gradient[10 x 1 x *]] }
|
||||
000000CBB0834F50: {[conv1.c.c Gradient[28 x 28 x 16 x *]] [conv1.out Value[28 x 28 x 16 x *]] }
|
||||
000000CBB0834FF0: {[conv1.c.c Value[28 x 28 x 16 x *]] }
|
||||
000000CBB08353B0: {[featScaled Value[28 x 28 x 1 x *]] }
|
||||
000000CBB0835770: {[ce Value[1]] }
|
||||
000000CBB0835950: {[conv2.c.c Value[14 x 14 x 32 x *]] }
|
||||
000000CBB0835B30: {[conv2.b.b Gradient[1 x 1 x 32]] [conv2.out Gradient[14 x 14 x 32 x *]] [h1.t Value[128 x *]] }
|
||||
000000CBB0835BD0: {[h1.W Gradient[128 x 7 x 7 x 32]] [h1.z Value[128 x 1 x *]] }
|
||||
000000CBB0835C70: {[h1.t Gradient[128 x *]] [h1.y Value[128 x 1 x *]] }
|
||||
000000CBB0835DB0: {[conv2.cpb Gradient[14 x 14 x 32 x *]] [pool1 Gradient[14 x 14 x 16 x *]] [pool2.p Value[7 x 7 x 32 x *]] }
|
||||
000000CBB0835F90: {[ce Gradient[1]] }
|
||||
000000CBB0836350: {[conv1.cpb Value[28 x 28 x 16 x *]] [conv1.w.W Gradient[16 x 25]] }
|
||||
000000CBB08363F0: {[conv1.b.b Gradient[1 x 1 x 16]] [conv1.out Gradient[28 x 28 x 16 x *]] }
|
||||
000000CBB0836490: {[h1.z Gradient[128 x 1 x *]] [ol.t Value[10 x 1 x *]] [pool2.p Gradient[7 x 7 x 32 x *]] }
|
||||
000000CBB0836670: {[ol.b Gradient[10 x 1]] }
|
||||
000000CBB0836990: {[conv2.c.c Gradient[14 x 14 x 32 x *]] [conv2.out Value[14 x 14 x 32 x *]] }
|
||||
000000CBB0836A30: {[ol.W Gradient[10 x 128]] [ol.z Gradient[10 x 1 x *]] }
|
||||
000000CBB0836B70: {[conv2.cpb Value[14 x 14 x 32 x *]] [conv2.w.W Gradient[32 x 400]] }
|
||||
000000CBB0836CB0: {[h1.b Gradient[128 x 1]] [h1.y Gradient[128 x 1 x *]] }
|
||||
000000CBB0836E90: {[conv1.cpb Gradient[28 x 28 x 16 x *]] [pool1 Value[14 x 14 x 16 x *]] }
|
||||
|
||||
05/13/2016 08:16:18: No PreCompute nodes found, skipping PreCompute step.
|
||||
|
||||
05/13/2016 08:16:18: Starting Epoch 1: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
|
||||
05/13/2016 08:16:18: Starting minibatch loop.
|
||||
05/13/2016 08:16:22: Epoch[ 1 of 3]-Minibatch[1-500, 26.67%]: ce = 1.52245886 * 16000; errs = 53.5312% * 16000; time = 4.2213s; samplesPerSecond = 3790.3
|
||||
05/13/2016 08:16:24: Epoch[ 1 of 3]-Minibatch[501-1000, 53.33%]: ce = 0.20213049 * 16000; errs = 5.7375% * 16000; time = 1.6650s; samplesPerSecond = 9609.8
|
||||
05/13/2016 08:16:26: Epoch[ 1 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.11822998 * 16000; errs = 3.4% * 16000; time = 1.6662s; samplesPerSecond = 9602.5
|
||||
05/13/2016 08:16:27: Finished Epoch[ 1 of 3]: [Training] ce = 0.51029333 * 60000; errs = 17.25% * 60000; totalSamplesSeen = 60000; learningRatePerSample = 0.003125; epochTime=8.83729s
|
||||
05/13/2016 08:16:27: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu/Models/02_Convolution.1'
|
||||
|
||||
05/13/2016 08:16:27: Starting Epoch 2: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
|
||||
05/13/2016 08:16:27: Starting minibatch loop.
|
||||
05/13/2016 08:16:29: Epoch[ 2 of 3]-Minibatch[1-500, 26.67%]: ce = 0.07765988 * 16000; errs = 2.28125% * 16000; time = 1.6655s; samplesPerSecond = 9606.6
|
||||
05/13/2016 08:16:30: Epoch[ 2 of 3]-Minibatch[501-1000, 53.33%]: ce = 0.06650398 * 16000; errs = 1.94375% * 16000; time = 1.6661s; samplesPerSecond = 9603.4
|
||||
05/13/2016 08:16:32: Epoch[ 2 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.06597272 * 16000; errs = 2.025% * 16000; time = 1.6655s; samplesPerSecond = 9607.0
|
||||
05/13/2016 08:16:33: Finished Epoch[ 2 of 3]: [Training] ce = 0.06707618 * 60000; errs = 1.99333% * 60000; totalSamplesSeen = 120000; learningRatePerSample = 0.003125; epochTime=6.26303s
|
||||
05/13/2016 08:16:33: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu/Models/02_Convolution.2'
|
||||
|
||||
05/13/2016 08:16:33: Starting Epoch 3: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
|
||||
05/13/2016 08:16:33: Starting minibatch loop.
|
||||
05/13/2016 08:16:35: Epoch[ 3 of 3]-Minibatch[1-500, 26.67%]: ce = 0.04900096 * 16000; errs = 1.53125% * 16000; time = 1.6660s; samplesPerSecond = 9603.7
|
||||
05/13/2016 08:16:37: Epoch[ 3 of 3]-Minibatch[501-1000, 53.33%]: ce = 0.04317124 * 16000; errs = 1.3% * 16000; time = 1.6655s; samplesPerSecond = 9606.5
|
||||
05/13/2016 08:16:38: Epoch[ 3 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.04517576 * 16000; errs = 1.29375% * 16000; time = 1.6628s; samplesPerSecond = 9622.2
|
||||
05/13/2016 08:16:40: Finished Epoch[ 3 of 3]: [Training] ce = 0.04463579 * 60000; errs = 1.335% * 60000; totalSamplesSeen = 180000; learningRatePerSample = 0.003125; epochTime=6.25721s
|
||||
05/13/2016 08:16:40: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_02_Convolution@release_gpu/Models/02_Convolution'
|
||||
05/13/2016 08:16:40: CNTKCommandTrainEnd: train
|
||||
|
||||
05/13/2016 08:16:40: Action "train" complete.
|
||||
{ conv2.c.c : [14 x 14 x 32 x *] (gradient)
|
||||
conv2.out : [14 x 14 x 32 x *] }
|
||||
{ h1.W : [128 x 7 x 7 x 32] (gradient)
|
||||
h1.z : [128 x 1 x *] }
|
||||
{ h1.t : [128 x *] (gradient)
|
||||
h1.y : [128 x 1 x *] }
|
||||
{ h1.z : [128 x 1 x *] (gradient)
|
||||
ol.t : [10 x 1 x *]
|
||||
pool2.p : [7 x 7 x 32 x *] (gradient) }
|
||||
{ ol.W : [10 x 128] (gradient)
|
||||
ol.z : [10 x 1 x *] (gradient) }
|
||||
{ conv1.b.b : [1 x 1 x 16] (gradient)
|
||||
conv1.out : [28 x 28 x 16 x *] (gradient) }
|
||||
{ conv2.b.b : [1 x 1 x 32] (gradient)
|
||||
conv2.out : [14 x 14 x 32 x *] (gradient)
|
||||
h1.t : [128 x *] }
|
||||
{ conv2.cpb : [14 x 14 x 32 x *]
|
||||
conv2.w.W : [32 x 400] (gradient) }
|
||||
{ conv2.cpb : [14 x 14 x 32 x *] (gradient)
|
||||
pool1 : [14 x 14 x 16 x *] (gradient)
|
||||
pool2.p : [7 x 7 x 32 x *] }
|
||||
{ conv1.c.c : [28 x 28 x 16 x *] (gradient)
|
||||
conv1.out : [28 x 28 x 16 x *] }
|
||||
{ h1.b : [128 x 1] (gradient)
|
||||
h1.y : [128 x 1 x *] (gradient) }
|
||||
{ conv1.cpb : [28 x 28 x 16 x *]
|
||||
conv1.w.W : [16 x 25] (gradient) }
|
||||
{ conv1.cpb : [28 x 28 x 16 x *] (gradient)
|
||||
pool1 : [14 x 14 x 16 x *] }
|
||||
|
||||
|
||||
05/13/2016 08:16:40: ##############################################################################
|
||||
05/13/2016 08:16:40: # #
|
||||
05/13/2016 08:16:40: # Action "test" #
|
||||
05/13/2016 08:16:40: # #
|
||||
05/13/2016 08:16:40: ##############################################################################
|
||||
08/16/2016 03:01:08: Training 215370 parameters in 8 out of 8 parameter tensors and 22 nodes with gradient:
|
||||
|
||||
08/16/2016 03:01:08: Node 'conv1.b.b' (LearnableParameter operation) : [1 x 1 x 16]
|
||||
08/16/2016 03:01:08: Node 'conv1.w.W' (LearnableParameter operation) : [16 x 25]
|
||||
08/16/2016 03:01:08: Node 'conv2.b.b' (LearnableParameter operation) : [1 x 1 x 32]
|
||||
08/16/2016 03:01:08: Node 'conv2.w.W' (LearnableParameter operation) : [32 x 400]
|
||||
08/16/2016 03:01:08: Node 'h1.W' (LearnableParameter operation) : [128 x 7 x 7 x 32]
|
||||
08/16/2016 03:01:08: Node 'h1.b' (LearnableParameter operation) : [128 x 1]
|
||||
08/16/2016 03:01:08: Node 'ol.W' (LearnableParameter operation) : [10 x 128]
|
||||
08/16/2016 03:01:08: Node 'ol.b' (LearnableParameter operation) : [10 x 1]
|
||||
|
||||
08/16/2016 03:01:08: No PreCompute nodes found, or all already computed. Skipping pre-computation step.
|
||||
|
||||
08/16/2016 03:01:08: Starting Epoch 1: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..60000] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:01:08: Starting minibatch loop.
|
||||
08/16/2016 03:01:12: Epoch[ 1 of 3]-Minibatch[ 1- 500, 26.67%]: ce = 1.27430457 * 16000; errs = 44.075% * 16000; time = 3.3942s; samplesPerSecond = 4714.0
|
||||
08/16/2016 03:01:13: Epoch[ 1 of 3]-Minibatch[ 501-1000, 53.33%]: ce = 0.19224133 * 16000; errs = 5.400% * 16000; time = 1.7150s; samplesPerSecond = 9329.3
|
||||
08/16/2016 03:01:15: Epoch[ 1 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.11038135 * 16000; errs = 3.231% * 16000; time = 1.7153s; samplesPerSecond = 9327.6
|
||||
08/16/2016 03:01:16: Finished Epoch[ 1 of 3]: [Training] ce = 0.43859844 * 60000; errs = 14.615% * 60000; totalSamplesSeen = 60000; learningRatePerSample = 0.003125; epochTime=8.13585s
|
||||
08/16/2016 03:01:16: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu/Models/02_Convolution.1'
|
||||
|
||||
08/16/2016 03:01:16: Starting Epoch 2: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [60000..120000] (first sequence at sample 60000), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:01:16: Starting minibatch loop.
|
||||
08/16/2016 03:01:18: Epoch[ 2 of 3]-Minibatch[ 1- 500, 26.67%]: ce = 0.07473590 * 16000; errs = 2.250% * 16000; time = 1.7130s; samplesPerSecond = 9340.4
|
||||
08/16/2016 03:01:20: Epoch[ 2 of 3]-Minibatch[ 501-1000, 53.33%]: ce = 0.06083897 * 16000; errs = 1.825% * 16000; time = 1.7090s; samplesPerSecond = 9362.3
|
||||
08/16/2016 03:01:21: Epoch[ 2 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.06430908 * 16000; errs = 1.881% * 16000; time = 1.7094s; samplesPerSecond = 9359.8
|
||||
08/16/2016 03:01:23: Finished Epoch[ 2 of 3]: [Training] ce = 0.06608532 * 60000; errs = 1.973% * 60000; totalSamplesSeen = 120000; learningRatePerSample = 0.003125; epochTime=6.42906s
|
||||
08/16/2016 03:01:23: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu/Models/02_Convolution.2'
|
||||
|
||||
08/16/2016 03:01:23: Starting Epoch 3: learning rate per sample = 0.003125 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [120000..180000] (first sequence at sample 120000), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:01:23: Starting minibatch loop.
|
||||
08/16/2016 03:01:25: Epoch[ 3 of 3]-Minibatch[ 1- 500, 26.67%]: ce = 0.04609646 * 16000; errs = 1.450% * 16000; time = 1.7107s; samplesPerSecond = 9352.7
|
||||
08/16/2016 03:01:26: Epoch[ 3 of 3]-Minibatch[ 501-1000, 53.33%]: ce = 0.04193843 * 16000; errs = 1.256% * 16000; time = 1.7151s; samplesPerSecond = 9328.9
|
||||
08/16/2016 03:01:28: Epoch[ 3 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.04465855 * 16000; errs = 1.300% * 16000; time = 1.6923s; samplesPerSecond = 9454.4
|
||||
08/16/2016 03:01:29: Finished Epoch[ 3 of 3]: [Training] ce = 0.04399961 * 60000; errs = 1.310% * 60000; totalSamplesSeen = 180000; learningRatePerSample = 0.003125; epochTime=6.40462s
|
||||
08/16/2016 03:01:29: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_02_Convolution@release_gpu/Models/02_Convolution'
|
||||
08/16/2016 03:01:29: CNTKCommandTrainEnd: train
|
||||
|
||||
08/16/2016 03:01:29: Action "train" complete.
|
||||
|
||||
|
||||
08/16/2016 03:01:29: ##############################################################################
|
||||
08/16/2016 03:01:29: # #
|
||||
08/16/2016 03:01:29: # Action "test" #
|
||||
08/16/2016 03:01:29: # #
|
||||
08/16/2016 03:01:29: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
||||
3 roots:
|
||||
ce = CrossEntropyWithSoftmax()
|
||||
err = ErrorPrediction()
|
||||
errs = ErrorPrediction()
|
||||
ol.z = Plus()
|
||||
|
||||
Validating network. 27 nodes to process in pass 1.
|
||||
|
|
@ -458,7 +491,7 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 128], [128 x 1 x *1] -> [10 x 1
|
|||
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
|
||||
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x 1 x *1], [10 x 1] -> [10 x 1 x *1]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
|
||||
Validating --> errs = ErrorPrediction (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
|
||||
|
||||
Validating network. 16 nodes to process in pass 2.
|
||||
|
||||
|
|
@ -466,13 +499,13 @@ Validating network. 16 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 28 x 28 x 1, Output: 28 x 28 x 16, Kernel: 5 x 5 x 1, Map: 1 x 1 x 16, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c.c: using cuDNN convolution engine for geometry: Input: 28 x 28 x 1, Output: 28 x 28 x 16, Kernel: 5 x 5 x 1, Map: 1 x 1 x 16, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 28 x 28 x 16, Output: 14 x 14 x 16, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 28 x 28 x 16, Output: 14 x 14 x 16, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 14 x 14 x 16, Output: 14 x 14 x 32, Kernel: 5 x 5 x 16, Map: 32, Stride: 1 x 1 x 16, Sharing: (1, 1, 1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c.c: using cuDNN convolution engine for geometry: Input: 14 x 14 x 16, Output: 14 x 14 x 32, Kernel: 5 x 5 x 16, Map: 32, Stride: 1 x 1 x 16, Sharing: (1, 1, 1), AutoPad: (1, 1, 0), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 14 x 14 x 32, Output: 7 x 7 x 32, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (1, 1, 0), LowerPad: 0, UpperPad: 0.
|
||||
pool2.p: using cuDNN convolution engine for geometry: Input: 14 x 14 x 32, Output: 7 x 7 x 32, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1, 1, 1), AutoPad: (1, 1, 0), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.
|
||||
|
||||
|
||||
11 out of 27 nodes do not share the minibatch layout with the input data.
|
||||
|
|
@ -484,39 +517,13 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 27 matrices, 0 are shared as 0, and 27 are not shared.
|
||||
|
||||
0000000000000000: {[ce Gradient[1]] [conv1.b.b Gradient[1 x 1 x 16]] [conv1.c.c Gradient[28 x 28 x 16 x *1]] [conv1.cpb Gradient[28 x 28 x 16 x *1]] [conv1.out Gradient[28 x 28 x 16 x *1]] [conv1.w.W Gradient[16 x 25]] [conv2.b.b Gradient[1 x 1 x 32]] [conv2.c.c Gradient[14 x 14 x 32 x *1]] [conv2.cpb Gradient[14 x 14 x 32 x *1]] [conv2.out Gradient[14 x 14 x 32 x *1]] [conv2.w.W Gradient[32 x 400]] [err Gradient[1]] [featScale Gradient[1 x 1]] [featScaled Gradient[28 x 28 x 1 x *1]] [features Gradient[28 x 28 x 1 x *1]] [h1.W Gradient[128 x 7 x 7 x 32]] [h1.b Gradient[128 x 1]] [h1.t Gradient[128 x *1]] [h1.y Gradient[128 x 1 x *1]] [h1.z Gradient[128 x 1 x *1]] [labels Gradient[10 x *1]] [ol.W Gradient[10 x 128]] [ol.b Gradient[10 x 1]] [ol.t Gradient[10 x 1 x *1]] [ol.z Gradient[10 x 1 x *1]] [pool1 Gradient[14 x 14 x 16 x *1]] [pool2.p Gradient[7 x 7 x 32 x *1]] }
|
||||
000000CBAA188420: {[conv2.cpb Value[14 x 14 x 32 x *1]] }
|
||||
000000CBAA188BA0: {[pool2.p Value[7 x 7 x 32 x *1]] }
|
||||
000000CBAA1890A0: {[conv1.out Value[28 x 28 x 16 x *1]] }
|
||||
000000CBAA189140: {[conv2.c.c Value[14 x 14 x 32 x *1]] }
|
||||
000000CBAA1891E0: {[h1.t Value[128 x *1]] }
|
||||
000000CBAA189320: {[h1.z Value[128 x 1 x *1]] }
|
||||
000000CBAA1895A0: {[ol.t Value[10 x 1 x *1]] }
|
||||
000000CBAA189780: {[pool1 Value[14 x 14 x 16 x *1]] }
|
||||
000000CBAA189820: {[ol.z Value[10 x 1 x *1]] }
|
||||
000000CBAA189DC0: {[h1.y Value[128 x 1 x *1]] }
|
||||
000000CBAA18A0E0: {[conv2.out Value[14 x 14 x 32 x *1]] }
|
||||
000000CBB0834AF0: {[ol.W Value[10 x 128]] }
|
||||
000000CBB0834C30: {[h1.b Value[128 x 1]] }
|
||||
000000CBB0834FF0: {[features Value[28 x 28 x 1 x *1]] }
|
||||
000000CBB0835770: {[h1.W Value[128 x 7 x 7 x 32]] }
|
||||
000000CBB08358B0: {[featScale Value[1 x 1]] }
|
||||
000000CBB0835BD0: {[conv1.w.W Value[16 x 25]] }
|
||||
000000CBB08360D0: {[labels Value[10 x *1]] }
|
||||
000000CBB0836350: {[ol.b Value[10 x 1]] }
|
||||
000000CBB0836490: {[conv2.b.b Value[1 x 1 x 32]] }
|
||||
000000CBB0836A30: {[conv2.w.W Value[32 x 400]] }
|
||||
000000CBB0836CB0: {[conv1.b.b Value[1 x 1 x 16]] }
|
||||
000000CBB08371B0: {[err Value[1]] }
|
||||
000000CBB08372F0: {[conv1.cpb Value[28 x 28 x 16 x *1]] }
|
||||
000000CBB0837F70: {[featScaled Value[28 x 28 x 1 x *1]] }
|
||||
000000CBB08381F0: {[ce Value[1]] }
|
||||
000000CBB08383D0: {[conv1.c.c Value[28 x 28 x 16 x *1]] }
|
||||
|
||||
05/13/2016 08:16:51: Final Results: Minibatch[1-10]: errs = 1.52% * 10000; ce = 0.04488435 * 10000; perplexity = 1.04590689
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
08/16/2016 03:01:30: Minibatch[1-10]: errs = 1.380% * 10000; ce = 0.04422099 * 10000
|
||||
08/16/2016 03:01:30: Final Results: Minibatch[1-10]: errs = 1.380% * 10000; ce = 0.04422099 * 10000; perplexity = 1.04521331
|
||||
|
||||
05/13/2016 08:16:51: Action "test" complete.
|
||||
08/16/2016 03:01:30: Action "test" complete.
|
||||
|
||||
05/13/2016 08:16:51: __COMPLETED__
|
||||
08/16/2016 03:01:30: __COMPLETED__
|
||||
|
|
@ -1,64 +1,77 @@
|
|||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../../Examples/Image/MNIST/Config/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/Config/03_ConvBatchNorm.cntk currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../../Examples/Image/MNIST/Config OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 264172964 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../Examples/Image/MNIST/Config/03_ConvBatchNorm.cntk currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../Examples/Image/MNIST/Config OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 13 2016 14:50:25
|
||||
Last modified date: Thu May 12 14:00:37 2016
|
||||
Built time: Aug 16 2016 09:41:56
|
||||
Last modified date: Fri Aug 12 07:32:43 2016
|
||||
Build type: release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: no
|
||||
Math lib: acml
|
||||
Math lib: mkl
|
||||
CUDA_PATH: /usr/local/cuda-7.5
|
||||
CUB_PATH: /usr/local/cub-1.4.1
|
||||
CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
Built by philly on d8dc82703b0f
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by philly on f67b30a647de
|
||||
Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to /tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData
|
||||
05/13/2016 15:10:29: -------------------------------------------------------------------
|
||||
05/13/2016 15:10:29: Build info:
|
||||
Changed current directory to /tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData
|
||||
08/16/2016 10:50:05: -------------------------------------------------------------------
|
||||
08/16/2016 10:50:05: Build info:
|
||||
|
||||
05/13/2016 15:10:29: Built time: May 13 2016 14:50:25
|
||||
05/13/2016 15:10:29: Last modified date: Thu May 12 14:00:37 2016
|
||||
05/13/2016 15:10:29: Build type: release
|
||||
05/13/2016 15:10:29: Build target: GPU
|
||||
05/13/2016 15:10:29: With 1bit-SGD: no
|
||||
05/13/2016 15:10:29: Math lib: acml
|
||||
05/13/2016 15:10:29: CUDA_PATH: /usr/local/cuda-7.5
|
||||
05/13/2016 15:10:29: CUB_PATH: /usr/local/cub-1.4.1
|
||||
05/13/2016 15:10:29: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
05/13/2016 15:10:29: Build Branch: HEAD
|
||||
05/13/2016 15:10:29: Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
05/13/2016 15:10:29: Built by philly on d8dc82703b0f
|
||||
05/13/2016 15:10:29: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
05/13/2016 15:10:29: -------------------------------------------------------------------
|
||||
08/16/2016 10:50:05: Built time: Aug 16 2016 09:41:56
|
||||
08/16/2016 10:50:05: Last modified date: Fri Aug 12 07:32:43 2016
|
||||
08/16/2016 10:50:05: Build type: release
|
||||
08/16/2016 10:50:05: Build target: GPU
|
||||
08/16/2016 10:50:05: With 1bit-SGD: no
|
||||
08/16/2016 10:50:05: Math lib: mkl
|
||||
08/16/2016 10:50:05: CUDA_PATH: /usr/local/cuda-7.5
|
||||
08/16/2016 10:50:05: CUB_PATH: /usr/local/cub-1.4.1
|
||||
08/16/2016 10:50:05: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
08/16/2016 10:50:05: Build Branch: HEAD
|
||||
08/16/2016 10:50:05: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 10:50:05: Built by philly on f67b30a647de
|
||||
08/16/2016 10:50:05: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
08/16/2016 10:50:05: -------------------------------------------------------------------
|
||||
08/16/2016 10:50:06: -------------------------------------------------------------------
|
||||
08/16/2016 10:50:06: GPU info:
|
||||
|
||||
05/13/2016 15:10:29: Running on localhost at 2016/05/13 15:10:29
|
||||
05/13/2016 15:10:29: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../../Examples/Image/MNIST/Config/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/Config/03_ConvBatchNorm.cntk currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../../Examples/Image/MNIST/Config OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
08/16/2016 10:50:06: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:50:06: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:50:06: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:50:06: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:50:06: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 10:50:06: Running on localhost at 2016/08/16 10:50:06
|
||||
08/16/2016 10:50:06: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../Examples/Image/MNIST/Config/03_ConvBatchNorm.cntk currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../Examples/Image/MNIST/Config OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
|
||||
|
||||
|
||||
05/13/2016 15:10:29: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 15:10:29: RootDir = ".."
|
||||
08/16/2016 10:50:06: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:50:06: RootDir = ".."
|
||||
ConfigDir = "$RootDir$/Config"
|
||||
DataDir = "$RootDir$/Data"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
ModelDir = "$OutputDir$/Models"
|
||||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
command = train:test
|
||||
precision = "float"
|
||||
modelPath = "$ModelDir$/03_ConvBatchNorm"
|
||||
ndlMacros = "$ConfigDir$/Macros.ndl"
|
||||
traceLevel=1
|
||||
numMBsToShowResult=500
|
||||
initOnCPUOnly=true
|
||||
train = [
|
||||
action = "train"
|
||||
NDLNetworkBuilder = [
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly=true
|
||||
ndlMacros = "$ConfigDir$/Macros.ndl"
|
||||
networkDescription = "$ConfigDir$/03_ConvBatchNorm.ndl"
|
||||
]
|
||||
SGD = [
|
||||
|
|
@ -86,11 +99,8 @@ train = [
|
|||
]
|
||||
test = [
|
||||
action = "test"
|
||||
minibatchSize = 32
|
||||
minibatchSize = 1024
|
||||
modelPath=$ModelDir$/03_ConvBatchNorm
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "$ConfigDir$/03_ConvBatchNorm.ndl"
|
||||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "$DataDir$/Test-28x28_cntk_text.txt"
|
||||
|
|
@ -106,37 +116,37 @@ test = [
|
|||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../../Examples/Image/MNIST/Config
|
||||
OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu
|
||||
currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../Examples/Image/MNIST/Config
|
||||
OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
train=[SGD=[maxEpochs=3]]
|
||||
imageLayout="cudnn"
|
||||
|
||||
05/13/2016 15:10:29: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:50:06: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 15:10:29: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 15:10:29: RootDir = ".."
|
||||
08/16/2016 10:50:06: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:50:06: RootDir = ".."
|
||||
ConfigDir = "../Config"
|
||||
DataDir = "../Data"
|
||||
OutputDir = "../Output"
|
||||
ModelDir = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/Models"
|
||||
ModelDir = "/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/Models"
|
||||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
command = train:test
|
||||
precision = "float"
|
||||
modelPath = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm"
|
||||
ndlMacros = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../../Examples/Image/MNIST/Config/Macros.ndl"
|
||||
modelPath = "/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm"
|
||||
traceLevel=1
|
||||
numMBsToShowResult=500
|
||||
initOnCPUOnly=true
|
||||
train = [
|
||||
action = "train"
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../../Examples/Image/MNIST/Config/03_ConvBatchNorm.ndl"
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly=true
|
||||
ndlMacros = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../Examples/Image/MNIST/Config/Macros.ndl"
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../Examples/Image/MNIST/Config/03_ConvBatchNorm.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 60000
|
||||
|
|
@ -148,7 +158,7 @@ train = [
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData/Train-28x28_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData/Train-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
|
|
@ -163,14 +173,11 @@ train = [
|
|||
]
|
||||
test = [
|
||||
action = "test"
|
||||
minibatchSize = 32
|
||||
modelPath=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../../Examples/Image/MNIST/Config/03_ConvBatchNorm.ndl"
|
||||
]
|
||||
minibatchSize = 1024
|
||||
modelPath=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData/Test-28x28_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData/Test-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
|
|
@ -183,44 +190,39 @@ test = [
|
|||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../../Examples/Image/MNIST/Config
|
||||
OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu
|
||||
currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../Examples/Image/MNIST/Config
|
||||
OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
train=[SGD=[maxEpochs=3]]
|
||||
imageLayout="cudnn"
|
||||
|
||||
05/13/2016 15:10:29: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:50:06: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 15:10:29: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:50:06: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: 03_ConvBatchNorm.cntk:command=train:test
|
||||
configparameters: 03_ConvBatchNorm.cntk:ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../../Examples/Image/MNIST/Config
|
||||
configparameters: 03_ConvBatchNorm.cntk:currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData
|
||||
configparameters: 03_ConvBatchNorm.cntk:DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData
|
||||
configparameters: 03_ConvBatchNorm.cntk:ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../Examples/Image/MNIST/Config
|
||||
configparameters: 03_ConvBatchNorm.cntk:currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData
|
||||
configparameters: 03_ConvBatchNorm.cntk:DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData
|
||||
configparameters: 03_ConvBatchNorm.cntk:deviceId=0
|
||||
configparameters: 03_ConvBatchNorm.cntk:imageLayout=cudnn
|
||||
configparameters: 03_ConvBatchNorm.cntk:initOnCPUOnly=true
|
||||
configparameters: 03_ConvBatchNorm.cntk:ModelDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/Models
|
||||
configparameters: 03_ConvBatchNorm.cntk:modelPath=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm
|
||||
configparameters: 03_ConvBatchNorm.cntk:ndlMacros=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../../Examples/Image/MNIST/Config/Macros.ndl
|
||||
configparameters: 03_ConvBatchNorm.cntk:ModelDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/Models
|
||||
configparameters: 03_ConvBatchNorm.cntk:modelPath=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm
|
||||
configparameters: 03_ConvBatchNorm.cntk:numMBsToShowResult=500
|
||||
configparameters: 03_ConvBatchNorm.cntk:OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu
|
||||
configparameters: 03_ConvBatchNorm.cntk:OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu
|
||||
configparameters: 03_ConvBatchNorm.cntk:precision=float
|
||||
configparameters: 03_ConvBatchNorm.cntk:RootDir=..
|
||||
configparameters: 03_ConvBatchNorm.cntk:RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu
|
||||
configparameters: 03_ConvBatchNorm.cntk:RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu
|
||||
configparameters: 03_ConvBatchNorm.cntk:test=[
|
||||
action = "test"
|
||||
minibatchSize = 32
|
||||
modelPath=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../../Examples/Image/MNIST/Config/03_ConvBatchNorm.ndl"
|
||||
]
|
||||
minibatchSize = 1024
|
||||
modelPath=/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData/Test-28x28_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData/Test-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
|
|
@ -239,7 +241,10 @@ configparameters: 03_ConvBatchNorm.cntk:traceLevel=1
|
|||
configparameters: 03_ConvBatchNorm.cntk:train=[
|
||||
action = "train"
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../../Examples/Image/MNIST/Config/03_ConvBatchNorm.ndl"
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly=true
|
||||
ndlMacros = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../Examples/Image/MNIST/Config/Macros.ndl"
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/MNIST/03_ConvBatchNorm/../../../../../../Examples/Image/MNIST/Config/03_ConvBatchNorm.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 60000
|
||||
|
|
@ -251,7 +256,7 @@ configparameters: 03_ConvBatchNorm.cntk:train=[
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData/Train-28x28_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/TestData/Train-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
|
|
@ -265,29 +270,67 @@ configparameters: 03_ConvBatchNorm.cntk:train=[
|
|||
]
|
||||
] [SGD=[maxEpochs=3]]
|
||||
|
||||
05/13/2016 15:10:29: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/13/2016 15:10:29: Commands: train test
|
||||
05/13/2016 15:10:29: Precision = "float"
|
||||
05/13/2016 15:10:29: CNTKModelPath: /tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm
|
||||
05/13/2016 15:10:29: CNTKCommandTrainInfo: train : 3
|
||||
05/13/2016 15:10:29: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
08/16/2016 10:50:06: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:50:06: Commands: train test
|
||||
08/16/2016 10:50:06: Precision = "float"
|
||||
08/16/2016 10:50:06: CNTKModelPath: /tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm
|
||||
08/16/2016 10:50:06: CNTKCommandTrainInfo: train : 3
|
||||
08/16/2016 10:50:06: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
|
||||
05/13/2016 15:10:29: ##############################################################################
|
||||
05/13/2016 15:10:29: # #
|
||||
05/13/2016 15:10:29: # Action "train" #
|
||||
05/13/2016 15:10:29: # #
|
||||
05/13/2016 15:10:29: ##############################################################################
|
||||
08/16/2016 10:50:06: ##############################################################################
|
||||
08/16/2016 10:50:06: # #
|
||||
08/16/2016 10:50:06: # Action "train" #
|
||||
08/16/2016 10:50:06: # #
|
||||
08/16/2016 10:50:06: ##############################################################################
|
||||
|
||||
05/13/2016 15:10:29: CNTKCommandTrainBegin: train
|
||||
08/16/2016 10:50:06: CNTKCommandTrainBegin: train
|
||||
NDLBuilder Using GPU 0
|
||||
|
||||
05/13/2016 15:10:29: Creating virgin network.
|
||||
08/16/2016 10:50:06: Creating virgin network.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.000000.
|
||||
Node 'conv1.c.W' (LearnableParameter operation): Initializing Parameter[16 x 25] <- 0.000000.
|
||||
Node 'conv1.c.c.b' (LearnableParameter operation): Initializing Parameter[16 x 1] <- 0.000000.
|
||||
Node 'conv1.c.c.sc' (LearnableParameter operation): Initializing Parameter[16 x 1] <- 0.000000.
|
||||
Node 'conv1.c.c.m' (LearnableParameter operation): Initializing Parameter[16 x 1] <- 0.000000.
|
||||
Node 'conv1.c.c.isd' (LearnableParameter operation): Initializing Parameter[16 x 1] <- 0.000000.
|
||||
Node 'conv2.c.W' (LearnableParameter operation): Initializing Parameter[32 x 400] <- 0.000000.
|
||||
Node 'conv2.c.c.b' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.c.sc' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.c.m' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.c.isd' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[128 x 1568] <- 0.000000.
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[128 x 1] <- 0.000000.
|
||||
Node 'h1.sc' (LearnableParameter operation): Initializing Parameter[128 x 1] <- 0.000000.
|
||||
Node 'h1.m' (LearnableParameter operation): Initializing Parameter[128 x 1] <- 0.000000.
|
||||
Node 'h1.isd' (LearnableParameter operation): Initializing Parameter[128 x 1] <- 0.000000.
|
||||
Node 'ol.W' (LearnableParameter operation): Initializing Parameter[10 x 128] <- 0.000000.
|
||||
Node 'ol.b' (LearnableParameter operation): Initializing Parameter[10 x 1] <- 0.000000.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.003906.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.003906.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.003906.
|
||||
Node 'conv1.c.W' (LearnableParameter operation): Initializing Parameter[16 x 25] <- gaussian(seed=1, range=0.040000*10.000000, onCPU=true).
|
||||
Node 'conv1.c.c.b' (LearnableParameter operation): Initializing Parameter[16 x 1] <- 0.000000.
|
||||
Node 'conv1.c.c.sc' (LearnableParameter operation): Initializing Parameter[16 x 1] <- 1.000000.
|
||||
Node 'conv1.c.c.m' (LearnableParameter operation): Initializing Parameter[16 x 1] <- 0.000000.
|
||||
Node 'conv1.c.c.isd' (LearnableParameter operation): Initializing Parameter[16 x 1] <- 0.000000.
|
||||
Node 'conv2.c.W' (LearnableParameter operation): Initializing Parameter[32 x 400] <- gaussian(seed=2, range=0.010000*10.000000, onCPU=true).
|
||||
Node 'conv2.c.c.b' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.c.sc' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 1.000000.
|
||||
Node 'conv2.c.c.m' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.c.isd' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[128 x 1568] <- gaussian(seed=3, range=0.005051*1.000000, onCPU=true).
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[128 x 1] <- 0.000000.
|
||||
Node 'h1.sc' (LearnableParameter operation): Initializing Parameter[128 x 1] <- 1.000000.
|
||||
Node 'h1.m' (LearnableParameter operation): Initializing Parameter[128 x 1] <- 0.000000.
|
||||
Node 'h1.isd' (LearnableParameter operation): Initializing Parameter[128 x 1] <- 0.000000.
|
||||
Node 'ol.W' (LearnableParameter operation): Initializing Parameter[10 x 128] <- uniform(seed=4, range=0.050000*1.000000, onCPU=true).
|
||||
Node 'ol.b' (LearnableParameter operation): Initializing Parameter[10 x 1] <- uniform(seed=5, range=0.050000*1.000000, onCPU=true).
|
||||
|
||||
Post-processing network...
|
||||
|
||||
3 roots:
|
||||
ce = CrossEntropyWithSoftmax()
|
||||
err = ErrorPrediction()
|
||||
errs = ErrorPrediction()
|
||||
ol.z = Plus()
|
||||
|
||||
Validating network. 36 nodes to process in pass 1.
|
||||
|
|
@ -329,7 +372,7 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 128], [128 x *] -> [10 x *]
|
|||
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
|
||||
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x *], [10 x 1] -> [10 x 1 x *]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
|
||||
Validating --> errs = ErrorPrediction (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
|
||||
|
||||
Validating network. 16 nodes to process in pass 2.
|
||||
|
||||
|
|
@ -337,17 +380,17 @@ Validating network. 16 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 28 x 28 x 1, Output: 28 x 28 x 16, Kernel: 5 x 5 x 1, Map: 1 x 1 x 16, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c.c.c: using cuDNN convolution engine for geometry: Input: 28 x 28 x 1, Output: 28 x 28 x 16, Kernel: 5 x 5 x 1, Map: 1 x 1 x 16, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 28 x 28 x 16, Output: 14 x 14 x 16, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 28 x 28 x 16, Output: 14 x 14 x 16, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 14 x 14 x 16, Output: 14 x 14 x 32, Kernel: 5 x 5 x 16, Map: 1 x 1 x 32, Stride: 1 x 1 x 16, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c.c.c: using cuDNN convolution engine for geometry: Input: 14 x 14 x 16, Output: 14 x 14 x 32, Kernel: 5 x 5 x 16, Map: 1 x 1 x 32, Stride: 1 x 1 x 16, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 14 x 14 x 32, Output: 7 x 7 x 32, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 14 x 14 x 32, Output: 7 x 7 x 32, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
|
|
@ -356,113 +399,112 @@ Using CNTK batch normalization engine.
|
|||
|
||||
Post-processing network complete.
|
||||
|
||||
05/13/2016 15:10:29: Created model with 36 nodes on GPU 0.
|
||||
08/16/2016 10:50:07: Created model with 36 nodes on GPU 0.
|
||||
|
||||
05/13/2016 15:10:29: Training criterion node(s):
|
||||
05/13/2016 15:10:29: ce = CrossEntropyWithSoftmax
|
||||
08/16/2016 10:50:07: Training criterion node(s):
|
||||
08/16/2016 10:50:07: ce = CrossEntropyWithSoftmax
|
||||
|
||||
05/13/2016 15:10:29: Evaluation criterion node(s):
|
||||
|
||||
05/13/2016 15:10:29: err = ErrorPrediction
|
||||
08/16/2016 10:50:07: Evaluation criterion node(s):
|
||||
08/16/2016 10:50:07: errs = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 61 matrices, 28 are shared as 12, and 33 are not shared.
|
||||
|
||||
(nil): {[conv1.c.c.isd Gradient[16 x 1]] [conv1.c.c.m Gradient[16 x 1]] [conv2.c.c.isd Gradient[32 x 1]] [conv2.c.c.m Gradient[32 x 1]] [err Gradient[1]] [featScale Gradient[1 x 1]] [featScaled Gradient[28 x 28 x 1 x *]] [features Gradient[28 x 28 x 1 x *]] [h1.isd Gradient[128 x 1]] [h1.m Gradient[128 x 1]] [labels Gradient[10 x *]] }
|
||||
0x2643328: {[features Value[28 x 28 x 1 x *]] }
|
||||
0x33a9468: {[featScale Value[1 x 1]] }
|
||||
0x33aa5e8: {[labels Value[10 x *]] }
|
||||
0x33ab128: {[conv1.c.W Value[16 x 25]] }
|
||||
0x33ab818: {[conv1.c.c.b Value[16 x 1]] }
|
||||
0x33ac238: {[conv1.c.c.sc Value[16 x 1]] }
|
||||
0x33ad108: {[conv1.c.c.m Value[16 x 1]] }
|
||||
0x33adbf8: {[conv1.c.c.isd Value[16 x 1]] }
|
||||
0x33af968: {[conv2.c.W Value[32 x 400]] }
|
||||
0x33b0878: {[conv2.c.c.b Value[32 x 1]] }
|
||||
0x33b1258: {[conv2.c.c.sc Value[32 x 1]] }
|
||||
0x33b1908: {[ol.b Value[10 x 1]] }
|
||||
0x33b1e78: {[conv2.c.c.m Value[32 x 1]] }
|
||||
0x33b29c8: {[conv2.c.c.isd Value[32 x 1]] }
|
||||
0x33b3968: {[h1.W Value[128 x 7 x 7 x 32]] }
|
||||
0x33b5408: {[h1.b Value[128 x 1]] }
|
||||
0x33b5e38: {[h1.sc Value[128 x 1]] }
|
||||
0x33b6738: {[h1.m Value[128 x 1]] }
|
||||
0x33b70b8: {[h1.isd Value[128 x 1]] }
|
||||
0x33b7618: {[ol.W Value[10 x 128]] }
|
||||
0x33be778: {[ce Value[1]] }
|
||||
0x33bfba8: {[ol.z Value[10 x 1 x *]] }
|
||||
0x33ff558: {[err Value[1]] }
|
||||
0x788fe48: {[conv1.c.c.c Value[28 x 28 x 16 x *]] }
|
||||
0x7890188: {[featScaled Value[28 x 28 x 1 x *]] }
|
||||
0x7890438: {[conv1.c.c.y Value[28 x 28 x 16 x *]] }
|
||||
0x7891238: {[conv1.c.c.c Gradient[28 x 28 x 16 x *]] [conv1.y Value[28 x 28 x 16 x *]] }
|
||||
0x78913f8: {[conv1.c.c.y Gradient[28 x 28 x 16 x *]] [pool1 Value[14 x 14 x 16 x *]] }
|
||||
0x78915b8: {[conv1.c.W Gradient[16 x 25]] [conv2.c.c.c Value[14 x 14 x 32 x *]] }
|
||||
0x7891778: {[conv1.c.c.sc Gradient[16 x 1]] [conv1.y Gradient[28 x 28 x 16 x *]] }
|
||||
0x7891938: {[conv2.c.c.y Value[14 x 14 x 32 x *]] }
|
||||
0x7891e78: {[conv1.c.c.b Gradient[16 x 1]] [conv2.c.c.c Gradient[14 x 14 x 32 x *]] [conv2.y Value[14 x 14 x 32 x *]] }
|
||||
0x7892038: {[conv2.c.c.y Gradient[14 x 14 x 32 x *]] [pool2 Value[7 x 7 x 32 x *]] }
|
||||
0x78921f8: {[conv2.c.c.sc Gradient[32 x 1]] [conv2.y Gradient[14 x 14 x 32 x *]] [h1.t Value[128 x *]] }
|
||||
0x78923b8: {[h1.bn Value[128 x *]] }
|
||||
0x7892738: {[conv2.c.c.b Gradient[32 x 1]] }
|
||||
0x78928f8: {[conv2.c.W Gradient[32 x 400]] [h1.t Gradient[128 x *]] [h1.y Value[128 x *]] }
|
||||
0x7892ab8: {[h1.bn Gradient[128 x *]] [ol.t Value[10 x *]] }
|
||||
0x78999e8: {[ce Gradient[1]] }
|
||||
0x7899ba8: {[ol.W Gradient[10 x 128]] [ol.z Gradient[10 x 1 x *]] }
|
||||
0x7899d68: {[ol.t Gradient[10 x *]] [pool1 Gradient[14 x 14 x 16 x *]] [pool2 Gradient[7 x 7 x 32 x *]] }
|
||||
0x7899f28: {[ol.b Gradient[10 x 1]] }
|
||||
0x789a0e8: {[h1.sc Gradient[128 x 1]] [h1.y Gradient[128 x *]] }
|
||||
0x789a2d8: {[h1.W Gradient[128 x 7 x 7 x 32]] }
|
||||
0x789a498: {[h1.b Gradient[128 x 1]] }
|
||||
{ conv1.c.c.c : [28 x 28 x 16 x *] (gradient)
|
||||
conv1.y : [28 x 28 x 16 x *] }
|
||||
{ conv1.c.c.y : [28 x 28 x 16 x *] (gradient)
|
||||
pool1 : [14 x 14 x 16 x *] }
|
||||
{ conv1.c.W : [16 x 25] (gradient)
|
||||
conv2.c.c.c : [14 x 14 x 32 x *] }
|
||||
{ conv1.c.c.sc : [16 x 1] (gradient)
|
||||
conv1.y : [28 x 28 x 16 x *] (gradient) }
|
||||
{ conv1.c.c.b : [16 x 1] (gradient)
|
||||
conv2.c.c.c : [14 x 14 x 32 x *] (gradient)
|
||||
conv2.y : [14 x 14 x 32 x *] }
|
||||
{ conv2.c.c.y : [14 x 14 x 32 x *] (gradient)
|
||||
pool2 : [7 x 7 x 32 x *] }
|
||||
{ conv2.c.c.sc : [32 x 1] (gradient)
|
||||
conv2.y : [14 x 14 x 32 x *] (gradient)
|
||||
h1.t : [128 x *] }
|
||||
{ conv2.c.W : [32 x 400] (gradient)
|
||||
h1.t : [128 x *] (gradient)
|
||||
h1.y : [128 x *] }
|
||||
{ h1.bn : [128 x *] (gradient)
|
||||
ol.t : [10 x *] }
|
||||
{ ol.W : [10 x 128] (gradient)
|
||||
ol.z : [10 x 1 x *] (gradient) }
|
||||
{ ol.t : [10 x *] (gradient)
|
||||
pool1 : [14 x 14 x 16 x *] (gradient)
|
||||
pool2 : [7 x 7 x 32 x *] (gradient) }
|
||||
{ h1.sc : [128 x 1] (gradient)
|
||||
h1.y : [128 x *] (gradient) }
|
||||
|
||||
05/13/2016 15:10:29: No PreCompute nodes found, skipping PreCompute step.
|
||||
|
||||
05/13/2016 15:10:29: Starting Epoch 1: learning rate per sample = 0.015625 effective momentum = 0.900000 momentum as time constant = 303.7 samples
|
||||
08/16/2016 10:50:07: Training 215546 parameters in 11 out of 11 parameter tensors and 25 nodes with gradient:
|
||||
|
||||
05/13/2016 15:10:29: Starting minibatch loop.
|
||||
05/13/2016 15:10:31: Epoch[ 1 of 3]-Minibatch[1-500, 26.67%]: ce = 0.18369328 * 16000; errs = 5.75% * 16000; time = 2.0641s; samplesPerSecond = 7751.5
|
||||
05/13/2016 15:10:32: Epoch[ 1 of 3]-Minibatch[501-1000, 53.33%]: ce = 0.08101009 * 16000; errs = 2.425% * 16000; time = 1.0283s; samplesPerSecond = 15560.4
|
||||
05/13/2016 15:10:33: Epoch[ 1 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.06876743 * 16000; errs = 2.125% * 16000; time = 1.0403s; samplesPerSecond = 15380.9
|
||||
05/13/2016 15:10:34: Finished Epoch[ 1 of 3]: [Training] ce = 0.09983698 * 60000; errs = 3.09833% * 60000; totalSamplesSeen = 60000; learningRatePerSample = 0.015625; epochTime=4.9337s
|
||||
05/13/2016 15:10:34: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm.1'
|
||||
08/16/2016 10:50:07: Node 'conv1.c.W' (LearnableParameter operation) : [16 x 25]
|
||||
08/16/2016 10:50:07: Node 'conv1.c.c.b' (LearnableParameter operation) : [16 x 1]
|
||||
08/16/2016 10:50:07: Node 'conv1.c.c.sc' (LearnableParameter operation) : [16 x 1]
|
||||
08/16/2016 10:50:07: Node 'conv2.c.W' (LearnableParameter operation) : [32 x 400]
|
||||
08/16/2016 10:50:07: Node 'conv2.c.c.b' (LearnableParameter operation) : [32 x 1]
|
||||
08/16/2016 10:50:07: Node 'conv2.c.c.sc' (LearnableParameter operation) : [32 x 1]
|
||||
08/16/2016 10:50:07: Node 'h1.W' (LearnableParameter operation) : [128 x 7 x 7 x 32]
|
||||
08/16/2016 10:50:07: Node 'h1.b' (LearnableParameter operation) : [128 x 1]
|
||||
08/16/2016 10:50:07: Node 'h1.sc' (LearnableParameter operation) : [128 x 1]
|
||||
08/16/2016 10:50:07: Node 'ol.W' (LearnableParameter operation) : [10 x 128]
|
||||
08/16/2016 10:50:07: Node 'ol.b' (LearnableParameter operation) : [10 x 1]
|
||||
|
||||
08/16/2016 10:50:07: No PreCompute nodes found, or all already computed. Skipping pre-computation step.
|
||||
|
||||
08/16/2016 10:50:07: Starting Epoch 1: learning rate per sample = 0.015625 effective momentum = 0.900000 momentum as time constant = 303.7 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..60000] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:50:07: Starting minibatch loop.
|
||||
08/16/2016 10:50:09: Epoch[ 1 of 3]-Minibatch[ 1- 500, 26.67%]: ce = 0.17854112 * 16000; errs = 5.475% * 16000; time = 2.0153s; samplesPerSecond = 7939.3
|
||||
08/16/2016 10:50:10: Epoch[ 1 of 3]-Minibatch[ 501-1000, 53.33%]: ce = 0.08448637 * 16000; errs = 2.600% * 16000; time = 1.0438s; samplesPerSecond = 15328.0
|
||||
08/16/2016 10:50:11: Epoch[ 1 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.06565906 * 16000; errs = 2.013% * 16000; time = 1.0288s; samplesPerSecond = 15551.8
|
||||
08/16/2016 10:50:12: Finished Epoch[ 1 of 3]: [Training] ce = 0.09777317 * 60000; errs = 3.020% * 60000; totalSamplesSeen = 60000; learningRatePerSample = 0.015625; epochTime=4.87401s
|
||||
08/16/2016 10:50:12: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm.1'
|
||||
Setting batch normalization blend time constant to inf.
|
||||
|
||||
05/13/2016 15:10:34: Starting Epoch 2: learning rate per sample = 0.003125 effective momentum = 0.900000 momentum as time constant = 303.7 samples
|
||||
08/16/2016 10:50:12: Starting Epoch 2: learning rate per sample = 0.003125 effective momentum = 0.900000 momentum as time constant = 303.7 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [60000..120000] (first sequence at sample 60000), data subset 0 of 1
|
||||
|
||||
05/13/2016 15:10:34: Starting minibatch loop.
|
||||
05/13/2016 15:10:35: Epoch[ 2 of 3]-Minibatch[1-500, 26.67%]: ce = 0.02224222 * 16000; errs = 0.75625% * 16000; time = 1.0463s; samplesPerSecond = 15292.5
|
||||
05/13/2016 15:10:36: Epoch[ 2 of 3]-Minibatch[501-1000, 53.33%]: ce = 0.01788928 * 16000; errs = 0.56875% * 16000; time = 1.0489s; samplesPerSecond = 15254.3
|
||||
05/13/2016 15:10:37: Epoch[ 2 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.01989119 * 16000; errs = 0.54375% * 16000; time = 1.0414s; samplesPerSecond = 15363.9
|
||||
05/13/2016 15:10:38: Finished Epoch[ 2 of 3]: [Training] ce = 0.02009503 * 60000; errs = 0.623333% * 60000; totalSamplesSeen = 120000; learningRatePerSample = 0.003125; epochTime=3.92922s
|
||||
05/13/2016 15:10:38: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm.2'
|
||||
08/16/2016 10:50:12: Starting minibatch loop.
|
||||
08/16/2016 10:50:13: Epoch[ 2 of 3]-Minibatch[ 1- 500, 26.67%]: ce = 0.02472396 * 16000; errs = 0.831% * 16000; time = 1.0330s; samplesPerSecond = 15489.0
|
||||
08/16/2016 10:50:14: Epoch[ 2 of 3]-Minibatch[ 501-1000, 53.33%]: ce = 0.01743382 * 16000; errs = 0.500% * 16000; time = 1.0313s; samplesPerSecond = 15514.5
|
||||
08/16/2016 10:50:15: Epoch[ 2 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.02253625 * 16000; errs = 0.706% * 16000; time = 1.0300s; samplesPerSecond = 15534.0
|
||||
08/16/2016 10:50:15: Finished Epoch[ 2 of 3]: [Training] ce = 0.02161322 * 60000; errs = 0.687% * 60000; totalSamplesSeen = 120000; learningRatePerSample = 0.003125; epochTime=3.87243s
|
||||
08/16/2016 10:50:15: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm.2'
|
||||
|
||||
05/13/2016 15:10:38: Starting Epoch 3: learning rate per sample = 0.003125 effective momentum = 0.900000 momentum as time constant = 303.7 samples
|
||||
08/16/2016 10:50:15: Starting Epoch 3: learning rate per sample = 0.003125 effective momentum = 0.900000 momentum as time constant = 303.7 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [120000..180000] (first sequence at sample 120000), data subset 0 of 1
|
||||
|
||||
05/13/2016 15:10:38: Starting minibatch loop.
|
||||
05/13/2016 15:10:39: Epoch[ 3 of 3]-Minibatch[1-500, 26.67%]: ce = 0.01173781 * 16000; errs = 0.30625% * 16000; time = 1.0390s; samplesPerSecond = 15400.0
|
||||
05/13/2016 15:10:40: Epoch[ 3 of 3]-Minibatch[501-1000, 53.33%]: ce = 0.01463517 * 16000; errs = 0.43125% * 16000; time = 1.0397s; samplesPerSecond = 15388.4
|
||||
05/13/2016 15:10:41: Epoch[ 3 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.01582476 * 16000; errs = 0.49375% * 16000; time = 1.0373s; samplesPerSecond = 15425.2
|
||||
05/13/2016 15:10:42: Finished Epoch[ 3 of 3]: [Training] ce = 0.01382984 * 60000; errs = 0.401667% * 60000; totalSamplesSeen = 180000; learningRatePerSample = 0.003125; epochTime=3.9054s
|
||||
05/13/2016 15:10:42: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm'
|
||||
05/13/2016 15:10:42: CNTKCommandTrainEnd: train
|
||||
08/16/2016 10:50:15: Starting minibatch loop.
|
||||
08/16/2016 10:50:16: Epoch[ 3 of 3]-Minibatch[ 1- 500, 26.67%]: ce = 0.01485439 * 16000; errs = 0.419% * 16000; time = 1.0289s; samplesPerSecond = 15551.0
|
||||
08/16/2016 10:50:18: Epoch[ 3 of 3]-Minibatch[ 501-1000, 53.33%]: ce = 0.01477328 * 16000; errs = 0.419% * 16000; time = 1.0311s; samplesPerSecond = 15517.7
|
||||
08/16/2016 10:50:19: Epoch[ 3 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.01663958 * 16000; errs = 0.519% * 16000; time = 1.0372s; samplesPerSecond = 15426.6
|
||||
08/16/2016 10:50:19: Finished Epoch[ 3 of 3]: [Training] ce = 0.01594585 * 60000; errs = 0.482% * 60000; totalSamplesSeen = 180000; learningRatePerSample = 0.003125; epochTime=3.87525s
|
||||
08/16/2016 10:50:19: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm'
|
||||
08/16/2016 10:50:19: CNTKCommandTrainEnd: train
|
||||
|
||||
05/13/2016 15:10:42: Action "train" complete.
|
||||
08/16/2016 10:50:19: Action "train" complete.
|
||||
|
||||
|
||||
05/13/2016 15:10:42: ##############################################################################
|
||||
05/13/2016 15:10:42: # #
|
||||
05/13/2016 15:10:42: # Action "test" #
|
||||
05/13/2016 15:10:42: # #
|
||||
05/13/2016 15:10:42: ##############################################################################
|
||||
08/16/2016 10:50:19: ##############################################################################
|
||||
08/16/2016 10:50:19: # #
|
||||
08/16/2016 10:50:19: # Action "test" #
|
||||
08/16/2016 10:50:19: # #
|
||||
08/16/2016 10:50:19: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
||||
3 roots:
|
||||
ce = CrossEntropyWithSoftmax()
|
||||
err = ErrorPrediction()
|
||||
errs = ErrorPrediction()
|
||||
ol.z = Plus()
|
||||
|
||||
Validating network. 36 nodes to process in pass 1.
|
||||
|
|
@ -502,7 +544,7 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 128], [128 x *1] -> [10 x *1]
|
|||
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
|
||||
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x *1], [10 x 1] -> [10 x 1 x *1]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
|
||||
Validating --> errs = ErrorPrediction (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
|
||||
|
||||
Validating network. 16 nodes to process in pass 2.
|
||||
|
||||
|
|
@ -510,17 +552,17 @@ Validating network. 16 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 28 x 28 x 1, Output: 28 x 28 x 16, Kernel: 5 x 5 x 1, Map: 1 x 1 x 16, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c.c.c: using cuDNN convolution engine for geometry: Input: 28 x 28 x 1, Output: 28 x 28 x 16, Kernel: 5 x 5 x 1, Map: 1 x 1 x 16, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 28 x 28 x 16, Output: 14 x 14 x 16, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 28 x 28 x 16, Output: 14 x 14 x 16, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 14 x 14 x 16, Output: 14 x 14 x 32, Kernel: 5 x 5 x 16, Map: 1 x 1 x 32, Stride: 1 x 1 x 16, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c.c.c: using cuDNN convolution engine for geometry: Input: 14 x 14 x 16, Output: 14 x 14 x 32, Kernel: 5 x 5 x 16, Map: 1 x 1 x 32, Stride: 1 x 1 x 16, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 14 x 14 x 32, Output: 7 x 7 x 32, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 14 x 14 x 32, Output: 7 x 7 x 32, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
|
|
@ -534,48 +576,13 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 36 matrices, 0 are shared as 0, and 36 are not shared.
|
||||
|
||||
(nil): {[ce Gradient[1]] [conv1.c.W Gradient[16 x 25]] [conv1.c.c.b Gradient[16 x 1]] [conv1.c.c.c Gradient[28 x 28 x 16 x *1]] [conv1.c.c.isd Gradient[16 x 1]] [conv1.c.c.m Gradient[16 x 1]] [conv1.c.c.sc Gradient[16 x 1]] [conv1.c.c.y Gradient[28 x 28 x 16 x *1]] [conv1.y Gradient[28 x 28 x 16 x *1]] [conv2.c.W Gradient[32 x 400]] [conv2.c.c.b Gradient[32 x 1]] [conv2.c.c.c Gradient[14 x 14 x 32 x *1]] [conv2.c.c.isd Gradient[32 x 1]] [conv2.c.c.m Gradient[32 x 1]] [conv2.c.c.sc Gradient[32 x 1]] [conv2.c.c.y Gradient[14 x 14 x 32 x *1]] [conv2.y Gradient[14 x 14 x 32 x *1]] [err Gradient[1]] [featScale Gradient[1 x 1]] [featScaled Gradient[28 x 28 x 1 x *1]] [features Gradient[28 x 28 x 1 x *1]] [h1.W Gradient[128 x 7 x 7 x 32]] [h1.b Gradient[128 x 1]] [h1.bn Gradient[128 x *1]] [h1.isd Gradient[128 x 1]] [h1.m Gradient[128 x 1]] [h1.sc Gradient[128 x 1]] [h1.t Gradient[128 x *1]] [h1.y Gradient[128 x *1]] [labels Gradient[10 x *1]] [ol.W Gradient[10 x 128]] [ol.b Gradient[10 x 1]] [ol.t Gradient[10 x *1]] [ol.z Gradient[10 x 1 x *1]] [pool1 Gradient[14 x 14 x 16 x *1]] [pool2 Gradient[7 x 7 x 32 x *1]] }
|
||||
0x7f50cab10a28: {[h1.sc Value[128 x 1]] }
|
||||
0x7f50cab11988: {[h1.W Value[128 x 7 x 7 x 32]] }
|
||||
0x7f50cab132e8: {[labels Value[10 x *1]] }
|
||||
0x7f50cab13968: {[ol.b Value[10 x 1]] }
|
||||
0x7f50cab14c88: {[h1.b Value[128 x 1]] }
|
||||
0x7f50cab15368: {[h1.isd Value[128 x 1]] }
|
||||
0x7f50cab15da8: {[h1.m Value[128 x 1]] }
|
||||
0x7f50cab160c8: {[conv1.c.c.isd Value[16 x 1]] }
|
||||
0x7f50cab17e68: {[ol.W Value[10 x 128]] }
|
||||
0x7f50cab1ac98: {[ce Value[1]] }
|
||||
0x7f50cab1c4f8: {[err Value[1]] }
|
||||
0x7f50cabd0b58: {[conv1.c.c.c Value[28 x 28 x 16 x *1]] }
|
||||
0x7f50cabd0e98: {[featScaled Value[28 x 28 x 1 x *1]] }
|
||||
0x7f50cabd1148: {[conv1.c.c.y Value[28 x 28 x 16 x *1]] }
|
||||
0x7f50cabd1f48: {[conv1.y Value[28 x 28 x 16 x *1]] }
|
||||
0x7f50cabd2108: {[pool1 Value[14 x 14 x 16 x *1]] }
|
||||
0x7f50cabd22c8: {[conv2.c.c.c Value[14 x 14 x 32 x *1]] }
|
||||
0x7f50cabd2648: {[conv2.c.c.y Value[14 x 14 x 32 x *1]] }
|
||||
0x7f50cabd2b88: {[conv2.y Value[14 x 14 x 32 x *1]] }
|
||||
0x7f50cabd2d48: {[pool2 Value[7 x 7 x 32 x *1]] }
|
||||
0x7f50cabd2f08: {[h1.t Value[128 x *1]] }
|
||||
0x7f50cabd9558: {[h1.bn Value[128 x *1]] }
|
||||
0x7f50cabd9a98: {[h1.y Value[128 x *1]] }
|
||||
0x7f50cabd9c58: {[ol.t Value[10 x *1]] }
|
||||
0x7f50cabd9e18: {[ol.z Value[10 x 1 x *1]] }
|
||||
0x7f50cad85a38: {[conv1.c.c.b Value[16 x 1]] }
|
||||
0x7f50d5601148: {[conv2.c.c.sc Value[32 x 1]] }
|
||||
0x7f50d5601ea8: {[conv2.c.W Value[32 x 400]] }
|
||||
0x7f50d5602728: {[conv1.c.W Value[16 x 25]] }
|
||||
0x7f50d5602e58: {[conv2.c.c.b Value[32 x 1]] }
|
||||
0x7f50d5603b28: {[conv1.c.c.sc Value[16 x 1]] }
|
||||
0x7f50d56045d8: {[conv1.c.c.m Value[16 x 1]] }
|
||||
0x7f50d5606dd8: {[conv2.c.c.isd Value[32 x 1]] }
|
||||
0x7f50d5608478: {[conv2.c.c.m Value[32 x 1]] }
|
||||
0x7f50d5609d38: {[featScale Value[1 x 1]] }
|
||||
0x7f50d560a658: {[features Value[28 x 28 x 1 x *1]] }
|
||||
|
||||
05/13/2016 15:10:47: Final Results: Minibatch[1-10]: errs = 0.66% * 10000; ce = 0.02083102 * 10000; perplexity = 1.02104950
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
08/16/2016 10:50:20: Minibatch[1-10]: errs = 0.840% * 10000; ce = 0.02491569 * 10000
|
||||
08/16/2016 10:50:20: Final Results: Minibatch[1-10]: errs = 0.840% * 10000; ce = 0.02491569 * 10000; perplexity = 1.02522868
|
||||
|
||||
05/13/2016 15:10:47: Action "test" complete.
|
||||
08/16/2016 10:50:20: Action "test" complete.
|
||||
|
||||
05/13/2016 15:10:47: __COMPLETED__
|
||||
08/16/2016 10:50:20: __COMPLETED__
|
||||
|
|
@ -1,62 +1,77 @@
|
|||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/Config/03_ConvBatchNorm.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 268381192 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/03_ConvBatchNorm.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 13 2016 08:06:01
|
||||
Last modified date: Thu May 12 07:31:50 2016
|
||||
Built time: Aug 16 2016 02:54:53
|
||||
Last modified date: Fri Aug 12 05:31:21 2016
|
||||
Build type: Release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: no
|
||||
Math lib: mkl
|
||||
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
CUB_PATH: c:\src\cub-1.4.1
|
||||
CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by svcphil on Philly-Pool3
|
||||
Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData
|
||||
05/13/2016 08:16:56: -------------------------------------------------------------------
|
||||
05/13/2016 08:16:56: Build info:
|
||||
Changed current directory to C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData
|
||||
08/16/2016 03:01:34: -------------------------------------------------------------------
|
||||
08/16/2016 03:01:34: Build info:
|
||||
|
||||
05/13/2016 08:16:56: Built time: May 13 2016 08:06:01
|
||||
05/13/2016 08:16:56: Last modified date: Thu May 12 07:31:50 2016
|
||||
05/13/2016 08:16:56: Build type: Release
|
||||
05/13/2016 08:16:56: Build target: GPU
|
||||
05/13/2016 08:16:56: With 1bit-SGD: no
|
||||
05/13/2016 08:16:56: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
05/13/2016 08:16:56: CUB_PATH: c:\src\cub-1.4.1
|
||||
05/13/2016 08:16:56: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
05/13/2016 08:16:56: Build Branch: HEAD
|
||||
05/13/2016 08:16:56: Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
05/13/2016 08:16:56: Built by svcphil on Philly-Pool3
|
||||
05/13/2016 08:16:56: Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
05/13/2016 08:16:56: -------------------------------------------------------------------
|
||||
08/16/2016 03:01:34: Built time: Aug 16 2016 02:54:53
|
||||
08/16/2016 03:01:34: Last modified date: Fri Aug 12 05:31:21 2016
|
||||
08/16/2016 03:01:34: Build type: Release
|
||||
08/16/2016 03:01:34: Build target: GPU
|
||||
08/16/2016 03:01:34: With 1bit-SGD: no
|
||||
08/16/2016 03:01:34: Math lib: mkl
|
||||
08/16/2016 03:01:34: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
08/16/2016 03:01:34: CUB_PATH: c:\src\cub-1.4.1
|
||||
08/16/2016 03:01:34: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
08/16/2016 03:01:34: Build Branch: HEAD
|
||||
08/16/2016 03:01:34: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 03:01:34: Built by svcphil on Philly-Pool3
|
||||
08/16/2016 03:01:34: Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
08/16/2016 03:01:34: -------------------------------------------------------------------
|
||||
08/16/2016 03:01:36: -------------------------------------------------------------------
|
||||
08/16/2016 03:01:36: GPU info:
|
||||
|
||||
05/13/2016 08:16:56: Running on Philly-Pool2 at 2016/05/13 08:16:56
|
||||
05/13/2016 08:16:56: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/MNIST/Config/03_ConvBatchNorm.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
08/16/2016 03:01:36: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:01:36: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:01:36: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:01:36: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:01:36: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 03:01:36: Running on DPHAIM-24 at 2016/08/16 03:01:36
|
||||
08/16/2016 03:01:36: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/03_ConvBatchNorm.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu DeviceId=0 timestamping=true train=[SGD=[maxEpochs=3]] imageLayout="cudnn"
|
||||
|
||||
|
||||
|
||||
05/13/2016 08:16:56: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 08:16:56: RootDir = ".."
|
||||
08/16/2016 03:01:36: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:01:36: RootDir = ".."
|
||||
ConfigDir = "$RootDir$/Config"
|
||||
DataDir = "$RootDir$/Data"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
ModelDir = "$OutputDir$/Models"
|
||||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
command = train:test
|
||||
precision = "float"
|
||||
modelPath = "$ModelDir$/03_ConvBatchNorm"
|
||||
ndlMacros = "$ConfigDir$/Macros.ndl"
|
||||
traceLevel=1
|
||||
numMBsToShowResult=500
|
||||
initOnCPUOnly=true
|
||||
train = [
|
||||
action = "train"
|
||||
NDLNetworkBuilder = [
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly=true
|
||||
ndlMacros = "$ConfigDir$/Macros.ndl"
|
||||
networkDescription = "$ConfigDir$/03_ConvBatchNorm.ndl"
|
||||
]
|
||||
SGD = [
|
||||
|
|
@ -84,11 +99,8 @@ train = [
|
|||
]
|
||||
test = [
|
||||
action = "test"
|
||||
minibatchSize = 32
|
||||
minibatchSize = 1024
|
||||
modelPath=$ModelDir$/03_ConvBatchNorm
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "$ConfigDir$/03_ConvBatchNorm.ndl"
|
||||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "$DataDir$/Test-28x28_cntk_text.txt"
|
||||
|
|
@ -104,36 +116,36 @@ test = [
|
|||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
train=[SGD=[maxEpochs=3]]
|
||||
imageLayout="cudnn"
|
||||
|
||||
05/13/2016 08:16:56: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:01:36: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 08:16:56: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 08:16:56: RootDir = ".."
|
||||
08/16/2016 03:01:36: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:01:36: RootDir = ".."
|
||||
ConfigDir = "../Config"
|
||||
DataDir = "../Data"
|
||||
OutputDir = "../Output"
|
||||
ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu/Models"
|
||||
ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu/Models"
|
||||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
command = train:test
|
||||
precision = "float"
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm"
|
||||
ndlMacros = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/Macros.ndl"
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm"
|
||||
traceLevel=1
|
||||
numMBsToShowResult=500
|
||||
initOnCPUOnly=true
|
||||
train = [
|
||||
action = "train"
|
||||
NDLNetworkBuilder = [
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly=true
|
||||
ndlMacros = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/Macros.ndl"
|
||||
networkDescription = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/03_ConvBatchNorm.ndl"
|
||||
]
|
||||
SGD = [
|
||||
|
|
@ -146,7 +158,7 @@ train = [
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData/Train-28x28_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData/Train-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
|
|
@ -161,14 +173,11 @@ train = [
|
|||
]
|
||||
test = [
|
||||
action = "test"
|
||||
minibatchSize = 32
|
||||
modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/03_ConvBatchNorm.ndl"
|
||||
]
|
||||
minibatchSize = 1024
|
||||
modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData/Test-28x28_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData/Test-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
|
|
@ -181,44 +190,39 @@ test = [
|
|||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
train=[SGD=[maxEpochs=3]]
|
||||
imageLayout="cudnn"
|
||||
|
||||
05/13/2016 08:16:56: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:01:36: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 08:16:56: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:01:36: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: 03_ConvBatchNorm.cntk:command=train:test
|
||||
configparameters: 03_ConvBatchNorm.cntk:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config
|
||||
configparameters: 03_ConvBatchNorm.cntk:currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData
|
||||
configparameters: 03_ConvBatchNorm.cntk:DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData
|
||||
configparameters: 03_ConvBatchNorm.cntk:currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData
|
||||
configparameters: 03_ConvBatchNorm.cntk:DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData
|
||||
configparameters: 03_ConvBatchNorm.cntk:deviceId=0
|
||||
configparameters: 03_ConvBatchNorm.cntk:imageLayout=cudnn
|
||||
configparameters: 03_ConvBatchNorm.cntk:initOnCPUOnly=true
|
||||
configparameters: 03_ConvBatchNorm.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu/Models
|
||||
configparameters: 03_ConvBatchNorm.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm
|
||||
configparameters: 03_ConvBatchNorm.cntk:ndlMacros=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/Macros.ndl
|
||||
configparameters: 03_ConvBatchNorm.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu/Models
|
||||
configparameters: 03_ConvBatchNorm.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm
|
||||
configparameters: 03_ConvBatchNorm.cntk:numMBsToShowResult=500
|
||||
configparameters: 03_ConvBatchNorm.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu
|
||||
configparameters: 03_ConvBatchNorm.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu
|
||||
configparameters: 03_ConvBatchNorm.cntk:precision=float
|
||||
configparameters: 03_ConvBatchNorm.cntk:RootDir=..
|
||||
configparameters: 03_ConvBatchNorm.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu
|
||||
configparameters: 03_ConvBatchNorm.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu
|
||||
configparameters: 03_ConvBatchNorm.cntk:test=[
|
||||
action = "test"
|
||||
minibatchSize = 32
|
||||
modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/03_ConvBatchNorm.ndl"
|
||||
]
|
||||
minibatchSize = 1024
|
||||
modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData/Test-28x28_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData/Test-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
|
|
@ -237,6 +241,9 @@ configparameters: 03_ConvBatchNorm.cntk:traceLevel=1
|
|||
configparameters: 03_ConvBatchNorm.cntk:train=[
|
||||
action = "train"
|
||||
NDLNetworkBuilder = [
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly=true
|
||||
ndlMacros = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/Macros.ndl"
|
||||
networkDescription = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\MNIST\Config/03_ConvBatchNorm.ndl"
|
||||
]
|
||||
SGD = [
|
||||
|
|
@ -249,7 +256,7 @@ configparameters: 03_ConvBatchNorm.cntk:train=[
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData/Train-28x28_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu\TestData/Train-28x28_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 784
|
||||
|
|
@ -263,29 +270,67 @@ configparameters: 03_ConvBatchNorm.cntk:train=[
|
|||
]
|
||||
] [SGD=[maxEpochs=3]]
|
||||
|
||||
05/13/2016 08:16:56: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/13/2016 08:16:56: Commands: train test
|
||||
05/13/2016 08:16:56: Precision = "float"
|
||||
05/13/2016 08:16:56: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm
|
||||
05/13/2016 08:16:56: CNTKCommandTrainInfo: train : 3
|
||||
05/13/2016 08:16:56: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
08/16/2016 03:01:36: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:01:36: Commands: train test
|
||||
08/16/2016 03:01:36: Precision = "float"
|
||||
08/16/2016 03:01:36: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm
|
||||
08/16/2016 03:01:36: CNTKCommandTrainInfo: train : 3
|
||||
08/16/2016 03:01:36: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
|
||||
05/13/2016 08:16:56: ##############################################################################
|
||||
05/13/2016 08:16:56: # #
|
||||
05/13/2016 08:16:56: # Action "train" #
|
||||
05/13/2016 08:16:56: # #
|
||||
05/13/2016 08:16:56: ##############################################################################
|
||||
08/16/2016 03:01:36: ##############################################################################
|
||||
08/16/2016 03:01:36: # #
|
||||
08/16/2016 03:01:36: # Action "train" #
|
||||
08/16/2016 03:01:36: # #
|
||||
08/16/2016 03:01:36: ##############################################################################
|
||||
|
||||
05/13/2016 08:16:56: CNTKCommandTrainBegin: train
|
||||
08/16/2016 03:01:36: CNTKCommandTrainBegin: train
|
||||
NDLBuilder Using GPU 0
|
||||
|
||||
05/13/2016 08:16:57: Creating virgin network.
|
||||
08/16/2016 03:01:37: Creating virgin network.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.000000.
|
||||
Node 'conv1.c.W' (LearnableParameter operation): Initializing Parameter[16 x 25] <- 0.000000.
|
||||
Node 'conv1.c.c.b' (LearnableParameter operation): Initializing Parameter[16 x 1] <- 0.000000.
|
||||
Node 'conv1.c.c.sc' (LearnableParameter operation): Initializing Parameter[16 x 1] <- 0.000000.
|
||||
Node 'conv1.c.c.m' (LearnableParameter operation): Initializing Parameter[16 x 1] <- 0.000000.
|
||||
Node 'conv1.c.c.isd' (LearnableParameter operation): Initializing Parameter[16 x 1] <- 0.000000.
|
||||
Node 'conv2.c.W' (LearnableParameter operation): Initializing Parameter[32 x 400] <- 0.000000.
|
||||
Node 'conv2.c.c.b' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.c.sc' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.c.m' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.c.isd' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[128 x 1568] <- 0.000000.
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[128 x 1] <- 0.000000.
|
||||
Node 'h1.sc' (LearnableParameter operation): Initializing Parameter[128 x 1] <- 0.000000.
|
||||
Node 'h1.m' (LearnableParameter operation): Initializing Parameter[128 x 1] <- 0.000000.
|
||||
Node 'h1.isd' (LearnableParameter operation): Initializing Parameter[128 x 1] <- 0.000000.
|
||||
Node 'ol.W' (LearnableParameter operation): Initializing Parameter[10 x 128] <- 0.000000.
|
||||
Node 'ol.b' (LearnableParameter operation): Initializing Parameter[10 x 1] <- 0.000000.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.003906.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.003906.
|
||||
Node 'featScale' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.003906.
|
||||
Node 'conv1.c.W' (LearnableParameter operation): Initializing Parameter[16 x 25] <- gaussian(seed=1, range=0.040000*10.000000, onCPU=true).
|
||||
Node 'conv1.c.c.b' (LearnableParameter operation): Initializing Parameter[16 x 1] <- 0.000000.
|
||||
Node 'conv1.c.c.sc' (LearnableParameter operation): Initializing Parameter[16 x 1] <- 1.000000.
|
||||
Node 'conv1.c.c.m' (LearnableParameter operation): Initializing Parameter[16 x 1] <- 0.000000.
|
||||
Node 'conv1.c.c.isd' (LearnableParameter operation): Initializing Parameter[16 x 1] <- 0.000000.
|
||||
Node 'conv2.c.W' (LearnableParameter operation): Initializing Parameter[32 x 400] <- gaussian(seed=2, range=0.010000*10.000000, onCPU=true).
|
||||
Node 'conv2.c.c.b' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.c.sc' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 1.000000.
|
||||
Node 'conv2.c.c.m' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.c.isd' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[128 x 1568] <- gaussian(seed=3, range=0.005051*1.000000, onCPU=true).
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[128 x 1] <- 0.000000.
|
||||
Node 'h1.sc' (LearnableParameter operation): Initializing Parameter[128 x 1] <- 1.000000.
|
||||
Node 'h1.m' (LearnableParameter operation): Initializing Parameter[128 x 1] <- 0.000000.
|
||||
Node 'h1.isd' (LearnableParameter operation): Initializing Parameter[128 x 1] <- 0.000000.
|
||||
Node 'ol.W' (LearnableParameter operation): Initializing Parameter[10 x 128] <- uniform(seed=4, range=0.050000*1.000000, onCPU=true).
|
||||
Node 'ol.b' (LearnableParameter operation): Initializing Parameter[10 x 1] <- uniform(seed=5, range=0.050000*1.000000, onCPU=true).
|
||||
|
||||
Post-processing network...
|
||||
|
||||
3 roots:
|
||||
ce = CrossEntropyWithSoftmax()
|
||||
err = ErrorPrediction()
|
||||
errs = ErrorPrediction()
|
||||
ol.z = Plus()
|
||||
|
||||
Validating network. 36 nodes to process in pass 1.
|
||||
|
|
@ -327,7 +372,7 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 128], [128 x *] -> [10 x *]
|
|||
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
|
||||
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x *], [10 x 1] -> [10 x 1 x *]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
|
||||
Validating --> errs = ErrorPrediction (labels, ol.z) : [10 x *], [10 x 1 x *] -> [1]
|
||||
|
||||
Validating network. 16 nodes to process in pass 2.
|
||||
|
||||
|
|
@ -335,17 +380,17 @@ Validating network. 16 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 28 x 28 x 1, Output: 28 x 28 x 16, Kernel: 5 x 5 x 1, Map: 1 x 1 x 16, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c.c.c: using cuDNN convolution engine for geometry: Input: 28 x 28 x 1, Output: 28 x 28 x 16, Kernel: 5 x 5 x 1, Map: 1 x 1 x 16, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 28 x 28 x 16, Output: 14 x 14 x 16, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 28 x 28 x 16, Output: 14 x 14 x 16, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 14 x 14 x 16, Output: 14 x 14 x 32, Kernel: 5 x 5 x 16, Map: 1 x 1 x 32, Stride: 1 x 1 x 16, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c.c.c: using cuDNN convolution engine for geometry: Input: 14 x 14 x 16, Output: 14 x 14 x 32, Kernel: 5 x 5 x 16, Map: 1 x 1 x 32, Stride: 1 x 1 x 16, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 14 x 14 x 32, Output: 7 x 7 x 32, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 14 x 14 x 32, Output: 7 x 7 x 32, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
|
|
@ -354,113 +399,112 @@ Using CNTK batch normalization engine.
|
|||
|
||||
Post-processing network complete.
|
||||
|
||||
05/13/2016 08:16:58: Created model with 36 nodes on GPU 0.
|
||||
08/16/2016 03:01:37: Created model with 36 nodes on GPU 0.
|
||||
|
||||
05/13/2016 08:16:58: Training criterion node(s):
|
||||
05/13/2016 08:16:58: ce = CrossEntropyWithSoftmax
|
||||
08/16/2016 03:01:37: Training criterion node(s):
|
||||
08/16/2016 03:01:37: ce = CrossEntropyWithSoftmax
|
||||
|
||||
05/13/2016 08:16:58: Evaluation criterion node(s):
|
||||
|
||||
05/13/2016 08:16:58: err = ErrorPrediction
|
||||
08/16/2016 03:01:37: Evaluation criterion node(s):
|
||||
08/16/2016 03:01:37: errs = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 61 matrices, 28 are shared as 12, and 33 are not shared.
|
||||
|
||||
0000000000000000: {[conv1.c.c.isd Gradient[16 x 1]] [conv1.c.c.m Gradient[16 x 1]] [conv2.c.c.isd Gradient[32 x 1]] [conv2.c.c.m Gradient[32 x 1]] [err Gradient[1]] [featScale Gradient[1 x 1]] [featScaled Gradient[28 x 28 x 1 x *]] [features Gradient[28 x 28 x 1 x *]] [h1.isd Gradient[128 x 1]] [h1.m Gradient[128 x 1]] [labels Gradient[10 x *]] }
|
||||
00000093B2DC5E20: {[features Value[28 x 28 x 1 x *]] }
|
||||
00000093CB445890: {[ol.W Value[10 x 128]] }
|
||||
00000093CB446290: {[ol.b Value[10 x 1]] }
|
||||
00000093CB5EF4D0: {[conv2.c.c.m Value[32 x 1]] }
|
||||
00000093CB5EF570: {[conv1.c.W Value[16 x 25]] }
|
||||
00000093CB5EF610: {[h1.sc Value[128 x 1]] }
|
||||
00000093CB5EF9D0: {[conv2.c.c.b Value[32 x 1]] }
|
||||
00000093CB5EFBB0: {[h1.b Value[128 x 1]] }
|
||||
00000093CB5EFCF0: {[h1.isd Value[128 x 1]] }
|
||||
00000093CB5EFD90: {[h1.m Value[128 x 1]] }
|
||||
00000093CB5F03D0: {[conv1.c.c.b Value[16 x 1]] }
|
||||
00000093CB5F0470: {[conv1.c.c.sc Value[16 x 1]] }
|
||||
00000093CB5F05B0: {[conv1.c.c.isd Value[16 x 1]] }
|
||||
00000093CB5F06F0: {[conv2.c.W Value[32 x 400]] }
|
||||
00000093CB5F0830: {[conv2.c.c.sc Value[32 x 1]] }
|
||||
00000093CB5F08D0: {[conv2.c.c.isd Value[32 x 1]] }
|
||||
00000093CB5F0970: {[labels Value[10 x *]] }
|
||||
00000093CB5F0BF0: {[conv1.c.c.m Value[16 x 1]] }
|
||||
00000093CB5F0D30: {[featScale Value[1 x 1]] }
|
||||
00000093CB5F0DD0: {[h1.W Value[128 x 7 x 7 x 32]] }
|
||||
00000093D1AAE180: {[conv2.c.c.b Gradient[32 x 1]] }
|
||||
00000093D1AAE360: {[ol.t Gradient[10 x *]] [pool1 Gradient[14 x 14 x 16 x *]] [pool2 Gradient[7 x 7 x 32 x *]] }
|
||||
00000093D1AAE400: {[h1.W Gradient[128 x 7 x 7 x 32]] }
|
||||
00000093D1AAE5E0: {[conv1.c.c.c Gradient[28 x 28 x 16 x *]] [conv1.y Value[28 x 28 x 16 x *]] }
|
||||
00000093D1AAE680: {[h1.b Gradient[128 x 1]] }
|
||||
00000093D1AAE9A0: {[err Value[1]] }
|
||||
00000093D1AAED60: {[ol.z Value[10 x 1 x *]] }
|
||||
00000093D1AAEE00: {[ce Value[1]] }
|
||||
00000093D1AAEF40: {[conv1.c.c.y Value[28 x 28 x 16 x *]] }
|
||||
00000093D1AAF080: {[ol.b Gradient[10 x 1]] }
|
||||
00000093D1AAF120: {[conv1.c.W Gradient[16 x 25]] [conv2.c.c.c Value[14 x 14 x 32 x *]] }
|
||||
00000093D1AAF1C0: {[h1.bn Gradient[128 x *]] [ol.t Value[10 x *]] }
|
||||
00000093D1AAF260: {[conv1.c.c.y Gradient[28 x 28 x 16 x *]] [pool1 Value[14 x 14 x 16 x *]] }
|
||||
00000093D1AAF440: {[ol.W Gradient[10 x 128]] [ol.z Gradient[10 x 1 x *]] }
|
||||
00000093D1AAFB20: {[h1.bn Value[128 x *]] }
|
||||
00000093D1AAFDA0: {[conv1.c.c.c Value[28 x 28 x 16 x *]] }
|
||||
00000093D1AAFE40: {[conv2.c.c.y Value[14 x 14 x 32 x *]] }
|
||||
00000093D1AAFF80: {[conv2.c.c.y Gradient[14 x 14 x 32 x *]] [pool2 Value[7 x 7 x 32 x *]] }
|
||||
00000093D1AB0020: {[conv2.c.W Gradient[32 x 400]] [h1.t Gradient[128 x *]] [h1.y Value[128 x *]] }
|
||||
00000093D1AB00C0: {[h1.sc Gradient[128 x 1]] [h1.y Gradient[128 x *]] }
|
||||
00000093D1AB0200: {[ce Gradient[1]] }
|
||||
00000093D1AB03E0: {[conv1.c.c.sc Gradient[16 x 1]] [conv1.y Gradient[28 x 28 x 16 x *]] }
|
||||
00000093D1AB0480: {[conv1.c.c.b Gradient[16 x 1]] [conv2.c.c.c Gradient[14 x 14 x 32 x *]] [conv2.y Value[14 x 14 x 32 x *]] }
|
||||
00000093D1AB0660: {[featScaled Value[28 x 28 x 1 x *]] }
|
||||
00000093D1AB0700: {[conv2.c.c.sc Gradient[32 x 1]] [conv2.y Gradient[14 x 14 x 32 x *]] [h1.t Value[128 x *]] }
|
||||
{ conv2.c.W : [32 x 400] (gradient)
|
||||
h1.t : [128 x *] (gradient)
|
||||
h1.y : [128 x *] }
|
||||
{ conv1.c.c.b : [16 x 1] (gradient)
|
||||
conv2.c.c.c : [14 x 14 x 32 x *] (gradient)
|
||||
conv2.y : [14 x 14 x 32 x *] }
|
||||
{ conv1.c.W : [16 x 25] (gradient)
|
||||
conv2.c.c.c : [14 x 14 x 32 x *] }
|
||||
{ conv1.c.c.sc : [16 x 1] (gradient)
|
||||
conv1.y : [28 x 28 x 16 x *] (gradient) }
|
||||
{ conv2.c.c.y : [14 x 14 x 32 x *] (gradient)
|
||||
pool2 : [7 x 7 x 32 x *] }
|
||||
{ ol.W : [10 x 128] (gradient)
|
||||
ol.z : [10 x 1 x *] (gradient) }
|
||||
{ conv2.c.c.sc : [32 x 1] (gradient)
|
||||
conv2.y : [14 x 14 x 32 x *] (gradient)
|
||||
h1.t : [128 x *] }
|
||||
{ ol.t : [10 x *] (gradient)
|
||||
pool1 : [14 x 14 x 16 x *] (gradient)
|
||||
pool2 : [7 x 7 x 32 x *] (gradient) }
|
||||
{ h1.sc : [128 x 1] (gradient)
|
||||
h1.y : [128 x *] (gradient) }
|
||||
{ h1.bn : [128 x *] (gradient)
|
||||
ol.t : [10 x *] }
|
||||
{ conv1.c.c.c : [28 x 28 x 16 x *] (gradient)
|
||||
conv1.y : [28 x 28 x 16 x *] }
|
||||
{ conv1.c.c.y : [28 x 28 x 16 x *] (gradient)
|
||||
pool1 : [14 x 14 x 16 x *] }
|
||||
|
||||
05/13/2016 08:16:58: No PreCompute nodes found, skipping PreCompute step.
|
||||
|
||||
05/13/2016 08:16:58: Starting Epoch 1: learning rate per sample = 0.015625 effective momentum = 0.900000 momentum as time constant = 303.7 samples
|
||||
08/16/2016 03:01:37: Training 215546 parameters in 11 out of 11 parameter tensors and 25 nodes with gradient:
|
||||
|
||||
05/13/2016 08:16:58: Starting minibatch loop.
|
||||
05/13/2016 08:17:02: Epoch[ 1 of 3]-Minibatch[1-500, 26.67%]: ce = 0.17330922 * 16000; errs = 5.325% * 16000; time = 4.3656s; samplesPerSecond = 3665.0
|
||||
05/13/2016 08:17:04: Epoch[ 1 of 3]-Minibatch[501-1000, 53.33%]: ce = 0.07897408 * 16000; errs = 2.45625% * 16000; time = 1.7980s; samplesPerSecond = 8899.0
|
||||
05/13/2016 08:17:06: Epoch[ 1 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.06288062 * 16000; errs = 2.0125% * 16000; time = 1.7989s; samplesPerSecond = 8894.1
|
||||
05/13/2016 08:17:07: Finished Epoch[ 1 of 3]: [Training] ce = 0.09585953 * 60000; errs = 2.95667% * 60000; totalSamplesSeen = 60000; learningRatePerSample = 0.015625; epochTime=9.34707s
|
||||
05/13/2016 08:17:07: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm.1'
|
||||
08/16/2016 03:01:37: Node 'conv1.c.W' (LearnableParameter operation) : [16 x 25]
|
||||
08/16/2016 03:01:37: Node 'conv1.c.c.b' (LearnableParameter operation) : [16 x 1]
|
||||
08/16/2016 03:01:37: Node 'conv1.c.c.sc' (LearnableParameter operation) : [16 x 1]
|
||||
08/16/2016 03:01:37: Node 'conv2.c.W' (LearnableParameter operation) : [32 x 400]
|
||||
08/16/2016 03:01:37: Node 'conv2.c.c.b' (LearnableParameter operation) : [32 x 1]
|
||||
08/16/2016 03:01:37: Node 'conv2.c.c.sc' (LearnableParameter operation) : [32 x 1]
|
||||
08/16/2016 03:01:37: Node 'h1.W' (LearnableParameter operation) : [128 x 7 x 7 x 32]
|
||||
08/16/2016 03:01:37: Node 'h1.b' (LearnableParameter operation) : [128 x 1]
|
||||
08/16/2016 03:01:37: Node 'h1.sc' (LearnableParameter operation) : [128 x 1]
|
||||
08/16/2016 03:01:37: Node 'ol.W' (LearnableParameter operation) : [10 x 128]
|
||||
08/16/2016 03:01:37: Node 'ol.b' (LearnableParameter operation) : [10 x 1]
|
||||
|
||||
08/16/2016 03:01:37: No PreCompute nodes found, or all already computed. Skipping pre-computation step.
|
||||
|
||||
08/16/2016 03:01:37: Starting Epoch 1: learning rate per sample = 0.015625 effective momentum = 0.900000 momentum as time constant = 303.7 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..60000] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:01:37: Starting minibatch loop.
|
||||
08/16/2016 03:01:41: Epoch[ 1 of 3]-Minibatch[ 1- 500, 26.67%]: ce = 0.17254086 * 16000; errs = 5.425% * 16000; time = 3.5089s; samplesPerSecond = 4559.8
|
||||
08/16/2016 03:01:43: Epoch[ 1 of 3]-Minibatch[ 501-1000, 53.33%]: ce = 0.08539883 * 16000; errs = 2.644% * 16000; time = 1.8509s; samplesPerSecond = 8644.2
|
||||
08/16/2016 03:01:45: Epoch[ 1 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.06454272 * 16000; errs = 1.994% * 16000; time = 1.8515s; samplesPerSecond = 8641.7
|
||||
08/16/2016 03:01:46: Finished Epoch[ 1 of 3]: [Training] ce = 0.09637989 * 60000; errs = 3.007% * 60000; totalSamplesSeen = 60000; learningRatePerSample = 0.015625; epochTime=8.6331s
|
||||
08/16/2016 03:01:46: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm.1'
|
||||
Setting batch normalization blend time constant to 1.#INF.
|
||||
|
||||
05/13/2016 08:17:07: Starting Epoch 2: learning rate per sample = 0.003125 effective momentum = 0.900000 momentum as time constant = 303.7 samples
|
||||
08/16/2016 03:01:46: Starting Epoch 2: learning rate per sample = 0.003125 effective momentum = 0.900000 momentum as time constant = 303.7 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [60000..120000] (first sequence at sample 60000), data subset 0 of 1
|
||||
|
||||
05/13/2016 08:17:07: Starting minibatch loop.
|
||||
05/13/2016 08:17:09: Epoch[ 2 of 3]-Minibatch[1-500, 26.67%]: ce = 0.02381749 * 16000; errs = 0.7% * 16000; time = 1.7975s; samplesPerSecond = 8901.2
|
||||
05/13/2016 08:17:11: Epoch[ 2 of 3]-Minibatch[501-1000, 53.33%]: ce = 0.02147904 * 16000; errs = 0.6875% * 16000; time = 1.7971s; samplesPerSecond = 8903.3
|
||||
05/13/2016 08:17:13: Epoch[ 2 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.01875302 * 16000; errs = 0.58125% * 16000; time = 1.7965s; samplesPerSecond = 8906.1
|
||||
05/13/2016 08:17:14: Finished Epoch[ 2 of 3]: [Training] ce = 0.02042361 * 60000; errs = 0.626667% * 60000; totalSamplesSeen = 120000; learningRatePerSample = 0.003125; epochTime=6.7551s
|
||||
05/13/2016 08:17:14: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm.2'
|
||||
08/16/2016 03:01:46: Starting minibatch loop.
|
||||
08/16/2016 03:01:48: Epoch[ 2 of 3]-Minibatch[ 1- 500, 26.67%]: ce = 0.02438868 * 16000; errs = 0.813% * 16000; time = 1.8523s; samplesPerSecond = 8637.8
|
||||
08/16/2016 03:01:50: Epoch[ 2 of 3]-Minibatch[ 501-1000, 53.33%]: ce = 0.01921910 * 16000; errs = 0.619% * 16000; time = 1.8499s; samplesPerSecond = 8649.0
|
||||
08/16/2016 03:01:52: Epoch[ 2 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.02277288 * 16000; errs = 0.681% * 16000; time = 1.8499s; samplesPerSecond = 8649.0
|
||||
08/16/2016 03:01:53: Finished Epoch[ 2 of 3]: [Training] ce = 0.02211336 * 60000; errs = 0.702% * 60000; totalSamplesSeen = 120000; learningRatePerSample = 0.003125; epochTime=6.95395s
|
||||
08/16/2016 03:01:53: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm.2'
|
||||
|
||||
05/13/2016 08:17:14: Starting Epoch 3: learning rate per sample = 0.003125 effective momentum = 0.900000 momentum as time constant = 303.7 samples
|
||||
08/16/2016 03:01:53: Starting Epoch 3: learning rate per sample = 0.003125 effective momentum = 0.900000 momentum as time constant = 303.7 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [120000..180000] (first sequence at sample 120000), data subset 0 of 1
|
||||
|
||||
05/13/2016 08:17:14: Starting minibatch loop.
|
||||
05/13/2016 08:17:16: Epoch[ 3 of 3]-Minibatch[1-500, 26.67%]: ce = 0.01552748 * 16000; errs = 0.4% * 16000; time = 1.7980s; samplesPerSecond = 8899.0
|
||||
05/13/2016 08:17:18: Epoch[ 3 of 3]-Minibatch[501-1000, 53.33%]: ce = 0.01295741 * 16000; errs = 0.35625% * 16000; time = 1.7961s; samplesPerSecond = 8908.2
|
||||
05/13/2016 08:17:20: Epoch[ 3 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.01382423 * 16000; errs = 0.39375% * 16000; time = 1.7964s; samplesPerSecond = 8906.7
|
||||
05/13/2016 08:17:21: Finished Epoch[ 3 of 3]: [Training] ce = 0.01415997 * 60000; errs = 0.391667% * 60000; totalSamplesSeen = 180000; learningRatePerSample = 0.003125; epochTime=6.75556s
|
||||
05/13/2016 08:17:21: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm'
|
||||
05/13/2016 08:17:21: CNTKCommandTrainEnd: train
|
||||
08/16/2016 03:01:53: Starting minibatch loop.
|
||||
08/16/2016 03:01:55: Epoch[ 3 of 3]-Minibatch[ 1- 500, 26.67%]: ce = 0.01539427 * 16000; errs = 0.444% * 16000; time = 1.8502s; samplesPerSecond = 8647.8
|
||||
08/16/2016 03:01:57: Epoch[ 3 of 3]-Minibatch[ 501-1000, 53.33%]: ce = 0.01512400 * 16000; errs = 0.419% * 16000; time = 1.8489s; samplesPerSecond = 8653.8
|
||||
08/16/2016 03:01:59: Epoch[ 3 of 3]-Minibatch[1001-1500, 80.00%]: ce = 0.01669427 * 16000; errs = 0.487% * 16000; time = 1.8506s; samplesPerSecond = 8645.8
|
||||
08/16/2016 03:02:00: Finished Epoch[ 3 of 3]: [Training] ce = 0.01622233 * 60000; errs = 0.478% * 60000; totalSamplesSeen = 180000; learningRatePerSample = 0.003125; epochTime=6.94912s
|
||||
08/16/2016 03:02:00: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\MNIST_03_ConvBatchNorm@release_gpu/Models/03_ConvBatchNorm'
|
||||
08/16/2016 03:02:00: CNTKCommandTrainEnd: train
|
||||
|
||||
05/13/2016 08:17:21: Action "train" complete.
|
||||
08/16/2016 03:02:00: Action "train" complete.
|
||||
|
||||
|
||||
05/13/2016 08:17:21: ##############################################################################
|
||||
05/13/2016 08:17:21: # #
|
||||
05/13/2016 08:17:21: # Action "test" #
|
||||
05/13/2016 08:17:21: # #
|
||||
05/13/2016 08:17:21: ##############################################################################
|
||||
08/16/2016 03:02:00: ##############################################################################
|
||||
08/16/2016 03:02:00: # #
|
||||
08/16/2016 03:02:00: # Action "test" #
|
||||
08/16/2016 03:02:00: # #
|
||||
08/16/2016 03:02:00: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
||||
3 roots:
|
||||
ce = CrossEntropyWithSoftmax()
|
||||
err = ErrorPrediction()
|
||||
errs = ErrorPrediction()
|
||||
ol.z = Plus()
|
||||
|
||||
Validating network. 36 nodes to process in pass 1.
|
||||
|
|
@ -500,7 +544,7 @@ Validating --> ol.t = Times (ol.W, h1.y) : [10 x 128], [128 x *1] -> [10 x *1]
|
|||
Validating --> ol.b = LearnableParameter() : -> [10 x 1]
|
||||
Validating --> ol.z = Plus (ol.t, ol.b) : [10 x *1], [10 x 1] -> [10 x 1 x *1]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
|
||||
Validating --> errs = ErrorPrediction (labels, ol.z) : [10 x *1], [10 x 1 x *1] -> [1]
|
||||
|
||||
Validating network. 16 nodes to process in pass 2.
|
||||
|
||||
|
|
@ -508,17 +552,17 @@ Validating network. 16 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 28 x 28 x 1, Output: 28 x 28 x 16, Kernel: 5 x 5 x 1, Map: 1 x 1 x 16, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c.c.c: using cuDNN convolution engine for geometry: Input: 28 x 28 x 1, Output: 28 x 28 x 16, Kernel: 5 x 5 x 1, Map: 1 x 1 x 16, Stride: 1 x 1 x 1, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 28 x 28 x 16, Output: 14 x 14 x 16, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 28 x 28 x 16, Output: 14 x 14 x 16, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 14 x 14 x 16, Output: 14 x 14 x 32, Kernel: 5 x 5 x 16, Map: 1 x 1 x 32, Stride: 1 x 1 x 16, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c.c.c: using cuDNN convolution engine for geometry: Input: 14 x 14 x 16, Output: 14 x 14 x 32, Kernel: 5 x 5 x 16, Map: 1 x 1 x 32, Stride: 1 x 1 x 16, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 14 x 14 x 32, Output: 7 x 7 x 32, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 14 x 14 x 32, Output: 7 x 7 x 32, Kernel: 2 x 2 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
|
|
@ -532,48 +576,13 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 36 matrices, 0 are shared as 0, and 36 are not shared.
|
||||
|
||||
0000000000000000: {[ce Gradient[1]] [conv1.c.W Gradient[16 x 25]] [conv1.c.c.b Gradient[16 x 1]] [conv1.c.c.c Gradient[28 x 28 x 16 x *1]] [conv1.c.c.isd Gradient[16 x 1]] [conv1.c.c.m Gradient[16 x 1]] [conv1.c.c.sc Gradient[16 x 1]] [conv1.c.c.y Gradient[28 x 28 x 16 x *1]] [conv1.y Gradient[28 x 28 x 16 x *1]] [conv2.c.W Gradient[32 x 400]] [conv2.c.c.b Gradient[32 x 1]] [conv2.c.c.c Gradient[14 x 14 x 32 x *1]] [conv2.c.c.isd Gradient[32 x 1]] [conv2.c.c.m Gradient[32 x 1]] [conv2.c.c.sc Gradient[32 x 1]] [conv2.c.c.y Gradient[14 x 14 x 32 x *1]] [conv2.y Gradient[14 x 14 x 32 x *1]] [err Gradient[1]] [featScale Gradient[1 x 1]] [featScaled Gradient[28 x 28 x 1 x *1]] [features Gradient[28 x 28 x 1 x *1]] [h1.W Gradient[128 x 7 x 7 x 32]] [h1.b Gradient[128 x 1]] [h1.bn Gradient[128 x *1]] [h1.isd Gradient[128 x 1]] [h1.m Gradient[128 x 1]] [h1.sc Gradient[128 x 1]] [h1.t Gradient[128 x *1]] [h1.y Gradient[128 x *1]] [labels Gradient[10 x *1]] [ol.W Gradient[10 x 128]] [ol.b Gradient[10 x 1]] [ol.t Gradient[10 x *1]] [ol.z Gradient[10 x 1 x *1]] [pool1 Gradient[14 x 14 x 16 x *1]] [pool2 Gradient[7 x 7 x 32 x *1]] }
|
||||
00000093D1AAEE00: {[pool2 Value[7 x 7 x 32 x *1]] }
|
||||
00000093D1AAEEA0: {[conv2.c.c.y Value[14 x 14 x 32 x *1]] }
|
||||
00000093D1AAF300: {[h1.y Value[128 x *1]] }
|
||||
00000093D1AAF580: {[conv2.y Value[14 x 14 x 32 x *1]] }
|
||||
00000093D1AAF760: {[h1.bn Value[128 x *1]] }
|
||||
00000093D1AAF940: {[ol.t Value[10 x *1]] }
|
||||
00000093D1AB0200: {[h1.t Value[128 x *1]] }
|
||||
00000093D1AB0700: {[ol.z Value[10 x 1 x *1]] }
|
||||
00000093D1CA31E0: {[conv2.c.c.sc Value[32 x 1]] }
|
||||
00000093D1CA3320: {[conv2.c.c.isd Value[32 x 1]] }
|
||||
00000093D1CA35A0: {[conv2.c.W Value[32 x 400]] }
|
||||
00000093D1CA3BE0: {[conv2.c.c.m Value[32 x 1]] }
|
||||
00000093D1CA3FA0: {[conv2.c.c.b Value[32 x 1]] }
|
||||
00000093D1CA40E0: {[featScale Value[1 x 1]] }
|
||||
00000093D1CA4720: {[features Value[28 x 28 x 1 x *1]] }
|
||||
00000093D1CA4C20: {[conv1.c.c.isd Value[16 x 1]] }
|
||||
00000093D1CA5440: {[conv1.c.c.m Value[16 x 1]] }
|
||||
00000093D1CA54E0: {[conv1.c.c.b Value[16 x 1]] }
|
||||
00000093D1CA5580: {[conv1.c.c.sc Value[16 x 1]] }
|
||||
00000093D1CA5620: {[conv1.c.W Value[16 x 25]] }
|
||||
00000093D1CA5A80: {[ol.b Value[10 x 1]] }
|
||||
00000093D1CA5B20: {[ol.W Value[10 x 128]] }
|
||||
00000093D1CA60C0: {[err Value[1]] }
|
||||
00000093D1CA6160: {[h1.W Value[128 x 7 x 7 x 32]] }
|
||||
00000093D1CA6200: {[conv1.c.c.c Value[28 x 28 x 16 x *1]] }
|
||||
00000093D1CA62A0: {[h1.isd Value[128 x 1]] }
|
||||
00000093D1CA6340: {[ce Value[1]] }
|
||||
00000093D1CA6480: {[h1.b Value[128 x 1]] }
|
||||
00000093D1CA6660: {[conv1.c.c.y Value[28 x 28 x 16 x *1]] }
|
||||
00000093D1CA6700: {[featScaled Value[28 x 28 x 1 x *1]] }
|
||||
00000093D1CA68E0: {[h1.sc Value[128 x 1]] }
|
||||
00000093D1CA6980: {[conv1.y Value[28 x 28 x 16 x *1]] }
|
||||
00000093D1CA6AC0: {[pool1 Value[14 x 14 x 16 x *1]] }
|
||||
00000093D1CA6B60: {[conv2.c.c.c Value[14 x 14 x 32 x *1]] }
|
||||
00000093D1CA6D40: {[h1.m Value[128 x 1]] }
|
||||
00000093D1CA6DE0: {[labels Value[10 x *1]] }
|
||||
|
||||
05/13/2016 08:17:32: Final Results: Minibatch[1-10]: errs = 0.71% * 10000; ce = 0.02063067 * 10000; perplexity = 1.02084496
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
08/16/2016 03:02:01: Minibatch[1-10]: errs = 0.770% * 10000; ce = 0.02265451 * 10000
|
||||
08/16/2016 03:02:01: Final Results: Minibatch[1-10]: errs = 0.770% * 10000; ce = 0.02265451 * 10000; perplexity = 1.02291308
|
||||
|
||||
05/13/2016 08:17:32: Action "test" complete.
|
||||
08/16/2016 03:02:01: Action "test" complete.
|
||||
|
||||
05/13/2016 08:17:32: __COMPLETED__
|
||||
08/16/2016 03:02:01: __COMPLETED__
|
||||
|
|
@ -1,49 +1,62 @@
|
|||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/Config/01_Conv.cntk currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10 OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=10]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 264172964 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/01_Conv.cntk currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10 OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=10]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 13 2016 14:50:25
|
||||
Last modified date: Thu May 12 14:00:37 2016
|
||||
Built time: Aug 16 2016 09:41:56
|
||||
Last modified date: Fri Aug 12 07:32:43 2016
|
||||
Build type: release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: no
|
||||
Math lib: acml
|
||||
Math lib: mkl
|
||||
CUDA_PATH: /usr/local/cuda-7.5
|
||||
CUB_PATH: /usr/local/cub-1.4.1
|
||||
CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
Built by philly on d8dc82703b0f
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by philly on f67b30a647de
|
||||
Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to /tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData
|
||||
05/13/2016 15:10:47: Redirecting stderr to file -_Train_Test.log
|
||||
05/13/2016 15:10:47: -------------------------------------------------------------------
|
||||
05/13/2016 15:10:47: Build info:
|
||||
Changed current directory to /tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData
|
||||
08/16/2016 10:50:36: Redirecting stderr to file -_Train_Test.log
|
||||
08/16/2016 10:50:36: -------------------------------------------------------------------
|
||||
08/16/2016 10:50:36: Build info:
|
||||
|
||||
05/13/2016 15:10:47: Built time: May 13 2016 14:50:25
|
||||
05/13/2016 15:10:47: Last modified date: Thu May 12 14:00:37 2016
|
||||
05/13/2016 15:10:47: Build type: release
|
||||
05/13/2016 15:10:47: Build target: GPU
|
||||
05/13/2016 15:10:47: With 1bit-SGD: no
|
||||
05/13/2016 15:10:47: Math lib: acml
|
||||
05/13/2016 15:10:47: CUDA_PATH: /usr/local/cuda-7.5
|
||||
05/13/2016 15:10:47: CUB_PATH: /usr/local/cub-1.4.1
|
||||
05/13/2016 15:10:47: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
05/13/2016 15:10:47: Build Branch: HEAD
|
||||
05/13/2016 15:10:47: Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
05/13/2016 15:10:47: Built by philly on d8dc82703b0f
|
||||
05/13/2016 15:10:47: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
05/13/2016 15:10:47: -------------------------------------------------------------------
|
||||
08/16/2016 10:50:36: Built time: Aug 16 2016 09:41:56
|
||||
08/16/2016 10:50:36: Last modified date: Fri Aug 12 07:32:43 2016
|
||||
08/16/2016 10:50:36: Build type: release
|
||||
08/16/2016 10:50:36: Build target: GPU
|
||||
08/16/2016 10:50:36: With 1bit-SGD: no
|
||||
08/16/2016 10:50:36: Math lib: mkl
|
||||
08/16/2016 10:50:36: CUDA_PATH: /usr/local/cuda-7.5
|
||||
08/16/2016 10:50:36: CUB_PATH: /usr/local/cub-1.4.1
|
||||
08/16/2016 10:50:36: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
08/16/2016 10:50:36: Build Branch: HEAD
|
||||
08/16/2016 10:50:36: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 10:50:36: Built by philly on f67b30a647de
|
||||
08/16/2016 10:50:36: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
08/16/2016 10:50:36: -------------------------------------------------------------------
|
||||
08/16/2016 10:50:37: -------------------------------------------------------------------
|
||||
08/16/2016 10:50:37: GPU info:
|
||||
|
||||
05/13/2016 15:10:47: Running on localhost at 2016/05/13 15:10:47
|
||||
05/13/2016 15:10:47: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/Config/01_Conv.cntk currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10 OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=10]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
08/16/2016 10:50:37: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:50:37: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:50:37: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:50:37: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:50:37: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 10:50:37: Running on localhost at 2016/08/16 10:50:37
|
||||
08/16/2016 10:50:37: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/01_Conv.cntk currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10 OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=10]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
|
||||
|
||||
|
||||
05/13/2016 15:10:47: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 15:10:47: RootDir = "."
|
||||
08/16/2016 10:50:37: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:50:37: RootDir = "."
|
||||
ConfigDir = "$RootDir$"
|
||||
DataDir = "$RootDir$"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
|
|
@ -53,7 +66,6 @@ precision = "float"
|
|||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly=true
|
||||
prefetch = "true"
|
||||
command = Train:Test
|
||||
modelPath = "$ModelDir$/01_Convolution"
|
||||
stderr = "$OutputDir$/01_Conv"
|
||||
|
|
@ -86,7 +98,7 @@ Train = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
Test = [
|
||||
action = "test"
|
||||
|
|
@ -104,42 +116,41 @@ Test = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10
|
||||
OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu
|
||||
currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10
|
||||
OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Train=[SGD=[maxEpochs=10]]
|
||||
Train=[SGD=[epochSize=100]]
|
||||
stderr=-
|
||||
|
||||
05/13/2016 15:10:47: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:50:37: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 15:10:47: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 15:10:47: RootDir = "."
|
||||
08/16/2016 10:50:37: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:50:37: RootDir = "."
|
||||
ConfigDir = "."
|
||||
DataDir = "."
|
||||
OutputDir = "./Output"
|
||||
ModelDir = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models"
|
||||
ndlMacros = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/Macros.ndl"
|
||||
ModelDir = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models"
|
||||
ndlMacros = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/Macros.ndl"
|
||||
precision = "float"
|
||||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly=true
|
||||
prefetch = "true"
|
||||
command = Train:Test
|
||||
modelPath = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution"
|
||||
stderr = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/01_Conv"
|
||||
modelPath = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution"
|
||||
stderr = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/01_Conv"
|
||||
traceLevel = 1
|
||||
numMBsToShowResult = 500
|
||||
Train = [
|
||||
action = "train"
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/01_Convolution.ndl"
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/01_Convolution.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 49984
|
||||
|
|
@ -152,7 +163,7 @@ Train = [
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData/Train_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData/Train_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -163,14 +174,14 @@ Train = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
Test = [
|
||||
action = "test"
|
||||
minibatchSize = 16
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData/Test_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData/Test_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -181,45 +192,44 @@ Test = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10
|
||||
OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu
|
||||
currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10
|
||||
OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Train=[SGD=[maxEpochs=10]]
|
||||
Train=[SGD=[epochSize=100]]
|
||||
stderr=-
|
||||
|
||||
05/13/2016 15:10:47: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:50:37: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 15:10:47: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:50:37: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: 01_Conv.cntk:command=Train:Test
|
||||
configparameters: 01_Conv.cntk:ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10
|
||||
configparameters: 01_Conv.cntk:currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData
|
||||
configparameters: 01_Conv.cntk:DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData
|
||||
configparameters: 01_Conv.cntk:ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10
|
||||
configparameters: 01_Conv.cntk:currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData
|
||||
configparameters: 01_Conv.cntk:DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData
|
||||
configparameters: 01_Conv.cntk:deviceId=0
|
||||
configparameters: 01_Conv.cntk:imageLayout=cudnn
|
||||
configparameters: 01_Conv.cntk:initOnCPUOnly=true
|
||||
configparameters: 01_Conv.cntk:ModelDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models
|
||||
configparameters: 01_Conv.cntk:modelPath=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution
|
||||
configparameters: 01_Conv.cntk:ndlMacros=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/Macros.ndl
|
||||
configparameters: 01_Conv.cntk:ModelDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models
|
||||
configparameters: 01_Conv.cntk:modelPath=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution
|
||||
configparameters: 01_Conv.cntk:ndlMacros=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/Macros.ndl
|
||||
configparameters: 01_Conv.cntk:numMBsToShowResult=500
|
||||
configparameters: 01_Conv.cntk:OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu
|
||||
configparameters: 01_Conv.cntk:OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu
|
||||
configparameters: 01_Conv.cntk:precision=float
|
||||
configparameters: 01_Conv.cntk:prefetch=true
|
||||
configparameters: 01_Conv.cntk:RootDir=.
|
||||
configparameters: 01_Conv.cntk:RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu
|
||||
configparameters: 01_Conv.cntk:RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu
|
||||
configparameters: 01_Conv.cntk:stderr=-
|
||||
configparameters: 01_Conv.cntk:Test=[
|
||||
action = "test"
|
||||
minibatchSize = 16
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData/Test_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData/Test_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -230,7 +240,7 @@ configparameters: 01_Conv.cntk:Test=[
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
configparameters: 01_Conv.cntk:timestamping=true
|
||||
|
|
@ -238,7 +248,7 @@ configparameters: 01_Conv.cntk:traceLevel=1
|
|||
configparameters: 01_Conv.cntk:Train=[
|
||||
action = "train"
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/01_Convolution.ndl"
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/01_Convolution/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/01_Convolution.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 49984
|
||||
|
|
@ -251,7 +261,7 @@ configparameters: 01_Conv.cntk:Train=[
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData/Train_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/TestData/Train_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -262,27 +272,51 @@ configparameters: 01_Conv.cntk:Train=[
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
] [SGD=[maxEpochs=10]] [SGD=[epochSize=100]]
|
||||
|
||||
05/13/2016 15:10:47: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/13/2016 15:10:47: Commands: Train Test
|
||||
05/13/2016 15:10:47: Precision = "float"
|
||||
05/13/2016 15:10:47: CNTKModelPath: /tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution
|
||||
05/13/2016 15:10:47: CNTKCommandTrainInfo: Train : 10
|
||||
05/13/2016 15:10:47: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 10
|
||||
08/16/2016 10:50:37: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:50:37: Commands: Train Test
|
||||
08/16/2016 10:50:37: Precision = "float"
|
||||
08/16/2016 10:50:37: CNTKModelPath: /tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution
|
||||
08/16/2016 10:50:37: CNTKCommandTrainInfo: Train : 10
|
||||
08/16/2016 10:50:37: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 10
|
||||
|
||||
05/13/2016 15:10:47: ##############################################################################
|
||||
05/13/2016 15:10:47: # #
|
||||
05/13/2016 15:10:47: # Action "train" #
|
||||
05/13/2016 15:10:47: # #
|
||||
05/13/2016 15:10:47: ##############################################################################
|
||||
08/16/2016 10:50:37: ##############################################################################
|
||||
08/16/2016 10:50:37: # #
|
||||
08/16/2016 10:50:37: # Action "train" #
|
||||
08/16/2016 10:50:37: # #
|
||||
08/16/2016 10:50:37: ##############################################################################
|
||||
|
||||
05/13/2016 15:10:47: CNTKCommandTrainBegin: Train
|
||||
08/16/2016 10:50:37: CNTKCommandTrainBegin: Train
|
||||
NDLBuilder Using GPU 0
|
||||
|
||||
05/13/2016 15:10:47: Creating virgin network.
|
||||
08/16/2016 10:50:37: Creating virgin network.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.000000.
|
||||
Node 'conv1_act.W' (LearnableParameter operation): Initializing Parameter[32 x 75] <- 0.000000.
|
||||
Node 'conv1_act.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 32] <- 0.000000.
|
||||
Node 'conv2_act.W' (LearnableParameter operation): Initializing Parameter[32 x 800] <- 0.000000.
|
||||
Node 'conv2_act.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 32] <- 0.000000.
|
||||
Node 'conv3_act.W' (LearnableParameter operation): Initializing Parameter[64 x 800] <- 0.000000.
|
||||
Node 'conv3_act.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[64 x 3 x 3 x 64] <- 0.000000.
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializing Parameter[10 x 64] <- 0.000000.
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[10] <- 0.000000.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 128.000000.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 128.000000.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 128.000000.
|
||||
Node 'conv1_act.W' (LearnableParameter operation): Initializing Parameter[32 x 75] <- gaussian(seed=1, range=0.023094*0.004300, onCPU=false).
|
||||
SetGaussianRandomValue (GPU): creating curand object with seed 1, sizeof(ElemType)==4
|
||||
Node 'conv1_act.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 32] <- 0.000000.
|
||||
Node 'conv2_act.W' (LearnableParameter operation): Initializing Parameter[32 x 800] <- gaussian(seed=2, range=0.007071*1.414000, onCPU=false).
|
||||
Node 'conv2_act.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 32] <- 0.000000.
|
||||
Node 'conv3_act.W' (LearnableParameter operation): Initializing Parameter[64 x 800] <- gaussian(seed=3, range=0.007071*1.414000, onCPU=false).
|
||||
Node 'conv3_act.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[64 x 3 x 3 x 64] <- gaussian(seed=4, range=0.008333*12.000000, onCPU=false).
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializing Parameter[10 x 64] <- gaussian(seed=5, range=0.025000*1.500000, onCPU=false).
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[10] <- 0.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -334,158 +368,176 @@ Validating network. 21 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 32, Kernel: 5 x 5 x 3, Map: 1 x 1 x 32, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1_act.c: using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 32, Kernel: 5 x 5 x 3, Map: 1 x 1 x 32, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 32, Output: 15 x 15 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 32 x 32 x 32, Output: 15 x 15 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 15 x 15 x 32, Kernel: 5 x 5 x 32, Map: 1 x 1 x 32, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2_act.c: using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 15 x 15 x 32, Kernel: 5 x 5 x 32, Map: 1 x 1 x 32, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 7 x 7 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 7 x 7 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 7 x 7 x 32, Output: 7 x 7 x 64, Kernel: 5 x 5 x 32, Map: 1 x 1 x 64, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3_act.c: using cuDNN convolution engine for geometry: Input: 7 x 7 x 32, Output: 7 x 7 x 64, Kernel: 5 x 5 x 32, Map: 1 x 1 x 64, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 7 x 7 x 64, Output: 3 x 3 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool3: using cuDNN convolution engine for geometry: Input: 7 x 7 x 64, Output: 3 x 3 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
|
||||
13 out of 34 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
05/13/2016 15:10:48: Created model with 34 nodes on GPU 0.
|
||||
08/16/2016 10:50:38: Created model with 34 nodes on GPU 0.
|
||||
|
||||
05/13/2016 15:10:48: Training criterion node(s):
|
||||
05/13/2016 15:10:48: CE = CrossEntropyWithSoftmax
|
||||
08/16/2016 10:50:38: Training criterion node(s):
|
||||
08/16/2016 10:50:38: CE = CrossEntropyWithSoftmax
|
||||
|
||||
05/13/2016 15:10:48: Evaluation criterion node(s):
|
||||
|
||||
05/13/2016 15:10:48: Err = ErrorPrediction
|
||||
08/16/2016 10:50:38: Evaluation criterion node(s):
|
||||
08/16/2016 10:50:38: Err = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 63 matrices, 38 are shared as 17, and 25 are not shared.
|
||||
|
||||
(nil): {[Err Gradient[1]] [featOffs Gradient[1 x 1]] [featScaled Gradient[32 x 32 x 3 x *]] [features Gradient[32 x 32 x 3 x *]] [labels Gradient[10 x *]] }
|
||||
0x2485d28: {[OutputNodes.z Value[10 x 1 x *]] }
|
||||
0x2485ee8: {[CE Value[1]] }
|
||||
0x2486168: {[conv1_act.W Gradient[32 x 75]] [conv1_act.p Value[32 x 32 x 32 x *]] }
|
||||
0x2486328: {[conv1_act.c Gradient[32 x 32 x 32 x *]] [conv1_act.y Value[32 x 32 x 32 x *]] }
|
||||
0x24864e8: {[conv1_act.p Gradient[32 x 32 x 32 x *]] [pool1 Value[15 x 15 x 32 x *]] }
|
||||
0x249a638: {[features Value[32 x 32 x 3 x *]] }
|
||||
0x2975298: {[conv1_act.b Value[1 x 1 x 32]] }
|
||||
0x2976b48: {[conv2_act.W Value[32 x 800]] }
|
||||
0x2977ae8: {[conv2_act.b Value[1 x 1 x 32]] }
|
||||
0x2979668: {[conv3_act.W Value[64 x 800]] }
|
||||
0x2979f08: {[conv3_act.b Value[1 x 1 x 64]] }
|
||||
0x297bae8: {[h1.W Value[64 x 3 x 3 x 64]] }
|
||||
0x297c538: {[h1.b Value[64 x 1]] }
|
||||
0x297d5c8: {[OutputNodes.W Value[10 x 64]] }
|
||||
0x297ea98: {[OutputNodes.b Value[10]] }
|
||||
0x2dd1458: {[featOffs Value[1 x 1]] }
|
||||
0x2dd2678: {[labels Value[10 x *]] }
|
||||
0x2dd2eb8: {[conv1_act.W Value[32 x 75]] }
|
||||
0x7a59dd8: {[Err Value[1]] }
|
||||
0x7a5d378: {[featScaled Value[32 x 32 x 3 x *]] }
|
||||
0x7a5d6d8: {[conv1_act.c Value[32 x 32 x 32 x *]] }
|
||||
0x7a5e478: {[conv2_act.c Value[15 x 15 x 32 x *]] }
|
||||
0x7a5e638: {[conv1_act.b Gradient[1 x 1 x 32]] [conv1_act.y Gradient[32 x 32 x 32 x *]] }
|
||||
0x7a5e7f8: {[conv2_act.W Gradient[32 x 800]] [conv2_act.p Value[15 x 15 x 32 x *]] }
|
||||
0x7a7ade8: {[conv2_act.c Gradient[15 x 15 x 32 x *]] [conv2_act.y Value[15 x 15 x 32 x *]] }
|
||||
0x7a7afa8: {[conv2_act.p Gradient[15 x 15 x 32 x *]] [pool1 Gradient[15 x 15 x 32 x *]] [pool2 Value[7 x 7 x 32 x *]] }
|
||||
0x7a7b168: {[conv3_act.c Value[7 x 7 x 64 x *]] }
|
||||
0x7a7b328: {[conv2_act.b Gradient[1 x 1 x 32]] [conv2_act.y Gradient[15 x 15 x 32 x *]] }
|
||||
0x7a7b4e8: {[conv3_act.W Gradient[64 x 800]] [conv3_act.p Value[7 x 7 x 64 x *]] }
|
||||
0x7a7b6a8: {[conv3_act.c Gradient[7 x 7 x 64 x *]] [conv3_act.y Value[7 x 7 x 64 x *]] }
|
||||
0x7a7b868: {[conv3_act.p Gradient[7 x 7 x 64 x *]] [pool2 Gradient[7 x 7 x 32 x *]] [pool3 Value[3 x 3 x 64 x *]] }
|
||||
0x7a7ba28: {[conv3_act.b Gradient[1 x 1 x 64]] [conv3_act.y Gradient[7 x 7 x 64 x *]] [h1.t Value[64 x *]] }
|
||||
0x7a7bbe8: {[h1.W Gradient[64 x 3 x 3 x 64]] [h1.z Value[64 x 1 x *]] }
|
||||
0x7a7bda8: {[h1.t Gradient[64 x *]] [h1.y Value[64 x 1 x *]] }
|
||||
0x7a7bf68: {[h1_d Value[64 x 1 x *]] }
|
||||
0x7a7c128: {[h1.z Gradient[64 x 1 x *]] [pool3 Gradient[3 x 3 x 64 x *]] }
|
||||
0x7a7c2e8: {[OutputNodes.t Value[10 x 1 x *]] [h1.b Gradient[64 x 1]] [h1.y Gradient[64 x 1 x *]] }
|
||||
0x7a7cdc8: {[CE Gradient[1]] }
|
||||
0x7a7cf88: {[OutputNodes.W Gradient[10 x 64]] [OutputNodes.z Gradient[10 x 1 x *]] }
|
||||
0x7a7d148: {[OutputNodes.t Gradient[10 x 1 x *]] }
|
||||
0x7a7d308: {[OutputNodes.b Gradient[10]] }
|
||||
0x7a7d4c8: {[h1_d Gradient[64 x 1 x *]] }
|
||||
{ conv1_act.W : [32 x 75] (gradient)
|
||||
conv1_act.p : [32 x 32 x 32 x *] }
|
||||
{ conv1_act.c : [32 x 32 x 32 x *] (gradient)
|
||||
conv1_act.y : [32 x 32 x 32 x *] }
|
||||
{ conv1_act.p : [32 x 32 x 32 x *] (gradient)
|
||||
pool1 : [15 x 15 x 32 x *] }
|
||||
{ conv1_act.b : [1 x 1 x 32] (gradient)
|
||||
conv1_act.y : [32 x 32 x 32 x *] (gradient) }
|
||||
{ conv2_act.W : [32 x 800] (gradient)
|
||||
conv2_act.p : [15 x 15 x 32 x *] }
|
||||
{ conv2_act.c : [15 x 15 x 32 x *] (gradient)
|
||||
conv2_act.y : [15 x 15 x 32 x *] }
|
||||
{ conv2_act.p : [15 x 15 x 32 x *] (gradient)
|
||||
pool1 : [15 x 15 x 32 x *] (gradient)
|
||||
pool2 : [7 x 7 x 32 x *] }
|
||||
{ conv2_act.b : [1 x 1 x 32] (gradient)
|
||||
conv2_act.y : [15 x 15 x 32 x *] (gradient) }
|
||||
{ conv3_act.W : [64 x 800] (gradient)
|
||||
conv3_act.p : [7 x 7 x 64 x *] }
|
||||
{ conv3_act.c : [7 x 7 x 64 x *] (gradient)
|
||||
conv3_act.y : [7 x 7 x 64 x *] }
|
||||
{ conv3_act.p : [7 x 7 x 64 x *] (gradient)
|
||||
pool2 : [7 x 7 x 32 x *] (gradient)
|
||||
pool3 : [3 x 3 x 64 x *] }
|
||||
{ conv3_act.b : [1 x 1 x 64] (gradient)
|
||||
conv3_act.y : [7 x 7 x 64 x *] (gradient)
|
||||
h1.t : [64 x *] }
|
||||
{ h1.W : [64 x 3 x 3 x 64] (gradient)
|
||||
h1.z : [64 x 1 x *] }
|
||||
{ h1.t : [64 x *] (gradient)
|
||||
h1.y : [64 x 1 x *] }
|
||||
{ h1.z : [64 x 1 x *] (gradient)
|
||||
pool3 : [3 x 3 x 64 x *] (gradient) }
|
||||
{ OutputNodes.t : [10 x 1 x *]
|
||||
h1.b : [64 x 1] (gradient)
|
||||
h1.y : [64 x 1 x *] (gradient) }
|
||||
{ OutputNodes.W : [10 x 64] (gradient)
|
||||
OutputNodes.z : [10 x 1 x *] (gradient) }
|
||||
|
||||
05/13/2016 15:10:48: No PreCompute nodes found, skipping PreCompute step.
|
||||
|
||||
05/13/2016 15:10:48: Starting Epoch 1: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
08/16/2016 10:50:38: Training 116906 parameters in 10 out of 10 parameter tensors and 29 nodes with gradient:
|
||||
|
||||
05/13/2016 15:10:48: Starting minibatch loop.
|
||||
05/13/2016 15:10:51: Finished Epoch[ 1 of 10]: [Training] CE = 2.30242050 * 100; Err = 0.88000000 * 100; totalSamplesSeen = 100; learningRatePerSample = 0.00015625; epochTime=3.55904s
|
||||
05/13/2016 15:10:51: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.1'
|
||||
08/16/2016 10:50:38: Node 'OutputNodes.W' (LearnableParameter operation) : [10 x 64]
|
||||
08/16/2016 10:50:38: Node 'OutputNodes.b' (LearnableParameter operation) : [10]
|
||||
08/16/2016 10:50:38: Node 'conv1_act.W' (LearnableParameter operation) : [32 x 75]
|
||||
08/16/2016 10:50:38: Node 'conv1_act.b' (LearnableParameter operation) : [1 x 1 x 32]
|
||||
08/16/2016 10:50:38: Node 'conv2_act.W' (LearnableParameter operation) : [32 x 800]
|
||||
08/16/2016 10:50:38: Node 'conv2_act.b' (LearnableParameter operation) : [1 x 1 x 32]
|
||||
08/16/2016 10:50:38: Node 'conv3_act.W' (LearnableParameter operation) : [64 x 800]
|
||||
08/16/2016 10:50:38: Node 'conv3_act.b' (LearnableParameter operation) : [1 x 1 x 64]
|
||||
08/16/2016 10:50:38: Node 'h1.W' (LearnableParameter operation) : [64 x 3 x 3 x 64]
|
||||
08/16/2016 10:50:38: Node 'h1.b' (LearnableParameter operation) : [64 x 1]
|
||||
|
||||
05/13/2016 15:10:51: Starting Epoch 2: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
08/16/2016 10:50:38: No PreCompute nodes found, or all already computed. Skipping pre-computation step.
|
||||
|
||||
05/13/2016 15:10:51: Starting minibatch loop.
|
||||
05/13/2016 15:10:51: Finished Epoch[ 2 of 10]: [Training] CE = 2.30175842 * 100; Err = 0.94000000 * 100; totalSamplesSeen = 200; learningRatePerSample = 0.00015625; epochTime=0.011903s
|
||||
05/13/2016 15:10:51: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.2'
|
||||
08/16/2016 10:50:38: Starting Epoch 1: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..100] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
05/13/2016 15:10:51: Starting Epoch 3: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
08/16/2016 10:50:38: Starting minibatch loop.
|
||||
08/16/2016 10:50:41: Finished Epoch[ 1 of 10]: [Training] CE = 2.30223602 * 100; Err = 0.90000000 * 100; totalSamplesSeen = 100; learningRatePerSample = 0.00015625; epochTime=3.51082s
|
||||
08/16/2016 10:50:41: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.1'
|
||||
|
||||
05/13/2016 15:10:51: Starting minibatch loop.
|
||||
05/13/2016 15:10:51: Finished Epoch[ 3 of 10]: [Training] CE = 2.30054413 * 100; Err = 0.90000000 * 100; totalSamplesSeen = 300; learningRatePerSample = 0.00015625; epochTime=0.012701s
|
||||
05/13/2016 15:10:51: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.3'
|
||||
08/16/2016 10:50:41: Starting Epoch 2: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [100..200] (first sequence at sample 100), data subset 0 of 1
|
||||
|
||||
05/13/2016 15:10:51: Starting Epoch 4: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
08/16/2016 10:50:41: Starting minibatch loop.
|
||||
08/16/2016 10:50:41: Finished Epoch[ 2 of 10]: [Training] CE = 2.30189240 * 100; Err = 0.87000000 * 100; totalSamplesSeen = 200; learningRatePerSample = 0.00015625; epochTime=0.012555s
|
||||
08/16/2016 10:50:41: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.2'
|
||||
|
||||
05/13/2016 15:10:51: Starting minibatch loop.
|
||||
05/13/2016 15:10:51: Finished Epoch[ 4 of 10]: [Training] CE = 2.30022812 * 100; Err = 0.88000000 * 100; totalSamplesSeen = 400; learningRatePerSample = 0.00015625; epochTime=0.01144s
|
||||
05/13/2016 15:10:51: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.4'
|
||||
08/16/2016 10:50:41: Starting Epoch 3: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [200..300] (first sequence at sample 200), data subset 0 of 1
|
||||
|
||||
05/13/2016 15:10:51: Starting Epoch 5: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
08/16/2016 10:50:41: Starting minibatch loop.
|
||||
08/16/2016 10:50:41: Finished Epoch[ 3 of 10]: [Training] CE = 2.29965256 * 100; Err = 0.86000000 * 100; totalSamplesSeen = 300; learningRatePerSample = 0.00015625; epochTime=0.012394s
|
||||
08/16/2016 10:50:41: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.3'
|
||||
|
||||
05/13/2016 15:10:51: Starting minibatch loop.
|
||||
05/13/2016 15:10:51: Finished Epoch[ 5 of 10]: [Training] CE = 2.29579636 * 100; Err = 0.87000000 * 100; totalSamplesSeen = 500; learningRatePerSample = 0.00015625; epochTime=0.011529s
|
||||
05/13/2016 15:10:51: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.5'
|
||||
08/16/2016 10:50:41: Starting Epoch 4: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 3: frames [300..400] (first sequence at sample 300), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:50:41: Starting minibatch loop.
|
||||
08/16/2016 10:50:41: Finished Epoch[ 4 of 10]: [Training] CE = 2.29966064 * 100; Err = 0.91000000 * 100; totalSamplesSeen = 400; learningRatePerSample = 0.00015625; epochTime=0.0124s
|
||||
08/16/2016 10:50:41: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.4'
|
||||
|
||||
08/16/2016 10:50:41: Starting Epoch 5: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 4: frames [400..500] (first sequence at sample 400), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:50:41: Starting minibatch loop.
|
||||
08/16/2016 10:50:41: Finished Epoch[ 5 of 10]: [Training] CE = 2.30450394 * 100; Err = 0.94000000 * 100; totalSamplesSeen = 500; learningRatePerSample = 0.00015625; epochTime=0.012302s
|
||||
08/16/2016 10:50:41: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.5'
|
||||
Setting dropout rate to 0.5.
|
||||
|
||||
05/13/2016 15:10:51: Starting Epoch 6: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
08/16/2016 10:50:41: Starting Epoch 6: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 5: frames [500..600] (first sequence at sample 500), data subset 0 of 1
|
||||
|
||||
05/13/2016 15:10:51: Starting minibatch loop.
|
||||
08/16/2016 10:50:41: Starting minibatch loop.
|
||||
(GPU): creating curand object with seed 5
|
||||
05/13/2016 15:10:51: Finished Epoch[ 6 of 10]: [Training] CE = 2.30121231 * 100; Err = 0.84000000 * 100; totalSamplesSeen = 600; learningRatePerSample = 0.00015625; epochTime=0.012276s
|
||||
05/13/2016 15:10:51: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.6'
|
||||
08/16/2016 10:50:41: Finished Epoch[ 6 of 10]: [Training] CE = 2.29013916 * 100; Err = 0.81000000 * 100; totalSamplesSeen = 600; learningRatePerSample = 0.00015625; epochTime=0.012412s
|
||||
08/16/2016 10:50:41: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.6'
|
||||
|
||||
05/13/2016 15:10:51: Starting Epoch 7: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
08/16/2016 10:50:41: Starting Epoch 7: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 6: frames [600..700] (first sequence at sample 600), data subset 0 of 1
|
||||
|
||||
05/13/2016 15:10:51: Starting minibatch loop.
|
||||
08/16/2016 10:50:41: Starting minibatch loop.
|
||||
(GPU): creating curand object with seed 6
|
||||
05/13/2016 15:10:52: Finished Epoch[ 7 of 10]: [Training] CE = 2.28975647 * 100; Err = 0.93000000 * 100; totalSamplesSeen = 700; learningRatePerSample = 0.00015625; epochTime=0.011495s
|
||||
05/13/2016 15:10:52: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.7'
|
||||
08/16/2016 10:50:41: Finished Epoch[ 7 of 10]: [Training] CE = 2.29815765 * 100; Err = 0.93000000 * 100; totalSamplesSeen = 700; learningRatePerSample = 0.00015625; epochTime=0.012303s
|
||||
08/16/2016 10:50:41: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.7'
|
||||
|
||||
05/13/2016 15:10:52: Starting Epoch 8: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
08/16/2016 10:50:41: Starting Epoch 8: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 7: frames [700..800] (first sequence at sample 700), data subset 0 of 1
|
||||
|
||||
05/13/2016 15:10:52: Starting minibatch loop.
|
||||
08/16/2016 10:50:41: Starting minibatch loop.
|
||||
(GPU): creating curand object with seed 7
|
||||
05/13/2016 15:10:52: Finished Epoch[ 8 of 10]: [Training] CE = 2.29035095 * 100; Err = 0.91000000 * 100; totalSamplesSeen = 800; learningRatePerSample = 0.00015625; epochTime=0.012157s
|
||||
05/13/2016 15:10:52: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.8'
|
||||
08/16/2016 10:50:41: Finished Epoch[ 8 of 10]: [Training] CE = 2.28805603 * 100; Err = 0.89000000 * 100; totalSamplesSeen = 800; learningRatePerSample = 0.00015625; epochTime=0.012517s
|
||||
08/16/2016 10:50:41: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.8'
|
||||
|
||||
05/13/2016 15:10:52: Starting Epoch 9: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
08/16/2016 10:50:41: Starting Epoch 9: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 8: frames [800..900] (first sequence at sample 800), data subset 0 of 1
|
||||
|
||||
05/13/2016 15:10:52: Starting minibatch loop.
|
||||
08/16/2016 10:50:41: Starting minibatch loop.
|
||||
(GPU): creating curand object with seed 8
|
||||
05/13/2016 15:10:52: Finished Epoch[ 9 of 10]: [Training] CE = 2.29797729 * 100; Err = 0.87000000 * 100; totalSamplesSeen = 900; learningRatePerSample = 0.00015625; epochTime=0.011451s
|
||||
05/13/2016 15:10:52: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.9'
|
||||
08/16/2016 10:50:41: Finished Epoch[ 9 of 10]: [Training] CE = 2.29380524 * 100; Err = 0.88000000 * 100; totalSamplesSeen = 900; learningRatePerSample = 0.00015625; epochTime=0.012463s
|
||||
08/16/2016 10:50:41: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.9'
|
||||
|
||||
05/13/2016 15:10:52: Starting Epoch 10: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
08/16/2016 10:50:41: Starting Epoch 10: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 9: frames [900..1000] (first sequence at sample 900), data subset 0 of 1
|
||||
|
||||
05/13/2016 15:10:52: Starting minibatch loop.
|
||||
08/16/2016 10:50:41: Starting minibatch loop.
|
||||
(GPU): creating curand object with seed 9
|
||||
05/13/2016 15:10:52: Finished Epoch[10 of 10]: [Training] CE = 2.29764435 * 100; Err = 0.87000000 * 100; totalSamplesSeen = 1000; learningRatePerSample = 0.00015625; epochTime=0.012689s
|
||||
05/13/2016 15:10:52: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution'
|
||||
05/13/2016 15:10:52: CNTKCommandTrainEnd: Train
|
||||
08/16/2016 10:50:41: Finished Epoch[10 of 10]: [Training] CE = 2.27814423 * 100; Err = 0.87000000 * 100; totalSamplesSeen = 1000; learningRatePerSample = 0.00015625; epochTime=0.012432s
|
||||
08/16/2016 10:50:41: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution'
|
||||
08/16/2016 10:50:41: CNTKCommandTrainEnd: Train
|
||||
|
||||
05/13/2016 15:10:52: Action "train" complete.
|
||||
08/16/2016 10:50:41: Action "train" complete.
|
||||
|
||||
|
||||
05/13/2016 15:10:52: ##############################################################################
|
||||
05/13/2016 15:10:52: # #
|
||||
05/13/2016 15:10:52: # Action "test" #
|
||||
05/13/2016 15:10:52: # #
|
||||
05/13/2016 15:10:52: ##############################################################################
|
||||
08/16/2016 10:50:41: ##############################################################################
|
||||
08/16/2016 10:50:41: # #
|
||||
08/16/2016 10:50:41: # Action "test" #
|
||||
08/16/2016 10:50:41: # #
|
||||
08/16/2016 10:50:41: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
|
@ -538,17 +590,17 @@ Validating network. 21 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 32, Kernel: 5 x 5 x 3, Map: 1 x 1 x 32, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1_act.c: using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 32, Kernel: 5 x 5 x 3, Map: 1 x 1 x 32, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 32, Output: 15 x 15 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 32 x 32 x 32, Output: 15 x 15 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 15 x 15 x 32, Kernel: 5 x 5 x 32, Map: 1 x 1 x 32, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2_act.c: using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 15 x 15 x 32, Kernel: 5 x 5 x 32, Map: 1 x 1 x 32, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 7 x 7 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 7 x 7 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 7 x 7 x 32, Output: 7 x 7 x 64, Kernel: 5 x 5 x 32, Map: 1 x 1 x 64, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3_act.c: using cuDNN convolution engine for geometry: Input: 7 x 7 x 32, Output: 7 x 7 x 64, Kernel: 5 x 5 x 32, Map: 1 x 1 x 64, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 7 x 7 x 64, Output: 3 x 3 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool3: using cuDNN convolution engine for geometry: Input: 7 x 7 x 64, Output: 3 x 3 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
|
||||
13 out of 34 nodes do not share the minibatch layout with the input data.
|
||||
|
|
@ -560,46 +612,14 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 34 matrices, 0 are shared as 0, and 34 are not shared.
|
||||
|
||||
(nil): {[CE Gradient[1]] [Err Gradient[1]] [OutputNodes.W Gradient[10 x 64]] [OutputNodes.b Gradient[10]] [OutputNodes.t Gradient[10 x 1 x *1]] [OutputNodes.z Gradient[10 x 1 x *1]] [conv1_act.W Gradient[32 x 75]] [conv1_act.b Gradient[1 x 1 x 32]] [conv1_act.c Gradient[32 x 32 x 32 x *1]] [conv1_act.p Gradient[32 x 32 x 32 x *1]] [conv1_act.y Gradient[32 x 32 x 32 x *1]] [conv2_act.W Gradient[32 x 800]] [conv2_act.b Gradient[1 x 1 x 32]] [conv2_act.c Gradient[15 x 15 x 32 x *1]] [conv2_act.p Gradient[15 x 15 x 32 x *1]] [conv2_act.y Gradient[15 x 15 x 32 x *1]] [conv3_act.W Gradient[64 x 800]] [conv3_act.b Gradient[1 x 1 x 64]] [conv3_act.c Gradient[7 x 7 x 64 x *1]] [conv3_act.p Gradient[7 x 7 x 64 x *1]] [conv3_act.y Gradient[7 x 7 x 64 x *1]] [featOffs Gradient[1 x 1]] [featScaled Gradient[32 x 32 x 3 x *1]] [features Gradient[32 x 32 x 3 x *1]] [h1.W Gradient[64 x 3 x 3 x 64]] [h1.b Gradient[64 x 1]] [h1.t Gradient[64 x *1]] [h1.y Gradient[64 x 1 x *1]] [h1.z Gradient[64 x 1 x *1]] [h1_d Gradient[64 x 1 x *1]] [labels Gradient[10 x *1]] [pool1 Gradient[15 x 15 x 32 x *1]] [pool2 Gradient[7 x 7 x 32 x *1]] [pool3 Gradient[3 x 3 x 64 x *1]] }
|
||||
0x7fc883e04ba8: {[conv1_act.b Value[1 x 1 x 32]] }
|
||||
0x7fc883e05fc8: {[conv1_act.W Value[32 x 75]] }
|
||||
0x7fc883e06768: {[conv2_act.b Value[1 x 1 x 32]] }
|
||||
0x7fc883e06928: {[conv2_act.W Value[32 x 800]] }
|
||||
0x7fc883e085b8: {[conv3_act.b Value[1 x 1 x 64]] }
|
||||
0x7fc883e09528: {[conv3_act.W Value[64 x 800]] }
|
||||
0x7fc883e0b568: {[featOffs Value[1 x 1]] }
|
||||
0x7fc883e0c1e8: {[features Value[32 x 32 x 3 x *1]] }
|
||||
0x7fc883e0cc38: {[h1.b Value[64 x 1]] }
|
||||
0x7fc883e0cf08: {[h1.W Value[64 x 3 x 3 x 64]] }
|
||||
0x7fc883e0eb48: {[labels Value[10 x *1]] }
|
||||
0x7fc883e0f558: {[OutputNodes.b Value[10]] }
|
||||
0x7fc883e10068: {[OutputNodes.W Value[10 x 64]] }
|
||||
0x7fc883e286b8: {[Err Value[1]] }
|
||||
0x7fc883e2bd28: {[CE Value[1]] }
|
||||
0x7fc883e2bfa8: {[conv1_act.y Value[32 x 32 x 32 x *1]] }
|
||||
0x7fc883e54728: {[conv1_act.c Value[32 x 32 x 32 x *1]] }
|
||||
0x7fc883e54a88: {[featScaled Value[32 x 32 x 3 x *1]] }
|
||||
0x7fc883e54c18: {[conv1_act.p Value[32 x 32 x 32 x *1]] }
|
||||
0x7fc883e71a78: {[pool1 Value[15 x 15 x 32 x *1]] }
|
||||
0x7fc883e71c38: {[conv2_act.c Value[15 x 15 x 32 x *1]] }
|
||||
0x7fc883e71fb8: {[conv2_act.p Value[15 x 15 x 32 x *1]] }
|
||||
0x7fc883e72178: {[conv2_act.y Value[15 x 15 x 32 x *1]] }
|
||||
0x7fc883e72338: {[pool2 Value[7 x 7 x 32 x *1]] }
|
||||
0x7fc883e724f8: {[conv3_act.c Value[7 x 7 x 64 x *1]] }
|
||||
0x7fc883e72878: {[conv3_act.p Value[7 x 7 x 64 x *1]] }
|
||||
0x7fc883e72a38: {[conv3_act.y Value[7 x 7 x 64 x *1]] }
|
||||
0x7fc883e72bf8: {[pool3 Value[3 x 3 x 64 x *1]] }
|
||||
0x7fc883e72db8: {[h1.t Value[64 x *1]] }
|
||||
0x7fc883e72f78: {[h1.z Value[64 x 1 x *1]] }
|
||||
0x7fc883e73138: {[h1.y Value[64 x 1 x *1]] }
|
||||
0x7fc883e732f8: {[h1_d Value[64 x 1 x *1]] }
|
||||
0x7fc883e73678: {[OutputNodes.t Value[10 x 1 x *1]] }
|
||||
0x7fc883e73838: {[OutputNodes.z Value[10 x 1 x *1]] }
|
||||
|
||||
05/13/2016 15:10:58: Final Results: Minibatch[1-625]: Err = 0.86430000 * 10000; CE = 2.28476029 * 10000; perplexity = 9.82333117
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
08/16/2016 10:50:43: Minibatch[1-500]: Err = 0.86125000 * 8000; CE = 2.28389484 * 8000
|
||||
08/16/2016 10:50:43: Minibatch[501-625]: Err = 0.86350000 * 2000; CE = 2.28027481 * 2000
|
||||
08/16/2016 10:50:43: Final Results: Minibatch[1-625]: Err = 0.86170000 * 10000; CE = 2.28317084 * 10000; perplexity = 9.80772986
|
||||
|
||||
05/13/2016 15:10:58: Action "test" complete.
|
||||
08/16/2016 10:50:43: Action "test" complete.
|
||||
|
||||
05/13/2016 15:10:58: __COMPLETED__
|
||||
08/16/2016 10:50:43: __COMPLETED__
|
||||
|
|
@ -1,47 +1,62 @@
|
|||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/Config/01_Conv.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10 OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=10]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 268381192 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/01_Conv.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10 OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=10]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 13 2016 08:06:01
|
||||
Last modified date: Thu May 12 07:31:50 2016
|
||||
Built time: Aug 16 2016 02:54:53
|
||||
Last modified date: Fri Aug 12 05:31:21 2016
|
||||
Build type: Release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: no
|
||||
Math lib: mkl
|
||||
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
CUB_PATH: c:\src\cub-1.4.1
|
||||
CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by svcphil on Philly-Pool3
|
||||
Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData
|
||||
05/13/2016 08:17:50: Redirecting stderr to file -_Train_Test.log
|
||||
05/13/2016 08:17:50: -------------------------------------------------------------------
|
||||
05/13/2016 08:17:50: Build info:
|
||||
Changed current directory to C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData
|
||||
08/16/2016 03:02:05: Redirecting stderr to file -_Train_Test.log
|
||||
08/16/2016 03:02:05: -------------------------------------------------------------------
|
||||
08/16/2016 03:02:05: Build info:
|
||||
|
||||
05/13/2016 08:17:50: Built time: May 13 2016 08:06:01
|
||||
05/13/2016 08:17:50: Last modified date: Thu May 12 07:31:50 2016
|
||||
05/13/2016 08:17:50: Build type: Release
|
||||
05/13/2016 08:17:50: Build target: GPU
|
||||
05/13/2016 08:17:50: With 1bit-SGD: no
|
||||
05/13/2016 08:17:50: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
05/13/2016 08:17:50: CUB_PATH: c:\src\cub-1.4.1
|
||||
05/13/2016 08:17:50: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
05/13/2016 08:17:50: Build Branch: HEAD
|
||||
05/13/2016 08:17:50: Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
05/13/2016 08:17:50: Built by svcphil on Philly-Pool3
|
||||
05/13/2016 08:17:50: Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
05/13/2016 08:17:50: -------------------------------------------------------------------
|
||||
08/16/2016 03:02:05: Built time: Aug 16 2016 02:54:53
|
||||
08/16/2016 03:02:05: Last modified date: Fri Aug 12 05:31:21 2016
|
||||
08/16/2016 03:02:05: Build type: Release
|
||||
08/16/2016 03:02:05: Build target: GPU
|
||||
08/16/2016 03:02:05: With 1bit-SGD: no
|
||||
08/16/2016 03:02:05: Math lib: mkl
|
||||
08/16/2016 03:02:05: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
08/16/2016 03:02:05: CUB_PATH: c:\src\cub-1.4.1
|
||||
08/16/2016 03:02:05: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
08/16/2016 03:02:05: Build Branch: HEAD
|
||||
08/16/2016 03:02:05: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 03:02:05: Built by svcphil on Philly-Pool3
|
||||
08/16/2016 03:02:05: Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
08/16/2016 03:02:05: -------------------------------------------------------------------
|
||||
08/16/2016 03:02:07: -------------------------------------------------------------------
|
||||
08/16/2016 03:02:07: GPU info:
|
||||
|
||||
05/13/2016 08:17:50: Running on Philly-Pool2 at 2016/05/13 08:17:50
|
||||
05/13/2016 08:17:50: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/Config/01_Conv.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10 OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=10]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
08/16/2016 03:02:07: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:02:07: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:02:07: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:02:07: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:02:07: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 03:02:07: Running on DPHAIM-24 at 2016/08/16 03:02:07
|
||||
08/16/2016 03:02:07: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/01_Conv.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10 OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=10]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
|
||||
|
||||
|
||||
05/13/2016 08:17:50: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 08:17:50: RootDir = "."
|
||||
08/16/2016 03:02:07: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:02:07: RootDir = "."
|
||||
ConfigDir = "$RootDir$"
|
||||
DataDir = "$RootDir$"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
|
|
@ -51,7 +66,6 @@ precision = "float"
|
|||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly=true
|
||||
prefetch = "true"
|
||||
command = Train:Test
|
||||
modelPath = "$ModelDir$/01_Convolution"
|
||||
stderr = "$OutputDir$/01_Conv"
|
||||
|
|
@ -84,7 +98,7 @@ Train = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
Test = [
|
||||
action = "test"
|
||||
|
|
@ -102,36 +116,35 @@ Test = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Train=[SGD=[maxEpochs=10]]
|
||||
Train=[SGD=[epochSize=100]]
|
||||
stderr=-
|
||||
|
||||
05/13/2016 08:17:50: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:02:07: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 08:17:50: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 08:17:50: RootDir = "."
|
||||
08/16/2016 03:02:07: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:02:07: RootDir = "."
|
||||
ConfigDir = "."
|
||||
DataDir = "."
|
||||
OutputDir = "./Output"
|
||||
ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models"
|
||||
ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models"
|
||||
ndlMacros = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/Macros.ndl"
|
||||
precision = "float"
|
||||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly=true
|
||||
prefetch = "true"
|
||||
command = Train:Test
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution"
|
||||
stderr = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/01_Conv"
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution"
|
||||
stderr = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/01_Conv"
|
||||
traceLevel = 1
|
||||
numMBsToShowResult = 500
|
||||
Train = [
|
||||
|
|
@ -150,7 +163,7 @@ Train = [
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData/Train_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData/Train_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -161,14 +174,14 @@ Train = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
Test = [
|
||||
action = "test"
|
||||
minibatchSize = 16
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData/Test_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData/Test_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -179,45 +192,44 @@ Test = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Train=[SGD=[maxEpochs=10]]
|
||||
Train=[SGD=[epochSize=100]]
|
||||
stderr=-
|
||||
|
||||
05/13/2016 08:17:50: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:02:07: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 08:17:50: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:02:07: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: 01_Conv.cntk:command=Train:Test
|
||||
configparameters: 01_Conv.cntk:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10
|
||||
configparameters: 01_Conv.cntk:currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData
|
||||
configparameters: 01_Conv.cntk:DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData
|
||||
configparameters: 01_Conv.cntk:currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData
|
||||
configparameters: 01_Conv.cntk:DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData
|
||||
configparameters: 01_Conv.cntk:deviceId=0
|
||||
configparameters: 01_Conv.cntk:imageLayout=cudnn
|
||||
configparameters: 01_Conv.cntk:initOnCPUOnly=true
|
||||
configparameters: 01_Conv.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models
|
||||
configparameters: 01_Conv.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution
|
||||
configparameters: 01_Conv.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models
|
||||
configparameters: 01_Conv.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution
|
||||
configparameters: 01_Conv.cntk:ndlMacros=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/Macros.ndl
|
||||
configparameters: 01_Conv.cntk:numMBsToShowResult=500
|
||||
configparameters: 01_Conv.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu
|
||||
configparameters: 01_Conv.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu
|
||||
configparameters: 01_Conv.cntk:precision=float
|
||||
configparameters: 01_Conv.cntk:prefetch=true
|
||||
configparameters: 01_Conv.cntk:RootDir=.
|
||||
configparameters: 01_Conv.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu
|
||||
configparameters: 01_Conv.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu
|
||||
configparameters: 01_Conv.cntk:stderr=-
|
||||
configparameters: 01_Conv.cntk:Test=[
|
||||
action = "test"
|
||||
minibatchSize = 16
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData/Test_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData/Test_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -228,7 +240,7 @@ configparameters: 01_Conv.cntk:Test=[
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
configparameters: 01_Conv.cntk:timestamping=true
|
||||
|
|
@ -249,7 +261,7 @@ configparameters: 01_Conv.cntk:Train=[
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData/Train_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData/Train_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -260,27 +272,51 @@ configparameters: 01_Conv.cntk:Train=[
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
] [SGD=[maxEpochs=10]] [SGD=[epochSize=100]]
|
||||
|
||||
05/13/2016 08:17:50: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/13/2016 08:17:50: Commands: Train Test
|
||||
05/13/2016 08:17:50: Precision = "float"
|
||||
05/13/2016 08:17:50: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution
|
||||
05/13/2016 08:17:50: CNTKCommandTrainInfo: Train : 10
|
||||
05/13/2016 08:17:50: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 10
|
||||
08/16/2016 03:02:07: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:02:07: Commands: Train Test
|
||||
08/16/2016 03:02:07: Precision = "float"
|
||||
08/16/2016 03:02:07: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution
|
||||
08/16/2016 03:02:07: CNTKCommandTrainInfo: Train : 10
|
||||
08/16/2016 03:02:07: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 10
|
||||
|
||||
05/13/2016 08:17:50: ##############################################################################
|
||||
05/13/2016 08:17:50: # #
|
||||
05/13/2016 08:17:50: # Action "train" #
|
||||
05/13/2016 08:17:50: # #
|
||||
05/13/2016 08:17:50: ##############################################################################
|
||||
08/16/2016 03:02:07: ##############################################################################
|
||||
08/16/2016 03:02:07: # #
|
||||
08/16/2016 03:02:07: # Action "train" #
|
||||
08/16/2016 03:02:07: # #
|
||||
08/16/2016 03:02:07: ##############################################################################
|
||||
|
||||
05/13/2016 08:17:50: CNTKCommandTrainBegin: Train
|
||||
08/16/2016 03:02:07: CNTKCommandTrainBegin: Train
|
||||
NDLBuilder Using GPU 0
|
||||
|
||||
05/13/2016 08:17:52: Creating virgin network.
|
||||
08/16/2016 03:02:08: Creating virgin network.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.000000.
|
||||
Node 'conv1_act.W' (LearnableParameter operation): Initializing Parameter[32 x 75] <- 0.000000.
|
||||
Node 'conv1_act.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 32] <- 0.000000.
|
||||
Node 'conv2_act.W' (LearnableParameter operation): Initializing Parameter[32 x 800] <- 0.000000.
|
||||
Node 'conv2_act.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 32] <- 0.000000.
|
||||
Node 'conv3_act.W' (LearnableParameter operation): Initializing Parameter[64 x 800] <- 0.000000.
|
||||
Node 'conv3_act.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[64 x 3 x 3 x 64] <- 0.000000.
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializing Parameter[10 x 64] <- 0.000000.
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[10] <- 0.000000.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 128.000000.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 128.000000.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 128.000000.
|
||||
Node 'conv1_act.W' (LearnableParameter operation): Initializing Parameter[32 x 75] <- gaussian(seed=1, range=0.023094*0.004300, onCPU=false).
|
||||
Microsoft::MSR::CNTK::GPUMatrix<ElemType>::SetGaussianRandomValue (GPU): creating curand object with seed 1, sizeof(ElemType)==4
|
||||
Node 'conv1_act.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 32] <- 0.000000.
|
||||
Node 'conv2_act.W' (LearnableParameter operation): Initializing Parameter[32 x 800] <- gaussian(seed=2, range=0.007071*1.414000, onCPU=false).
|
||||
Node 'conv2_act.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 32] <- 0.000000.
|
||||
Node 'conv3_act.W' (LearnableParameter operation): Initializing Parameter[64 x 800] <- gaussian(seed=3, range=0.007071*1.414000, onCPU=false).
|
||||
Node 'conv3_act.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[64 x 3 x 3 x 64] <- gaussian(seed=4, range=0.008333*12.000000, onCPU=false).
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializing Parameter[10 x 64] <- gaussian(seed=5, range=0.025000*1.500000, onCPU=false).
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[10] <- 0.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -332,158 +368,176 @@ Validating network. 21 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 32, Kernel: 5 x 5 x 3, Map: 1 x 1 x 32, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1_act.c: using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 32, Kernel: 5 x 5 x 3, Map: 1 x 1 x 32, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 32, Output: 15 x 15 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 32 x 32 x 32, Output: 15 x 15 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 15 x 15 x 32, Kernel: 5 x 5 x 32, Map: 1 x 1 x 32, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2_act.c: using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 15 x 15 x 32, Kernel: 5 x 5 x 32, Map: 1 x 1 x 32, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 7 x 7 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 7 x 7 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 7 x 7 x 32, Output: 7 x 7 x 64, Kernel: 5 x 5 x 32, Map: 1 x 1 x 64, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3_act.c: using cuDNN convolution engine for geometry: Input: 7 x 7 x 32, Output: 7 x 7 x 64, Kernel: 5 x 5 x 32, Map: 1 x 1 x 64, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 7 x 7 x 64, Output: 3 x 3 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool3: using cuDNN convolution engine for geometry: Input: 7 x 7 x 64, Output: 3 x 3 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
|
||||
13 out of 34 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
05/13/2016 08:17:53: Created model with 34 nodes on GPU 0.
|
||||
08/16/2016 03:02:09: Created model with 34 nodes on GPU 0.
|
||||
|
||||
05/13/2016 08:17:53: Training criterion node(s):
|
||||
05/13/2016 08:17:53: CE = CrossEntropyWithSoftmax
|
||||
08/16/2016 03:02:09: Training criterion node(s):
|
||||
08/16/2016 03:02:09: CE = CrossEntropyWithSoftmax
|
||||
|
||||
05/13/2016 08:17:53: Evaluation criterion node(s):
|
||||
|
||||
05/13/2016 08:17:53: Err = ErrorPrediction
|
||||
08/16/2016 03:02:09: Evaluation criterion node(s):
|
||||
08/16/2016 03:02:09: Err = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 63 matrices, 38 are shared as 17, and 25 are not shared.
|
||||
|
||||
0000000000000000: {[Err Gradient[1]] [featOffs Gradient[1 x 1]] [featScaled Gradient[32 x 32 x 3 x *]] [features Gradient[32 x 32 x 3 x *]] [labels Gradient[10 x *]] }
|
||||
0000004790872560: {[features Value[32 x 32 x 3 x *]] }
|
||||
00000047A69D0CD0: {[h1.W Value[64 x 3 x 3 x 64]] }
|
||||
00000047A69D1130: {[conv1_act.b Value[1 x 1 x 32]] }
|
||||
00000047A69D11D0: {[h1.b Value[64 x 1]] }
|
||||
00000047A69D1450: {[conv1_act.W Value[32 x 75]] }
|
||||
00000047A69D14F0: {[conv2_act.b Value[1 x 1 x 32]] }
|
||||
00000047A69D1810: {[featOffs Value[1 x 1]] }
|
||||
00000047A69D1950: {[OutputNodes.W Value[10 x 64]] }
|
||||
00000047A69D1C70: {[conv3_act.W Value[64 x 800]] }
|
||||
00000047A69D1D10: {[OutputNodes.b Value[10]] }
|
||||
00000047A69D1F90: {[conv3_act.b Value[1 x 1 x 64]] }
|
||||
00000047A69D2490: {[labels Value[10 x *]] }
|
||||
00000047A69D2850: {[conv2_act.W Value[32 x 800]] }
|
||||
00000047AD12D4B0: {[conv3_act.p Gradient[7 x 7 x 64 x *]] [pool2 Gradient[7 x 7 x 32 x *]] [pool3 Value[3 x 3 x 64 x *]] }
|
||||
00000047AD12D690: {[conv3_act.W Gradient[64 x 800]] [conv3_act.p Value[7 x 7 x 64 x *]] }
|
||||
00000047AD12D7D0: {[CE Value[1]] }
|
||||
00000047AD12D870: {[featScaled Value[32 x 32 x 3 x *]] }
|
||||
00000047AD12D910: {[conv1_act.c Value[32 x 32 x 32 x *]] }
|
||||
00000047AD12DC30: {[OutputNodes.t Value[10 x 1 x *]] [h1.b Gradient[64 x 1]] [h1.y Gradient[64 x 1 x *]] }
|
||||
00000047AD12DEB0: {[conv2_act.p Gradient[15 x 15 x 32 x *]] [pool1 Gradient[15 x 15 x 32 x *]] [pool2 Value[7 x 7 x 32 x *]] }
|
||||
00000047AD12DF50: {[conv3_act.c Gradient[7 x 7 x 64 x *]] [conv3_act.y Value[7 x 7 x 64 x *]] }
|
||||
00000047AD12E090: {[OutputNodes.t Gradient[10 x 1 x *]] }
|
||||
00000047AD12E130: {[conv1_act.p Gradient[32 x 32 x 32 x *]] [pool1 Value[15 x 15 x 32 x *]] }
|
||||
00000047AD12E270: {[conv2_act.c Gradient[15 x 15 x 32 x *]] [conv2_act.y Value[15 x 15 x 32 x *]] }
|
||||
00000047AD12E3B0: {[Err Value[1]] }
|
||||
00000047AD12E450: {[conv2_act.c Value[15 x 15 x 32 x *]] }
|
||||
00000047AD12E590: {[h1_d Gradient[64 x 1 x *]] }
|
||||
00000047AD12E6D0: {[conv1_act.W Gradient[32 x 75]] [conv1_act.p Value[32 x 32 x 32 x *]] }
|
||||
00000047AD12E810: {[h1.t Gradient[64 x *]] [h1.y Value[64 x 1 x *]] }
|
||||
00000047AD12E8B0: {[h1_d Value[64 x 1 x *]] }
|
||||
00000047AD12E950: {[conv3_act.c Value[7 x 7 x 64 x *]] }
|
||||
00000047AD12EA90: {[OutputNodes.W Gradient[10 x 64]] [OutputNodes.z Gradient[10 x 1 x *]] }
|
||||
00000047AD12EBD0: {[conv2_act.W Gradient[32 x 800]] [conv2_act.p Value[15 x 15 x 32 x *]] }
|
||||
00000047AD12EC70: {[h1.z Gradient[64 x 1 x *]] [pool3 Gradient[3 x 3 x 64 x *]] }
|
||||
00000047AD12ED10: {[OutputNodes.b Gradient[10]] }
|
||||
00000047AD12F2B0: {[CE Gradient[1]] }
|
||||
00000047AD12F490: {[conv2_act.b Gradient[1 x 1 x 32]] [conv2_act.y Gradient[15 x 15 x 32 x *]] }
|
||||
00000047AD12F530: {[conv3_act.b Gradient[1 x 1 x 64]] [conv3_act.y Gradient[7 x 7 x 64 x *]] [h1.t Value[64 x *]] }
|
||||
00000047AD12F5D0: {[h1.W Gradient[64 x 3 x 3 x 64]] [h1.z Value[64 x 1 x *]] }
|
||||
00000047AD12F670: {[conv1_act.b Gradient[1 x 1 x 32]] [conv1_act.y Gradient[32 x 32 x 32 x *]] }
|
||||
00000047AD12F710: {[OutputNodes.z Value[10 x 1 x *]] }
|
||||
00000047AD12F7B0: {[conv1_act.c Gradient[32 x 32 x 32 x *]] [conv1_act.y Value[32 x 32 x 32 x *]] }
|
||||
{ conv2_act.c : [15 x 15 x 32 x *] (gradient)
|
||||
conv2_act.y : [15 x 15 x 32 x *] }
|
||||
{ h1.t : [64 x *] (gradient)
|
||||
h1.y : [64 x 1 x *] }
|
||||
{ conv2_act.W : [32 x 800] (gradient)
|
||||
conv2_act.p : [15 x 15 x 32 x *] }
|
||||
{ conv2_act.b : [1 x 1 x 32] (gradient)
|
||||
conv2_act.y : [15 x 15 x 32 x *] (gradient) }
|
||||
{ h1.z : [64 x 1 x *] (gradient)
|
||||
pool3 : [3 x 3 x 64 x *] (gradient) }
|
||||
{ conv3_act.c : [7 x 7 x 64 x *] (gradient)
|
||||
conv3_act.y : [7 x 7 x 64 x *] }
|
||||
{ conv3_act.W : [64 x 800] (gradient)
|
||||
conv3_act.p : [7 x 7 x 64 x *] }
|
||||
{ conv3_act.p : [7 x 7 x 64 x *] (gradient)
|
||||
pool2 : [7 x 7 x 32 x *] (gradient)
|
||||
pool3 : [3 x 3 x 64 x *] }
|
||||
{ conv3_act.b : [1 x 1 x 64] (gradient)
|
||||
conv3_act.y : [7 x 7 x 64 x *] (gradient)
|
||||
h1.t : [64 x *] }
|
||||
{ conv1_act.c : [32 x 32 x 32 x *] (gradient)
|
||||
conv1_act.y : [32 x 32 x 32 x *] }
|
||||
{ h1.W : [64 x 3 x 3 x 64] (gradient)
|
||||
h1.z : [64 x 1 x *] }
|
||||
{ OutputNodes.t : [10 x 1 x *]
|
||||
h1.b : [64 x 1] (gradient)
|
||||
h1.y : [64 x 1 x *] (gradient) }
|
||||
{ conv2_act.p : [15 x 15 x 32 x *] (gradient)
|
||||
pool1 : [15 x 15 x 32 x *] (gradient)
|
||||
pool2 : [7 x 7 x 32 x *] }
|
||||
{ conv1_act.W : [32 x 75] (gradient)
|
||||
conv1_act.p : [32 x 32 x 32 x *] }
|
||||
{ conv1_act.b : [1 x 1 x 32] (gradient)
|
||||
conv1_act.y : [32 x 32 x 32 x *] (gradient) }
|
||||
{ OutputNodes.W : [10 x 64] (gradient)
|
||||
OutputNodes.z : [10 x 1 x *] (gradient) }
|
||||
{ conv1_act.p : [32 x 32 x 32 x *] (gradient)
|
||||
pool1 : [15 x 15 x 32 x *] }
|
||||
|
||||
05/13/2016 08:17:53: No PreCompute nodes found, skipping PreCompute step.
|
||||
|
||||
05/13/2016 08:17:53: Starting Epoch 1: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
08/16/2016 03:02:09: Training 116906 parameters in 10 out of 10 parameter tensors and 29 nodes with gradient:
|
||||
|
||||
05/13/2016 08:17:53: Starting minibatch loop.
|
||||
05/13/2016 08:18:02: Finished Epoch[ 1 of 10]: [Training] CE = 2.30266907 * 100; Err = 0.91000000 * 100; totalSamplesSeen = 100; learningRatePerSample = 0.00015625; epochTime=9.23399s
|
||||
05/13/2016 08:18:02: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.1'
|
||||
08/16/2016 03:02:09: Node 'OutputNodes.W' (LearnableParameter operation) : [10 x 64]
|
||||
08/16/2016 03:02:09: Node 'OutputNodes.b' (LearnableParameter operation) : [10]
|
||||
08/16/2016 03:02:09: Node 'conv1_act.W' (LearnableParameter operation) : [32 x 75]
|
||||
08/16/2016 03:02:09: Node 'conv1_act.b' (LearnableParameter operation) : [1 x 1 x 32]
|
||||
08/16/2016 03:02:09: Node 'conv2_act.W' (LearnableParameter operation) : [32 x 800]
|
||||
08/16/2016 03:02:09: Node 'conv2_act.b' (LearnableParameter operation) : [1 x 1 x 32]
|
||||
08/16/2016 03:02:09: Node 'conv3_act.W' (LearnableParameter operation) : [64 x 800]
|
||||
08/16/2016 03:02:09: Node 'conv3_act.b' (LearnableParameter operation) : [1 x 1 x 64]
|
||||
08/16/2016 03:02:09: Node 'h1.W' (LearnableParameter operation) : [64 x 3 x 3 x 64]
|
||||
08/16/2016 03:02:09: Node 'h1.b' (LearnableParameter operation) : [64 x 1]
|
||||
|
||||
05/13/2016 08:18:02: Starting Epoch 2: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
08/16/2016 03:02:09: No PreCompute nodes found, or all already computed. Skipping pre-computation step.
|
||||
|
||||
05/13/2016 08:18:02: Starting minibatch loop.
|
||||
05/13/2016 08:18:02: Finished Epoch[ 2 of 10]: [Training] CE = 2.30141006 * 100; Err = 0.86000000 * 100; totalSamplesSeen = 200; learningRatePerSample = 0.00015625; epochTime=0.0203s
|
||||
05/13/2016 08:18:02: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.2'
|
||||
08/16/2016 03:02:09: Starting Epoch 1: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..100] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
05/13/2016 08:18:02: Starting Epoch 3: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
08/16/2016 03:02:09: Starting minibatch loop.
|
||||
08/16/2016 03:02:14: Finished Epoch[ 1 of 10]: [Training] CE = 2.30223602 * 100; Err = 0.90000000 * 100; totalSamplesSeen = 100; learningRatePerSample = 0.00015625; epochTime=4.93739s
|
||||
08/16/2016 03:02:14: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.1'
|
||||
|
||||
05/13/2016 08:18:02: Starting minibatch loop.
|
||||
05/13/2016 08:18:02: Finished Epoch[ 3 of 10]: [Training] CE = 2.30164764 * 100; Err = 0.92000000 * 100; totalSamplesSeen = 300; learningRatePerSample = 0.00015625; epochTime=0.020255s
|
||||
05/13/2016 08:18:02: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.3'
|
||||
08/16/2016 03:02:14: Starting Epoch 2: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [100..200] (first sequence at sample 100), data subset 0 of 1
|
||||
|
||||
05/13/2016 08:18:02: Starting Epoch 4: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
08/16/2016 03:02:14: Starting minibatch loop.
|
||||
08/16/2016 03:02:14: Finished Epoch[ 2 of 10]: [Training] CE = 2.30189240 * 100; Err = 0.87000000 * 100; totalSamplesSeen = 200; learningRatePerSample = 0.00015625; epochTime=0.016498s
|
||||
08/16/2016 03:02:14: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.2'
|
||||
|
||||
05/13/2016 08:18:02: Starting minibatch loop.
|
||||
05/13/2016 08:18:02: Finished Epoch[ 4 of 10]: [Training] CE = 2.29509628 * 100; Err = 0.87000000 * 100; totalSamplesSeen = 400; learningRatePerSample = 0.00015625; epochTime=0.020212s
|
||||
05/13/2016 08:18:02: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.4'
|
||||
08/16/2016 03:02:15: Starting Epoch 3: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [200..300] (first sequence at sample 200), data subset 0 of 1
|
||||
|
||||
05/13/2016 08:18:03: Starting Epoch 5: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
08/16/2016 03:02:15: Starting minibatch loop.
|
||||
08/16/2016 03:02:15: Finished Epoch[ 3 of 10]: [Training] CE = 2.29965256 * 100; Err = 0.86000000 * 100; totalSamplesSeen = 300; learningRatePerSample = 0.00015625; epochTime=0.0146s
|
||||
08/16/2016 03:02:15: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.3'
|
||||
|
||||
05/13/2016 08:18:03: Starting minibatch loop.
|
||||
05/13/2016 08:18:03: Finished Epoch[ 5 of 10]: [Training] CE = 2.29264740 * 100; Err = 0.87000000 * 100; totalSamplesSeen = 500; learningRatePerSample = 0.00015625; epochTime=0.02033s
|
||||
05/13/2016 08:18:03: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.5'
|
||||
08/16/2016 03:02:15: Starting Epoch 4: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 3: frames [300..400] (first sequence at sample 300), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:02:15: Starting minibatch loop.
|
||||
08/16/2016 03:02:15: Finished Epoch[ 4 of 10]: [Training] CE = 2.29966064 * 100; Err = 0.91000000 * 100; totalSamplesSeen = 400; learningRatePerSample = 0.00015625; epochTime=0.01451s
|
||||
08/16/2016 03:02:15: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.4'
|
||||
|
||||
08/16/2016 03:02:15: Starting Epoch 5: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 4: frames [400..500] (first sequence at sample 400), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:02:15: Starting minibatch loop.
|
||||
08/16/2016 03:02:15: Finished Epoch[ 5 of 10]: [Training] CE = 2.30450378 * 100; Err = 0.94000000 * 100; totalSamplesSeen = 500; learningRatePerSample = 0.00015625; epochTime=0.014432s
|
||||
08/16/2016 03:02:15: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.5'
|
||||
Setting dropout rate to 0.5.
|
||||
|
||||
05/13/2016 08:18:03: Starting Epoch 6: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
08/16/2016 03:02:15: Starting Epoch 6: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 5: frames [500..600] (first sequence at sample 500), data subset 0 of 1
|
||||
|
||||
05/13/2016 08:18:03: Starting minibatch loop.
|
||||
08/16/2016 03:02:15: Starting minibatch loop.
|
||||
(GPU): creating curand object with seed 5
|
||||
05/13/2016 08:18:03: Finished Epoch[ 6 of 10]: [Training] CE = 2.30378311 * 100; Err = 0.92000000 * 100; totalSamplesSeen = 600; learningRatePerSample = 0.00015625; epochTime=0.026637s
|
||||
05/13/2016 08:18:03: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.6'
|
||||
08/16/2016 03:02:15: Finished Epoch[ 6 of 10]: [Training] CE = 2.29013901 * 100; Err = 0.81000000 * 100; totalSamplesSeen = 600; learningRatePerSample = 0.00015625; epochTime=0.023069s
|
||||
08/16/2016 03:02:15: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.6'
|
||||
|
||||
05/13/2016 08:18:03: Starting Epoch 7: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
08/16/2016 03:02:15: Starting Epoch 7: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 6: frames [600..700] (first sequence at sample 600), data subset 0 of 1
|
||||
|
||||
05/13/2016 08:18:03: Starting minibatch loop.
|
||||
08/16/2016 03:02:15: Starting minibatch loop.
|
||||
(GPU): creating curand object with seed 6
|
||||
05/13/2016 08:18:03: Finished Epoch[ 7 of 10]: [Training] CE = 2.28946518 * 100; Err = 0.90000000 * 100; totalSamplesSeen = 700; learningRatePerSample = 0.00015625; epochTime=0.02618s
|
||||
05/13/2016 08:18:03: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.7'
|
||||
08/16/2016 03:02:15: Finished Epoch[ 7 of 10]: [Training] CE = 2.29815735 * 100; Err = 0.93000000 * 100; totalSamplesSeen = 700; learningRatePerSample = 0.00015625; epochTime=0.030436s
|
||||
08/16/2016 03:02:15: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.7'
|
||||
|
||||
05/13/2016 08:18:03: Starting Epoch 8: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
08/16/2016 03:02:15: Starting Epoch 8: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 7: frames [700..800] (first sequence at sample 700), data subset 0 of 1
|
||||
|
||||
05/13/2016 08:18:03: Starting minibatch loop.
|
||||
08/16/2016 03:02:15: Starting minibatch loop.
|
||||
(GPU): creating curand object with seed 7
|
||||
05/13/2016 08:18:03: Finished Epoch[ 8 of 10]: [Training] CE = 2.29619675 * 100; Err = 0.93000000 * 100; totalSamplesSeen = 800; learningRatePerSample = 0.00015625; epochTime=0.026196s
|
||||
05/13/2016 08:18:03: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.8'
|
||||
08/16/2016 03:02:15: Finished Epoch[ 8 of 10]: [Training] CE = 2.28805984 * 100; Err = 0.89000000 * 100; totalSamplesSeen = 800; learningRatePerSample = 0.00015625; epochTime=0.022867s
|
||||
08/16/2016 03:02:15: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.8'
|
||||
|
||||
05/13/2016 08:18:03: Starting Epoch 9: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
08/16/2016 03:02:15: Starting Epoch 9: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 8: frames [800..900] (first sequence at sample 800), data subset 0 of 1
|
||||
|
||||
05/13/2016 08:18:03: Starting minibatch loop.
|
||||
08/16/2016 03:02:15: Starting minibatch loop.
|
||||
(GPU): creating curand object with seed 8
|
||||
05/13/2016 08:18:03: Finished Epoch[ 9 of 10]: [Training] CE = 2.27065186 * 100; Err = 0.83000000 * 100; totalSamplesSeen = 900; learningRatePerSample = 0.00015625; epochTime=0.026126s
|
||||
05/13/2016 08:18:03: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.9'
|
||||
08/16/2016 03:02:15: Finished Epoch[ 9 of 10]: [Training] CE = 2.29377136 * 100; Err = 0.88000000 * 100; totalSamplesSeen = 900; learningRatePerSample = 0.00015625; epochTime=0.022876s
|
||||
08/16/2016 03:02:15: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.9'
|
||||
|
||||
05/13/2016 08:18:03: Starting Epoch 10: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
08/16/2016 03:02:15: Starting Epoch 10: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 9: frames [900..1000] (first sequence at sample 900), data subset 0 of 1
|
||||
|
||||
05/13/2016 08:18:03: Starting minibatch loop.
|
||||
08/16/2016 03:02:15: Starting minibatch loop.
|
||||
(GPU): creating curand object with seed 9
|
||||
05/13/2016 08:18:03: Finished Epoch[10 of 10]: [Training] CE = 2.31216217 * 100; Err = 0.92000000 * 100; totalSamplesSeen = 1000; learningRatePerSample = 0.00015625; epochTime=0.026148s
|
||||
05/13/2016 08:18:03: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution'
|
||||
05/13/2016 08:18:03: CNTKCommandTrainEnd: Train
|
||||
08/16/2016 03:02:15: Finished Epoch[10 of 10]: [Training] CE = 2.27813766 * 100; Err = 0.88000000 * 100; totalSamplesSeen = 1000; learningRatePerSample = 0.00015625; epochTime=0.022892s
|
||||
08/16/2016 03:02:15: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution'
|
||||
08/16/2016 03:02:15: CNTKCommandTrainEnd: Train
|
||||
|
||||
05/13/2016 08:18:03: Action "train" complete.
|
||||
08/16/2016 03:02:15: Action "train" complete.
|
||||
|
||||
|
||||
05/13/2016 08:18:03: ##############################################################################
|
||||
05/13/2016 08:18:03: # #
|
||||
05/13/2016 08:18:03: # Action "test" #
|
||||
05/13/2016 08:18:03: # #
|
||||
05/13/2016 08:18:03: ##############################################################################
|
||||
08/16/2016 03:02:15: ##############################################################################
|
||||
08/16/2016 03:02:15: # #
|
||||
08/16/2016 03:02:15: # Action "test" #
|
||||
08/16/2016 03:02:15: # #
|
||||
08/16/2016 03:02:15: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
|
@ -536,17 +590,17 @@ Validating network. 21 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 32, Kernel: 5 x 5 x 3, Map: 1 x 1 x 32, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1_act.c: using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 32, Kernel: 5 x 5 x 3, Map: 1 x 1 x 32, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 32, Output: 15 x 15 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 32 x 32 x 32, Output: 15 x 15 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 15 x 15 x 32, Kernel: 5 x 5 x 32, Map: 1 x 1 x 32, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2_act.c: using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 15 x 15 x 32, Kernel: 5 x 5 x 32, Map: 1 x 1 x 32, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 7 x 7 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 7 x 7 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 7 x 7 x 32, Output: 7 x 7 x 64, Kernel: 5 x 5 x 32, Map: 1 x 1 x 64, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3_act.c: using cuDNN convolution engine for geometry: Input: 7 x 7 x 32, Output: 7 x 7 x 64, Kernel: 5 x 5 x 32, Map: 1 x 1 x 64, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 7 x 7 x 64, Output: 3 x 3 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool3: using cuDNN convolution engine for geometry: Input: 7 x 7 x 64, Output: 3 x 3 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
|
||||
13 out of 34 nodes do not share the minibatch layout with the input data.
|
||||
|
|
@ -558,46 +612,14 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 34 matrices, 0 are shared as 0, and 34 are not shared.
|
||||
|
||||
0000000000000000: {[CE Gradient[1]] [Err Gradient[1]] [OutputNodes.W Gradient[10 x 64]] [OutputNodes.b Gradient[10]] [OutputNodes.t Gradient[10 x 1 x *1]] [OutputNodes.z Gradient[10 x 1 x *1]] [conv1_act.W Gradient[32 x 75]] [conv1_act.b Gradient[1 x 1 x 32]] [conv1_act.c Gradient[32 x 32 x 32 x *1]] [conv1_act.p Gradient[32 x 32 x 32 x *1]] [conv1_act.y Gradient[32 x 32 x 32 x *1]] [conv2_act.W Gradient[32 x 800]] [conv2_act.b Gradient[1 x 1 x 32]] [conv2_act.c Gradient[15 x 15 x 32 x *1]] [conv2_act.p Gradient[15 x 15 x 32 x *1]] [conv2_act.y Gradient[15 x 15 x 32 x *1]] [conv3_act.W Gradient[64 x 800]] [conv3_act.b Gradient[1 x 1 x 64]] [conv3_act.c Gradient[7 x 7 x 64 x *1]] [conv3_act.p Gradient[7 x 7 x 64 x *1]] [conv3_act.y Gradient[7 x 7 x 64 x *1]] [featOffs Gradient[1 x 1]] [featScaled Gradient[32 x 32 x 3 x *1]] [features Gradient[32 x 32 x 3 x *1]] [h1.W Gradient[64 x 3 x 3 x 64]] [h1.b Gradient[64 x 1]] [h1.t Gradient[64 x *1]] [h1.y Gradient[64 x 1 x *1]] [h1.z Gradient[64 x 1 x *1]] [h1_d Gradient[64 x 1 x *1]] [labels Gradient[10 x *1]] [pool1 Gradient[15 x 15 x 32 x *1]] [pool2 Gradient[7 x 7 x 32 x *1]] [pool3 Gradient[3 x 3 x 64 x *1]] }
|
||||
00000047A69D0FF0: {[h1.t Value[64 x *1]] }
|
||||
00000047A69D16D0: {[h1_d Value[64 x 1 x *1]] }
|
||||
00000047A69D1D10: {[OutputNodes.z Value[10 x 1 x *1]] }
|
||||
00000047A69D1DB0: {[OutputNodes.t Value[10 x 1 x *1]] }
|
||||
00000047A69D1E50: {[h1.y Value[64 x 1 x *1]] }
|
||||
00000047A69D2530: {[h1.z Value[64 x 1 x *1]] }
|
||||
00000047AD12D7D0: {[featOffs Value[1 x 1]] }
|
||||
00000047AD12D910: {[OutputNodes.W Value[10 x 64]] }
|
||||
00000047AD12DC30: {[conv1_act.b Value[1 x 1 x 32]] }
|
||||
00000047AD12DF50: {[conv2_act.W Value[32 x 800]] }
|
||||
00000047AD12E090: {[conv3_act.W Value[64 x 800]] }
|
||||
00000047AD12E3B0: {[features Value[32 x 32 x 3 x *1]] }
|
||||
00000047AD12E9F0: {[h1.W Value[64 x 3 x 3 x 64]] }
|
||||
00000047AD12EA90: {[conv1_act.W Value[32 x 75]] }
|
||||
00000047AD12EBD0: {[conv2_act.b Value[1 x 1 x 32]] }
|
||||
00000047AD12ED10: {[labels Value[10 x *1]] }
|
||||
00000047AD12F210: {[h1.b Value[64 x 1]] }
|
||||
00000047AD12F670: {[conv3_act.b Value[1 x 1 x 64]] }
|
||||
00000047AD12FB70: {[OutputNodes.b Value[10]] }
|
||||
00000047AD12FCB0: {[conv2_act.y Value[15 x 15 x 32 x *1]] }
|
||||
00000047AD12FD50: {[pool2 Value[7 x 7 x 32 x *1]] }
|
||||
00000047AD12FFD0: {[conv2_act.p Value[15 x 15 x 32 x *1]] }
|
||||
00000047AD130110: {[featScaled Value[32 x 32 x 3 x *1]] }
|
||||
00000047AD130250: {[conv3_act.c Value[7 x 7 x 64 x *1]] }
|
||||
00000047AD130390: {[conv3_act.p Value[7 x 7 x 64 x *1]] }
|
||||
00000047AD130430: {[conv3_act.y Value[7 x 7 x 64 x *1]] }
|
||||
00000047AD1304D0: {[conv1_act.p Value[32 x 32 x 32 x *1]] }
|
||||
00000047AD130570: {[pool3 Value[3 x 3 x 64 x *1]] }
|
||||
00000047AD1307F0: {[conv1_act.y Value[32 x 32 x 32 x *1]] }
|
||||
00000047AD130BB0: {[conv1_act.c Value[32 x 32 x 32 x *1]] }
|
||||
00000047AD1310B0: {[Err Value[1]] }
|
||||
00000047AD131150: {[pool1 Value[15 x 15 x 32 x *1]] }
|
||||
00000047AD1311F0: {[CE Value[1]] }
|
||||
00000047AD131330: {[conv2_act.c Value[15 x 15 x 32 x *1]] }
|
||||
|
||||
05/13/2016 08:18:17: Final Results: Minibatch[1-625]: Err = 0.84020000 * 10000; CE = 2.27465317 * 10000; perplexity = 9.72454569
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
08/16/2016 03:02:17: Minibatch[1-500]: Err = 0.86112500 * 8000; CE = 2.28394067 * 8000
|
||||
08/16/2016 03:02:18: Minibatch[501-625]: Err = 0.86300000 * 2000; CE = 2.28036680 * 2000
|
||||
08/16/2016 03:02:18: Final Results: Minibatch[1-625]: Err = 0.86150000 * 10000; CE = 2.28322590 * 10000; perplexity = 9.80826991
|
||||
|
||||
05/13/2016 08:18:17: Action "test" complete.
|
||||
08/16/2016 03:02:18: Action "test" complete.
|
||||
|
||||
05/13/2016 08:18:17: __COMPLETED__
|
||||
08/16/2016 03:02:18: __COMPLETED__
|
||||
|
|
@ -1,49 +1,62 @@
|
|||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/Config/02_BatchNormConv.cntk currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10 OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=5]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 264172964 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv.cntk currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10 OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=5]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 13 2016 14:50:25
|
||||
Last modified date: Thu May 12 14:00:37 2016
|
||||
Built time: Aug 16 2016 09:41:56
|
||||
Last modified date: Fri Aug 12 07:32:43 2016
|
||||
Build type: release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: no
|
||||
Math lib: acml
|
||||
Math lib: mkl
|
||||
CUDA_PATH: /usr/local/cuda-7.5
|
||||
CUB_PATH: /usr/local/cub-1.4.1
|
||||
CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
Built by philly on d8dc82703b0f
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by philly on f67b30a647de
|
||||
Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to /tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData
|
||||
05/13/2016 15:10:59: Redirecting stderr to file -_Train_Test.log
|
||||
05/13/2016 15:10:59: -------------------------------------------------------------------
|
||||
05/13/2016 15:10:59: Build info:
|
||||
Changed current directory to /tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData
|
||||
08/16/2016 10:50:44: Redirecting stderr to file -_Train_Test.log
|
||||
08/16/2016 10:50:44: -------------------------------------------------------------------
|
||||
08/16/2016 10:50:44: Build info:
|
||||
|
||||
05/13/2016 15:10:59: Built time: May 13 2016 14:50:25
|
||||
05/13/2016 15:10:59: Last modified date: Thu May 12 14:00:37 2016
|
||||
05/13/2016 15:10:59: Build type: release
|
||||
05/13/2016 15:10:59: Build target: GPU
|
||||
05/13/2016 15:10:59: With 1bit-SGD: no
|
||||
05/13/2016 15:10:59: Math lib: acml
|
||||
05/13/2016 15:10:59: CUDA_PATH: /usr/local/cuda-7.5
|
||||
05/13/2016 15:10:59: CUB_PATH: /usr/local/cub-1.4.1
|
||||
05/13/2016 15:10:59: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
05/13/2016 15:10:59: Build Branch: HEAD
|
||||
05/13/2016 15:10:59: Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
05/13/2016 15:10:59: Built by philly on d8dc82703b0f
|
||||
05/13/2016 15:10:59: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
05/13/2016 15:10:59: -------------------------------------------------------------------
|
||||
08/16/2016 10:50:44: Built time: Aug 16 2016 09:41:56
|
||||
08/16/2016 10:50:44: Last modified date: Fri Aug 12 07:32:43 2016
|
||||
08/16/2016 10:50:44: Build type: release
|
||||
08/16/2016 10:50:44: Build target: GPU
|
||||
08/16/2016 10:50:44: With 1bit-SGD: no
|
||||
08/16/2016 10:50:44: Math lib: mkl
|
||||
08/16/2016 10:50:44: CUDA_PATH: /usr/local/cuda-7.5
|
||||
08/16/2016 10:50:44: CUB_PATH: /usr/local/cub-1.4.1
|
||||
08/16/2016 10:50:44: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
08/16/2016 10:50:44: Build Branch: HEAD
|
||||
08/16/2016 10:50:44: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 10:50:44: Built by philly on f67b30a647de
|
||||
08/16/2016 10:50:44: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
08/16/2016 10:50:44: -------------------------------------------------------------------
|
||||
08/16/2016 10:50:45: -------------------------------------------------------------------
|
||||
08/16/2016 10:50:45: GPU info:
|
||||
|
||||
05/13/2016 15:10:59: Running on localhost at 2016/05/13 15:10:59
|
||||
05/13/2016 15:10:59: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/Config/02_BatchNormConv.cntk currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10 OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=5]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
08/16/2016 10:50:45: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:50:45: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:50:45: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:50:45: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:50:45: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 10:50:45: Running on localhost at 2016/08/16 10:50:45
|
||||
08/16/2016 10:50:45: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv.cntk currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10 OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=5]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
|
||||
|
||||
|
||||
05/13/2016 15:10:59: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 15:10:59: RootDir = "."
|
||||
08/16/2016 10:50:45: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:50:45: RootDir = "."
|
||||
ConfigDir = "$RootDir$"
|
||||
DataDir = "$RootDir$"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
|
|
@ -53,7 +66,6 @@ precision = "float"
|
|||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly=true
|
||||
prefetch = "true"
|
||||
command = Train:Test
|
||||
stderr = "$OutputDir$/02_BatchNormConv"
|
||||
traceLevel = 1
|
||||
|
|
@ -86,7 +98,7 @@ Train = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
Test = [
|
||||
action = "test"
|
||||
|
|
@ -105,42 +117,41 @@ Test = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10
|
||||
OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu
|
||||
currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10
|
||||
OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Train=[SGD=[maxEpochs=5]]
|
||||
Train=[SGD=[epochSize=100]]
|
||||
stderr=-
|
||||
|
||||
05/13/2016 15:10:59: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:50:45: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 15:10:59: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 15:10:59: RootDir = "."
|
||||
08/16/2016 10:50:45: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:50:45: RootDir = "."
|
||||
ConfigDir = "."
|
||||
DataDir = "."
|
||||
OutputDir = "./Output"
|
||||
ModelDir = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models"
|
||||
ndlMacros = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/Macros.ndl"
|
||||
ModelDir = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models"
|
||||
ndlMacros = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/Macros.ndl"
|
||||
precision = "float"
|
||||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly=true
|
||||
prefetch = "true"
|
||||
command = Train:Test
|
||||
stderr = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/02_BatchNormConv"
|
||||
stderr = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/02_BatchNormConv"
|
||||
traceLevel = 1
|
||||
numMBsToShowResult = 500
|
||||
Train = [
|
||||
action = "train"
|
||||
modelPath = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv"
|
||||
modelPath = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv"
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv.ndl"
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 49984
|
||||
|
|
@ -153,7 +164,7 @@ Train = [
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData/Train_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData/Train_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -164,15 +175,15 @@ Train = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
Test = [
|
||||
action = "test"
|
||||
modelPath = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv"
|
||||
modelPath = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv"
|
||||
minibatchSize = 16
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData/Test_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData/Test_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -183,45 +194,44 @@ Test = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10
|
||||
OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu
|
||||
currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10
|
||||
OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Train=[SGD=[maxEpochs=5]]
|
||||
Train=[SGD=[epochSize=100]]
|
||||
stderr=-
|
||||
|
||||
05/13/2016 15:10:59: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:50:45: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 15:10:59: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:50:45: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: 02_BatchNormConv.cntk:command=Train:Test
|
||||
configparameters: 02_BatchNormConv.cntk:ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10
|
||||
configparameters: 02_BatchNormConv.cntk:currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData
|
||||
configparameters: 02_BatchNormConv.cntk:DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData
|
||||
configparameters: 02_BatchNormConv.cntk:ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10
|
||||
configparameters: 02_BatchNormConv.cntk:currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData
|
||||
configparameters: 02_BatchNormConv.cntk:DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData
|
||||
configparameters: 02_BatchNormConv.cntk:deviceId=0
|
||||
configparameters: 02_BatchNormConv.cntk:imageLayout=cudnn
|
||||
configparameters: 02_BatchNormConv.cntk:initOnCPUOnly=true
|
||||
configparameters: 02_BatchNormConv.cntk:ModelDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models
|
||||
configparameters: 02_BatchNormConv.cntk:ndlMacros=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/Macros.ndl
|
||||
configparameters: 02_BatchNormConv.cntk:ModelDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models
|
||||
configparameters: 02_BatchNormConv.cntk:ndlMacros=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/Macros.ndl
|
||||
configparameters: 02_BatchNormConv.cntk:numMBsToShowResult=500
|
||||
configparameters: 02_BatchNormConv.cntk:OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu
|
||||
configparameters: 02_BatchNormConv.cntk:OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu
|
||||
configparameters: 02_BatchNormConv.cntk:precision=float
|
||||
configparameters: 02_BatchNormConv.cntk:prefetch=true
|
||||
configparameters: 02_BatchNormConv.cntk:RootDir=.
|
||||
configparameters: 02_BatchNormConv.cntk:RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu
|
||||
configparameters: 02_BatchNormConv.cntk:RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu
|
||||
configparameters: 02_BatchNormConv.cntk:stderr=-
|
||||
configparameters: 02_BatchNormConv.cntk:Test=[
|
||||
action = "test"
|
||||
modelPath = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv"
|
||||
modelPath = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv"
|
||||
minibatchSize = 16
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData/Test_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData/Test_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -232,16 +242,16 @@ configparameters: 02_BatchNormConv.cntk:Test=[
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
configparameters: 02_BatchNormConv.cntk:timestamping=true
|
||||
configparameters: 02_BatchNormConv.cntk:traceLevel=1
|
||||
configparameters: 02_BatchNormConv.cntk:Train=[
|
||||
action = "train"
|
||||
modelPath = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv"
|
||||
modelPath = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv"
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv.ndl"
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/02_BatchNormConv.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 49984
|
||||
|
|
@ -254,7 +264,7 @@ configparameters: 02_BatchNormConv.cntk:Train=[
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData/Train_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/TestData/Train_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -265,27 +275,75 @@ configparameters: 02_BatchNormConv.cntk:Train=[
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
] [SGD=[maxEpochs=5]] [SGD=[epochSize=100]]
|
||||
|
||||
05/13/2016 15:10:59: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/13/2016 15:10:59: Commands: Train Test
|
||||
05/13/2016 15:10:59: Precision = "float"
|
||||
05/13/2016 15:10:59: CNTKModelPath: /tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv
|
||||
05/13/2016 15:10:59: CNTKCommandTrainInfo: Train : 5
|
||||
05/13/2016 15:10:59: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 5
|
||||
08/16/2016 10:50:45: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:50:45: Commands: Train Test
|
||||
08/16/2016 10:50:45: Precision = "float"
|
||||
08/16/2016 10:50:45: CNTKModelPath: /tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv
|
||||
08/16/2016 10:50:45: CNTKCommandTrainInfo: Train : 5
|
||||
08/16/2016 10:50:45: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 5
|
||||
|
||||
05/13/2016 15:10:59: ##############################################################################
|
||||
05/13/2016 15:10:59: # #
|
||||
05/13/2016 15:10:59: # Action "train" #
|
||||
05/13/2016 15:10:59: # #
|
||||
05/13/2016 15:10:59: ##############################################################################
|
||||
08/16/2016 10:50:45: ##############################################################################
|
||||
08/16/2016 10:50:45: # #
|
||||
08/16/2016 10:50:45: # Action "train" #
|
||||
08/16/2016 10:50:45: # #
|
||||
08/16/2016 10:50:45: ##############################################################################
|
||||
|
||||
05/13/2016 15:10:59: CNTKCommandTrainBegin: Train
|
||||
08/16/2016 10:50:45: CNTKCommandTrainBegin: Train
|
||||
NDLBuilder Using GPU 0
|
||||
|
||||
05/13/2016 15:10:59: Creating virgin network.
|
||||
08/16/2016 10:50:46: Creating virgin network.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.000000.
|
||||
Node 'conv1.c.W' (LearnableParameter operation): Initializing Parameter[32 x 75] <- 0.000000.
|
||||
Node 'conv1.c.c.b' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv1.c.c.sc' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv1.c.c.m' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv1.c.c.isd' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.W' (LearnableParameter operation): Initializing Parameter[32 x 800] <- 0.000000.
|
||||
Node 'conv2.c.c.b' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.c.sc' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.c.m' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.c.isd' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv3.c.W' (LearnableParameter operation): Initializing Parameter[64 x 800] <- 0.000000.
|
||||
Node 'conv3.c.c.b' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'conv3.c.c.sc' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'conv3.c.c.m' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'conv3.c.c.isd' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[64 x 3 x 3 x 64] <- 0.000000.
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'h1.sc' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'h1.m' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'h1.isd' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializing Parameter[10 x 64] <- 0.000000.
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[10] <- 0.000000.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 128.000000.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 128.000000.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 128.000000.
|
||||
Node 'conv1.c.W' (LearnableParameter operation): Initializing Parameter[32 x 75] <- gaussian(seed=1, range=0.023094*0.004300, onCPU=false).
|
||||
SetGaussianRandomValue (GPU): creating curand object with seed 1, sizeof(ElemType)==4
|
||||
Node 'conv1.c.c.b' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv1.c.c.sc' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 1.000000.
|
||||
Node 'conv1.c.c.m' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv1.c.c.isd' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.W' (LearnableParameter operation): Initializing Parameter[32 x 800] <- gaussian(seed=2, range=0.007071*1.414000, onCPU=false).
|
||||
Node 'conv2.c.c.b' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.c.sc' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 1.000000.
|
||||
Node 'conv2.c.c.m' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.c.isd' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv3.c.W' (LearnableParameter operation): Initializing Parameter[64 x 800] <- gaussian(seed=3, range=0.007071*1.414000, onCPU=false).
|
||||
Node 'conv3.c.c.b' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'conv3.c.c.sc' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 1.000000.
|
||||
Node 'conv3.c.c.m' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'conv3.c.c.isd' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[64 x 3 x 3 x 64] <- gaussian(seed=4, range=0.008333*12.000000, onCPU=false).
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'h1.sc' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 1.000000.
|
||||
Node 'h1.m' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'h1.isd' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializing Parameter[10 x 64] <- gaussian(seed=5, range=0.025000*1.500000, onCPU=false).
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[10] <- 0.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -348,23 +406,23 @@ Validating network. 20 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 32, Kernel: 5 x 5 x 3, Map: 1 x 1 x 32, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c.c.c: using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 32, Kernel: 5 x 5 x 3, Map: 1 x 1 x 32, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 32, Output: 15 x 15 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 32 x 32 x 32, Output: 15 x 15 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 15 x 15 x 32, Kernel: 5 x 5 x 32, Map: 1 x 1 x 32, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c.c.c: using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 15 x 15 x 32, Kernel: 5 x 5 x 32, Map: 1 x 1 x 32, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 7 x 7 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 7 x 7 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 7 x 7 x 32, Output: 7 x 7 x 64, Kernel: 5 x 5 x 32, Map: 1 x 1 x 64, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3.c.c.c: using cuDNN convolution engine for geometry: Input: 7 x 7 x 32, Output: 7 x 7 x 64, Kernel: 5 x 5 x 32, Map: 1 x 1 x 64, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 7 x 7 x 64, Output: 3 x 3 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool3: using cuDNN convolution engine for geometry: Input: 7 x 7 x 64, Output: 3 x 3 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
|
|
@ -373,118 +431,122 @@ Using CNTK batch normalization engine.
|
|||
|
||||
Post-processing network complete.
|
||||
|
||||
05/13/2016 15:10:59: Created model with 45 nodes on GPU 0.
|
||||
08/16/2016 10:50:46: Created model with 45 nodes on GPU 0.
|
||||
|
||||
05/13/2016 15:10:59: Training criterion node(s):
|
||||
05/13/2016 15:10:59: CE = CrossEntropyWithSoftmax
|
||||
08/16/2016 10:50:46: Training criterion node(s):
|
||||
08/16/2016 10:50:46: CE = CrossEntropyWithSoftmax
|
||||
|
||||
05/13/2016 15:10:59: Evaluation criterion node(s):
|
||||
|
||||
05/13/2016 15:10:59: Err = ErrorPrediction
|
||||
08/16/2016 10:50:46: Evaluation criterion node(s):
|
||||
08/16/2016 10:50:46: Err = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 77 matrices, 38 are shared as 16, and 39 are not shared.
|
||||
|
||||
(nil): {[Err Gradient[1]] [conv1.c.c.isd Gradient[32 x 1]] [conv1.c.c.m Gradient[32 x 1]] [conv2.c.c.isd Gradient[32 x 1]] [conv2.c.c.m Gradient[32 x 1]] [conv3.c.c.isd Gradient[64 x 1]] [conv3.c.c.m Gradient[64 x 1]] [featOffs Gradient[1 x 1]] [featScaled Gradient[32 x 32 x 3 x *]] [features Gradient[32 x 32 x 3 x *]] [h1.isd Gradient[64 x 1]] [h1.m Gradient[64 x 1]] [labels Gradient[10 x *]] }
|
||||
0x28d4ae8: {[features Value[32 x 32 x 3 x *]] }
|
||||
0x31e4be8: {[featOffs Value[1 x 1]] }
|
||||
0x31e6988: {[labels Value[10 x *]] }
|
||||
0x31e74c8: {[conv1.c.W Value[32 x 75]] }
|
||||
0x31e7a08: {[conv1.c.c.b Value[32 x 1]] }
|
||||
0x31e86c8: {[conv1.c.c.sc Value[32 x 1]] }
|
||||
0x31e9228: {[conv1.c.c.m Value[32 x 1]] }
|
||||
0x31e9d78: {[conv1.c.c.isd Value[32 x 1]] }
|
||||
0x31ead98: {[conv2.c.W Value[32 x 800]] }
|
||||
0x31ec628: {[conv2.c.c.b Value[32 x 1]] }
|
||||
0x31ed2e8: {[conv2.c.c.sc Value[32 x 1]] }
|
||||
0x31ed9b8: {[h1.b Value[64 x 1]] }
|
||||
0x31edfc8: {[conv2.c.c.m Value[32 x 1]] }
|
||||
0x31eeb38: {[conv2.c.c.isd Value[32 x 1]] }
|
||||
0x31efbe8: {[conv3.c.W Value[64 x 800]] }
|
||||
0x31f0ca8: {[conv3.c.c.b Value[64 x 1]] }
|
||||
0x31f16a8: {[conv3.c.c.sc Value[64 x 1]] }
|
||||
0x31f2548: {[conv3.c.c.m Value[64 x 1]] }
|
||||
0x31f30b8: {[conv3.c.c.isd Value[64 x 1]] }
|
||||
0x31f4a48: {[h1.W Value[64 x 3 x 3 x 64]] }
|
||||
0x31f6098: {[h1.sc Value[64 x 1]] }
|
||||
0x31f6b48: {[h1.m Value[64 x 1]] }
|
||||
0x3629fb8: {[h1.isd Value[64 x 1]] }
|
||||
0x362b3f8: {[OutputNodes.W Value[10 x 64]] }
|
||||
0x362c2e8: {[OutputNodes.b Value[10]] }
|
||||
0x7f51399894d8: {[Err Value[1]] }
|
||||
0x7f513998b9d8: {[conv1.c.c.y Value[32 x 32 x 32 x *]] }
|
||||
0x7f51399ab038: {[featScaled Value[32 x 32 x 3 x *]] }
|
||||
0x7f51399ab378: {[conv1.c.c.c Value[32 x 32 x 32 x *]] }
|
||||
0x7f51399ac5f8: {[conv1.c.c.c Gradient[32 x 32 x 32 x *]] [conv1.y Value[32 x 32 x 32 x *]] }
|
||||
0x7f51399ac7b8: {[conv1.c.c.y Gradient[32 x 32 x 32 x *]] [pool1 Value[15 x 15 x 32 x *]] }
|
||||
0x7f51399ac978: {[conv1.c.W Gradient[32 x 75]] [conv2.c.c.c Value[15 x 15 x 32 x *]] }
|
||||
0x7f51399acb38: {[conv1.c.c.sc Gradient[32 x 1]] [conv1.y Gradient[32 x 32 x 32 x *]] }
|
||||
0x7f51399accf8: {[conv2.c.c.y Value[15 x 15 x 32 x *]] }
|
||||
0x7f51399ad238: {[conv1.c.c.b Gradient[32 x 1]] [conv2.c.c.c Gradient[15 x 15 x 32 x *]] [conv2.y Value[15 x 15 x 32 x *]] }
|
||||
0x7f51399ad3f8: {[conv2.c.c.y Gradient[15 x 15 x 32 x *]] [pool2 Value[7 x 7 x 32 x *]] }
|
||||
0x7f51399ad5b8: {[conv2.c.W Gradient[32 x 800]] [conv3.c.c.c Value[7 x 7 x 64 x *]] }
|
||||
0x7f51399ad778: {[conv2.c.c.sc Gradient[32 x 1]] [conv2.y Gradient[15 x 15 x 32 x *]] }
|
||||
0x7f51399ad938: {[conv3.c.c.y Value[7 x 7 x 64 x *]] }
|
||||
0x7f51399ade78: {[conv2.c.c.b Gradient[32 x 1]] [conv3.c.c.c Gradient[7 x 7 x 64 x *]] [conv3.y Value[7 x 7 x 64 x *]] }
|
||||
0x7f51399ae038: {[conv3.c.c.y Gradient[7 x 7 x 64 x *]] [pool3 Value[3 x 3 x 64 x *]] }
|
||||
0x7f51399ae1f8: {[conv3.c.c.sc Gradient[64 x 1]] [conv3.y Gradient[7 x 7 x 64 x *]] [h1.t Value[64 x *]] }
|
||||
0x7f51399ae3b8: {[h1.bn Value[64 x *]] }
|
||||
0x7f51399ae738: {[conv3.c.c.b Gradient[64 x 1]] }
|
||||
0x7f51399ae8f8: {[conv3.c.W Gradient[64 x 800]] [h1.t Gradient[64 x *]] [h1.y Value[64 x *]] }
|
||||
0x7f51399aeab8: {[OutputNodes.t Value[10 x *]] [h1.bn Gradient[64 x *]] }
|
||||
0x7f51399af598: {[CE Gradient[1]] }
|
||||
0x7f51399af758: {[OutputNodes.W Gradient[10 x 64]] [OutputNodes.z Gradient[10 x *]] }
|
||||
0x7f51399af918: {[OutputNodes.t Gradient[10 x *]] [pool1 Gradient[15 x 15 x 32 x *]] [pool2 Gradient[7 x 7 x 32 x *]] [pool3 Gradient[3 x 3 x 64 x *]] }
|
||||
0x7f51399afad8: {[OutputNodes.b Gradient[10]] }
|
||||
0x7f51399afc98: {[h1.sc Gradient[64 x 1]] [h1.y Gradient[64 x *]] }
|
||||
0x7f51399afe88: {[h1.W Gradient[64 x 3 x 3 x 64]] }
|
||||
0x7f51399b0048: {[h1.b Gradient[64 x 1]] }
|
||||
0x7f51399b6728: {[OutputNodes.z Value[10 x *]] }
|
||||
0x7f51399b68e8: {[CE Value[1]] }
|
||||
|
||||
05/13/2016 15:10:59: No PreCompute nodes found, skipping PreCompute step.
|
||||
|
||||
05/13/2016 15:10:59: Starting Epoch 1: learning rate per sample = 0.000469 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
|
||||
05/13/2016 15:10:59: Starting minibatch loop.
|
||||
05/13/2016 15:11:03: Finished Epoch[ 1 of 5]: [Training] CE = 2.29343704 * 100; Err = 0.87000000 * 100; totalSamplesSeen = 100; learningRatePerSample = 0.00046874999; epochTime=3.58144s
|
||||
05/13/2016 15:11:03: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv.1'
|
||||
|
||||
05/13/2016 15:11:03: Starting Epoch 2: learning rate per sample = 0.000469 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
|
||||
05/13/2016 15:11:03: Starting minibatch loop.
|
||||
05/13/2016 15:11:03: Finished Epoch[ 2 of 5]: [Training] CE = 2.22764633 * 100; Err = 0.88000000 * 100; totalSamplesSeen = 200; learningRatePerSample = 0.00046874999; epochTime=0.01264s
|
||||
05/13/2016 15:11:03: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv.2'
|
||||
|
||||
05/13/2016 15:11:03: Starting Epoch 3: learning rate per sample = 0.000469 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
|
||||
05/13/2016 15:11:03: Starting minibatch loop.
|
||||
05/13/2016 15:11:03: Finished Epoch[ 3 of 5]: [Training] CE = 2.20062683 * 100; Err = 0.77000000 * 100; totalSamplesSeen = 300; learningRatePerSample = 0.00046874999; epochTime=0.01151s
|
||||
05/13/2016 15:11:03: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv.3'
|
||||
|
||||
05/13/2016 15:11:03: Starting Epoch 4: learning rate per sample = 0.000469 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
|
||||
05/13/2016 15:11:03: Starting minibatch loop.
|
||||
05/13/2016 15:11:03: Finished Epoch[ 4 of 5]: [Training] CE = 2.19534531 * 100; Err = 0.81000000 * 100; totalSamplesSeen = 400; learningRatePerSample = 0.00046874999; epochTime=0.012353s
|
||||
05/13/2016 15:11:03: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv.4'
|
||||
|
||||
05/13/2016 15:11:03: Starting Epoch 5: learning rate per sample = 0.000469 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
|
||||
05/13/2016 15:11:03: Starting minibatch loop.
|
||||
05/13/2016 15:11:03: Finished Epoch[ 5 of 5]: [Training] CE = 2.16844864 * 100; Err = 0.79000000 * 100; totalSamplesSeen = 500; learningRatePerSample = 0.00046874999; epochTime=0.01142s
|
||||
05/13/2016 15:11:03: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv'
|
||||
05/13/2016 15:11:03: CNTKCommandTrainEnd: Train
|
||||
|
||||
05/13/2016 15:11:03: Action "train" complete.
|
||||
{ conv1.c.c.y : [32 x 32 x 32 x *] (gradient)
|
||||
pool1 : [15 x 15 x 32 x *] }
|
||||
{ conv1.c.W : [32 x 75] (gradient)
|
||||
conv2.c.c.c : [15 x 15 x 32 x *] }
|
||||
{ conv1.c.c.sc : [32 x 1] (gradient)
|
||||
conv1.y : [32 x 32 x 32 x *] (gradient) }
|
||||
{ conv1.c.c.b : [32 x 1] (gradient)
|
||||
conv2.c.c.c : [15 x 15 x 32 x *] (gradient)
|
||||
conv2.y : [15 x 15 x 32 x *] }
|
||||
{ conv2.c.c.y : [15 x 15 x 32 x *] (gradient)
|
||||
pool2 : [7 x 7 x 32 x *] }
|
||||
{ conv2.c.W : [32 x 800] (gradient)
|
||||
conv3.c.c.c : [7 x 7 x 64 x *] }
|
||||
{ conv2.c.c.sc : [32 x 1] (gradient)
|
||||
conv2.y : [15 x 15 x 32 x *] (gradient) }
|
||||
{ conv2.c.c.b : [32 x 1] (gradient)
|
||||
conv3.c.c.c : [7 x 7 x 64 x *] (gradient)
|
||||
conv3.y : [7 x 7 x 64 x *] }
|
||||
{ conv3.c.c.y : [7 x 7 x 64 x *] (gradient)
|
||||
pool3 : [3 x 3 x 64 x *] }
|
||||
{ conv3.c.c.sc : [64 x 1] (gradient)
|
||||
conv3.y : [7 x 7 x 64 x *] (gradient)
|
||||
h1.t : [64 x *] }
|
||||
{ conv3.c.W : [64 x 800] (gradient)
|
||||
h1.t : [64 x *] (gradient)
|
||||
h1.y : [64 x *] }
|
||||
{ OutputNodes.t : [10 x *]
|
||||
h1.bn : [64 x *] (gradient) }
|
||||
{ OutputNodes.W : [10 x 64] (gradient)
|
||||
OutputNodes.z : [10 x *] (gradient) }
|
||||
{ OutputNodes.t : [10 x *] (gradient)
|
||||
pool1 : [15 x 15 x 32 x *] (gradient)
|
||||
pool2 : [7 x 7 x 32 x *] (gradient)
|
||||
pool3 : [3 x 3 x 64 x *] (gradient) }
|
||||
{ h1.sc : [64 x 1] (gradient)
|
||||
h1.y : [64 x *] (gradient) }
|
||||
{ conv1.c.c.c : [32 x 32 x 32 x *] (gradient)
|
||||
conv1.y : [32 x 32 x 32 x *] }
|
||||
|
||||
|
||||
05/13/2016 15:11:03: ##############################################################################
|
||||
05/13/2016 15:11:03: # #
|
||||
05/13/2016 15:11:03: # Action "test" #
|
||||
05/13/2016 15:11:03: # #
|
||||
05/13/2016 15:11:03: ##############################################################################
|
||||
08/16/2016 10:50:46: Training 117098 parameters in 14 out of 14 parameter tensors and 32 nodes with gradient:
|
||||
|
||||
08/16/2016 10:50:46: Node 'OutputNodes.W' (LearnableParameter operation) : [10 x 64]
|
||||
08/16/2016 10:50:46: Node 'OutputNodes.b' (LearnableParameter operation) : [10]
|
||||
08/16/2016 10:50:46: Node 'conv1.c.W' (LearnableParameter operation) : [32 x 75]
|
||||
08/16/2016 10:50:46: Node 'conv1.c.c.b' (LearnableParameter operation) : [32 x 1]
|
||||
08/16/2016 10:50:46: Node 'conv1.c.c.sc' (LearnableParameter operation) : [32 x 1]
|
||||
08/16/2016 10:50:46: Node 'conv2.c.W' (LearnableParameter operation) : [32 x 800]
|
||||
08/16/2016 10:50:46: Node 'conv2.c.c.b' (LearnableParameter operation) : [32 x 1]
|
||||
08/16/2016 10:50:46: Node 'conv2.c.c.sc' (LearnableParameter operation) : [32 x 1]
|
||||
08/16/2016 10:50:46: Node 'conv3.c.W' (LearnableParameter operation) : [64 x 800]
|
||||
08/16/2016 10:50:46: Node 'conv3.c.c.b' (LearnableParameter operation) : [64 x 1]
|
||||
08/16/2016 10:50:46: Node 'conv3.c.c.sc' (LearnableParameter operation) : [64 x 1]
|
||||
08/16/2016 10:50:46: Node 'h1.W' (LearnableParameter operation) : [64 x 3 x 3 x 64]
|
||||
08/16/2016 10:50:46: Node 'h1.b' (LearnableParameter operation) : [64 x 1]
|
||||
08/16/2016 10:50:46: Node 'h1.sc' (LearnableParameter operation) : [64 x 1]
|
||||
|
||||
08/16/2016 10:50:46: No PreCompute nodes found, or all already computed. Skipping pre-computation step.
|
||||
|
||||
08/16/2016 10:50:46: Starting Epoch 1: learning rate per sample = 0.000469 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..100] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:50:46: Starting minibatch loop.
|
||||
08/16/2016 10:50:49: Finished Epoch[ 1 of 5]: [Training] CE = 2.26618500 * 100; Err = 0.87000000 * 100; totalSamplesSeen = 100; learningRatePerSample = 0.00046874999; epochTime=3.51442s
|
||||
08/16/2016 10:50:49: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv.1'
|
||||
|
||||
08/16/2016 10:50:49: Starting Epoch 2: learning rate per sample = 0.000469 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [100..200] (first sequence at sample 100), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:50:49: Starting minibatch loop.
|
||||
08/16/2016 10:50:49: Finished Epoch[ 2 of 5]: [Training] CE = 2.24375671 * 100; Err = 0.82000000 * 100; totalSamplesSeen = 200; learningRatePerSample = 0.00046874999; epochTime=0.011601s
|
||||
08/16/2016 10:50:50: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv.2'
|
||||
|
||||
08/16/2016 10:50:50: Starting Epoch 3: learning rate per sample = 0.000469 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [200..300] (first sequence at sample 200), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:50:50: Starting minibatch loop.
|
||||
08/16/2016 10:50:50: Finished Epoch[ 3 of 5]: [Training] CE = 2.21250885 * 100; Err = 0.84000000 * 100; totalSamplesSeen = 300; learningRatePerSample = 0.00046874999; epochTime=0.012328s
|
||||
08/16/2016 10:50:50: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv.3'
|
||||
|
||||
08/16/2016 10:50:50: Starting Epoch 4: learning rate per sample = 0.000469 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
BlockRandomizer::StartEpoch: epoch 3: frames [300..400] (first sequence at sample 300), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:50:50: Starting minibatch loop.
|
||||
08/16/2016 10:50:50: Finished Epoch[ 4 of 5]: [Training] CE = 2.20485107 * 100; Err = 0.82000000 * 100; totalSamplesSeen = 400; learningRatePerSample = 0.00046874999; epochTime=0.011359s
|
||||
08/16/2016 10:50:50: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv.4'
|
||||
|
||||
08/16/2016 10:50:50: Starting Epoch 5: learning rate per sample = 0.000469 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
BlockRandomizer::StartEpoch: epoch 4: frames [400..500] (first sequence at sample 400), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:50:50: Starting minibatch loop.
|
||||
08/16/2016 10:50:50: Finished Epoch[ 5 of 5]: [Training] CE = 2.17108704 * 100; Err = 0.78000000 * 100; totalSamplesSeen = 500; learningRatePerSample = 0.00046874999; epochTime=0.011981s
|
||||
08/16/2016 10:50:50: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv'
|
||||
08/16/2016 10:50:50: CNTKCommandTrainEnd: Train
|
||||
|
||||
08/16/2016 10:50:50: Action "train" complete.
|
||||
|
||||
|
||||
08/16/2016 10:50:50: ##############################################################################
|
||||
08/16/2016 10:50:50: # #
|
||||
08/16/2016 10:50:50: # Action "test" #
|
||||
08/16/2016 10:50:50: # #
|
||||
08/16/2016 10:50:50: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
|
@ -548,23 +610,23 @@ Validating network. 20 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 32, Kernel: 5 x 5 x 3, Map: 1 x 1 x 32, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c.c.c: using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 32, Kernel: 5 x 5 x 3, Map: 1 x 1 x 32, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 32, Output: 15 x 15 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 32 x 32 x 32, Output: 15 x 15 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 15 x 15 x 32, Kernel: 5 x 5 x 32, Map: 1 x 1 x 32, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c.c.c: using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 15 x 15 x 32, Kernel: 5 x 5 x 32, Map: 1 x 1 x 32, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 7 x 7 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 7 x 7 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 7 x 7 x 32, Output: 7 x 7 x 64, Kernel: 5 x 5 x 32, Map: 1 x 1 x 64, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3.c.c.c: using cuDNN convolution engine for geometry: Input: 7 x 7 x 32, Output: 7 x 7 x 64, Kernel: 5 x 5 x 32, Map: 1 x 1 x 64, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 7 x 7 x 64, Output: 3 x 3 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool3: using cuDNN convolution engine for geometry: Input: 7 x 7 x 64, Output: 3 x 3 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
|
|
@ -578,57 +640,14 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 45 matrices, 0 are shared as 0, and 45 are not shared.
|
||||
|
||||
(nil): {[CE Gradient[1]] [Err Gradient[1]] [OutputNodes.W Gradient[10 x 64]] [OutputNodes.b Gradient[10]] [OutputNodes.t Gradient[10 x *1]] [OutputNodes.z Gradient[10 x *1]] [conv1.c.W Gradient[32 x 75]] [conv1.c.c.b Gradient[32 x 1]] [conv1.c.c.c Gradient[32 x 32 x 32 x *1]] [conv1.c.c.isd Gradient[32 x 1]] [conv1.c.c.m Gradient[32 x 1]] [conv1.c.c.sc Gradient[32 x 1]] [conv1.c.c.y Gradient[32 x 32 x 32 x *1]] [conv1.y Gradient[32 x 32 x 32 x *1]] [conv2.c.W Gradient[32 x 800]] [conv2.c.c.b Gradient[32 x 1]] [conv2.c.c.c Gradient[15 x 15 x 32 x *1]] [conv2.c.c.isd Gradient[32 x 1]] [conv2.c.c.m Gradient[32 x 1]] [conv2.c.c.sc Gradient[32 x 1]] [conv2.c.c.y Gradient[15 x 15 x 32 x *1]] [conv2.y Gradient[15 x 15 x 32 x *1]] [conv3.c.W Gradient[64 x 800]] [conv3.c.c.b Gradient[64 x 1]] [conv3.c.c.c Gradient[7 x 7 x 64 x *1]] [conv3.c.c.isd Gradient[64 x 1]] [conv3.c.c.m Gradient[64 x 1]] [conv3.c.c.sc Gradient[64 x 1]] [conv3.c.c.y Gradient[7 x 7 x 64 x *1]] [conv3.y Gradient[7 x 7 x 64 x *1]] [featOffs Gradient[1 x 1]] [featScaled Gradient[32 x 32 x 3 x *1]] [features Gradient[32 x 32 x 3 x *1]] [h1.W Gradient[64 x 3 x 3 x 64]] [h1.b Gradient[64 x 1]] [h1.bn Gradient[64 x *1]] [h1.isd Gradient[64 x 1]] [h1.m Gradient[64 x 1]] [h1.sc Gradient[64 x 1]] [h1.t Gradient[64 x *1]] [h1.y Gradient[64 x *1]] [labels Gradient[10 x *1]] [pool1 Gradient[15 x 15 x 32 x *1]] [pool2 Gradient[7 x 7 x 32 x *1]] [pool3 Gradient[3 x 3 x 64 x *1]] }
|
||||
0x7f5132919b38: {[conv2.c.c.isd Value[32 x 1]] }
|
||||
0x7f5132919cf8: {[conv2.c.c.m Value[32 x 1]] }
|
||||
0x7f513291a7b8: {[conv2.c.c.sc Value[32 x 1]] }
|
||||
0x7f5132974258: {[conv1.c.W Value[32 x 75]] }
|
||||
0x7f51329744c8: {[conv2.c.c.b Value[32 x 1]] }
|
||||
0x7f5132974fa8: {[conv1.c.c.isd Value[32 x 1]] }
|
||||
0x7f5132975168: {[conv1.c.c.m Value[32 x 1]] }
|
||||
0x7f513298b078: {[conv1.y Value[32 x 32 x 32 x *1]] }
|
||||
0x7f513298b238: {[pool1 Value[15 x 15 x 32 x *1]] }
|
||||
0x7f513298b3f8: {[conv2.c.c.c Value[15 x 15 x 32 x *1]] }
|
||||
0x7f513298b778: {[conv2.c.c.y Value[15 x 15 x 32 x *1]] }
|
||||
0x7f513298bcb8: {[conv2.y Value[15 x 15 x 32 x *1]] }
|
||||
0x7f513298be78: {[pool2 Value[7 x 7 x 32 x *1]] }
|
||||
0x7f513298c038: {[conv3.c.c.c Value[7 x 7 x 64 x *1]] }
|
||||
0x7f513298c3b8: {[conv3.c.c.y Value[7 x 7 x 64 x *1]] }
|
||||
0x7f513298c8f8: {[conv3.y Value[7 x 7 x 64 x *1]] }
|
||||
0x7f513298cab8: {[pool3 Value[3 x 3 x 64 x *1]] }
|
||||
0x7f513298cc78: {[h1.t Value[64 x *1]] }
|
||||
0x7f513298ce38: {[h1.bn Value[64 x *1]] }
|
||||
0x7f513298d378: {[h1.y Value[64 x *1]] }
|
||||
0x7f513298d538: {[OutputNodes.t Value[10 x *1]] }
|
||||
0x7f513298d6f8: {[OutputNodes.z Value[10 x *1]] }
|
||||
0x7f51397eafd8: {[featScaled Value[32 x 32 x 3 x *1]] }
|
||||
0x7f51397eb318: {[conv1.c.c.c Value[32 x 32 x 32 x *1]] }
|
||||
0x7f5139989708: {[labels Value[10 x *1]] }
|
||||
0x7f513998a198: {[OutputNodes.b Value[10]] }
|
||||
0x7f513998aea8: {[OutputNodes.W Value[10 x 64]] }
|
||||
0x7f513998d7f8: {[conv1.c.c.sc Value[32 x 1]] }
|
||||
0x7f51399aaca8: {[CE Value[1]] }
|
||||
0x7f51399ab038: {[Err Value[1]] }
|
||||
0x7f51399cc578: {[conv1.c.c.y Value[32 x 32 x 32 x *1]] }
|
||||
0x7f51399ce9c8: {[conv3.c.c.sc Value[64 x 1]] }
|
||||
0x7f51399cebd8: {[conv3.c.W Value[64 x 800]] }
|
||||
0x7f51399d04a8: {[featOffs Value[1 x 1]] }
|
||||
0x7f51399d1148: {[features Value[32 x 32 x 3 x *1]] }
|
||||
0x7f51399d1b98: {[h1.b Value[64 x 1]] }
|
||||
0x7f51399d1e78: {[h1.isd Value[64 x 1]] }
|
||||
0x7f51399d2ab8: {[h1.m Value[64 x 1]] }
|
||||
0x7f51399d3518: {[h1.sc Value[64 x 1]] }
|
||||
0x7f51399d4f88: {[conv2.c.W Value[32 x 800]] }
|
||||
0x7f51399d5a88: {[conv3.c.c.b Value[64 x 1]] }
|
||||
0x7f51399d6bf8: {[conv3.c.c.isd Value[64 x 1]] }
|
||||
0x7f51399d6df8: {[conv3.c.c.m Value[64 x 1]] }
|
||||
0x7f51399f43a8: {[h1.W Value[64 x 3 x 3 x 64]] }
|
||||
0x7f51399fb388: {[conv1.c.c.b Value[32 x 1]] }
|
||||
|
||||
05/13/2016 15:11:09: Final Results: Minibatch[1-625]: Err = 0.86810000 * 10000; CE = 2.32970283 * 10000; perplexity = 10.27488769
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
08/16/2016 10:50:51: Minibatch[1-500]: Err = 0.86100000 * 8000; CE = 2.27391421 * 8000
|
||||
08/16/2016 10:50:51: Minibatch[501-625]: Err = 0.85550000 * 2000; CE = 2.27178036 * 2000
|
||||
08/16/2016 10:50:51: Final Results: Minibatch[1-625]: Err = 0.85990000 * 10000; CE = 2.27348744 * 10000; perplexity = 9.71321604
|
||||
|
||||
05/13/2016 15:11:09: Action "test" complete.
|
||||
08/16/2016 10:50:51: Action "test" complete.
|
||||
|
||||
05/13/2016 15:11:09: __COMPLETED__
|
||||
08/16/2016 10:50:51: __COMPLETED__
|
||||
|
|
@ -1,47 +1,62 @@
|
|||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/Config/02_BatchNormConv.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10 OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=5]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 268381192 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/02_BatchNormConv.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10 OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=5]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 13 2016 08:06:01
|
||||
Last modified date: Thu May 12 07:31:50 2016
|
||||
Built time: Aug 16 2016 02:54:53
|
||||
Last modified date: Fri Aug 12 05:31:21 2016
|
||||
Build type: Release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: no
|
||||
Math lib: mkl
|
||||
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
CUB_PATH: c:\src\cub-1.4.1
|
||||
CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by svcphil on Philly-Pool3
|
||||
Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData
|
||||
05/13/2016 08:18:23: Redirecting stderr to file -_Train_Test.log
|
||||
05/13/2016 08:18:23: -------------------------------------------------------------------
|
||||
05/13/2016 08:18:23: Build info:
|
||||
Changed current directory to C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData
|
||||
08/16/2016 03:02:22: Redirecting stderr to file -_Train_Test.log
|
||||
08/16/2016 03:02:22: -------------------------------------------------------------------
|
||||
08/16/2016 03:02:22: Build info:
|
||||
|
||||
05/13/2016 08:18:23: Built time: May 13 2016 08:06:01
|
||||
05/13/2016 08:18:23: Last modified date: Thu May 12 07:31:50 2016
|
||||
05/13/2016 08:18:23: Build type: Release
|
||||
05/13/2016 08:18:23: Build target: GPU
|
||||
05/13/2016 08:18:23: With 1bit-SGD: no
|
||||
05/13/2016 08:18:23: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
05/13/2016 08:18:23: CUB_PATH: c:\src\cub-1.4.1
|
||||
05/13/2016 08:18:23: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
05/13/2016 08:18:23: Build Branch: HEAD
|
||||
05/13/2016 08:18:23: Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
05/13/2016 08:18:23: Built by svcphil on Philly-Pool3
|
||||
05/13/2016 08:18:23: Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
05/13/2016 08:18:23: -------------------------------------------------------------------
|
||||
08/16/2016 03:02:22: Built time: Aug 16 2016 02:54:53
|
||||
08/16/2016 03:02:22: Last modified date: Fri Aug 12 05:31:21 2016
|
||||
08/16/2016 03:02:22: Build type: Release
|
||||
08/16/2016 03:02:22: Build target: GPU
|
||||
08/16/2016 03:02:22: With 1bit-SGD: no
|
||||
08/16/2016 03:02:22: Math lib: mkl
|
||||
08/16/2016 03:02:22: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
08/16/2016 03:02:22: CUB_PATH: c:\src\cub-1.4.1
|
||||
08/16/2016 03:02:22: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
08/16/2016 03:02:22: Build Branch: HEAD
|
||||
08/16/2016 03:02:22: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 03:02:22: Built by svcphil on Philly-Pool3
|
||||
08/16/2016 03:02:22: Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
08/16/2016 03:02:22: -------------------------------------------------------------------
|
||||
08/16/2016 03:02:25: -------------------------------------------------------------------
|
||||
08/16/2016 03:02:25: GPU info:
|
||||
|
||||
05/13/2016 08:18:23: Running on Philly-Pool2 at 2016/05/13 08:18:23
|
||||
05/13/2016 08:18:23: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/Config/02_BatchNormConv.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10 OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=5]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
08/16/2016 03:02:25: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:02:25: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:02:25: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:02:25: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:02:25: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 03:02:25: Running on DPHAIM-24 at 2016/08/16 03:02:25
|
||||
08/16/2016 03:02:25: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/02_BatchNormConv.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10 OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=5]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
|
||||
|
||||
|
||||
05/13/2016 08:18:23: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 08:18:23: RootDir = "."
|
||||
08/16/2016 03:02:25: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:02:25: RootDir = "."
|
||||
ConfigDir = "$RootDir$"
|
||||
DataDir = "$RootDir$"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
|
|
@ -51,7 +66,6 @@ precision = "float"
|
|||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly=true
|
||||
prefetch = "true"
|
||||
command = Train:Test
|
||||
stderr = "$OutputDir$/02_BatchNormConv"
|
||||
traceLevel = 1
|
||||
|
|
@ -84,7 +98,7 @@ Train = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
Test = [
|
||||
action = "test"
|
||||
|
|
@ -103,40 +117,39 @@ Test = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Train=[SGD=[maxEpochs=5]]
|
||||
Train=[SGD=[epochSize=100]]
|
||||
stderr=-
|
||||
|
||||
05/13/2016 08:18:23: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:02:25: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 08:18:23: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 08:18:23: RootDir = "."
|
||||
08/16/2016 03:02:25: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:02:25: RootDir = "."
|
||||
ConfigDir = "."
|
||||
DataDir = "."
|
||||
OutputDir = "./Output"
|
||||
ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models"
|
||||
ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models"
|
||||
ndlMacros = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/Macros.ndl"
|
||||
precision = "float"
|
||||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
initOnCPUOnly=true
|
||||
prefetch = "true"
|
||||
command = Train:Test
|
||||
stderr = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/02_BatchNormConv"
|
||||
stderr = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/02_BatchNormConv"
|
||||
traceLevel = 1
|
||||
numMBsToShowResult = 500
|
||||
Train = [
|
||||
action = "train"
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv"
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv"
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/02_BatchNormConv.ndl"
|
||||
]
|
||||
|
|
@ -151,7 +164,7 @@ Train = [
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData/Train_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData/Train_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -162,15 +175,15 @@ Train = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
Test = [
|
||||
action = "test"
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv"
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv"
|
||||
minibatchSize = 16
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData/Test_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData/Test_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -181,45 +194,44 @@ Test = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Train=[SGD=[maxEpochs=5]]
|
||||
Train=[SGD=[epochSize=100]]
|
||||
stderr=-
|
||||
|
||||
05/13/2016 08:18:23: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:02:25: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 08:18:23: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:02:25: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: 02_BatchNormConv.cntk:command=Train:Test
|
||||
configparameters: 02_BatchNormConv.cntk:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10
|
||||
configparameters: 02_BatchNormConv.cntk:currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData
|
||||
configparameters: 02_BatchNormConv.cntk:DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData
|
||||
configparameters: 02_BatchNormConv.cntk:currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData
|
||||
configparameters: 02_BatchNormConv.cntk:DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData
|
||||
configparameters: 02_BatchNormConv.cntk:deviceId=0
|
||||
configparameters: 02_BatchNormConv.cntk:imageLayout=cudnn
|
||||
configparameters: 02_BatchNormConv.cntk:initOnCPUOnly=true
|
||||
configparameters: 02_BatchNormConv.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models
|
||||
configparameters: 02_BatchNormConv.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models
|
||||
configparameters: 02_BatchNormConv.cntk:ndlMacros=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/Macros.ndl
|
||||
configparameters: 02_BatchNormConv.cntk:numMBsToShowResult=500
|
||||
configparameters: 02_BatchNormConv.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu
|
||||
configparameters: 02_BatchNormConv.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu
|
||||
configparameters: 02_BatchNormConv.cntk:precision=float
|
||||
configparameters: 02_BatchNormConv.cntk:prefetch=true
|
||||
configparameters: 02_BatchNormConv.cntk:RootDir=.
|
||||
configparameters: 02_BatchNormConv.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu
|
||||
configparameters: 02_BatchNormConv.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu
|
||||
configparameters: 02_BatchNormConv.cntk:stderr=-
|
||||
configparameters: 02_BatchNormConv.cntk:Test=[
|
||||
action = "test"
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv"
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv"
|
||||
minibatchSize = 16
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData/Test_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData/Test_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -230,14 +242,14 @@ configparameters: 02_BatchNormConv.cntk:Test=[
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
configparameters: 02_BatchNormConv.cntk:timestamping=true
|
||||
configparameters: 02_BatchNormConv.cntk:traceLevel=1
|
||||
configparameters: 02_BatchNormConv.cntk:Train=[
|
||||
action = "train"
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv"
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv"
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/02_BatchNormConv.ndl"
|
||||
]
|
||||
|
|
@ -252,7 +264,7 @@ configparameters: 02_BatchNormConv.cntk:Train=[
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData/Train_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu\TestData/Train_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -263,27 +275,75 @@ configparameters: 02_BatchNormConv.cntk:Train=[
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
] [SGD=[maxEpochs=5]] [SGD=[epochSize=100]]
|
||||
|
||||
05/13/2016 08:18:23: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/13/2016 08:18:23: Commands: Train Test
|
||||
05/13/2016 08:18:23: Precision = "float"
|
||||
05/13/2016 08:18:23: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv
|
||||
05/13/2016 08:18:23: CNTKCommandTrainInfo: Train : 5
|
||||
05/13/2016 08:18:23: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 5
|
||||
08/16/2016 03:02:25: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:02:25: Commands: Train Test
|
||||
08/16/2016 03:02:25: Precision = "float"
|
||||
08/16/2016 03:02:25: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv
|
||||
08/16/2016 03:02:25: CNTKCommandTrainInfo: Train : 5
|
||||
08/16/2016 03:02:25: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 5
|
||||
|
||||
05/13/2016 08:18:23: ##############################################################################
|
||||
05/13/2016 08:18:23: # #
|
||||
05/13/2016 08:18:23: # Action "train" #
|
||||
05/13/2016 08:18:23: # #
|
||||
05/13/2016 08:18:23: ##############################################################################
|
||||
08/16/2016 03:02:25: ##############################################################################
|
||||
08/16/2016 03:02:25: # #
|
||||
08/16/2016 03:02:25: # Action "train" #
|
||||
08/16/2016 03:02:25: # #
|
||||
08/16/2016 03:02:25: ##############################################################################
|
||||
|
||||
05/13/2016 08:18:23: CNTKCommandTrainBegin: Train
|
||||
08/16/2016 03:02:25: CNTKCommandTrainBegin: Train
|
||||
NDLBuilder Using GPU 0
|
||||
|
||||
05/13/2016 08:18:24: Creating virgin network.
|
||||
08/16/2016 03:02:26: Creating virgin network.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.000000.
|
||||
Node 'conv1.c.W' (LearnableParameter operation): Initializing Parameter[32 x 75] <- 0.000000.
|
||||
Node 'conv1.c.c.b' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv1.c.c.sc' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv1.c.c.m' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv1.c.c.isd' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.W' (LearnableParameter operation): Initializing Parameter[32 x 800] <- 0.000000.
|
||||
Node 'conv2.c.c.b' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.c.sc' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.c.m' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.c.isd' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv3.c.W' (LearnableParameter operation): Initializing Parameter[64 x 800] <- 0.000000.
|
||||
Node 'conv3.c.c.b' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'conv3.c.c.sc' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'conv3.c.c.m' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'conv3.c.c.isd' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[64 x 3 x 3 x 64] <- 0.000000.
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'h1.sc' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'h1.m' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'h1.isd' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializing Parameter[10 x 64] <- 0.000000.
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[10] <- 0.000000.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 128.000000.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 128.000000.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 128.000000.
|
||||
Node 'conv1.c.W' (LearnableParameter operation): Initializing Parameter[32 x 75] <- gaussian(seed=1, range=0.023094*0.004300, onCPU=false).
|
||||
Microsoft::MSR::CNTK::GPUMatrix<ElemType>::SetGaussianRandomValue (GPU): creating curand object with seed 1, sizeof(ElemType)==4
|
||||
Node 'conv1.c.c.b' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv1.c.c.sc' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 1.000000.
|
||||
Node 'conv1.c.c.m' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv1.c.c.isd' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.W' (LearnableParameter operation): Initializing Parameter[32 x 800] <- gaussian(seed=2, range=0.007071*1.414000, onCPU=false).
|
||||
Node 'conv2.c.c.b' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.c.sc' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 1.000000.
|
||||
Node 'conv2.c.c.m' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv2.c.c.isd' (LearnableParameter operation): Initializing Parameter[32 x 1] <- 0.000000.
|
||||
Node 'conv3.c.W' (LearnableParameter operation): Initializing Parameter[64 x 800] <- gaussian(seed=3, range=0.007071*1.414000, onCPU=false).
|
||||
Node 'conv3.c.c.b' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'conv3.c.c.sc' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 1.000000.
|
||||
Node 'conv3.c.c.m' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'conv3.c.c.isd' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[64 x 3 x 3 x 64] <- gaussian(seed=4, range=0.008333*12.000000, onCPU=false).
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'h1.sc' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 1.000000.
|
||||
Node 'h1.m' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'h1.isd' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializing Parameter[10 x 64] <- gaussian(seed=5, range=0.025000*1.500000, onCPU=false).
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[10] <- 0.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -346,23 +406,23 @@ Validating network. 20 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 32, Kernel: 5 x 5 x 3, Map: 1 x 1 x 32, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c.c.c: using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 32, Kernel: 5 x 5 x 3, Map: 1 x 1 x 32, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 32, Output: 15 x 15 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 32 x 32 x 32, Output: 15 x 15 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 15 x 15 x 32, Kernel: 5 x 5 x 32, Map: 1 x 1 x 32, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c.c.c: using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 15 x 15 x 32, Kernel: 5 x 5 x 32, Map: 1 x 1 x 32, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 7 x 7 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 7 x 7 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 7 x 7 x 32, Output: 7 x 7 x 64, Kernel: 5 x 5 x 32, Map: 1 x 1 x 64, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3.c.c.c: using cuDNN convolution engine for geometry: Input: 7 x 7 x 32, Output: 7 x 7 x 64, Kernel: 5 x 5 x 32, Map: 1 x 1 x 64, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 7 x 7 x 64, Output: 3 x 3 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool3: using cuDNN convolution engine for geometry: Input: 7 x 7 x 64, Output: 3 x 3 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
|
|
@ -371,118 +431,122 @@ Using CNTK batch normalization engine.
|
|||
|
||||
Post-processing network complete.
|
||||
|
||||
05/13/2016 08:18:26: Created model with 45 nodes on GPU 0.
|
||||
08/16/2016 03:02:27: Created model with 45 nodes on GPU 0.
|
||||
|
||||
05/13/2016 08:18:26: Training criterion node(s):
|
||||
05/13/2016 08:18:26: CE = CrossEntropyWithSoftmax
|
||||
08/16/2016 03:02:27: Training criterion node(s):
|
||||
08/16/2016 03:02:27: CE = CrossEntropyWithSoftmax
|
||||
|
||||
05/13/2016 08:18:26: Evaluation criterion node(s):
|
||||
|
||||
05/13/2016 08:18:26: Err = ErrorPrediction
|
||||
08/16/2016 03:02:27: Evaluation criterion node(s):
|
||||
08/16/2016 03:02:27: Err = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 77 matrices, 38 are shared as 16, and 39 are not shared.
|
||||
|
||||
0000000000000000: {[Err Gradient[1]] [conv1.c.c.isd Gradient[32 x 1]] [conv1.c.c.m Gradient[32 x 1]] [conv2.c.c.isd Gradient[32 x 1]] [conv2.c.c.m Gradient[32 x 1]] [conv3.c.c.isd Gradient[64 x 1]] [conv3.c.c.m Gradient[64 x 1]] [featOffs Gradient[1 x 1]] [featScaled Gradient[32 x 32 x 3 x *]] [features Gradient[32 x 32 x 3 x *]] [h1.isd Gradient[64 x 1]] [h1.m Gradient[64 x 1]] [labels Gradient[10 x *]] }
|
||||
000000E89AC81140: {[conv3.c.c.sc Value[64 x 1]] }
|
||||
000000E89AC813C0: {[conv2.c.c.sc Value[32 x 1]] }
|
||||
000000E89AC815A0: {[conv2.c.c.b Value[32 x 1]] }
|
||||
000000E89AC81820: {[h1.isd Value[64 x 1]] }
|
||||
000000E89AC81A00: {[OutputNodes.W Value[10 x 64]] }
|
||||
000000E89AC81BE0: {[OutputNodes.b Value[10]] }
|
||||
000000E89AC81F00: {[h1.W Value[64 x 3 x 3 x 64]] }
|
||||
000000E89AC81FA0: {[h1.m Value[64 x 1]] }
|
||||
000000E89AC82180: {[conv2.c.c.m Value[32 x 1]] }
|
||||
000000E89AC822C0: {[conv3.c.c.isd Value[64 x 1]] }
|
||||
000000E89AC82540: {[h1.sc Value[64 x 1]] }
|
||||
000000E89AC825E0: {[conv2.c.c.isd Value[32 x 1]] }
|
||||
000000E89AC82680: {[conv3.c.c.m Value[64 x 1]] }
|
||||
000000E89AC82720: {[h1.b Value[64 x 1]] }
|
||||
000000E89AC82860: {[conv3.c.W Value[64 x 800]] }
|
||||
000000E89AC82AE0: {[conv2.c.W Value[32 x 800]] }
|
||||
000000E89AC82B80: {[conv3.c.c.b Value[64 x 1]] }
|
||||
000000E8A0568140: {[featScaled Value[32 x 32 x 3 x *]] }
|
||||
000000E8A05681E0: {[conv2.c.c.y Gradient[15 x 15 x 32 x *]] [pool2 Value[7 x 7 x 32 x *]] }
|
||||
000000E8A0568280: {[conv2.c.c.sc Gradient[32 x 1]] [conv2.y Gradient[15 x 15 x 32 x *]] }
|
||||
000000E8A0568320: {[conv3.c.c.y Value[7 x 7 x 64 x *]] }
|
||||
000000E8A0568460: {[conv2.c.c.b Gradient[32 x 1]] [conv3.c.c.c Gradient[7 x 7 x 64 x *]] [conv3.y Value[7 x 7 x 64 x *]] }
|
||||
000000E8A05685A0: {[OutputNodes.t Value[10 x *]] [h1.bn Gradient[64 x *]] }
|
||||
000000E8A0568A00: {[Err Value[1]] }
|
||||
000000E8A0568AA0: {[conv2.c.c.y Value[15 x 15 x 32 x *]] }
|
||||
000000E8A0568BE0: {[conv1.c.c.b Gradient[32 x 1]] [conv2.c.c.c Gradient[15 x 15 x 32 x *]] [conv2.y Value[15 x 15 x 32 x *]] }
|
||||
000000E8A0568D20: {[conv3.c.c.b Gradient[64 x 1]] }
|
||||
000000E8A0568DC0: {[conv3.c.c.sc Gradient[64 x 1]] [conv3.y Gradient[7 x 7 x 64 x *]] [h1.t Value[64 x *]] }
|
||||
000000E8A0568E60: {[conv3.c.W Gradient[64 x 800]] [h1.t Gradient[64 x *]] [h1.y Value[64 x *]] }
|
||||
000000E8A0569040: {[conv1.c.c.y Gradient[32 x 32 x 32 x *]] [pool1 Value[15 x 15 x 32 x *]] }
|
||||
000000E8A0569400: {[conv1.c.c.y Value[32 x 32 x 32 x *]] }
|
||||
000000E8A05694A0: {[conv2.c.W Gradient[32 x 800]] [conv3.c.c.c Value[7 x 7 x 64 x *]] }
|
||||
000000E8A0569540: {[OutputNodes.W Gradient[10 x 64]] [OutputNodes.z Gradient[10 x *]] }
|
||||
000000E8A0569680: {[OutputNodes.t Gradient[10 x *]] [pool1 Gradient[15 x 15 x 32 x *]] [pool2 Gradient[7 x 7 x 32 x *]] [pool3 Gradient[3 x 3 x 64 x *]] }
|
||||
000000E8A0569720: {[OutputNodes.b Gradient[10]] }
|
||||
000000E8A05697C0: {[h1.sc Gradient[64 x 1]] [h1.y Gradient[64 x *]] }
|
||||
000000E8A0569860: {[conv1.c.W Gradient[32 x 75]] [conv2.c.c.c Value[15 x 15 x 32 x *]] }
|
||||
000000E8A0569900: {[conv1.c.c.c Gradient[32 x 32 x 32 x *]] [conv1.y Value[32 x 32 x 32 x *]] }
|
||||
000000E8A05699A0: {[CE Gradient[1]] }
|
||||
000000E8A0569A40: {[h1.W Gradient[64 x 3 x 3 x 64]] }
|
||||
000000E8A0569B80: {[conv3.c.c.y Gradient[7 x 7 x 64 x *]] [pool3 Value[3 x 3 x 64 x *]] }
|
||||
000000E8A0569E00: {[h1.bn Value[64 x *]] }
|
||||
000000E8A0569FE0: {[h1.b Gradient[64 x 1]] }
|
||||
000000E8A056A120: {[conv1.c.c.sc Gradient[32 x 1]] [conv1.y Gradient[32 x 32 x 32 x *]] }
|
||||
000000E8A056A3A0: {[CE Value[1]] }
|
||||
000000E8A056A620: {[OutputNodes.z Value[10 x *]] }
|
||||
000000E8A056A760: {[conv1.c.c.c Value[32 x 32 x 32 x *]] }
|
||||
000000E8FC080980: {[featOffs Value[1 x 1]] }
|
||||
000000E8FC0811A0: {[conv1.c.W Value[32 x 75]] }
|
||||
000000E8FC081240: {[conv1.c.c.b Value[32 x 1]] }
|
||||
000000E8FC081740: {[conv1.c.c.sc Value[32 x 1]] }
|
||||
000000E8FC081920: {[labels Value[10 x *]] }
|
||||
000000E8FC081D80: {[features Value[32 x 32 x 3 x *]] }
|
||||
000000E8FC081EC0: {[conv1.c.c.m Value[32 x 1]] }
|
||||
000000E8FC081F60: {[conv1.c.c.isd Value[32 x 1]] }
|
||||
|
||||
05/13/2016 08:18:26: No PreCompute nodes found, skipping PreCompute step.
|
||||
|
||||
05/13/2016 08:18:26: Starting Epoch 1: learning rate per sample = 0.000469 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
|
||||
05/13/2016 08:18:26: Starting minibatch loop.
|
||||
05/13/2016 08:18:35: Finished Epoch[ 1 of 5]: [Training] CE = 2.31451355 * 100; Err = 0.87000000 * 100; totalSamplesSeen = 100; learningRatePerSample = 0.00046874999; epochTime=9.33323s
|
||||
05/13/2016 08:18:36: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv.1'
|
||||
|
||||
05/13/2016 08:18:37: Starting Epoch 2: learning rate per sample = 0.000469 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
|
||||
05/13/2016 08:18:37: Starting minibatch loop.
|
||||
05/13/2016 08:18:37: Finished Epoch[ 2 of 5]: [Training] CE = 2.27380722 * 100; Err = 0.82000000 * 100; totalSamplesSeen = 200; learningRatePerSample = 0.00046874999; epochTime=0.020597s
|
||||
05/13/2016 08:18:37: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv.2'
|
||||
|
||||
05/13/2016 08:18:37: Starting Epoch 3: learning rate per sample = 0.000469 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
|
||||
05/13/2016 08:18:37: Starting minibatch loop.
|
||||
05/13/2016 08:18:37: Finished Epoch[ 3 of 5]: [Training] CE = 2.25248398 * 100; Err = 0.83000000 * 100; totalSamplesSeen = 300; learningRatePerSample = 0.00046874999; epochTime=0.020236s
|
||||
05/13/2016 08:18:37: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv.3'
|
||||
|
||||
05/13/2016 08:18:37: Starting Epoch 4: learning rate per sample = 0.000469 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
|
||||
05/13/2016 08:18:37: Starting minibatch loop.
|
||||
05/13/2016 08:18:37: Finished Epoch[ 4 of 5]: [Training] CE = 2.15781601 * 100; Err = 0.77000000 * 100; totalSamplesSeen = 400; learningRatePerSample = 0.00046874999; epochTime=0.020351s
|
||||
05/13/2016 08:18:37: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv.4'
|
||||
|
||||
05/13/2016 08:18:37: Starting Epoch 5: learning rate per sample = 0.000469 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
|
||||
05/13/2016 08:18:37: Starting minibatch loop.
|
||||
05/13/2016 08:18:37: Finished Epoch[ 5 of 5]: [Training] CE = 2.12939789 * 100; Err = 0.71000000 * 100; totalSamplesSeen = 500; learningRatePerSample = 0.00046874999; epochTime=0.02018s
|
||||
05/13/2016 08:18:37: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv'
|
||||
05/13/2016 08:18:37: CNTKCommandTrainEnd: Train
|
||||
|
||||
05/13/2016 08:18:37: Action "train" complete.
|
||||
{ OutputNodes.W : [10 x 64] (gradient)
|
||||
OutputNodes.z : [10 x *] (gradient) }
|
||||
{ conv2.c.W : [32 x 800] (gradient)
|
||||
conv3.c.c.c : [7 x 7 x 64 x *] }
|
||||
{ conv3.c.W : [64 x 800] (gradient)
|
||||
h1.t : [64 x *] (gradient)
|
||||
h1.y : [64 x *] }
|
||||
{ OutputNodes.t : [10 x *]
|
||||
h1.bn : [64 x *] (gradient) }
|
||||
{ conv1.c.W : [32 x 75] (gradient)
|
||||
conv2.c.c.c : [15 x 15 x 32 x *] }
|
||||
{ conv1.c.c.c : [32 x 32 x 32 x *] (gradient)
|
||||
conv1.y : [32 x 32 x 32 x *] }
|
||||
{ conv2.c.c.y : [15 x 15 x 32 x *] (gradient)
|
||||
pool2 : [7 x 7 x 32 x *] }
|
||||
{ conv2.c.c.sc : [32 x 1] (gradient)
|
||||
conv2.y : [15 x 15 x 32 x *] (gradient) }
|
||||
{ conv3.c.c.sc : [64 x 1] (gradient)
|
||||
conv3.y : [7 x 7 x 64 x *] (gradient)
|
||||
h1.t : [64 x *] }
|
||||
{ conv1.c.c.sc : [32 x 1] (gradient)
|
||||
conv1.y : [32 x 32 x 32 x *] (gradient) }
|
||||
{ conv1.c.c.b : [32 x 1] (gradient)
|
||||
conv2.c.c.c : [15 x 15 x 32 x *] (gradient)
|
||||
conv2.y : [15 x 15 x 32 x *] }
|
||||
{ conv2.c.c.b : [32 x 1] (gradient)
|
||||
conv3.c.c.c : [7 x 7 x 64 x *] (gradient)
|
||||
conv3.y : [7 x 7 x 64 x *] }
|
||||
{ conv3.c.c.y : [7 x 7 x 64 x *] (gradient)
|
||||
pool3 : [3 x 3 x 64 x *] }
|
||||
{ conv1.c.c.y : [32 x 32 x 32 x *] (gradient)
|
||||
pool1 : [15 x 15 x 32 x *] }
|
||||
{ OutputNodes.t : [10 x *] (gradient)
|
||||
pool1 : [15 x 15 x 32 x *] (gradient)
|
||||
pool2 : [7 x 7 x 32 x *] (gradient)
|
||||
pool3 : [3 x 3 x 64 x *] (gradient) }
|
||||
{ h1.sc : [64 x 1] (gradient)
|
||||
h1.y : [64 x *] (gradient) }
|
||||
|
||||
|
||||
05/13/2016 08:18:37: ##############################################################################
|
||||
05/13/2016 08:18:37: # #
|
||||
05/13/2016 08:18:37: # Action "test" #
|
||||
05/13/2016 08:18:37: # #
|
||||
05/13/2016 08:18:37: ##############################################################################
|
||||
08/16/2016 03:02:27: Training 117098 parameters in 14 out of 14 parameter tensors and 32 nodes with gradient:
|
||||
|
||||
08/16/2016 03:02:27: Node 'OutputNodes.W' (LearnableParameter operation) : [10 x 64]
|
||||
08/16/2016 03:02:27: Node 'OutputNodes.b' (LearnableParameter operation) : [10]
|
||||
08/16/2016 03:02:27: Node 'conv1.c.W' (LearnableParameter operation) : [32 x 75]
|
||||
08/16/2016 03:02:27: Node 'conv1.c.c.b' (LearnableParameter operation) : [32 x 1]
|
||||
08/16/2016 03:02:27: Node 'conv1.c.c.sc' (LearnableParameter operation) : [32 x 1]
|
||||
08/16/2016 03:02:27: Node 'conv2.c.W' (LearnableParameter operation) : [32 x 800]
|
||||
08/16/2016 03:02:27: Node 'conv2.c.c.b' (LearnableParameter operation) : [32 x 1]
|
||||
08/16/2016 03:02:27: Node 'conv2.c.c.sc' (LearnableParameter operation) : [32 x 1]
|
||||
08/16/2016 03:02:27: Node 'conv3.c.W' (LearnableParameter operation) : [64 x 800]
|
||||
08/16/2016 03:02:27: Node 'conv3.c.c.b' (LearnableParameter operation) : [64 x 1]
|
||||
08/16/2016 03:02:27: Node 'conv3.c.c.sc' (LearnableParameter operation) : [64 x 1]
|
||||
08/16/2016 03:02:27: Node 'h1.W' (LearnableParameter operation) : [64 x 3 x 3 x 64]
|
||||
08/16/2016 03:02:27: Node 'h1.b' (LearnableParameter operation) : [64 x 1]
|
||||
08/16/2016 03:02:27: Node 'h1.sc' (LearnableParameter operation) : [64 x 1]
|
||||
|
||||
08/16/2016 03:02:27: No PreCompute nodes found, or all already computed. Skipping pre-computation step.
|
||||
|
||||
08/16/2016 03:02:27: Starting Epoch 1: learning rate per sample = 0.000469 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..100] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:02:27: Starting minibatch loop.
|
||||
08/16/2016 03:02:32: Finished Epoch[ 1 of 5]: [Training] CE = 2.26618500 * 100; Err = 0.87000000 * 100; totalSamplesSeen = 100; learningRatePerSample = 0.00046874999; epochTime=5.56244s
|
||||
08/16/2016 03:02:32: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv.1'
|
||||
|
||||
08/16/2016 03:02:32: Starting Epoch 2: learning rate per sample = 0.000469 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [100..200] (first sequence at sample 100), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:02:32: Starting minibatch loop.
|
||||
08/16/2016 03:02:32: Finished Epoch[ 2 of 5]: [Training] CE = 2.24384949 * 100; Err = 0.82000000 * 100; totalSamplesSeen = 200; learningRatePerSample = 0.00046874999; epochTime=0.015922s
|
||||
08/16/2016 03:02:32: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv.2'
|
||||
|
||||
08/16/2016 03:02:32: Starting Epoch 3: learning rate per sample = 0.000469 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [200..300] (first sequence at sample 200), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:02:32: Starting minibatch loop.
|
||||
08/16/2016 03:02:33: Finished Epoch[ 3 of 5]: [Training] CE = 2.20850739 * 100; Err = 0.81000000 * 100; totalSamplesSeen = 300; learningRatePerSample = 0.00046874999; epochTime=0.015231s
|
||||
08/16/2016 03:02:33: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv.3'
|
||||
|
||||
08/16/2016 03:02:33: Starting Epoch 4: learning rate per sample = 0.000469 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
BlockRandomizer::StartEpoch: epoch 3: frames [300..400] (first sequence at sample 300), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:02:33: Starting minibatch loop.
|
||||
08/16/2016 03:02:33: Finished Epoch[ 4 of 5]: [Training] CE = 2.21282410 * 100; Err = 0.85000000 * 100; totalSamplesSeen = 400; learningRatePerSample = 0.00046874999; epochTime=0.015851s
|
||||
08/16/2016 03:02:33: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv.4'
|
||||
|
||||
08/16/2016 03:02:33: Starting Epoch 5: learning rate per sample = 0.000469 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
BlockRandomizer::StartEpoch: epoch 4: frames [400..500] (first sequence at sample 400), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:02:33: Starting minibatch loop.
|
||||
08/16/2016 03:02:33: Finished Epoch[ 5 of 5]: [Training] CE = 2.16235260 * 100; Err = 0.79000000 * 100; totalSamplesSeen = 500; learningRatePerSample = 0.00046874999; epochTime=0.015383s
|
||||
08/16/2016 03:02:33: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_02_BatchNormConv@release_gpu/Models/02_BatchNormConv'
|
||||
08/16/2016 03:02:33: CNTKCommandTrainEnd: Train
|
||||
|
||||
08/16/2016 03:02:33: Action "train" complete.
|
||||
|
||||
|
||||
08/16/2016 03:02:33: ##############################################################################
|
||||
08/16/2016 03:02:33: # #
|
||||
08/16/2016 03:02:33: # Action "test" #
|
||||
08/16/2016 03:02:33: # #
|
||||
08/16/2016 03:02:33: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
|
@ -546,23 +610,23 @@ Validating network. 20 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 32, Kernel: 5 x 5 x 3, Map: 1 x 1 x 32, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c.c.c: using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 32, Kernel: 5 x 5 x 3, Map: 1 x 1 x 32, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 32, Output: 15 x 15 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 32 x 32 x 32, Output: 15 x 15 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 15 x 15 x 32, Kernel: 5 x 5 x 32, Map: 1 x 1 x 32, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c.c.c: using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 15 x 15 x 32, Kernel: 5 x 5 x 32, Map: 1 x 1 x 32, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 7 x 7 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 7 x 7 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 7 x 7 x 32, Output: 7 x 7 x 64, Kernel: 5 x 5 x 32, Map: 1 x 1 x 64, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3.c.c.c: using cuDNN convolution engine for geometry: Input: 7 x 7 x 32, Output: 7 x 7 x 64, Kernel: 5 x 5 x 32, Map: 1 x 1 x 64, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 7 x 7 x 64, Output: 3 x 3 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool3: using cuDNN convolution engine for geometry: Input: 7 x 7 x 64, Output: 3 x 3 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using CNTK batch normalization engine.
|
||||
|
||||
|
|
@ -576,57 +640,14 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 45 matrices, 0 are shared as 0, and 45 are not shared.
|
||||
|
||||
0000000000000000: {[CE Gradient[1]] [Err Gradient[1]] [OutputNodes.W Gradient[10 x 64]] [OutputNodes.b Gradient[10]] [OutputNodes.t Gradient[10 x *1]] [OutputNodes.z Gradient[10 x *1]] [conv1.c.W Gradient[32 x 75]] [conv1.c.c.b Gradient[32 x 1]] [conv1.c.c.c Gradient[32 x 32 x 32 x *1]] [conv1.c.c.isd Gradient[32 x 1]] [conv1.c.c.m Gradient[32 x 1]] [conv1.c.c.sc Gradient[32 x 1]] [conv1.c.c.y Gradient[32 x 32 x 32 x *1]] [conv1.y Gradient[32 x 32 x 32 x *1]] [conv2.c.W Gradient[32 x 800]] [conv2.c.c.b Gradient[32 x 1]] [conv2.c.c.c Gradient[15 x 15 x 32 x *1]] [conv2.c.c.isd Gradient[32 x 1]] [conv2.c.c.m Gradient[32 x 1]] [conv2.c.c.sc Gradient[32 x 1]] [conv2.c.c.y Gradient[15 x 15 x 32 x *1]] [conv2.y Gradient[15 x 15 x 32 x *1]] [conv3.c.W Gradient[64 x 800]] [conv3.c.c.b Gradient[64 x 1]] [conv3.c.c.c Gradient[7 x 7 x 64 x *1]] [conv3.c.c.isd Gradient[64 x 1]] [conv3.c.c.m Gradient[64 x 1]] [conv3.c.c.sc Gradient[64 x 1]] [conv3.c.c.y Gradient[7 x 7 x 64 x *1]] [conv3.y Gradient[7 x 7 x 64 x *1]] [featOffs Gradient[1 x 1]] [featScaled Gradient[32 x 32 x 3 x *1]] [features Gradient[32 x 32 x 3 x *1]] [h1.W Gradient[64 x 3 x 3 x 64]] [h1.b Gradient[64 x 1]] [h1.bn Gradient[64 x *1]] [h1.isd Gradient[64 x 1]] [h1.m Gradient[64 x 1]] [h1.sc Gradient[64 x 1]] [h1.t Gradient[64 x *1]] [h1.y Gradient[64 x *1]] [labels Gradient[10 x *1]] [pool1 Gradient[15 x 15 x 32 x *1]] [pool2 Gradient[7 x 7 x 32 x *1]] [pool3 Gradient[3 x 3 x 64 x *1]] }
|
||||
000000E8A05681E0: {[conv2.c.c.c Value[15 x 15 x 32 x *1]] }
|
||||
000000E8A0568460: {[conv1.c.c.c Value[32 x 32 x 32 x *1]] }
|
||||
000000E8A05685A0: {[conv1.c.c.y Value[32 x 32 x 32 x *1]] }
|
||||
000000E8A0568A00: {[CE Value[1]] }
|
||||
000000E8A0568AA0: {[conv2.y Value[15 x 15 x 32 x *1]] }
|
||||
000000E8A0568B40: {[h1.y Value[64 x *1]] }
|
||||
000000E8A0568D20: {[featScaled Value[32 x 32 x 3 x *1]] }
|
||||
000000E8A0568DC0: {[pool1 Value[15 x 15 x 32 x *1]] }
|
||||
000000E8A05694A0: {[conv1.y Value[32 x 32 x 32 x *1]] }
|
||||
000000E8A0569540: {[pool3 Value[3 x 3 x 64 x *1]] }
|
||||
000000E8A0569680: {[OutputNodes.t Value[10 x *1]] }
|
||||
000000E8A0569720: {[OutputNodes.z Value[10 x *1]] }
|
||||
000000E8A05697C0: {[conv3.y Value[7 x 7 x 64 x *1]] }
|
||||
000000E8A05699A0: {[conv3.c.c.y Value[7 x 7 x 64 x *1]] }
|
||||
000000E8A0569D60: {[conv2.c.c.y Value[15 x 15 x 32 x *1]] }
|
||||
000000E8A0569E00: {[h1.t Value[64 x *1]] }
|
||||
000000E8A0569F40: {[conv3.c.c.c Value[7 x 7 x 64 x *1]] }
|
||||
000000E8A056A080: {[Err Value[1]] }
|
||||
000000E8A056A3A0: {[pool2 Value[7 x 7 x 32 x *1]] }
|
||||
000000E8A056A620: {[h1.bn Value[64 x *1]] }
|
||||
000000E8A16A32D0: {[h1.sc Value[64 x 1]] }
|
||||
000000E8A16A3870: {[conv2.c.c.b Value[32 x 1]] }
|
||||
000000E8A16A3C30: {[conv1.c.c.isd Value[32 x 1]] }
|
||||
000000E8A16A3CD0: {[conv2.c.c.sc Value[32 x 1]] }
|
||||
000000E8A16A3E10: {[conv3.c.c.b Value[64 x 1]] }
|
||||
000000E8A16A3F50: {[conv1.c.c.b Value[32 x 1]] }
|
||||
000000E8A16A4090: {[conv2.c.c.isd Value[32 x 1]] }
|
||||
000000E8A16A4310: {[conv3.c.c.sc Value[64 x 1]] }
|
||||
000000E8A16A4630: {[conv1.c.c.sc Value[32 x 1]] }
|
||||
000000E8A16A46D0: {[conv1.c.W Value[32 x 75]] }
|
||||
000000E8A16A4A90: {[conv1.c.c.m Value[32 x 1]] }
|
||||
000000E8A16A4B30: {[conv3.c.W Value[64 x 800]] }
|
||||
000000E8A16A4EF0: {[conv2.c.W Value[32 x 800]] }
|
||||
000000E8A16A4F90: {[conv3.c.c.m Value[64 x 1]] }
|
||||
000000E8A16A5030: {[featOffs Value[1 x 1]] }
|
||||
000000E8A16A50D0: {[conv3.c.c.isd Value[64 x 1]] }
|
||||
000000E8A16A5350: {[conv2.c.c.m Value[32 x 1]] }
|
||||
000000E8A16A53F0: {[features Value[32 x 32 x 3 x *1]] }
|
||||
000000E8A16A5530: {[h1.b Value[64 x 1]] }
|
||||
000000E8A16A57B0: {[h1.isd Value[64 x 1]] }
|
||||
000000E8A16A58F0: {[h1.m Value[64 x 1]] }
|
||||
000000E8A16A5CB0: {[labels Value[10 x *1]] }
|
||||
000000E8A16A6110: {[OutputNodes.W Value[10 x 64]] }
|
||||
000000E8A16A61B0: {[OutputNodes.b Value[10]] }
|
||||
000000E8A16A6930: {[h1.W Value[64 x 3 x 3 x 64]] }
|
||||
|
||||
05/13/2016 08:18:52: Final Results: Minibatch[1-625]: Err = 0.84580000 * 10000; CE = 2.27296712 * 10000; perplexity = 9.70816338
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
08/16/2016 03:02:35: Minibatch[1-500]: Err = 0.87962500 * 8000; CE = 2.28452046 * 8000
|
||||
08/16/2016 03:02:35: Minibatch[501-625]: Err = 0.88300000 * 2000; CE = 2.28575908 * 2000
|
||||
08/16/2016 03:02:35: Final Results: Minibatch[1-625]: Err = 0.88030000 * 10000; CE = 2.28476819 * 10000; perplexity = 9.82340875
|
||||
|
||||
05/13/2016 08:18:52: Action "test" complete.
|
||||
08/16/2016 03:02:35: Action "test" complete.
|
||||
|
||||
05/13/2016 08:18:52: __COMPLETED__
|
||||
08/16/2016 03:02:35: __COMPLETED__
|
||||
Различия файлов скрыты, потому что одна или несколько строк слишком длинны
Различия файлов скрыты, потому что одна или несколько строк слишком длинны
Различия файлов скрыты, потому что одна или несколько строк слишком длинны
Различия файлов скрыты, потому что одна или несколько строк слишком длинны
|
|
@ -1,49 +1,62 @@
|
|||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/Config/05_ConvLocal.cntk currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10 OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=5]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 264172964 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal.cntk currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10 OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=5]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 13 2016 14:50:25
|
||||
Last modified date: Thu May 12 14:00:37 2016
|
||||
Built time: Aug 16 2016 09:41:56
|
||||
Last modified date: Fri Aug 12 07:32:43 2016
|
||||
Build type: release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: no
|
||||
Math lib: acml
|
||||
Math lib: mkl
|
||||
CUDA_PATH: /usr/local/cuda-7.5
|
||||
CUB_PATH: /usr/local/cub-1.4.1
|
||||
CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
Built by philly on d8dc82703b0f
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by philly on f67b30a647de
|
||||
Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to /tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData
|
||||
05/13/2016 15:11:10: Redirecting stderr to file -_Train_Test.log
|
||||
05/13/2016 15:11:10: -------------------------------------------------------------------
|
||||
05/13/2016 15:11:10: Build info:
|
||||
Changed current directory to /tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData
|
||||
08/16/2016 10:51:22: Redirecting stderr to file -_Train_Test.log
|
||||
08/16/2016 10:51:22: -------------------------------------------------------------------
|
||||
08/16/2016 10:51:22: Build info:
|
||||
|
||||
05/13/2016 15:11:10: Built time: May 13 2016 14:50:25
|
||||
05/13/2016 15:11:10: Last modified date: Thu May 12 14:00:37 2016
|
||||
05/13/2016 15:11:10: Build type: release
|
||||
05/13/2016 15:11:10: Build target: GPU
|
||||
05/13/2016 15:11:10: With 1bit-SGD: no
|
||||
05/13/2016 15:11:10: Math lib: acml
|
||||
05/13/2016 15:11:10: CUDA_PATH: /usr/local/cuda-7.5
|
||||
05/13/2016 15:11:10: CUB_PATH: /usr/local/cub-1.4.1
|
||||
05/13/2016 15:11:10: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
05/13/2016 15:11:10: Build Branch: HEAD
|
||||
05/13/2016 15:11:10: Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
05/13/2016 15:11:10: Built by philly on d8dc82703b0f
|
||||
05/13/2016 15:11:10: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
05/13/2016 15:11:10: -------------------------------------------------------------------
|
||||
08/16/2016 10:51:22: Built time: Aug 16 2016 09:41:56
|
||||
08/16/2016 10:51:22: Last modified date: Fri Aug 12 07:32:43 2016
|
||||
08/16/2016 10:51:22: Build type: release
|
||||
08/16/2016 10:51:22: Build target: GPU
|
||||
08/16/2016 10:51:22: With 1bit-SGD: no
|
||||
08/16/2016 10:51:22: Math lib: mkl
|
||||
08/16/2016 10:51:22: CUDA_PATH: /usr/local/cuda-7.5
|
||||
08/16/2016 10:51:22: CUB_PATH: /usr/local/cub-1.4.1
|
||||
08/16/2016 10:51:22: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
08/16/2016 10:51:22: Build Branch: HEAD
|
||||
08/16/2016 10:51:22: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 10:51:22: Built by philly on f67b30a647de
|
||||
08/16/2016 10:51:22: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
08/16/2016 10:51:22: -------------------------------------------------------------------
|
||||
08/16/2016 10:51:23: -------------------------------------------------------------------
|
||||
08/16/2016 10:51:23: GPU info:
|
||||
|
||||
05/13/2016 15:11:10: Running on localhost at 2016/05/13 15:11:10
|
||||
05/13/2016 15:11:10: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/Config/05_ConvLocal.cntk currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10 OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=5]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
08/16/2016 10:51:23: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:51:23: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:51:23: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:51:23: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:51:23: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 10:51:23: Running on localhost at 2016/08/16 10:51:23
|
||||
08/16/2016 10:51:23: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal.cntk currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10 OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=5]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
|
||||
|
||||
|
||||
05/13/2016 15:11:10: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 15:11:10: RootDir = "."
|
||||
08/16/2016 10:51:23: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:51:23: RootDir = "."
|
||||
ConfigDir = "$RootDir$"
|
||||
DataDir = "$RootDir$"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
|
|
@ -52,7 +65,6 @@ ndlMacros = "$ConfigDir$/Macros.ndl"
|
|||
precision = "float"
|
||||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
prefetch = "true"
|
||||
command = Train:Test
|
||||
modelPath = "$ModelDir$/05_ConvLocal"
|
||||
stderr = "$OutputDir$/05_ConvLocal"
|
||||
|
|
@ -84,7 +96,7 @@ Train = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
Test = [
|
||||
action = "test"
|
||||
|
|
@ -102,41 +114,40 @@ Test = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10
|
||||
OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu
|
||||
currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10
|
||||
OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Train=[SGD=[maxEpochs=5]]
|
||||
Train=[SGD=[epochSize=100]]
|
||||
stderr=-
|
||||
|
||||
05/13/2016 15:11:10: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:51:23: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 15:11:10: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 15:11:10: RootDir = "."
|
||||
08/16/2016 10:51:23: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:51:23: RootDir = "."
|
||||
ConfigDir = "."
|
||||
DataDir = "."
|
||||
OutputDir = "./Output"
|
||||
ModelDir = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/Models"
|
||||
ndlMacros = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/Macros.ndl"
|
||||
ModelDir = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/Models"
|
||||
ndlMacros = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/Macros.ndl"
|
||||
precision = "float"
|
||||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
prefetch = "true"
|
||||
command = Train:Test
|
||||
modelPath = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal"
|
||||
stderr = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/05_ConvLocal"
|
||||
modelPath = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal"
|
||||
stderr = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/05_ConvLocal"
|
||||
traceLevel = 1
|
||||
numMBsToShowResult = 50
|
||||
Train = [
|
||||
action = "train"
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal.ndl"
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 49984
|
||||
|
|
@ -148,7 +159,7 @@ Train = [
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData/Train_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData/Train_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -159,14 +170,14 @@ Train = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
Test = [
|
||||
action = "test"
|
||||
minibatchSize = 16
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData/Test_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData/Test_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -177,44 +188,43 @@ Test = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10
|
||||
OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu
|
||||
currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10
|
||||
OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Train=[SGD=[maxEpochs=5]]
|
||||
Train=[SGD=[epochSize=100]]
|
||||
stderr=-
|
||||
|
||||
05/13/2016 15:11:10: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:51:23: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 15:11:10: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:51:23: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: 05_ConvLocal.cntk:command=Train:Test
|
||||
configparameters: 05_ConvLocal.cntk:ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10
|
||||
configparameters: 05_ConvLocal.cntk:currentDirectory=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData
|
||||
configparameters: 05_ConvLocal.cntk:DataDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData
|
||||
configparameters: 05_ConvLocal.cntk:ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10
|
||||
configparameters: 05_ConvLocal.cntk:currentDirectory=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData
|
||||
configparameters: 05_ConvLocal.cntk:DataDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData
|
||||
configparameters: 05_ConvLocal.cntk:deviceId=0
|
||||
configparameters: 05_ConvLocal.cntk:imageLayout=cudnn
|
||||
configparameters: 05_ConvLocal.cntk:ModelDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/Models
|
||||
configparameters: 05_ConvLocal.cntk:modelPath=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal
|
||||
configparameters: 05_ConvLocal.cntk:ndlMacros=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/Macros.ndl
|
||||
configparameters: 05_ConvLocal.cntk:ModelDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/Models
|
||||
configparameters: 05_ConvLocal.cntk:modelPath=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal
|
||||
configparameters: 05_ConvLocal.cntk:ndlMacros=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/Macros.ndl
|
||||
configparameters: 05_ConvLocal.cntk:numMBsToShowResult=50
|
||||
configparameters: 05_ConvLocal.cntk:OutputDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu
|
||||
configparameters: 05_ConvLocal.cntk:OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu
|
||||
configparameters: 05_ConvLocal.cntk:precision=float
|
||||
configparameters: 05_ConvLocal.cntk:prefetch=true
|
||||
configparameters: 05_ConvLocal.cntk:RootDir=.
|
||||
configparameters: 05_ConvLocal.cntk:RunDir=/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu
|
||||
configparameters: 05_ConvLocal.cntk:RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu
|
||||
configparameters: 05_ConvLocal.cntk:stderr=-
|
||||
configparameters: 05_ConvLocal.cntk:Test=[
|
||||
action = "test"
|
||||
minibatchSize = 16
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData/Test_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData/Test_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -225,7 +235,7 @@ configparameters: 05_ConvLocal.cntk:Test=[
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
configparameters: 05_ConvLocal.cntk:timestamping=true
|
||||
|
|
@ -233,7 +243,7 @@ configparameters: 05_ConvLocal.cntk:traceLevel=1
|
|||
configparameters: 05_ConvLocal.cntk:Train=[
|
||||
action = "train"
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/../../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal.ndl"
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal/../../../../../../../Examples/Image/Miscellaneous/CIFAR-10/05_ConvLocal.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 49984
|
||||
|
|
@ -245,7 +255,7 @@ configparameters: 05_ConvLocal.cntk:Train=[
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData/Train_cntk_text.txt"
|
||||
file = "/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/TestData/Train_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -256,27 +266,51 @@ configparameters: 05_ConvLocal.cntk:Train=[
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
] [SGD=[maxEpochs=5]] [SGD=[epochSize=100]]
|
||||
|
||||
05/13/2016 15:11:10: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/13/2016 15:11:10: Commands: Train Test
|
||||
05/13/2016 15:11:10: Precision = "float"
|
||||
05/13/2016 15:11:10: CNTKModelPath: /tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal
|
||||
05/13/2016 15:11:10: CNTKCommandTrainInfo: Train : 5
|
||||
05/13/2016 15:11:10: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 5
|
||||
08/16/2016 10:51:23: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:51:23: Commands: Train Test
|
||||
08/16/2016 10:51:23: Precision = "float"
|
||||
08/16/2016 10:51:23: CNTKModelPath: /tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal
|
||||
08/16/2016 10:51:23: CNTKCommandTrainInfo: Train : 5
|
||||
08/16/2016 10:51:23: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 5
|
||||
|
||||
05/13/2016 15:11:10: ##############################################################################
|
||||
05/13/2016 15:11:10: # #
|
||||
05/13/2016 15:11:10: # Action "train" #
|
||||
05/13/2016 15:11:10: # #
|
||||
05/13/2016 15:11:10: ##############################################################################
|
||||
08/16/2016 10:51:23: ##############################################################################
|
||||
08/16/2016 10:51:23: # #
|
||||
08/16/2016 10:51:23: # Action "train" #
|
||||
08/16/2016 10:51:23: # #
|
||||
08/16/2016 10:51:23: ##############################################################################
|
||||
|
||||
05/13/2016 15:11:10: CNTKCommandTrainBegin: Train
|
||||
08/16/2016 10:51:23: CNTKCommandTrainBegin: Train
|
||||
NDLBuilder Using GPU 0
|
||||
|
||||
05/13/2016 15:11:10: Creating virgin network.
|
||||
08/16/2016 10:51:23: Creating virgin network.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.000000.
|
||||
Node 'conv1.W' (LearnableParameter operation): Initializing Parameter[64 x 75] <- 0.000000.
|
||||
Node 'conv1.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv2.W' (LearnableParameter operation): Initializing Parameter[64 x 1600] <- 0.000000.
|
||||
Node 'conv2.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv3.W' (LearnableParameter operation): Initializing Parameter[3136 x 576] <- 0.000000.
|
||||
Node 'conv3.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv4.W' (LearnableParameter operation): Initializing Parameter[1568 x 576] <- 0.000000.
|
||||
Node 'conv4.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 32] <- 0.000000.
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializing Parameter[10 x 7 x 7 x 32] <- 0.000000.
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[10] <- 0.000000.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 128.000000.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 128.000000.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 128.000000.
|
||||
Node 'conv1.W' (LearnableParameter operation): Initializing Parameter[64 x 75] <- gaussian(seed=1, range=0.023094*0.004300, onCPU=false).
|
||||
SetGaussianRandomValue (GPU): creating curand object with seed 1, sizeof(ElemType)==4
|
||||
Node 'conv1.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv2.W' (LearnableParameter operation): Initializing Parameter[64 x 1600] <- gaussian(seed=2, range=0.005000*1.414000, onCPU=false).
|
||||
Node 'conv2.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv3.W' (LearnableParameter operation): Initializing Parameter[3136 x 576] <- gaussian(seed=3, range=0.008333*1.414000, onCPU=false).
|
||||
Node 'conv3.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv4.W' (LearnableParameter operation): Initializing Parameter[1568 x 576] <- gaussian(seed=4, range=0.008333*1.414000, onCPU=false).
|
||||
Node 'conv4.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 32] <- 0.000000.
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializing Parameter[10 x 7 x 7 x 32] <- gaussian(seed=5, range=0.005051*1.500000, onCPU=false).
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[10] <- 0.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -326,120 +360,132 @@ Validating network. 19 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 64, Kernel: 5 x 5 x 3, Map: 1 x 1 x 64, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c: using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 64, Kernel: 5 x 5 x 3, Map: 1 x 1 x 64, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 64, Output: 15 x 15 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 32 x 32 x 64, Output: 15 x 15 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 64, Output: 15 x 15 x 64, Kernel: 5 x 5 x 64, Map: 1 x 1 x 64, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c: using cuDNN convolution engine for geometry: Input: 15 x 15 x 64, Output: 15 x 15 x 64, Kernel: 5 x 5 x 64, Map: 1 x 1 x 64, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 64, Output: 7 x 7 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 15 x 15 x 64, Output: 7 x 7 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using reference convolution engine for geometry: Input: 7 x 7 x 64, Output: 7 x 7 x 64, Kernel: 3 x 3 x 64, Map: 64, Stride: 1 x 1 x 64, Sharing: (0, 0, 0), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3.c: using reference convolution engine for geometry: Input: 7 x 7 x 64, Output: 7 x 7 x 64, Kernel: 3 x 3 x 64, Map: 64, Stride: 1 x 1 x 64, Sharing: (0, 0, 0), AutoPad: (1, 1, 1), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.
|
||||
|
||||
Using reference convolution engine for geometry: Input: 7 x 7 x 64, Output: 7 x 7 x 32, Kernel: 3 x 3 x 64, Map: 32, Stride: 1 x 1 x 64, Sharing: (0, 0, 0), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv4.c: using reference convolution engine for geometry: Input: 7 x 7 x 64, Output: 7 x 7 x 32, Kernel: 3 x 3 x 64, Map: 32, Stride: 1 x 1 x 64, Sharing: (0, 0, 0), AutoPad: (1, 1, 1), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.
|
||||
|
||||
|
||||
13 out of 32 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
05/13/2016 15:11:11: Created model with 32 nodes on GPU 0.
|
||||
08/16/2016 10:51:24: Created model with 32 nodes on GPU 0.
|
||||
|
||||
05/13/2016 15:11:11: Training criterion node(s):
|
||||
05/13/2016 15:11:11: CE = CrossEntropyWithSoftmax
|
||||
08/16/2016 10:51:24: Training criterion node(s):
|
||||
08/16/2016 10:51:24: CE = CrossEntropyWithSoftmax
|
||||
|
||||
05/13/2016 15:11:11: Evaluation criterion node(s):
|
||||
|
||||
05/13/2016 15:11:11: Err = ErrorPrediction
|
||||
08/16/2016 10:51:24: Evaluation criterion node(s):
|
||||
08/16/2016 10:51:24: Err = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 59 matrices, 35 are shared as 16, and 24 are not shared.
|
||||
|
||||
(nil): {[Err Gradient[1]] [featOffs Gradient[1 x 1]] [featScaled Gradient[32 x 32 x 3 x *]] [features Gradient[32 x 32 x 3 x *]] [labels Gradient[10 x *]] }
|
||||
0x116d9a8: {[features Value[32 x 32 x 3 x *]] }
|
||||
0x1a7f8b8: {[labels Value[10 x *]] }
|
||||
0x1a80378: {[conv1.W Value[64 x 75]] }
|
||||
0x1a811e8: {[conv1.b Value[1 x 1 x 64]] }
|
||||
0x1a823c8: {[conv2.W Value[64 x 1600]] }
|
||||
0x1a82ad8: {[conv2.b Value[1 x 1 x 64]] }
|
||||
0x1a85518: {[conv3.W Value[3136 x 576]] }
|
||||
0x1a86638: {[conv3.b Value[1 x 1 x 64]] }
|
||||
0x1a877e8: {[conv4.W Value[1568 x 576]] }
|
||||
0x1a89748: {[conv4.b Value[1 x 1 x 32]] }
|
||||
0x1a8bb58: {[OutputNodes.W Value[10 x 7 x 7 x 32]] }
|
||||
0x1a8c538: {[OutputNodes.b Value[10]] }
|
||||
0x1a9f838: {[featOffs Value[1 x 1]] }
|
||||
0x22a7f78: {[Err Value[1]] }
|
||||
0x64a9888: {[conv1.c Gradient[32 x 32 x 64 x *]] [conv1.y Value[32 x 32 x 64 x *]] }
|
||||
0x64a9a48: {[conv1.p Gradient[32 x 32 x 64 x *]] [pool1 Value[15 x 15 x 64 x *]] }
|
||||
0x64a9c08: {[conv2.c Value[15 x 15 x 64 x *]] }
|
||||
0x64a9dc8: {[conv1.b Gradient[1 x 1 x 64]] [conv1.y Gradient[32 x 32 x 64 x *]] }
|
||||
0x64a9f88: {[conv2.W Gradient[64 x 1600]] [conv2.p Value[15 x 15 x 64 x *]] }
|
||||
0x64aa148: {[conv2.c Gradient[15 x 15 x 64 x *]] [conv2.y Value[15 x 15 x 64 x *]] }
|
||||
0x64aa308: {[conv2.p Gradient[15 x 15 x 64 x *]] [pool1 Gradient[15 x 15 x 64 x *]] [pool2 Value[7 x 7 x 64 x *]] }
|
||||
0x64aa4c8: {[conv3.c Value[7 x 7 x 64 x *]] }
|
||||
0x64aa688: {[conv2.b Gradient[1 x 1 x 64]] [conv2.y Gradient[15 x 15 x 64 x *]] }
|
||||
0x64aa848: {[conv3.W Gradient[3136 x 576]] [conv3.p Value[7 x 7 x 64 x *]] }
|
||||
0x64aaa08: {[conv3.c Gradient[7 x 7 x 64 x *]] [conv3.y Value[7 x 7 x 64 x *]] }
|
||||
0x64aabc8: {[conv4.c Value[7 x 7 x 32 x *]] }
|
||||
0x64aad88: {[conv3.p Gradient[7 x 7 x 64 x *]] [pool2 Gradient[7 x 7 x 64 x *]] }
|
||||
0x64aaf48: {[conv4.W Gradient[1568 x 576]] [conv4.p Value[7 x 7 x 32 x *]] }
|
||||
0x64ab108: {[conv4.c Gradient[7 x 7 x 32 x *]] [conv4.y Value[7 x 7 x 32 x *]] }
|
||||
0x64ab2c8: {[OutputNodes.t Value[10 x *]] [conv3.b Gradient[1 x 1 x 64]] [conv3.y Gradient[7 x 7 x 64 x *]] [conv4.p Gradient[7 x 7 x 32 x *]] }
|
||||
0x670e7f8: {[OutputNodes.z Value[10 x *]] }
|
||||
0x675a228: {[featScaled Value[32 x 32 x 3 x *]] }
|
||||
0x675ada8: {[conv1.W Gradient[64 x 75]] [conv1.p Value[32 x 32 x 64 x *]] }
|
||||
0x675b248: {[CE Value[1]] }
|
||||
0x675b4c8: {[conv1.c Value[32 x 32 x 64 x *]] }
|
||||
0x67cd168: {[CE Gradient[1]] }
|
||||
0x67cd328: {[OutputNodes.W Gradient[10 x 7 x 7 x 32]] [OutputNodes.z Gradient[10 x *]] }
|
||||
0x67cd4e8: {[OutputNodes.t Gradient[10 x *]] }
|
||||
0x67cd6a8: {[OutputNodes.b Gradient[10]] }
|
||||
0x67cd868: {[conv4.b Gradient[1 x 1 x 32]] [conv4.y Gradient[7 x 7 x 32 x *]] }
|
||||
|
||||
05/13/2016 15:11:11: No PreCompute nodes found, skipping PreCompute step.
|
||||
|
||||
05/13/2016 15:11:11: Starting Epoch 1: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
|
||||
05/13/2016 15:11:11: Starting minibatch loop.
|
||||
05/13/2016 15:11:14: Finished Epoch[ 1 of 5]: [Training] CE = 2.30261719 * 100; Err = 0.90000000 * 100; totalSamplesSeen = 100; learningRatePerSample = 0.00015625; epochTime=3.71954s
|
||||
05/13/2016 15:11:14: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal.1'
|
||||
|
||||
05/13/2016 15:11:15: Starting Epoch 2: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
|
||||
05/13/2016 15:11:15: Starting minibatch loop.
|
||||
05/13/2016 15:11:15: Finished Epoch[ 2 of 5]: [Training] CE = 2.30258881 * 100; Err = 0.94000000 * 100; totalSamplesSeen = 200; learningRatePerSample = 0.00015625; epochTime=0.184562s
|
||||
05/13/2016 15:11:15: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal.2'
|
||||
|
||||
05/13/2016 15:11:15: Starting Epoch 3: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
|
||||
05/13/2016 15:11:15: Starting minibatch loop.
|
||||
05/13/2016 15:11:15: Finished Epoch[ 3 of 5]: [Training] CE = 2.30256729 * 100; Err = 0.92000000 * 100; totalSamplesSeen = 300; learningRatePerSample = 0.00015625; epochTime=0.187378s
|
||||
05/13/2016 15:11:15: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal.3'
|
||||
|
||||
05/13/2016 15:11:15: Starting Epoch 4: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
|
||||
05/13/2016 15:11:15: Starting minibatch loop.
|
||||
05/13/2016 15:11:16: Finished Epoch[ 4 of 5]: [Training] CE = 2.30254120 * 100; Err = 0.89000000 * 100; totalSamplesSeen = 400; learningRatePerSample = 0.00015625; epochTime=0.181785s
|
||||
05/13/2016 15:11:16: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal.4'
|
||||
|
||||
05/13/2016 15:11:16: Starting Epoch 5: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
|
||||
05/13/2016 15:11:16: Starting minibatch loop.
|
||||
05/13/2016 15:11:16: Finished Epoch[ 5 of 5]: [Training] CE = 2.30259888 * 100; Err = 0.92000000 * 100; totalSamplesSeen = 500; learningRatePerSample = 0.00015625; epochTime=0.186221s
|
||||
05/13/2016 15:11:16: SGD: Saving checkpoint model '/tmp/cntk-test-20160513145544.775982/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal'
|
||||
05/13/2016 15:11:16: CNTKCommandTrainEnd: Train
|
||||
|
||||
05/13/2016 15:11:16: Action "train" complete.
|
||||
{ OutputNodes.W : [10 x 7 x 7 x 32] (gradient)
|
||||
OutputNodes.z : [10 x *] (gradient) }
|
||||
{ conv4.b : [1 x 1 x 32] (gradient)
|
||||
conv4.y : [7 x 7 x 32 x *] (gradient) }
|
||||
{ conv1.W : [64 x 75] (gradient)
|
||||
conv1.p : [32 x 32 x 64 x *] }
|
||||
{ conv1.c : [32 x 32 x 64 x *] (gradient)
|
||||
conv1.y : [32 x 32 x 64 x *] }
|
||||
{ conv1.p : [32 x 32 x 64 x *] (gradient)
|
||||
pool1 : [15 x 15 x 64 x *] }
|
||||
{ conv1.b : [1 x 1 x 64] (gradient)
|
||||
conv1.y : [32 x 32 x 64 x *] (gradient) }
|
||||
{ conv2.W : [64 x 1600] (gradient)
|
||||
conv2.p : [15 x 15 x 64 x *] }
|
||||
{ conv2.c : [15 x 15 x 64 x *] (gradient)
|
||||
conv2.y : [15 x 15 x 64 x *] }
|
||||
{ conv2.p : [15 x 15 x 64 x *] (gradient)
|
||||
pool1 : [15 x 15 x 64 x *] (gradient)
|
||||
pool2 : [7 x 7 x 64 x *] }
|
||||
{ conv2.b : [1 x 1 x 64] (gradient)
|
||||
conv2.y : [15 x 15 x 64 x *] (gradient) }
|
||||
{ conv3.W : [3136 x 576] (gradient)
|
||||
conv3.p : [7 x 7 x 64 x *] }
|
||||
{ conv3.c : [7 x 7 x 64 x *] (gradient)
|
||||
conv3.y : [7 x 7 x 64 x *] }
|
||||
{ conv3.p : [7 x 7 x 64 x *] (gradient)
|
||||
pool2 : [7 x 7 x 64 x *] (gradient) }
|
||||
{ conv4.W : [1568 x 576] (gradient)
|
||||
conv4.p : [7 x 7 x 32 x *] }
|
||||
{ conv4.c : [7 x 7 x 32 x *] (gradient)
|
||||
conv4.y : [7 x 7 x 32 x *] }
|
||||
{ OutputNodes.t : [10 x *]
|
||||
conv3.b : [1 x 1 x 64] (gradient)
|
||||
conv3.y : [7 x 7 x 64 x *] (gradient)
|
||||
conv4.p : [7 x 7 x 32 x *] (gradient) }
|
||||
|
||||
|
||||
05/13/2016 15:11:16: ##############################################################################
|
||||
05/13/2016 15:11:16: # #
|
||||
05/13/2016 15:11:16: # Action "test" #
|
||||
05/13/2016 15:11:16: # #
|
||||
05/13/2016 15:11:16: ##############################################################################
|
||||
08/16/2016 10:51:24: Training 2832618 parameters in 10 out of 10 parameter tensors and 27 nodes with gradient:
|
||||
|
||||
08/16/2016 10:51:24: Node 'OutputNodes.W' (LearnableParameter operation) : [10 x 7 x 7 x 32]
|
||||
08/16/2016 10:51:24: Node 'OutputNodes.b' (LearnableParameter operation) : [10]
|
||||
08/16/2016 10:51:24: Node 'conv1.W' (LearnableParameter operation) : [64 x 75]
|
||||
08/16/2016 10:51:24: Node 'conv1.b' (LearnableParameter operation) : [1 x 1 x 64]
|
||||
08/16/2016 10:51:24: Node 'conv2.W' (LearnableParameter operation) : [64 x 1600]
|
||||
08/16/2016 10:51:24: Node 'conv2.b' (LearnableParameter operation) : [1 x 1 x 64]
|
||||
08/16/2016 10:51:24: Node 'conv3.W' (LearnableParameter operation) : [3136 x 576]
|
||||
08/16/2016 10:51:24: Node 'conv3.b' (LearnableParameter operation) : [1 x 1 x 64]
|
||||
08/16/2016 10:51:24: Node 'conv4.W' (LearnableParameter operation) : [1568 x 576]
|
||||
08/16/2016 10:51:24: Node 'conv4.b' (LearnableParameter operation) : [1 x 1 x 32]
|
||||
|
||||
08/16/2016 10:51:24: No PreCompute nodes found, or all already computed. Skipping pre-computation step.
|
||||
|
||||
08/16/2016 10:51:24: Starting Epoch 1: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..100] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:51:24: Starting minibatch loop.
|
||||
08/16/2016 10:51:28: Finished Epoch[ 1 of 5]: [Training] CE = 2.30258331 * 100; Err = 0.90000000 * 100; totalSamplesSeen = 100; learningRatePerSample = 0.00015625; epochTime=3.83324s
|
||||
08/16/2016 10:51:28: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal.1'
|
||||
|
||||
08/16/2016 10:51:28: Starting Epoch 2: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [100..200] (first sequence at sample 100), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:51:28: Starting minibatch loop.
|
||||
08/16/2016 10:51:28: Finished Epoch[ 2 of 5]: [Training] CE = 2.30260956 * 100; Err = 0.91000000 * 100; totalSamplesSeen = 200; learningRatePerSample = 0.00015625; epochTime=0.190736s
|
||||
08/16/2016 10:51:28: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal.2'
|
||||
|
||||
08/16/2016 10:51:28: Starting Epoch 3: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [200..300] (first sequence at sample 200), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:51:28: Starting minibatch loop.
|
||||
08/16/2016 10:51:28: Finished Epoch[ 3 of 5]: [Training] CE = 2.30259949 * 100; Err = 0.90000000 * 100; totalSamplesSeen = 300; learningRatePerSample = 0.00015625; epochTime=0.190026s
|
||||
08/16/2016 10:51:29: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal.3'
|
||||
|
||||
08/16/2016 10:51:29: Starting Epoch 4: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 3: frames [300..400] (first sequence at sample 300), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:51:29: Starting minibatch loop.
|
||||
08/16/2016 10:51:29: Finished Epoch[ 4 of 5]: [Training] CE = 2.30261490 * 100; Err = 0.92000000 * 100; totalSamplesSeen = 400; learningRatePerSample = 0.00015625; epochTime=0.186068s
|
||||
08/16/2016 10:51:29: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal.4'
|
||||
|
||||
08/16/2016 10:51:29: Starting Epoch 5: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 4: frames [400..500] (first sequence at sample 400), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:51:29: Starting minibatch loop.
|
||||
08/16/2016 10:51:29: Finished Epoch[ 5 of 5]: [Training] CE = 2.30255005 * 100; Err = 0.92000000 * 100; totalSamplesSeen = 500; learningRatePerSample = 0.00015625; epochTime=0.187202s
|
||||
08/16/2016 10:51:29: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Image/Miscellaneous/CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal'
|
||||
08/16/2016 10:51:30: CNTKCommandTrainEnd: Train
|
||||
|
||||
08/16/2016 10:51:30: Action "train" complete.
|
||||
|
||||
|
||||
08/16/2016 10:51:30: ##############################################################################
|
||||
08/16/2016 10:51:30: # #
|
||||
08/16/2016 10:51:30: # Action "test" #
|
||||
08/16/2016 10:51:30: # #
|
||||
08/16/2016 10:51:30: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
|
@ -490,17 +536,17 @@ Validating network. 19 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 64, Kernel: 5 x 5 x 3, Map: 1 x 1 x 64, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c: using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 64, Kernel: 5 x 5 x 3, Map: 1 x 1 x 64, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 64, Output: 15 x 15 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 32 x 32 x 64, Output: 15 x 15 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 64, Output: 15 x 15 x 64, Kernel: 5 x 5 x 64, Map: 1 x 1 x 64, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c: using cuDNN convolution engine for geometry: Input: 15 x 15 x 64, Output: 15 x 15 x 64, Kernel: 5 x 5 x 64, Map: 1 x 1 x 64, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 64, Output: 7 x 7 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 15 x 15 x 64, Output: 7 x 7 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using reference convolution engine for geometry: Input: 7 x 7 x 64, Output: 7 x 7 x 64, Kernel: 3 x 3 x 64, Map: 64, Stride: 1 x 1 x 64, Sharing: (0, 0, 0), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3.c: using reference convolution engine for geometry: Input: 7 x 7 x 64, Output: 7 x 7 x 64, Kernel: 3 x 3 x 64, Map: 64, Stride: 1 x 1 x 64, Sharing: (0, 0, 0), AutoPad: (1, 1, 1), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.
|
||||
|
||||
Using reference convolution engine for geometry: Input: 7 x 7 x 64, Output: 7 x 7 x 32, Kernel: 3 x 3 x 64, Map: 32, Stride: 1 x 1 x 64, Sharing: (0, 0, 0), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv4.c: using reference convolution engine for geometry: Input: 7 x 7 x 64, Output: 7 x 7 x 32, Kernel: 3 x 3 x 64, Map: 32, Stride: 1 x 1 x 64, Sharing: (0, 0, 0), AutoPad: (1, 1, 1), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.
|
||||
|
||||
|
||||
13 out of 32 nodes do not share the minibatch layout with the input data.
|
||||
|
|
@ -512,44 +558,25 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 32 matrices, 0 are shared as 0, and 32 are not shared.
|
||||
|
||||
(nil): {[CE Gradient[1]] [Err Gradient[1]] [OutputNodes.W Gradient[10 x 7 x 7 x 32]] [OutputNodes.b Gradient[10]] [OutputNodes.t Gradient[10 x *1]] [OutputNodes.z Gradient[10 x *1]] [conv1.W Gradient[64 x 75]] [conv1.b Gradient[1 x 1 x 64]] [conv1.c Gradient[32 x 32 x 64 x *1]] [conv1.p Gradient[32 x 32 x 64 x *1]] [conv1.y Gradient[32 x 32 x 64 x *1]] [conv2.W Gradient[64 x 1600]] [conv2.b Gradient[1 x 1 x 64]] [conv2.c Gradient[15 x 15 x 64 x *1]] [conv2.p Gradient[15 x 15 x 64 x *1]] [conv2.y Gradient[15 x 15 x 64 x *1]] [conv3.W Gradient[3136 x 576]] [conv3.b Gradient[1 x 1 x 64]] [conv3.c Gradient[7 x 7 x 64 x *1]] [conv3.p Gradient[7 x 7 x 64 x *1]] [conv3.y Gradient[7 x 7 x 64 x *1]] [conv4.W Gradient[1568 x 576]] [conv4.b Gradient[1 x 1 x 32]] [conv4.c Gradient[7 x 7 x 32 x *1]] [conv4.p Gradient[7 x 7 x 32 x *1]] [conv4.y Gradient[7 x 7 x 32 x *1]] [featOffs Gradient[1 x 1]] [featScaled Gradient[32 x 32 x 3 x *1]] [features Gradient[32 x 32 x 3 x *1]] [labels Gradient[10 x *1]] [pool1 Gradient[15 x 15 x 64 x *1]] [pool2 Gradient[7 x 7 x 64 x *1]] }
|
||||
0x7f2a5c2042f8: {[conv1.W Value[64 x 75]] }
|
||||
0x7f2a5c204418: {[conv1.b Value[1 x 1 x 64]] }
|
||||
0x7f2a5c205fe8: {[conv2.b Value[1 x 1 x 64]] }
|
||||
0x7f2a5c206938: {[conv2.W Value[64 x 1600]] }
|
||||
0x7f2a5c2089d8: {[conv3.W Value[3136 x 576]] }
|
||||
0x7f2a5c208d08: {[conv3.b Value[1 x 1 x 64]] }
|
||||
0x7f2a5c20b888: {[conv4.b Value[1 x 1 x 32]] }
|
||||
0x7f2a5c20cd98: {[conv4.W Value[1568 x 576]] }
|
||||
0x7f2a5c20e228: {[featOffs Value[1 x 1]] }
|
||||
0x7f2a5c20eeb8: {[features Value[32 x 32 x 3 x *1]] }
|
||||
0x7f2a5c20fd18: {[labels Value[10 x *1]] }
|
||||
0x7f2a5c210718: {[OutputNodes.b Value[10]] }
|
||||
0x7f2a5c211278: {[OutputNodes.W Value[10 x 7 x 7 x 32]] }
|
||||
0x7f2a5c2287c8: {[Err Value[1]] }
|
||||
0x7f2a5c22af98: {[conv1.c Value[32 x 32 x 64 x *1]] }
|
||||
0x7f2a5c22b318: {[conv1.p Value[32 x 32 x 64 x *1]] }
|
||||
0x7f2a5c27cfd8: {[CE Value[1]] }
|
||||
0x7f2a5c2e8b08: {[featScaled Value[32 x 32 x 3 x *1]] }
|
||||
0x7f2a5c2e9748: {[conv1.y Value[32 x 32 x 64 x *1]] }
|
||||
0x7f2a5c2e9908: {[pool1 Value[15 x 15 x 64 x *1]] }
|
||||
0x7f2a5c2e9ac8: {[conv2.c Value[15 x 15 x 64 x *1]] }
|
||||
0x7f2a5c2ee708: {[conv2.p Value[15 x 15 x 64 x *1]] }
|
||||
0x7f2a5c2ee8c8: {[conv2.y Value[15 x 15 x 64 x *1]] }
|
||||
0x7f2a5c2eea88: {[pool2 Value[7 x 7 x 64 x *1]] }
|
||||
0x7f2a5c2eec48: {[conv3.c Value[7 x 7 x 64 x *1]] }
|
||||
0x7f2a5c2eefc8: {[conv3.p Value[7 x 7 x 64 x *1]] }
|
||||
0x7f2a5c2ef188: {[conv3.y Value[7 x 7 x 64 x *1]] }
|
||||
0x7f2a5c2ef348: {[conv4.c Value[7 x 7 x 32 x *1]] }
|
||||
0x7f2a5c2ef6c8: {[conv4.p Value[7 x 7 x 32 x *1]] }
|
||||
0x7f2a5c2ef888: {[conv4.y Value[7 x 7 x 32 x *1]] }
|
||||
0x7f2a5c2efa48: {[OutputNodes.t Value[10 x *1]] }
|
||||
0x7f2a5c2efc08: {[OutputNodes.z Value[10 x *1]] }
|
||||
|
||||
05/13/2016 15:11:30: Final Results: Minibatch[1-625]: Err = 0.84650000 * 10000; CE = 2.30252428 * 10000; perplexity = 9.99939189
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
08/16/2016 10:51:31: Minibatch[1-50]: Err = 0.88250000 * 800; CE = 2.30253527 * 800
|
||||
08/16/2016 10:51:31: Minibatch[51-100]: Err = 0.89625000 * 800; CE = 2.30253128 * 800
|
||||
08/16/2016 10:51:31: Minibatch[101-150]: Err = 0.89000000 * 800; CE = 2.30254278 * 800
|
||||
08/16/2016 10:51:32: Minibatch[151-200]: Err = 0.87625000 * 800; CE = 2.30252282 * 800
|
||||
08/16/2016 10:51:32: Minibatch[201-250]: Err = 0.89250000 * 800; CE = 2.30252040 * 800
|
||||
08/16/2016 10:51:32: Minibatch[251-300]: Err = 0.88625000 * 800; CE = 2.30254718 * 800
|
||||
08/16/2016 10:51:32: Minibatch[301-350]: Err = 0.87250000 * 800; CE = 2.30251737 * 800
|
||||
08/16/2016 10:51:32: Minibatch[351-400]: Err = 0.89875000 * 800; CE = 2.30257154 * 800
|
||||
08/16/2016 10:51:33: Minibatch[401-450]: Err = 0.90000000 * 800; CE = 2.30253825 * 800
|
||||
08/16/2016 10:51:33: Minibatch[451-500]: Err = 0.85625000 * 800; CE = 2.30253371 * 800
|
||||
08/16/2016 10:51:33: Minibatch[501-550]: Err = 0.90125000 * 800; CE = 2.30255184 * 800
|
||||
08/16/2016 10:51:33: Minibatch[551-600]: Err = 0.88125000 * 800; CE = 2.30251704 * 800
|
||||
08/16/2016 10:51:34: Minibatch[601-625]: Err = 0.89250000 * 400; CE = 2.30252918 * 400
|
||||
08/16/2016 10:51:34: Final Results: Minibatch[1-625]: Err = 0.88640000 * 10000; CE = 2.30253552 * 10000; perplexity = 9.99950432
|
||||
|
||||
05/13/2016 15:11:30: Action "test" complete.
|
||||
08/16/2016 10:51:34: Action "test" complete.
|
||||
|
||||
05/13/2016 15:11:30: __COMPLETED__
|
||||
08/16/2016 10:51:34: __COMPLETED__
|
||||
|
|
@ -1,47 +1,62 @@
|
|||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/Config/05_ConvLocal.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10 OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=5]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 268381192 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/05_ConvLocal.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10 OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=5]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 13 2016 08:06:01
|
||||
Last modified date: Thu May 12 07:31:50 2016
|
||||
Built time: Aug 16 2016 02:54:53
|
||||
Last modified date: Fri Aug 12 05:31:21 2016
|
||||
Build type: Release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: no
|
||||
Math lib: mkl
|
||||
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
CUB_PATH: c:\src\cub-1.4.1
|
||||
CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by svcphil on Philly-Pool3
|
||||
Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData
|
||||
05/13/2016 08:18:58: Redirecting stderr to file -_Train_Test.log
|
||||
05/13/2016 08:18:58: -------------------------------------------------------------------
|
||||
05/13/2016 08:18:58: Build info:
|
||||
Changed current directory to C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData
|
||||
08/16/2016 03:03:54: Redirecting stderr to file -_Train_Test.log
|
||||
08/16/2016 03:03:54: -------------------------------------------------------------------
|
||||
08/16/2016 03:03:54: Build info:
|
||||
|
||||
05/13/2016 08:18:58: Built time: May 13 2016 08:06:01
|
||||
05/13/2016 08:18:58: Last modified date: Thu May 12 07:31:50 2016
|
||||
05/13/2016 08:18:58: Build type: Release
|
||||
05/13/2016 08:18:58: Build target: GPU
|
||||
05/13/2016 08:18:58: With 1bit-SGD: no
|
||||
05/13/2016 08:18:58: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
05/13/2016 08:18:58: CUB_PATH: c:\src\cub-1.4.1
|
||||
05/13/2016 08:18:58: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
05/13/2016 08:18:58: Build Branch: HEAD
|
||||
05/13/2016 08:18:58: Build SHA1: 35fadc316f045d843bbd9b85061250a959268787
|
||||
05/13/2016 08:18:58: Built by svcphil on Philly-Pool3
|
||||
05/13/2016 08:18:58: Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
05/13/2016 08:18:58: -------------------------------------------------------------------
|
||||
08/16/2016 03:03:54: Built time: Aug 16 2016 02:54:53
|
||||
08/16/2016 03:03:54: Last modified date: Fri Aug 12 05:31:21 2016
|
||||
08/16/2016 03:03:54: Build type: Release
|
||||
08/16/2016 03:03:54: Build target: GPU
|
||||
08/16/2016 03:03:54: With 1bit-SGD: no
|
||||
08/16/2016 03:03:54: Math lib: mkl
|
||||
08/16/2016 03:03:54: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
08/16/2016 03:03:54: CUB_PATH: c:\src\cub-1.4.1
|
||||
08/16/2016 03:03:54: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
08/16/2016 03:03:54: Build Branch: HEAD
|
||||
08/16/2016 03:03:54: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 03:03:54: Built by svcphil on Philly-Pool3
|
||||
08/16/2016 03:03:54: Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
08/16/2016 03:03:54: -------------------------------------------------------------------
|
||||
08/16/2016 03:03:57: -------------------------------------------------------------------
|
||||
08/16/2016 03:03:57: GPU info:
|
||||
|
||||
05/13/2016 08:18:58: Running on Philly-Pool2 at 2016/05/13 08:18:58
|
||||
05/13/2016 08:18:58: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/../../../../Tests/EndToEndTests/CNTKTextFormatReader/Examples/Image/Miscellaneous/CIFAR-10/Config/05_ConvLocal.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10 OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=5]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
08/16/2016 03:03:57: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:03:57: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:03:57: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:03:57: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:03:57: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 03:03:57: Running on DPHAIM-24 at 2016/08/16 03:03:57
|
||||
08/16/2016 03:03:57: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/05_ConvLocal.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10 OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=5]] Train=[SGD=[epochSize=100]] stderr=-
|
||||
|
||||
|
||||
|
||||
05/13/2016 08:18:59: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 08:18:59: RootDir = "."
|
||||
08/16/2016 03:03:57: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:03:57: RootDir = "."
|
||||
ConfigDir = "$RootDir$"
|
||||
DataDir = "$RootDir$"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
|
|
@ -50,7 +65,6 @@ ndlMacros = "$ConfigDir$/Macros.ndl"
|
|||
precision = "float"
|
||||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
prefetch = "true"
|
||||
command = Train:Test
|
||||
modelPath = "$ModelDir$/05_ConvLocal"
|
||||
stderr = "$OutputDir$/05_ConvLocal"
|
||||
|
|
@ -82,7 +96,7 @@ Train = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
Test = [
|
||||
action = "test"
|
||||
|
|
@ -100,35 +114,34 @@ Test = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Train=[SGD=[maxEpochs=5]]
|
||||
Train=[SGD=[epochSize=100]]
|
||||
stderr=-
|
||||
|
||||
05/13/2016 08:18:59: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:03:57: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 08:18:59: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/13/2016 08:18:59: RootDir = "."
|
||||
08/16/2016 03:03:57: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:03:57: RootDir = "."
|
||||
ConfigDir = "."
|
||||
DataDir = "."
|
||||
OutputDir = "./Output"
|
||||
ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu/Models"
|
||||
ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu/Models"
|
||||
ndlMacros = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/Macros.ndl"
|
||||
precision = "float"
|
||||
deviceId = 0
|
||||
imageLayout = "cudnn"
|
||||
prefetch = "true"
|
||||
command = Train:Test
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal"
|
||||
stderr = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu/05_ConvLocal"
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal"
|
||||
stderr = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu/05_ConvLocal"
|
||||
traceLevel = 1
|
||||
numMBsToShowResult = 50
|
||||
Train = [
|
||||
|
|
@ -146,7 +159,7 @@ Train = [
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData/Train_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData/Train_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -157,14 +170,14 @@ Train = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
Test = [
|
||||
action = "test"
|
||||
minibatchSize = 16
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData/Test_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData/Test_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -175,44 +188,43 @@ Test = [
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Train=[SGD=[maxEpochs=5]]
|
||||
Train=[SGD=[epochSize=100]]
|
||||
stderr=-
|
||||
|
||||
05/13/2016 08:18:59: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:03:57: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/13/2016 08:18:59: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:03:57: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: 05_ConvLocal.cntk:command=Train:Test
|
||||
configparameters: 05_ConvLocal.cntk:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10
|
||||
configparameters: 05_ConvLocal.cntk:currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData
|
||||
configparameters: 05_ConvLocal.cntk:DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData
|
||||
configparameters: 05_ConvLocal.cntk:currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData
|
||||
configparameters: 05_ConvLocal.cntk:DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData
|
||||
configparameters: 05_ConvLocal.cntk:deviceId=0
|
||||
configparameters: 05_ConvLocal.cntk:imageLayout=cudnn
|
||||
configparameters: 05_ConvLocal.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu/Models
|
||||
configparameters: 05_ConvLocal.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal
|
||||
configparameters: 05_ConvLocal.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu/Models
|
||||
configparameters: 05_ConvLocal.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal
|
||||
configparameters: 05_ConvLocal.cntk:ndlMacros=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/Macros.ndl
|
||||
configparameters: 05_ConvLocal.cntk:numMBsToShowResult=50
|
||||
configparameters: 05_ConvLocal.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu
|
||||
configparameters: 05_ConvLocal.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu
|
||||
configparameters: 05_ConvLocal.cntk:precision=float
|
||||
configparameters: 05_ConvLocal.cntk:prefetch=true
|
||||
configparameters: 05_ConvLocal.cntk:RootDir=.
|
||||
configparameters: 05_ConvLocal.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu
|
||||
configparameters: 05_ConvLocal.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu
|
||||
configparameters: 05_ConvLocal.cntk:stderr=-
|
||||
configparameters: 05_ConvLocal.cntk:Test=[
|
||||
action = "test"
|
||||
minibatchSize = 16
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData/Test_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData/Test_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -223,7 +235,7 @@ configparameters: 05_ConvLocal.cntk:Test=[
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
configparameters: 05_ConvLocal.cntk:timestamping=true
|
||||
|
|
@ -243,7 +255,7 @@ configparameters: 05_ConvLocal.cntk:Train=[
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData/Train_cntk_text.txt"
|
||||
file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu\TestData/Train_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 3072
|
||||
|
|
@ -254,27 +266,51 @@ configparameters: 05_ConvLocal.cntk:Train=[
|
|||
format = "dense"
|
||||
]
|
||||
]
|
||||
]
|
||||
]
|
||||
] [SGD=[maxEpochs=5]] [SGD=[epochSize=100]]
|
||||
|
||||
05/13/2016 08:18:59: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/13/2016 08:18:59: Commands: Train Test
|
||||
05/13/2016 08:18:59: Precision = "float"
|
||||
05/13/2016 08:18:59: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal
|
||||
05/13/2016 08:18:59: CNTKCommandTrainInfo: Train : 5
|
||||
05/13/2016 08:18:59: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 5
|
||||
08/16/2016 03:03:57: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:03:57: Commands: Train Test
|
||||
08/16/2016 03:03:57: Precision = "float"
|
||||
08/16/2016 03:03:57: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal
|
||||
08/16/2016 03:03:57: CNTKCommandTrainInfo: Train : 5
|
||||
08/16/2016 03:03:57: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 5
|
||||
|
||||
05/13/2016 08:18:59: ##############################################################################
|
||||
05/13/2016 08:18:59: # #
|
||||
05/13/2016 08:18:59: # Action "train" #
|
||||
05/13/2016 08:18:59: # #
|
||||
05/13/2016 08:18:59: ##############################################################################
|
||||
08/16/2016 03:03:57: ##############################################################################
|
||||
08/16/2016 03:03:57: # #
|
||||
08/16/2016 03:03:57: # Action "train" #
|
||||
08/16/2016 03:03:57: # #
|
||||
08/16/2016 03:03:57: ##############################################################################
|
||||
|
||||
05/13/2016 08:18:59: CNTKCommandTrainBegin: Train
|
||||
08/16/2016 03:03:57: CNTKCommandTrainBegin: Train
|
||||
NDLBuilder Using GPU 0
|
||||
|
||||
05/13/2016 08:19:00: Creating virgin network.
|
||||
08/16/2016 03:03:58: Creating virgin network.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.000000.
|
||||
Node 'conv1.W' (LearnableParameter operation): Initializing Parameter[64 x 75] <- 0.000000.
|
||||
Node 'conv1.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv2.W' (LearnableParameter operation): Initializing Parameter[64 x 1600] <- 0.000000.
|
||||
Node 'conv2.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv3.W' (LearnableParameter operation): Initializing Parameter[3136 x 576] <- 0.000000.
|
||||
Node 'conv3.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv4.W' (LearnableParameter operation): Initializing Parameter[1568 x 576] <- 0.000000.
|
||||
Node 'conv4.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 32] <- 0.000000.
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializing Parameter[10 x 7 x 7 x 32] <- 0.000000.
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[10] <- 0.000000.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 128.000000.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 128.000000.
|
||||
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 128.000000.
|
||||
Node 'conv1.W' (LearnableParameter operation): Initializing Parameter[64 x 75] <- gaussian(seed=1, range=0.023094*0.004300, onCPU=false).
|
||||
Microsoft::MSR::CNTK::GPUMatrix<ElemType>::SetGaussianRandomValue (GPU): creating curand object with seed 1, sizeof(ElemType)==4
|
||||
Node 'conv1.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv2.W' (LearnableParameter operation): Initializing Parameter[64 x 1600] <- gaussian(seed=2, range=0.005000*1.414000, onCPU=false).
|
||||
Node 'conv2.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv3.W' (LearnableParameter operation): Initializing Parameter[3136 x 576] <- gaussian(seed=3, range=0.008333*1.414000, onCPU=false).
|
||||
Node 'conv3.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv4.W' (LearnableParameter operation): Initializing Parameter[1568 x 576] <- gaussian(seed=4, range=0.008333*1.414000, onCPU=false).
|
||||
Node 'conv4.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 32] <- 0.000000.
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializing Parameter[10 x 7 x 7 x 32] <- gaussian(seed=5, range=0.005051*1.500000, onCPU=false).
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[10] <- 0.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -324,120 +360,132 @@ Validating network. 19 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 64, Kernel: 5 x 5 x 3, Map: 1 x 1 x 64, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c: using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 64, Kernel: 5 x 5 x 3, Map: 1 x 1 x 64, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 64, Output: 15 x 15 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 32 x 32 x 64, Output: 15 x 15 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 64, Output: 15 x 15 x 64, Kernel: 5 x 5 x 64, Map: 1 x 1 x 64, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c: using cuDNN convolution engine for geometry: Input: 15 x 15 x 64, Output: 15 x 15 x 64, Kernel: 5 x 5 x 64, Map: 1 x 1 x 64, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 64, Output: 7 x 7 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 15 x 15 x 64, Output: 7 x 7 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using reference convolution engine for geometry: Input: 7 x 7 x 64, Output: 7 x 7 x 64, Kernel: 3 x 3 x 64, Map: 64, Stride: 1 x 1 x 64, Sharing: (0, 0, 0), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3.c: using reference convolution engine for geometry: Input: 7 x 7 x 64, Output: 7 x 7 x 64, Kernel: 3 x 3 x 64, Map: 64, Stride: 1 x 1 x 64, Sharing: (0, 0, 0), AutoPad: (1, 1, 1), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.
|
||||
|
||||
Using reference convolution engine for geometry: Input: 7 x 7 x 64, Output: 7 x 7 x 32, Kernel: 3 x 3 x 64, Map: 32, Stride: 1 x 1 x 64, Sharing: (0, 0, 0), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv4.c: using reference convolution engine for geometry: Input: 7 x 7 x 64, Output: 7 x 7 x 32, Kernel: 3 x 3 x 64, Map: 32, Stride: 1 x 1 x 64, Sharing: (0, 0, 0), AutoPad: (1, 1, 1), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.
|
||||
|
||||
|
||||
13 out of 32 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
05/13/2016 08:19:02: Created model with 32 nodes on GPU 0.
|
||||
08/16/2016 03:03:59: Created model with 32 nodes on GPU 0.
|
||||
|
||||
05/13/2016 08:19:02: Training criterion node(s):
|
||||
05/13/2016 08:19:02: CE = CrossEntropyWithSoftmax
|
||||
08/16/2016 03:03:59: Training criterion node(s):
|
||||
08/16/2016 03:03:59: CE = CrossEntropyWithSoftmax
|
||||
|
||||
05/13/2016 08:19:02: Evaluation criterion node(s):
|
||||
|
||||
05/13/2016 08:19:02: Err = ErrorPrediction
|
||||
08/16/2016 03:03:59: Evaluation criterion node(s):
|
||||
08/16/2016 03:03:59: Err = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 59 matrices, 35 are shared as 16, and 24 are not shared.
|
||||
|
||||
0000000000000000: {[Err Gradient[1]] [featOffs Gradient[1 x 1]] [featScaled Gradient[32 x 32 x 3 x *]] [features Gradient[32 x 32 x 3 x *]] [labels Gradient[10 x *]] }
|
||||
000000DE977F80B0: {[conv1.W Gradient[64 x 75]] [conv1.p Value[32 x 32 x 64 x *]] }
|
||||
000000DE977F8330: {[conv4.b Gradient[1 x 1 x 32]] [conv4.y Gradient[7 x 7 x 32 x *]] }
|
||||
000000DE977F8470: {[CE Value[1]] }
|
||||
000000DE977F8510: {[conv3.p Gradient[7 x 7 x 64 x *]] [pool2 Gradient[7 x 7 x 64 x *]] }
|
||||
000000DE977F8830: {[OutputNodes.t Gradient[10 x *]] }
|
||||
000000DE977F8A10: {[OutputNodes.z Value[10 x *]] }
|
||||
000000DE977F8AB0: {[conv2.c Gradient[15 x 15 x 64 x *]] [conv2.y Value[15 x 15 x 64 x *]] }
|
||||
000000DE977F8D30: {[CE Gradient[1]] }
|
||||
000000DE977F8DD0: {[conv3.c Value[7 x 7 x 64 x *]] }
|
||||
000000DE977F8FB0: {[conv2.b Gradient[1 x 1 x 64]] [conv2.y Gradient[15 x 15 x 64 x *]] }
|
||||
000000DE977F90F0: {[conv1.c Gradient[32 x 32 x 64 x *]] [conv1.y Value[32 x 32 x 64 x *]] }
|
||||
000000DE977F9230: {[conv3.W Gradient[3136 x 576]] [conv3.p Value[7 x 7 x 64 x *]] }
|
||||
000000DE977F92D0: {[conv2.c Value[15 x 15 x 64 x *]] }
|
||||
000000DE977F9410: {[OutputNodes.W Gradient[10 x 7 x 7 x 32]] [OutputNodes.z Gradient[10 x *]] }
|
||||
000000DE977F9550: {[conv1.c Value[32 x 32 x 64 x *]] }
|
||||
000000DE977F95F0: {[conv1.p Gradient[32 x 32 x 64 x *]] [pool1 Value[15 x 15 x 64 x *]] }
|
||||
000000DE977F9690: {[conv2.p Gradient[15 x 15 x 64 x *]] [pool1 Gradient[15 x 15 x 64 x *]] [pool2 Value[7 x 7 x 64 x *]] }
|
||||
000000DE977F9730: {[conv3.c Gradient[7 x 7 x 64 x *]] [conv3.y Value[7 x 7 x 64 x *]] }
|
||||
000000DE977F97D0: {[conv2.W Gradient[64 x 1600]] [conv2.p Value[15 x 15 x 64 x *]] }
|
||||
000000DE977F9910: {[featScaled Value[32 x 32 x 3 x *]] }
|
||||
000000DE977F99B0: {[conv4.W Gradient[1568 x 576]] [conv4.p Value[7 x 7 x 32 x *]] }
|
||||
000000DE977F9AF0: {[conv4.c Gradient[7 x 7 x 32 x *]] [conv4.y Value[7 x 7 x 32 x *]] }
|
||||
000000DE977FA130: {[conv4.c Value[7 x 7 x 32 x *]] }
|
||||
000000DE977FA1D0: {[Err Value[1]] }
|
||||
000000DE977FA270: {[OutputNodes.t Value[10 x *]] [conv3.b Gradient[1 x 1 x 64]] [conv3.y Gradient[7 x 7 x 64 x *]] [conv4.p Gradient[7 x 7 x 32 x *]] }
|
||||
000000DE977FA310: {[OutputNodes.b Gradient[10]] }
|
||||
000000DE977FA770: {[conv1.b Gradient[1 x 1 x 64]] [conv1.y Gradient[32 x 32 x 64 x *]] }
|
||||
000000DEF7E6FAA0: {[features Value[32 x 32 x 3 x *]] }
|
||||
000000DEFC647630: {[conv4.W Value[1568 x 576]] }
|
||||
000000DEFC647950: {[conv1.W Value[64 x 75]] }
|
||||
000000DEFC647C70: {[featOffs Value[1 x 1]] }
|
||||
000000DEFC647DB0: {[conv1.b Value[1 x 1 x 64]] }
|
||||
000000DEFC648350: {[conv2.W Value[64 x 1600]] }
|
||||
000000DEFC6483F0: {[conv3.b Value[1 x 1 x 64]] }
|
||||
000000DEFC6488F0: {[labels Value[10 x *]] }
|
||||
000000DEFC648A30: {[conv2.b Value[1 x 1 x 64]] }
|
||||
000000DEFC648B70: {[OutputNodes.W Value[10 x 7 x 7 x 32]] }
|
||||
000000DEFC648CB0: {[conv4.b Value[1 x 1 x 32]] }
|
||||
000000DEFC6491B0: {[OutputNodes.b Value[10]] }
|
||||
000000DEFC649430: {[conv3.W Value[3136 x 576]] }
|
||||
|
||||
05/13/2016 08:19:02: No PreCompute nodes found, skipping PreCompute step.
|
||||
|
||||
05/13/2016 08:19:02: Starting Epoch 1: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
|
||||
05/13/2016 08:19:02: Starting minibatch loop.
|
||||
05/13/2016 08:19:12: Finished Epoch[ 1 of 5]: [Training] CE = 2.30259964 * 100; Err = 0.92000000 * 100; totalSamplesSeen = 100; learningRatePerSample = 0.00015625; epochTime=10.1655s
|
||||
05/13/2016 08:19:12: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal.1'
|
||||
|
||||
05/13/2016 08:19:12: Starting Epoch 2: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
|
||||
05/13/2016 08:19:12: Starting minibatch loop.
|
||||
05/13/2016 08:19:13: Finished Epoch[ 2 of 5]: [Training] CE = 2.30259521 * 100; Err = 0.88000000 * 100; totalSamplesSeen = 200; learningRatePerSample = 0.00015625; epochTime=0.8044s
|
||||
05/13/2016 08:19:14: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal.2'
|
||||
|
||||
05/13/2016 08:19:14: Starting Epoch 3: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
|
||||
05/13/2016 08:19:14: Starting minibatch loop.
|
||||
05/13/2016 08:19:15: Finished Epoch[ 3 of 5]: [Training] CE = 2.30259445 * 100; Err = 0.89000000 * 100; totalSamplesSeen = 300; learningRatePerSample = 0.00015625; epochTime=0.807411s
|
||||
05/13/2016 08:19:15: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal.3'
|
||||
|
||||
05/13/2016 08:19:15: Starting Epoch 4: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
|
||||
05/13/2016 08:19:15: Starting minibatch loop.
|
||||
05/13/2016 08:19:16: Finished Epoch[ 4 of 5]: [Training] CE = 2.30256668 * 100; Err = 0.92000000 * 100; totalSamplesSeen = 400; learningRatePerSample = 0.00015625; epochTime=0.805702s
|
||||
05/13/2016 08:19:16: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal.4'
|
||||
|
||||
05/13/2016 08:19:16: Starting Epoch 5: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
|
||||
05/13/2016 08:19:16: Starting minibatch loop.
|
||||
05/13/2016 08:19:17: Finished Epoch[ 5 of 5]: [Training] CE = 2.30257889 * 100; Err = 0.93000000 * 100; totalSamplesSeen = 500; learningRatePerSample = 0.00015625; epochTime=0.799938s
|
||||
05/13/2016 08:19:17: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160513081543.861015\CNTKTextFormatReader\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal'
|
||||
05/13/2016 08:19:18: CNTKCommandTrainEnd: Train
|
||||
|
||||
05/13/2016 08:19:18: Action "train" complete.
|
||||
{ conv1.W : [64 x 75] (gradient)
|
||||
conv1.p : [32 x 32 x 64 x *] }
|
||||
{ conv1.b : [1 x 1 x 64] (gradient)
|
||||
conv1.y : [32 x 32 x 64 x *] (gradient) }
|
||||
{ conv2.W : [64 x 1600] (gradient)
|
||||
conv2.p : [15 x 15 x 64 x *] }
|
||||
{ conv2.c : [15 x 15 x 64 x *] (gradient)
|
||||
conv2.y : [15 x 15 x 64 x *] }
|
||||
{ conv2.p : [15 x 15 x 64 x *] (gradient)
|
||||
pool1 : [15 x 15 x 64 x *] (gradient)
|
||||
pool2 : [7 x 7 x 64 x *] }
|
||||
{ conv2.b : [1 x 1 x 64] (gradient)
|
||||
conv2.y : [15 x 15 x 64 x *] (gradient) }
|
||||
{ conv1.c : [32 x 32 x 64 x *] (gradient)
|
||||
conv1.y : [32 x 32 x 64 x *] }
|
||||
{ conv1.p : [32 x 32 x 64 x *] (gradient)
|
||||
pool1 : [15 x 15 x 64 x *] }
|
||||
{ conv4.b : [1 x 1 x 32] (gradient)
|
||||
conv4.y : [7 x 7 x 32 x *] (gradient) }
|
||||
{ OutputNodes.t : [10 x *]
|
||||
conv3.b : [1 x 1 x 64] (gradient)
|
||||
conv3.y : [7 x 7 x 64 x *] (gradient)
|
||||
conv4.p : [7 x 7 x 32 x *] (gradient) }
|
||||
{ OutputNodes.W : [10 x 7 x 7 x 32] (gradient)
|
||||
OutputNodes.z : [10 x *] (gradient) }
|
||||
{ conv3.c : [7 x 7 x 64 x *] (gradient)
|
||||
conv3.y : [7 x 7 x 64 x *] }
|
||||
{ conv3.W : [3136 x 576] (gradient)
|
||||
conv3.p : [7 x 7 x 64 x *] }
|
||||
{ conv4.c : [7 x 7 x 32 x *] (gradient)
|
||||
conv4.y : [7 x 7 x 32 x *] }
|
||||
{ conv3.p : [7 x 7 x 64 x *] (gradient)
|
||||
pool2 : [7 x 7 x 64 x *] (gradient) }
|
||||
{ conv4.W : [1568 x 576] (gradient)
|
||||
conv4.p : [7 x 7 x 32 x *] }
|
||||
|
||||
|
||||
05/13/2016 08:19:18: ##############################################################################
|
||||
05/13/2016 08:19:18: # #
|
||||
05/13/2016 08:19:18: # Action "test" #
|
||||
05/13/2016 08:19:18: # #
|
||||
05/13/2016 08:19:18: ##############################################################################
|
||||
08/16/2016 03:03:59: Training 2832618 parameters in 10 out of 10 parameter tensors and 27 nodes with gradient:
|
||||
|
||||
08/16/2016 03:03:59: Node 'OutputNodes.W' (LearnableParameter operation) : [10 x 7 x 7 x 32]
|
||||
08/16/2016 03:03:59: Node 'OutputNodes.b' (LearnableParameter operation) : [10]
|
||||
08/16/2016 03:03:59: Node 'conv1.W' (LearnableParameter operation) : [64 x 75]
|
||||
08/16/2016 03:03:59: Node 'conv1.b' (LearnableParameter operation) : [1 x 1 x 64]
|
||||
08/16/2016 03:03:59: Node 'conv2.W' (LearnableParameter operation) : [64 x 1600]
|
||||
08/16/2016 03:03:59: Node 'conv2.b' (LearnableParameter operation) : [1 x 1 x 64]
|
||||
08/16/2016 03:03:59: Node 'conv3.W' (LearnableParameter operation) : [3136 x 576]
|
||||
08/16/2016 03:03:59: Node 'conv3.b' (LearnableParameter operation) : [1 x 1 x 64]
|
||||
08/16/2016 03:03:59: Node 'conv4.W' (LearnableParameter operation) : [1568 x 576]
|
||||
08/16/2016 03:03:59: Node 'conv4.b' (LearnableParameter operation) : [1 x 1 x 32]
|
||||
|
||||
08/16/2016 03:03:59: No PreCompute nodes found, or all already computed. Skipping pre-computation step.
|
||||
|
||||
08/16/2016 03:03:59: Starting Epoch 1: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..100] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:03:59: Starting minibatch loop.
|
||||
08/16/2016 03:04:04: Finished Epoch[ 1 of 5]: [Training] CE = 2.30258331 * 100; Err = 0.90000000 * 100; totalSamplesSeen = 100; learningRatePerSample = 0.00015625; epochTime=5.21825s
|
||||
08/16/2016 03:04:05: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal.1'
|
||||
|
||||
08/16/2016 03:04:05: Starting Epoch 2: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [100..200] (first sequence at sample 100), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:04:05: Starting minibatch loop.
|
||||
08/16/2016 03:04:05: Finished Epoch[ 2 of 5]: [Training] CE = 2.30260956 * 100; Err = 0.91000000 * 100; totalSamplesSeen = 200; learningRatePerSample = 0.00015625; epochTime=0.191092s
|
||||
08/16/2016 03:04:05: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal.2'
|
||||
|
||||
08/16/2016 03:04:05: Starting Epoch 3: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [200..300] (first sequence at sample 200), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:04:05: Starting minibatch loop.
|
||||
08/16/2016 03:04:05: Finished Epoch[ 3 of 5]: [Training] CE = 2.30259949 * 100; Err = 0.90000000 * 100; totalSamplesSeen = 300; learningRatePerSample = 0.00015625; epochTime=0.18611s
|
||||
08/16/2016 03:04:06: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal.3'
|
||||
|
||||
08/16/2016 03:04:06: Starting Epoch 4: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 3: frames [300..400] (first sequence at sample 300), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:04:06: Starting minibatch loop.
|
||||
08/16/2016 03:04:06: Finished Epoch[ 4 of 5]: [Training] CE = 2.30261505 * 100; Err = 0.92000000 * 100; totalSamplesSeen = 400; learningRatePerSample = 0.00015625; epochTime=0.189814s
|
||||
08/16/2016 03:04:06: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal.4'
|
||||
|
||||
08/16/2016 03:04:06: Starting Epoch 5: learning rate per sample = 0.000156 effective momentum = 0.900000 momentum as time constant = 607.4 samples
|
||||
BlockRandomizer::StartEpoch: epoch 4: frames [400..500] (first sequence at sample 400), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:04:06: Starting minibatch loop.
|
||||
08/16/2016 03:04:06: Finished Epoch[ 5 of 5]: [Training] CE = 2.30255020 * 100; Err = 0.92000000 * 100; totalSamplesSeen = 500; learningRatePerSample = 0.00015625; epochTime=0.193188s
|
||||
08/16/2016 03:04:07: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_05_ConvLocal@release_gpu/Models/05_ConvLocal'
|
||||
08/16/2016 03:04:07: CNTKCommandTrainEnd: Train
|
||||
|
||||
08/16/2016 03:04:07: Action "train" complete.
|
||||
|
||||
|
||||
08/16/2016 03:04:07: ##############################################################################
|
||||
08/16/2016 03:04:07: # #
|
||||
08/16/2016 03:04:07: # Action "test" #
|
||||
08/16/2016 03:04:07: # #
|
||||
08/16/2016 03:04:07: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
|
@ -488,17 +536,17 @@ Validating network. 19 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 64, Kernel: 5 x 5 x 3, Map: 1 x 1 x 64, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c: using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 64, Kernel: 5 x 5 x 3, Map: 1 x 1 x 64, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 32 x 32 x 64, Output: 15 x 15 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 32 x 32 x 64, Output: 15 x 15 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 64, Output: 15 x 15 x 64, Kernel: 5 x 5 x 64, Map: 1 x 1 x 64, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c: using cuDNN convolution engine for geometry: Input: 15 x 15 x 64, Output: 15 x 15 x 64, Kernel: 5 x 5 x 64, Map: 1 x 1 x 64, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 15 x 15 x 64, Output: 7 x 7 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 15 x 15 x 64, Output: 7 x 7 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using reference convolution engine for geometry: Input: 7 x 7 x 64, Output: 7 x 7 x 64, Kernel: 3 x 3 x 64, Map: 64, Stride: 1 x 1 x 64, Sharing: (0, 0, 0), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3.c: using reference convolution engine for geometry: Input: 7 x 7 x 64, Output: 7 x 7 x 64, Kernel: 3 x 3 x 64, Map: 64, Stride: 1 x 1 x 64, Sharing: (0, 0, 0), AutoPad: (1, 1, 1), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.
|
||||
|
||||
Using reference convolution engine for geometry: Input: 7 x 7 x 64, Output: 7 x 7 x 32, Kernel: 3 x 3 x 64, Map: 32, Stride: 1 x 1 x 64, Sharing: (0, 0, 0), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv4.c: using reference convolution engine for geometry: Input: 7 x 7 x 64, Output: 7 x 7 x 32, Kernel: 3 x 3 x 64, Map: 32, Stride: 1 x 1 x 64, Sharing: (0, 0, 0), AutoPad: (1, 1, 1), LowerPad: 0 x 0 x 0, UpperPad: 0 x 0 x 0.
|
||||
|
||||
|
||||
13 out of 32 nodes do not share the minibatch layout with the input data.
|
||||
|
|
@ -510,44 +558,25 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 32 matrices, 0 are shared as 0, and 32 are not shared.
|
||||
|
||||
0000000000000000: {[CE Gradient[1]] [Err Gradient[1]] [OutputNodes.W Gradient[10 x 7 x 7 x 32]] [OutputNodes.b Gradient[10]] [OutputNodes.t Gradient[10 x *1]] [OutputNodes.z Gradient[10 x *1]] [conv1.W Gradient[64 x 75]] [conv1.b Gradient[1 x 1 x 64]] [conv1.c Gradient[32 x 32 x 64 x *1]] [conv1.p Gradient[32 x 32 x 64 x *1]] [conv1.y Gradient[32 x 32 x 64 x *1]] [conv2.W Gradient[64 x 1600]] [conv2.b Gradient[1 x 1 x 64]] [conv2.c Gradient[15 x 15 x 64 x *1]] [conv2.p Gradient[15 x 15 x 64 x *1]] [conv2.y Gradient[15 x 15 x 64 x *1]] [conv3.W Gradient[3136 x 576]] [conv3.b Gradient[1 x 1 x 64]] [conv3.c Gradient[7 x 7 x 64 x *1]] [conv3.p Gradient[7 x 7 x 64 x *1]] [conv3.y Gradient[7 x 7 x 64 x *1]] [conv4.W Gradient[1568 x 576]] [conv4.b Gradient[1 x 1 x 32]] [conv4.c Gradient[7 x 7 x 32 x *1]] [conv4.p Gradient[7 x 7 x 32 x *1]] [conv4.y Gradient[7 x 7 x 32 x *1]] [featOffs Gradient[1 x 1]] [featScaled Gradient[32 x 32 x 3 x *1]] [features Gradient[32 x 32 x 3 x *1]] [labels Gradient[10 x *1]] [pool1 Gradient[15 x 15 x 64 x *1]] [pool2 Gradient[7 x 7 x 64 x *1]] }
|
||||
000000DE977F8010: {[labels Value[10 x *1]] }
|
||||
000000DE977F8290: {[OutputNodes.b Value[10]] }
|
||||
000000DE977F8510: {[OutputNodes.W Value[10 x 7 x 7 x 32]] }
|
||||
000000DE977F8C90: {[conv1.W Value[64 x 75]] }
|
||||
000000DE977F8DD0: {[conv2.W Value[64 x 1600]] }
|
||||
000000DE977F90F0: {[conv3.W Value[3136 x 576]] }
|
||||
000000DE977F94B0: {[conv3.b Value[1 x 1 x 64]] }
|
||||
000000DE977F9C30: {[conv4.b Value[1 x 1 x 32]] }
|
||||
000000DE977FA1D0: {[conv4.W Value[1568 x 576]] }
|
||||
000000DE977FA270: {[featOffs Value[1 x 1]] }
|
||||
000000DE977FA310: {[conv2.b Value[1 x 1 x 64]] }
|
||||
000000DE977FA450: {[conv1.b Value[1 x 1 x 64]] }
|
||||
000000DE977FA630: {[features Value[32 x 32 x 3 x *1]] }
|
||||
000000DE977FAA90: {[pool1 Value[15 x 15 x 64 x *1]] }
|
||||
000000DE977FAB30: {[conv2.p Value[15 x 15 x 64 x *1]] }
|
||||
000000DE977FADB0: {[Err Value[1]] }
|
||||
000000DE977FAE50: {[conv2.c Value[15 x 15 x 64 x *1]] }
|
||||
000000DE977FB030: {[conv3.c Value[7 x 7 x 64 x *1]] }
|
||||
000000DE977FB210: {[conv3.p Value[7 x 7 x 64 x *1]] }
|
||||
000000DE977FB3F0: {[conv1.c Value[32 x 32 x 64 x *1]] }
|
||||
000000DE977FB530: {[conv1.p Value[32 x 32 x 64 x *1]] }
|
||||
000000DE977FB5D0: {[conv1.y Value[32 x 32 x 64 x *1]] }
|
||||
000000DE977FBAD0: {[featScaled Value[32 x 32 x 3 x *1]] }
|
||||
000000DE977FBC10: {[CE Value[1]] }
|
||||
000000DE977FBD50: {[conv2.y Value[15 x 15 x 64 x *1]] }
|
||||
000000DE977FBDF0: {[pool2 Value[7 x 7 x 64 x *1]] }
|
||||
000000DEFC647810: {[conv4.y Value[7 x 7 x 32 x *1]] }
|
||||
000000DEFC648030: {[conv4.p Value[7 x 7 x 32 x *1]] }
|
||||
000000DEFC648530: {[conv3.y Value[7 x 7 x 64 x *1]] }
|
||||
000000DEFC648DF0: {[OutputNodes.z Value[10 x *1]] }
|
||||
000000DEFC649070: {[conv4.c Value[7 x 7 x 32 x *1]] }
|
||||
000000DEFC6491B0: {[OutputNodes.t Value[10 x *1]] }
|
||||
|
||||
05/13/2016 08:19:48: Final Results: Minibatch[1-625]: Err = 0.85850000 * 10000; CE = 2.30251652 * 10000; perplexity = 9.99931430
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
08/16/2016 03:04:09: Minibatch[1-50]: Err = 0.88250000 * 800; CE = 2.30253532 * 800
|
||||
08/16/2016 03:04:09: Minibatch[51-100]: Err = 0.89625000 * 800; CE = 2.30253127 * 800
|
||||
08/16/2016 03:04:09: Minibatch[101-150]: Err = 0.89000000 * 800; CE = 2.30254280 * 800
|
||||
08/16/2016 03:04:10: Minibatch[151-200]: Err = 0.87625000 * 800; CE = 2.30252286 * 800
|
||||
08/16/2016 03:04:10: Minibatch[201-250]: Err = 0.89125000 * 800; CE = 2.30252037 * 800
|
||||
08/16/2016 03:04:10: Minibatch[251-300]: Err = 0.88500000 * 800; CE = 2.30254713 * 800
|
||||
08/16/2016 03:04:11: Minibatch[301-350]: Err = 0.87375000 * 800; CE = 2.30251743 * 800
|
||||
08/16/2016 03:04:11: Minibatch[351-400]: Err = 0.89875000 * 800; CE = 2.30257149 * 800
|
||||
08/16/2016 03:04:11: Minibatch[401-450]: Err = 0.90125000 * 800; CE = 2.30253827 * 800
|
||||
08/16/2016 03:04:11: Minibatch[451-500]: Err = 0.85625000 * 800; CE = 2.30253367 * 800
|
||||
08/16/2016 03:04:12: Minibatch[501-550]: Err = 0.90125000 * 800; CE = 2.30255186 * 800
|
||||
08/16/2016 03:04:12: Minibatch[551-600]: Err = 0.88125000 * 800; CE = 2.30251704 * 800
|
||||
08/16/2016 03:04:12: Minibatch[601-625]: Err = 0.89250000 * 400; CE = 2.30252914 * 400
|
||||
08/16/2016 03:04:12: Final Results: Minibatch[1-625]: Err = 0.88640000 * 10000; CE = 2.30253553 * 10000; perplexity = 9.99950434
|
||||
|
||||
05/13/2016 08:19:48: Action "test" complete.
|
||||
08/16/2016 03:04:12: Action "test" complete.
|
||||
|
||||
05/13/2016 08:19:48: __COMPLETED__
|
||||
08/16/2016 03:04:12: __COMPLETED__
|
||||
|
|
@ -1,22 +1,27 @@
|
|||
=== Running /home/alrezni/src/cntk_git/build/release/bin/cntk configFile=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/MultiGpu/../Config/Multigpu.cntk currentDirectory=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data RunDir=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu DataDir=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data ConfigDir=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/MultiGpu/../Config OutputDir=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu DeviceId=-1 timestamping=true Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 264172964 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/1bitsgd/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/MultiGpu/../../../../../../Examples/Other/Simple2d/Config/Multigpu.cntk currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_cpu DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/MultiGpu/../../../../../../Examples/Other/Simple2d/Config OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_cpu DeviceId=-1 timestamping=true Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 3 2016 15:08:09
|
||||
Last modified date: Tue Apr 5 16:01:37 2016
|
||||
Built time: Aug 16 2016 09:41:57
|
||||
Last modified date: Mon Aug 15 23:39:17 2016
|
||||
Build type: release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: yes
|
||||
Math lib: acml
|
||||
CUDA_PATH: /usr/local/cuda-7.0
|
||||
Math lib: mkl
|
||||
CUDA_PATH: /usr/local/cuda-7.5
|
||||
CUB_PATH: /usr/local/cub-1.4.1
|
||||
CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
Build Branch: alrezni/examples_text
|
||||
Build SHA1: e80dab7d66009531806ce70b4842146e0da00516
|
||||
Built by alrezni on atleneu04
|
||||
Build Path: /home/alrezni/src/cntk_git
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by philly on 643085f7f8c2
|
||||
Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to /home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
Changed current directory to /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
MPIWrapper: initializing MPI
|
||||
ping [requestnodes (before change)]: 1 nodes pinging each other
|
||||
ping [requestnodes (before change)]: all 1 nodes responded
|
||||
|
|
@ -26,32 +31,40 @@ ping [requestnodes (after change)]: all 1 nodes responded
|
|||
mpihelper: only one MPI process: MPI operation will be boring
|
||||
ping [mpihelper]: 1 nodes pinging each other
|
||||
ping [mpihelper]: all 1 nodes responded
|
||||
05/03/2016 15:21:43: -------------------------------------------------------------------
|
||||
05/03/2016 15:21:43: Build info:
|
||||
08/16/2016 10:01:26: -------------------------------------------------------------------
|
||||
08/16/2016 10:01:26: Build info:
|
||||
|
||||
05/03/2016 15:21:43: Built time: May 3 2016 15:08:09
|
||||
05/03/2016 15:21:43: Last modified date: Tue Apr 5 16:01:37 2016
|
||||
05/03/2016 15:21:43: Build type: release
|
||||
05/03/2016 15:21:43: Build target: GPU
|
||||
05/03/2016 15:21:43: With 1bit-SGD: yes
|
||||
05/03/2016 15:21:43: Math lib: acml
|
||||
05/03/2016 15:21:43: CUDA_PATH: /usr/local/cuda-7.0
|
||||
05/03/2016 15:21:43: CUB_PATH: /usr/local/cub-1.4.1
|
||||
05/03/2016 15:21:43: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
05/03/2016 15:21:43: Build Branch: alrezni/examples_text
|
||||
05/03/2016 15:21:43: Build SHA1: e80dab7d66009531806ce70b4842146e0da00516
|
||||
05/03/2016 15:21:43: Built by alrezni on atleneu04
|
||||
05/03/2016 15:21:43: Build Path: /home/alrezni/src/cntk_git
|
||||
05/03/2016 15:21:43: -------------------------------------------------------------------
|
||||
08/16/2016 10:01:26: Built time: Aug 16 2016 09:41:57
|
||||
08/16/2016 10:01:26: Last modified date: Mon Aug 15 23:39:17 2016
|
||||
08/16/2016 10:01:26: Build type: release
|
||||
08/16/2016 10:01:26: Build target: GPU
|
||||
08/16/2016 10:01:26: With 1bit-SGD: yes
|
||||
08/16/2016 10:01:26: Math lib: mkl
|
||||
08/16/2016 10:01:26: CUDA_PATH: /usr/local/cuda-7.5
|
||||
08/16/2016 10:01:26: CUB_PATH: /usr/local/cub-1.4.1
|
||||
08/16/2016 10:01:26: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
08/16/2016 10:01:26: Build Branch: HEAD
|
||||
08/16/2016 10:01:26: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 10:01:26: Built by philly on 643085f7f8c2
|
||||
08/16/2016 10:01:26: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
08/16/2016 10:01:26: -------------------------------------------------------------------
|
||||
08/16/2016 10:01:27: -------------------------------------------------------------------
|
||||
08/16/2016 10:01:27: GPU info:
|
||||
|
||||
05/03/2016 15:21:43: Running on localhost at 2016/05/03 15:21:43
|
||||
05/03/2016 15:21:43: Command line:
|
||||
/home/alrezni/src/cntk_git/build/release/bin/cntk configFile=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/MultiGpu/../Config/Multigpu.cntk currentDirectory=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data RunDir=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu DataDir=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data ConfigDir=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/MultiGpu/../Config OutputDir=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu DeviceId=-1 timestamping=true Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
08/16/2016 10:01:27: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:01:27: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:01:27: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:01:27: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:01:27: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 10:01:27: Running on localhost at 2016/08/16 10:01:27
|
||||
08/16/2016 10:01:27: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/1bitsgd/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/MultiGpu/../../../../../../Examples/Other/Simple2d/Config/Multigpu.cntk currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_cpu DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/MultiGpu/../../../../../../Examples/Other/Simple2d/Config OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_cpu DeviceId=-1 timestamping=true Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
|
||||
|
||||
05/03/2016 15:21:43: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 15:21:43: RootDir = ".."
|
||||
08/16/2016 10:01:27: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:01:27: RootDir = ".."
|
||||
ConfigDir = "$RootDir$/Config"
|
||||
DataDir = "$RootDir$/Data"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
|
|
@ -140,28 +153,28 @@ dim = 2
|
|||
]
|
||||
outputPath = "$OutputDir$/MultigpuOutput"
|
||||
]
|
||||
currentDirectory=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
RunDir=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu
|
||||
DataDir=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
ConfigDir=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/MultiGpu/../Config
|
||||
OutputDir=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu
|
||||
currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_cpu
|
||||
DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/MultiGpu/../../../../../../Examples/Other/Simple2d/Config
|
||||
OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_cpu
|
||||
DeviceId=-1
|
||||
timestamping=true
|
||||
Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
05/03/2016 15:21:43: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:01:27: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 15:21:43: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 15:21:43: RootDir = ".."
|
||||
08/16/2016 10:01:27: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:01:27: RootDir = ".."
|
||||
ConfigDir = "../Config"
|
||||
DataDir = "../Data"
|
||||
OutputDir = "../Output"
|
||||
ModelDir = "/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu/Models"
|
||||
ModelDir = "/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_cpu/Models"
|
||||
deviceId = "auto"
|
||||
command = Multigpu_Demo_Train:Multigpu_Demo_Test
|
||||
precision = "float"
|
||||
traceLevel = 1
|
||||
modelPath = "/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn"
|
||||
modelPath = "/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn"
|
||||
outputNodeNames = ScaledLogLikelihood
|
||||
parallelTrain = true
|
||||
Multigpu_Demo_Train=[
|
||||
|
|
@ -193,7 +206,7 @@ Multigpu_Demo_Train=[
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTrain_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTrain_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -210,7 +223,7 @@ Multigpu_Demo_Test=[
|
|||
action = "test"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -227,7 +240,7 @@ Multigpu_Demo_Output=[
|
|||
action = "write"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -239,32 +252,32 @@ dim = 2
|
|||
]
|
||||
]
|
||||
]
|
||||
outputPath = "/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu/MultigpuOutput"
|
||||
outputPath = "/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_cpu/MultigpuOutput"
|
||||
]
|
||||
currentDirectory=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
RunDir=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu
|
||||
DataDir=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
ConfigDir=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/MultiGpu/../Config
|
||||
OutputDir=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu
|
||||
currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_cpu
|
||||
DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/MultiGpu/../../../../../../Examples/Other/Simple2d/Config
|
||||
OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_cpu
|
||||
DeviceId=-1
|
||||
timestamping=true
|
||||
Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
05/03/2016 15:21:43: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:01:27: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 15:21:43: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:01:27: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: Multigpu.cntk:command=Multigpu_Demo_Train:Multigpu_Demo_Test
|
||||
configparameters: Multigpu.cntk:ConfigDir=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/MultiGpu/../Config
|
||||
configparameters: Multigpu.cntk:currentDirectory=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
configparameters: Multigpu.cntk:DataDir=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
configparameters: Multigpu.cntk:ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/MultiGpu/../../../../../../Examples/Other/Simple2d/Config
|
||||
configparameters: Multigpu.cntk:currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
configparameters: Multigpu.cntk:DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
configparameters: Multigpu.cntk:deviceId=-1
|
||||
configparameters: Multigpu.cntk:ModelDir=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu/Models
|
||||
configparameters: Multigpu.cntk:modelPath=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn
|
||||
configparameters: Multigpu.cntk:ModelDir=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_cpu/Models
|
||||
configparameters: Multigpu.cntk:modelPath=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn
|
||||
configparameters: Multigpu.cntk:Multigpu_Demo_Output=[
|
||||
action = "write"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -276,14 +289,14 @@ dim = 2
|
|||
]
|
||||
]
|
||||
]
|
||||
outputPath = "/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu/MultigpuOutput"
|
||||
outputPath = "/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_cpu/MultigpuOutput"
|
||||
]
|
||||
|
||||
configparameters: Multigpu.cntk:Multigpu_Demo_Test=[
|
||||
action = "test"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -326,7 +339,7 @@ configparameters: Multigpu.cntk:Multigpu_Demo_Train=[
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTrain_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTrain_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -340,31 +353,43 @@ dim = 2
|
|||
]
|
||||
] [SGD=[maxEpochs=3]]
|
||||
|
||||
configparameters: Multigpu.cntk:OutputDir=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu
|
||||
configparameters: Multigpu.cntk:OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_cpu
|
||||
configparameters: Multigpu.cntk:outputNodeNames=ScaledLogLikelihood
|
||||
configparameters: Multigpu.cntk:parallelTrain=true
|
||||
configparameters: Multigpu.cntk:precision=float
|
||||
configparameters: Multigpu.cntk:RootDir=..
|
||||
configparameters: Multigpu.cntk:RunDir=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu
|
||||
configparameters: Multigpu.cntk:RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_cpu
|
||||
configparameters: Multigpu.cntk:timestamping=true
|
||||
configparameters: Multigpu.cntk:traceLevel=1
|
||||
05/03/2016 15:21:43: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/03/2016 15:21:43: Commands: Multigpu_Demo_Train Multigpu_Demo_Test
|
||||
05/03/2016 15:21:43: Precision = "float"
|
||||
05/03/2016 15:21:43: CNTKModelPath: /tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn
|
||||
05/03/2016 15:21:43: CNTKCommandTrainInfo: Multigpu_Demo_Train : 3
|
||||
05/03/2016 15:21:43: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
08/16/2016 10:01:27: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:01:27: Commands: Multigpu_Demo_Train Multigpu_Demo_Test
|
||||
08/16/2016 10:01:27: Precision = "float"
|
||||
08/16/2016 10:01:27: CNTKModelPath: /tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn
|
||||
08/16/2016 10:01:27: CNTKCommandTrainInfo: Multigpu_Demo_Train : 3
|
||||
08/16/2016 10:01:27: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
|
||||
05/03/2016 15:21:43: ##############################################################################
|
||||
05/03/2016 15:21:43: # #
|
||||
05/03/2016 15:21:43: # Action "train" #
|
||||
05/03/2016 15:21:43: # #
|
||||
05/03/2016 15:21:43: ##############################################################################
|
||||
08/16/2016 10:01:27: ##############################################################################
|
||||
08/16/2016 10:01:27: # #
|
||||
08/16/2016 10:01:27: # Action "train" #
|
||||
08/16/2016 10:01:27: # #
|
||||
08/16/2016 10:01:27: ##############################################################################
|
||||
|
||||
05/03/2016 15:21:43: CNTKCommandTrainBegin: Multigpu_Demo_Train
|
||||
08/16/2016 10:01:27: CNTKCommandTrainBegin: Multigpu_Demo_Train
|
||||
SimpleNetworkBuilder Using CPU
|
||||
|
||||
05/03/2016 15:21:43: Creating virgin network.
|
||||
08/16/2016 10:01:27: Creating virgin network.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[50 x 2] <- 0.000000.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[50 x 2] <- uniform(seed=1, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[50 x 50] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[50 x 50] <- uniform(seed=2, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[2 x 50] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[2 x 50] <- uniform(seed=3, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[2 x 1] <- 0.000000.
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[2 x 1] <- 0.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -416,207 +441,210 @@ Validating network, final pass.
|
|||
|
||||
Post-processing network complete.
|
||||
|
||||
05/03/2016 15:21:43: Created model with 25 nodes on CPU.
|
||||
08/16/2016 10:01:27: Created model with 25 nodes on CPU.
|
||||
|
||||
05/03/2016 15:21:43: Training criterion node(s):
|
||||
05/03/2016 15:21:43: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
|
||||
08/16/2016 10:01:27: Training criterion node(s):
|
||||
08/16/2016 10:01:27: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
|
||||
|
||||
05/03/2016 15:21:43: Evaluation criterion node(s):
|
||||
|
||||
05/03/2016 15:21:43: EvalErrorPrediction = ErrorPrediction
|
||||
08/16/2016 10:01:27: Evaluation criterion node(s):
|
||||
08/16/2016 10:01:27: EvalErrorPrediction = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 40 matrices, 19 are shared as 8, and 21 are not shared.
|
||||
|
||||
(nil): {[EvalErrorPrediction Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
|
||||
0x1abc7c8: {[InvStdOfFeatures Value[2]] }
|
||||
0x1b40348: {[features Value[2 x *]] }
|
||||
0x1b408b8: {[MeanOfFeatures Value[2]] }
|
||||
0x1b40bb8: {[W0 Value[50 x 2]] }
|
||||
0x1b41058: {[B0 Value[50 x 1]] }
|
||||
0x1b41d88: {[W1 Value[50 x 50]] }
|
||||
0x1b448c8: {[B1 Value[50 x 1]] }
|
||||
0x1b45698: {[W2 Value[2 x 50]] }
|
||||
0x1b45c98: {[B2 Value[2 x 1]] }
|
||||
0x1b46708: {[labels Value[2 x *]] }
|
||||
0x1b473e8: {[Prior Value[2]] }
|
||||
0x1b4b138: {[ScaledLogLikelihood Value[2 x 1 x *]] }
|
||||
0x1b4cc28: {[EvalErrorPrediction Value[1]] }
|
||||
0x1b4cea8: {[CrossEntropyWithSoftmax Value[1]] }
|
||||
0x1b4d388: {[H1 Value[50 x 1 x *]] [W0*features Gradient[50 x *]] }
|
||||
0x1b4d548: {[W0*features+B0 Gradient[50 x 1 x *]] [W1*H1 Value[50 x 1 x *]] }
|
||||
0x1b4d6c8: {[LogOfPrior Value[2]] }
|
||||
0x1b4f7f8: {[W0 Gradient[50 x 2]] [W0*features+B0 Value[50 x 1 x *]] }
|
||||
0x1b4fa08: {[MVNormalizedFeatures Value[2 x *]] }
|
||||
0x1b4fd28: {[W0*features Value[50 x *]] }
|
||||
0x1b4fee8: {[W1 Gradient[50 x 50]] [W1*H1+B1 Value[50 x 1 x *]] }
|
||||
0x1b500a8: {[H2 Value[50 x 1 x *]] [W1*H1 Gradient[50 x 1 x *]] }
|
||||
0x1b50268: {[B0 Gradient[50 x 1]] [H1 Gradient[50 x 1 x *]] [W1*H1+B1 Gradient[50 x 1 x *]] [W2*H1 Value[2 x 1 x *]] }
|
||||
0x1b50428: {[HLast Value[2 x 1 x *]] [W2 Gradient[2 x 50]] }
|
||||
0x1b50fb8: {[CrossEntropyWithSoftmax Gradient[1]] }
|
||||
0x1b51178: {[B1 Gradient[50 x 1]] [H2 Gradient[50 x 1 x *]] [HLast Gradient[2 x 1 x *]] }
|
||||
0x1b51338: {[W2*H1 Gradient[2 x 1 x *]] }
|
||||
0x1b514f8: {[B2 Gradient[2 x 1]] }
|
||||
{ W0 : [50 x 2] (gradient)
|
||||
W0*features+B0 : [50 x 1 x *] }
|
||||
{ H1 : [50 x 1 x *]
|
||||
W0*features : [50 x *] (gradient) }
|
||||
{ W0*features+B0 : [50 x 1 x *] (gradient)
|
||||
W1*H1 : [50 x 1 x *] }
|
||||
{ W1 : [50 x 50] (gradient)
|
||||
W1*H1+B1 : [50 x 1 x *] }
|
||||
{ H2 : [50 x 1 x *]
|
||||
W1*H1 : [50 x 1 x *] (gradient) }
|
||||
{ B0 : [50 x 1] (gradient)
|
||||
H1 : [50 x 1 x *] (gradient)
|
||||
W1*H1+B1 : [50 x 1 x *] (gradient)
|
||||
W2*H1 : [2 x 1 x *] }
|
||||
{ HLast : [2 x 1 x *]
|
||||
W2 : [2 x 50] (gradient) }
|
||||
{ B1 : [50 x 1] (gradient)
|
||||
H2 : [50 x 1 x *] (gradient)
|
||||
HLast : [2 x 1 x *] (gradient) }
|
||||
|
||||
|
||||
05/03/2016 15:21:43: Precomputing --> 3 PreCompute nodes found.
|
||||
08/16/2016 10:01:27: Training 2802 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:
|
||||
|
||||
05/03/2016 15:21:43: MeanOfFeatures = Mean()
|
||||
05/03/2016 15:21:43: InvStdOfFeatures = InvStdDev()
|
||||
05/03/2016 15:21:43: Prior = Mean()
|
||||
|
||||
05/03/2016 15:21:44: Precomputing --> Completed.
|
||||
08/16/2016 10:01:27: Node 'B0' (LearnableParameter operation) : [50 x 1]
|
||||
08/16/2016 10:01:27: Node 'B1' (LearnableParameter operation) : [50 x 1]
|
||||
08/16/2016 10:01:27: Node 'B2' (LearnableParameter operation) : [2 x 1]
|
||||
08/16/2016 10:01:27: Node 'W0' (LearnableParameter operation) : [50 x 2]
|
||||
08/16/2016 10:01:27: Node 'W1' (LearnableParameter operation) : [50 x 50]
|
||||
08/16/2016 10:01:27: Node 'W2' (LearnableParameter operation) : [2 x 50]
|
||||
|
||||
|
||||
05/03/2016 15:21:44: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
08/16/2016 10:01:27: Precomputing --> 3 PreCompute nodes found.
|
||||
|
||||
05/03/2016 15:21:44: Starting minibatch loop.
|
||||
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.69966235 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0538s; samplesPerSecond = 4647.4
|
||||
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.70639648 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.1073s; samplesPerSecond = 2329.6
|
||||
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.70470264 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0631s; samplesPerSecond = 3961.3
|
||||
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.69813501 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0747s; samplesPerSecond = 3346.9
|
||||
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.73551416 * 250; EvalErrorPrediction = 0.57600000 * 250; time = 0.0900s; samplesPerSecond = 2778.4
|
||||
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72432324 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0605s; samplesPerSecond = 4135.0
|
||||
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.73327588 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.0619s; samplesPerSecond = 4039.0
|
||||
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.70092627 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0769s; samplesPerSecond = 3249.9
|
||||
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.72354980 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0799s; samplesPerSecond = 3129.0
|
||||
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.72148096 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0620s; samplesPerSecond = 4031.5
|
||||
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.69814941 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.1278s; samplesPerSecond = 1955.9
|
||||
05/03/2016 15:21:44: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.70699121 * 250; EvalErrorPrediction = 0.54800000 * 250; time = 0.0821s; samplesPerSecond = 3044.1
|
||||
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.69898437 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0755s; samplesPerSecond = 3312.4
|
||||
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.71712695 * 250; EvalErrorPrediction = 0.54000000 * 250; time = 0.0657s; samplesPerSecond = 3804.8
|
||||
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.69470703 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.1049s; samplesPerSecond = 2382.9
|
||||
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.71375879 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.1180s; samplesPerSecond = 2117.9
|
||||
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70381641 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.1065s; samplesPerSecond = 2347.9
|
||||
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.71748633 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.2709s; samplesPerSecond = 922.9
|
||||
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.71863281 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.1375s; samplesPerSecond = 1818.4
|
||||
05/03/2016 15:21:45: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.70715234 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.1143s; samplesPerSecond = 2186.6
|
||||
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.70401074 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.1079s; samplesPerSecond = 2317.1
|
||||
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70599414 * 250; EvalErrorPrediction = 0.48400000 * 250; time = 0.0917s; samplesPerSecond = 2727.7
|
||||
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69628711 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0923s; samplesPerSecond = 2707.6
|
||||
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.75920898 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0887s; samplesPerSecond = 2819.0
|
||||
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.70542578 * 250; EvalErrorPrediction = 0.43600000 * 250; time = 0.0634s; samplesPerSecond = 3945.8
|
||||
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.70643945 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0885s; samplesPerSecond = 2823.7
|
||||
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.72481641 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0601s; samplesPerSecond = 4162.6
|
||||
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.71133594 * 250; EvalErrorPrediction = 0.55600000 * 250; time = 0.0630s; samplesPerSecond = 3968.1
|
||||
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.68605664 * 250; EvalErrorPrediction = 0.47200000 * 250; time = 0.0849s; samplesPerSecond = 2944.1
|
||||
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.69535352 * 250; EvalErrorPrediction = 0.47200000 * 250; time = 0.0879s; samplesPerSecond = 2844.6
|
||||
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.68741797 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0752s; samplesPerSecond = 3325.7
|
||||
05/03/2016 15:21:46: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.67916406 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0958s; samplesPerSecond = 2610.3
|
||||
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.67841992 * 250; EvalErrorPrediction = 0.44800000 * 250; time = 0.1009s; samplesPerSecond = 2478.7
|
||||
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.68038477 * 250; EvalErrorPrediction = 0.49200000 * 250; time = 0.1607s; samplesPerSecond = 1555.6
|
||||
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.61937109 * 250; EvalErrorPrediction = 0.30400000 * 250; time = 0.1131s; samplesPerSecond = 2211.4
|
||||
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.57844141 * 250; EvalErrorPrediction = 0.27200000 * 250; time = 0.1047s; samplesPerSecond = 2388.5
|
||||
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.49124023 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0896s; samplesPerSecond = 2791.5
|
||||
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.39071289 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0727s; samplesPerSecond = 3438.8
|
||||
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.27650586 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.2624s; samplesPerSecond = 952.6
|
||||
05/03/2016 15:21:47: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.26430078 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0842s; samplesPerSecond = 2967.7
|
||||
05/03/2016 15:21:47: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.66664150 * 10000; EvalErrorPrediction = 0.44430000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=3.93174s
|
||||
05/03/2016 15:21:47: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn.1'
|
||||
08/16/2016 10:01:27: MeanOfFeatures = Mean()
|
||||
08/16/2016 10:01:27: InvStdOfFeatures = InvStdDev()
|
||||
08/16/2016 10:01:27: Prior = Mean()
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
05/03/2016 15:21:47: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
|
||||
05/03/2016 15:21:47: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
|
||||
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.20720006 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0545s; samplesPerSecond = 4583.4
|
||||
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.19690290 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0641s; samplesPerSecond = 3899.7
|
||||
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.16064646 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0770s; samplesPerSecond = 3247.1
|
||||
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.13547171 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0640s; samplesPerSecond = 3904.2
|
||||
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.18000261 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0732s; samplesPerSecond = 3413.6
|
||||
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.17787841 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0790s; samplesPerSecond = 3164.0
|
||||
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.16821879 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0880s; samplesPerSecond = 2839.4
|
||||
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.16363456 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0854s; samplesPerSecond = 2926.8
|
||||
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.19533907 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0774s; samplesPerSecond = 3228.6
|
||||
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.19318692 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0820s; samplesPerSecond = 3049.5
|
||||
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.12726279 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0766s; samplesPerSecond = 3261.6
|
||||
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.18620067 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0773s; samplesPerSecond = 3235.5
|
||||
05/03/2016 15:21:48: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.11547500 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0797s; samplesPerSecond = 3136.6
|
||||
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16675950 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0833s; samplesPerSecond = 2999.8
|
||||
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.15807389 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0822s; samplesPerSecond = 3042.5
|
||||
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.18389093 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0726s; samplesPerSecond = 3443.0
|
||||
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.18269750 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0897s; samplesPerSecond = 2787.7
|
||||
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18737841 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0963s; samplesPerSecond = 2597.3
|
||||
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.20174757 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0811s; samplesPerSecond = 3081.1
|
||||
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.13336708 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0732s; samplesPerSecond = 3414.6
|
||||
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13851332 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0879s; samplesPerSecond = 2843.0
|
||||
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.15422288 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0821s; samplesPerSecond = 3044.3
|
||||
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.15478799 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0815s; samplesPerSecond = 3069.2
|
||||
05/03/2016 15:21:49: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.14530201 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0810s; samplesPerSecond = 3086.3
|
||||
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.12192809 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.2596s; samplesPerSecond = 962.9
|
||||
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.13975597 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0569s; samplesPerSecond = 4394.5
|
||||
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12566363 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0911s; samplesPerSecond = 2744.6
|
||||
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.18963051 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0765s; samplesPerSecond = 3267.2
|
||||
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.17955467 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0914s; samplesPerSecond = 2736.4
|
||||
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.18862103 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0772s; samplesPerSecond = 3236.7
|
||||
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.17503073 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0775s; samplesPerSecond = 3225.8
|
||||
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.14741998 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0774s; samplesPerSecond = 3230.1
|
||||
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.13803981 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0726s; samplesPerSecond = 3443.0
|
||||
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.14139232 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0820s; samplesPerSecond = 3048.4
|
||||
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13886877 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0766s; samplesPerSecond = 3264.1
|
||||
05/03/2016 15:21:50: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.15025864 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0852s; samplesPerSecond = 2933.5
|
||||
05/03/2016 15:21:51: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.14659342 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0903s; samplesPerSecond = 2767.4
|
||||
05/03/2016 15:21:51: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.13078795 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0784s; samplesPerSecond = 3187.6
|
||||
05/03/2016 15:21:51: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.19832882 * 250; EvalErrorPrediction = 0.11600000 * 250; time = 0.0772s; samplesPerSecond = 3240.4
|
||||
05/03/2016 15:21:51: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15828904 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0721s; samplesPerSecond = 3468.7
|
||||
05/03/2016 15:21:51: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.16210811 * 10000; EvalErrorPrediction = 0.07480000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=3.34279s
|
||||
05/03/2016 15:21:51: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn.2'
|
||||
|
||||
05/03/2016 15:21:51: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
|
||||
05/03/2016 15:21:51: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
|
||||
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.19031988 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0960s; samplesPerSecond = 2604.5
|
||||
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.13920714 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0967s; samplesPerSecond = 2585.3
|
||||
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14595162 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0869s; samplesPerSecond = 2877.8
|
||||
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.13324012 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0817s; samplesPerSecond = 3060.5
|
||||
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.17358728 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0804s; samplesPerSecond = 3109.2
|
||||
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.17949159 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0660s; samplesPerSecond = 3788.1
|
||||
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.15009323 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0653s; samplesPerSecond = 3829.5
|
||||
05/03/2016 15:21:51: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.17060954 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0660s; samplesPerSecond = 3787.3
|
||||
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.10410764 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0762s; samplesPerSecond = 3280.0
|
||||
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.20572259 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.2571s; samplesPerSecond = 972.5
|
||||
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.16519130 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0640s; samplesPerSecond = 3906.2
|
||||
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.14908187 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0593s; samplesPerSecond = 4213.2
|
||||
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.19227612 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0688s; samplesPerSecond = 3632.8
|
||||
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13670934 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0532s; samplesPerSecond = 4700.3
|
||||
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.21113164 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0693s; samplesPerSecond = 3609.4
|
||||
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.13129944 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0882s; samplesPerSecond = 2833.6
|
||||
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17304376 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0840s; samplesPerSecond = 2975.2
|
||||
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16479250 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0685s; samplesPerSecond = 3648.5
|
||||
05/03/2016 15:21:52: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.14591786 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0976s; samplesPerSecond = 2561.0
|
||||
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.12562012 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0969s; samplesPerSecond = 2580.7
|
||||
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13442773 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0959s; samplesPerSecond = 2607.8
|
||||
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.17125328 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0754s; samplesPerSecond = 3314.6
|
||||
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.22482522 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.1037s; samplesPerSecond = 2410.8
|
||||
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.18291792 * 250; EvalErrorPrediction = 0.11600000 * 250; time = 0.0650s; samplesPerSecond = 3844.3
|
||||
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.20296558 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0823s; samplesPerSecond = 3038.9
|
||||
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.22849719 * 250; EvalErrorPrediction = 0.12400000 * 250; time = 0.0828s; samplesPerSecond = 3020.2
|
||||
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12500068 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0864s; samplesPerSecond = 2894.1
|
||||
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.15719802 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0840s; samplesPerSecond = 2976.4
|
||||
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.11520810 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0687s; samplesPerSecond = 3636.7
|
||||
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.14159592 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0974s; samplesPerSecond = 2567.1
|
||||
05/03/2016 15:21:53: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18509569 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0721s; samplesPerSecond = 3465.4
|
||||
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.15008345 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0905s; samplesPerSecond = 2763.6
|
||||
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.12866435 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0902s; samplesPerSecond = 2770.5
|
||||
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.17640526 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0896s; samplesPerSecond = 2789.2
|
||||
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.14982110 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.2845s; samplesPerSecond = 878.8
|
||||
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.11472753 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0867s; samplesPerSecond = 2882.5
|
||||
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16524783 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0755s; samplesPerSecond = 3312.4
|
||||
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.14961037 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0958s; samplesPerSecond = 2608.8
|
||||
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.15972387 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0972s; samplesPerSecond = 2572.7
|
||||
05/03/2016 15:21:54: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.17867958 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0969s; samplesPerSecond = 2581.0
|
||||
05/03/2016 15:21:54: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.16073358 * 10000; EvalErrorPrediction = 0.07780000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=3.65495s
|
||||
05/03/2016 15:21:54: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn'
|
||||
05/03/2016 15:21:54: CNTKCommandTrainEnd: Multigpu_Demo_Train
|
||||
|
||||
05/03/2016 15:21:54: Action "train" complete.
|
||||
08/16/2016 10:01:27: Precomputing --> Completed.
|
||||
|
||||
|
||||
05/03/2016 15:21:54: ##############################################################################
|
||||
05/03/2016 15:21:54: # #
|
||||
05/03/2016 15:21:54: # Action "test" #
|
||||
05/03/2016 15:21:54: # #
|
||||
05/03/2016 15:21:54: ##############################################################################
|
||||
08/16/2016 10:01:27: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:01:27: Starting minibatch loop.
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.69846765 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0208s; samplesPerSecond = 12032.5
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.76129944 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0104s; samplesPerSecond = 24029.2
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.72963208 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0263s; samplesPerSecond = 9510.0
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.74041528 * 250; EvalErrorPrediction = 0.56800000 * 250; time = 0.0289s; samplesPerSecond = 8665.2
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.70611035 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0197s; samplesPerSecond = 12660.8
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.74740723 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0259s; samplesPerSecond = 9634.3
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.75085840 * 250; EvalErrorPrediction = 0.40400000 * 250; time = 0.0103s; samplesPerSecond = 24163.9
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.78210742 * 250; EvalErrorPrediction = 0.48400000 * 250; time = 0.0168s; samplesPerSecond = 14848.3
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.70286572 * 250; EvalErrorPrediction = 0.47200000 * 250; time = 0.0170s; samplesPerSecond = 14742.3
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.69580322 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0292s; samplesPerSecond = 8552.3
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.70703613 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0334s; samplesPerSecond = 7480.3
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.74512988 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0104s; samplesPerSecond = 23941.8
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.70837598 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0166s; samplesPerSecond = 15043.0
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.69913086 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0166s; samplesPerSecond = 15038.5
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.70321875 * 250; EvalErrorPrediction = 0.53600000 * 250; time = 0.0206s; samplesPerSecond = 12148.9
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.69290918 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0260s; samplesPerSecond = 9610.2
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.74415527 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0107s; samplesPerSecond = 23353.6
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.73745117 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0166s; samplesPerSecond = 15081.1
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.71849609 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0168s; samplesPerSecond = 14905.8
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.71476953 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0136s; samplesPerSecond = 18331.1
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.69918457 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0290s; samplesPerSecond = 8620.1
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.69749512 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0107s; samplesPerSecond = 23454.4
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.70658887 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0104s; samplesPerSecond = 23973.9
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.69760742 * 250; EvalErrorPrediction = 0.56800000 * 250; time = 0.0199s; samplesPerSecond = 12538.9
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.69499219 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0135s; samplesPerSecond = 18504.8
|
||||
08/16/2016 10:01:27: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.69291211 * 250; EvalErrorPrediction = 0.49200000 * 250; time = 0.0293s; samplesPerSecond = 8538.8
|
||||
08/16/2016 10:01:28: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.70718945 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0305s; samplesPerSecond = 8199.1
|
||||
08/16/2016 10:01:28: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.69039453 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0169s; samplesPerSecond = 14832.4
|
||||
08/16/2016 10:01:28: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.70257422 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0167s; samplesPerSecond = 14931.6
|
||||
08/16/2016 10:01:28: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.71058984 * 250; EvalErrorPrediction = 0.42800000 * 250; time = 0.0166s; samplesPerSecond = 15085.7
|
||||
08/16/2016 10:01:28: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.69296875 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0167s; samplesPerSecond = 14995.2
|
||||
08/16/2016 10:01:28: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.69641211 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0168s; samplesPerSecond = 14916.5
|
||||
08/16/2016 10:01:28: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.69531055 * 250; EvalErrorPrediction = 0.49200000 * 250; time = 0.0329s; samplesPerSecond = 7601.3
|
||||
08/16/2016 10:01:28: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.69090430 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0208s; samplesPerSecond = 12036.6
|
||||
08/16/2016 10:01:28: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.68339063 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0168s; samplesPerSecond = 14893.4
|
||||
08/16/2016 10:01:28: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.67383984 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0330s; samplesPerSecond = 7576.2
|
||||
08/16/2016 10:01:28: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.65904102 * 250; EvalErrorPrediction = 0.26400000 * 250; time = 0.0104s; samplesPerSecond = 24010.8
|
||||
08/16/2016 10:01:28: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.64259766 * 250; EvalErrorPrediction = 0.36000000 * 250; time = 0.0135s; samplesPerSecond = 18487.0
|
||||
08/16/2016 10:01:28: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.60433398 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0167s; samplesPerSecond = 15004.2
|
||||
08/16/2016 10:01:28: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.56497070 * 250; EvalErrorPrediction = 0.11600000 * 250; time = 0.0136s; samplesPerSecond = 18390.5
|
||||
08/16/2016 10:01:28: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.70222344 * 10000; EvalErrorPrediction = 0.46820000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=0.776535s
|
||||
08/16/2016 10:01:28: SGD: Saving checkpoint model '/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn.1'
|
||||
|
||||
08/16/2016 10:01:28: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [10000..20000] (first sequence at sample 10000), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:01:28: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.50722371 * 250; EvalErrorPrediction = 0.14800000 * 250; time = 0.0397s; samplesPerSecond = 6295.5
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.45786101 * 250; EvalErrorPrediction = 0.12800000 * 250; time = 0.0285s; samplesPerSecond = 8776.9
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.37902995 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0178s; samplesPerSecond = 14020.5
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.34590577 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0176s; samplesPerSecond = 14178.0
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.29942918 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0174s; samplesPerSecond = 14344.7
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.28291648 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0301s; samplesPerSecond = 8297.1
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.25680062 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0530s; samplesPerSecond = 4715.7
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.21806843 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0388s; samplesPerSecond = 6450.9
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.22671616 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0293s; samplesPerSecond = 8533.6
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.20709374 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0177s; samplesPerSecond = 14159.5
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.18895447 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0365s; samplesPerSecond = 6855.7
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.17506560 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0441s; samplesPerSecond = 5669.8
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.18710038 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0252s; samplesPerSecond = 9901.0
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.18230681 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0205s; samplesPerSecond = 12218.4
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.18466931 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0175s; samplesPerSecond = 14290.6
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.17889979 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0174s; samplesPerSecond = 14329.9
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.18170165 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0215s; samplesPerSecond = 11627.4
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.21059295 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0206s; samplesPerSecond = 12147.1
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.16428288 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0301s; samplesPerSecond = 8297.9
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.17104948 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0500s; samplesPerSecond = 5002.3
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13190985 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0206s; samplesPerSecond = 12160.7
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.17235489 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0174s; samplesPerSecond = 14329.1
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.12426324 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0205s; samplesPerSecond = 12183.2
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.21852627 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0177s; samplesPerSecond = 14104.4
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.21640896 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0487s; samplesPerSecond = 5133.5
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.17959436 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0157s; samplesPerSecond = 15952.0
|
||||
08/16/2016 10:01:28: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.16189965 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0145s; samplesPerSecond = 17266.4
|
||||
08/16/2016 10:01:29: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.13475075 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0175s; samplesPerSecond = 14282.4
|
||||
08/16/2016 10:01:29: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.16423768 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0333s; samplesPerSecond = 7510.0
|
||||
08/16/2016 10:01:29: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.14635259 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0298s; samplesPerSecond = 8393.5
|
||||
08/16/2016 10:01:29: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.14974090 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0174s; samplesPerSecond = 14368.6
|
||||
08/16/2016 10:01:29: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.12504713 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0175s; samplesPerSecond = 14289.0
|
||||
08/16/2016 10:01:29: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.16433451 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0371s; samplesPerSecond = 6744.0
|
||||
08/16/2016 10:01:29: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.14200378 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0221s; samplesPerSecond = 11319.9
|
||||
08/16/2016 10:01:29: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13708748 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0208s; samplesPerSecond = 12010.0
|
||||
08/16/2016 10:01:29: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.13991044 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0149s; samplesPerSecond = 16734.7
|
||||
08/16/2016 10:01:29: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.15786864 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0208s; samplesPerSecond = 12029.1
|
||||
08/16/2016 10:01:29: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.16220493 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0155s; samplesPerSecond = 16121.8
|
||||
08/16/2016 10:01:29: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.13517917 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0199s; samplesPerSecond = 12571.7
|
||||
08/16/2016 10:01:29: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15440438 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0217s; samplesPerSecond = 11501.1
|
||||
08/16/2016 10:01:29: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.20309370 * 10000; EvalErrorPrediction = 0.08040000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=1.02227s
|
||||
08/16/2016 10:01:29: SGD: Saving checkpoint model '/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn.2'
|
||||
|
||||
08/16/2016 10:01:29: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [20000..30000] (first sequence at sample 20000), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:01:29: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
|
||||
08/16/2016 10:01:29: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.18478506 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0216s; samplesPerSecond = 11585.3
|
||||
08/16/2016 10:01:29: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.12741733 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0184s; samplesPerSecond = 13576.6
|
||||
08/16/2016 10:01:29: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.17535235 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0235s; samplesPerSecond = 10656.9
|
||||
08/16/2016 10:01:29: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.14042800 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0150s; samplesPerSecond = 16696.7
|
||||
08/16/2016 10:01:29: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.16643002 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0247s; samplesPerSecond = 10109.6
|
||||
08/16/2016 10:01:29: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.19327050 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0389s; samplesPerSecond = 6424.8
|
||||
08/16/2016 10:01:29: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.12260149 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0292s; samplesPerSecond = 8568.7
|
||||
08/16/2016 10:01:29: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.16504305 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0328s; samplesPerSecond = 7631.0
|
||||
08/16/2016 10:01:29: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.12425912 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0315s; samplesPerSecond = 7945.3
|
||||
08/16/2016 10:01:29: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.19996755 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0943s; samplesPerSecond = 2649.9
|
||||
08/16/2016 10:01:29: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.14253075 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0703s; samplesPerSecond = 3554.8
|
||||
08/16/2016 10:01:29: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.12335900 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0494s; samplesPerSecond = 5064.0
|
||||
08/16/2016 10:01:29: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.16695660 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0353s; samplesPerSecond = 7090.2
|
||||
08/16/2016 10:01:29: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.19907855 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0346s; samplesPerSecond = 7225.4
|
||||
08/16/2016 10:01:29: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.16895044 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0346s; samplesPerSecond = 7233.4
|
||||
08/16/2016 10:01:29: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.13285834 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0376s; samplesPerSecond = 6645.0
|
||||
08/16/2016 10:01:29: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.14406293 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0176s; samplesPerSecond = 14231.2
|
||||
08/16/2016 10:01:29: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.20987060 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0171s; samplesPerSecond = 14639.6
|
||||
08/16/2016 10:01:29: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.19265041 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0389s; samplesPerSecond = 6432.9
|
||||
08/16/2016 10:01:29: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.15040079 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0277s; samplesPerSecond = 9019.4
|
||||
08/16/2016 10:01:30: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.15551715 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0205s; samplesPerSecond = 12207.0
|
||||
08/16/2016 10:01:30: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.13682837 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0212s; samplesPerSecond = 11784.1
|
||||
08/16/2016 10:01:30: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.17235013 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0174s; samplesPerSecond = 14356.3
|
||||
08/16/2016 10:01:30: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.14431340 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0176s; samplesPerSecond = 14196.5
|
||||
08/16/2016 10:01:30: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.13791050 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0184s; samplesPerSecond = 13580.3
|
||||
08/16/2016 10:01:30: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.14160704 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0175s; samplesPerSecond = 14275.1
|
||||
08/16/2016 10:01:30: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.16921888 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0172s; samplesPerSecond = 14549.3
|
||||
08/16/2016 10:01:30: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.18580557 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0206s; samplesPerSecond = 12133.6
|
||||
08/16/2016 10:01:30: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.16487179 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0157s; samplesPerSecond = 15918.5
|
||||
08/16/2016 10:01:30: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.15450410 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0204s; samplesPerSecond = 12249.5
|
||||
08/16/2016 10:01:30: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18731137 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0540s; samplesPerSecond = 4628.8
|
||||
08/16/2016 10:01:30: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.13205502 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0113s; samplesPerSecond = 22137.6
|
||||
08/16/2016 10:01:30: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.14591704 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0174s; samplesPerSecond = 14338.2
|
||||
08/16/2016 10:01:30: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.13912720 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0175s; samplesPerSecond = 14267.0
|
||||
08/16/2016 10:01:30: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.20110201 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0199s; samplesPerSecond = 12535.1
|
||||
08/16/2016 10:01:30: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.12560399 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0245s; samplesPerSecond = 10196.2
|
||||
08/16/2016 10:01:30: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.18609894 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0184s; samplesPerSecond = 13563.4
|
||||
08/16/2016 10:01:30: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15309858 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0174s; samplesPerSecond = 14405.9
|
||||
08/16/2016 10:01:30: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.11872821 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0175s; samplesPerSecond = 14303.7
|
||||
08/16/2016 10:01:30: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.12948843 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0178s; samplesPerSecond = 14041.0
|
||||
08/16/2016 10:01:30: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15779327 * 10000; EvalErrorPrediction = 0.07250000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=1.10281s
|
||||
08/16/2016 10:01:30: SGD: Saving checkpoint model '/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn'
|
||||
08/16/2016 10:01:30: CNTKCommandTrainEnd: Multigpu_Demo_Train
|
||||
|
||||
08/16/2016 10:01:30: Action "train" complete.
|
||||
|
||||
|
||||
08/16/2016 10:01:30: ##############################################################################
|
||||
08/16/2016 10:01:30: # #
|
||||
08/16/2016 10:01:30: # Action "test" #
|
||||
08/16/2016 10:01:30: # #
|
||||
08/16/2016 10:01:30: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
|
@ -674,35 +702,17 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 25 matrices, 2 are shared as 1, and 23 are not shared.
|
||||
|
||||
(nil): {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalErrorPrediction Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
|
||||
0x1abbf28: {[B0 Value[50 x 1]] }
|
||||
0x1b47908: {[W1 Value[50 x 50]] }
|
||||
0x1b48278: {[W2 Value[2 x 50]] }
|
||||
0x1b49778: {[InvStdOfFeatures Value[2]] }
|
||||
0x1b49f18: {[labels Value[2 x *1]] }
|
||||
0x1b4a958: {[B2 Value[2 x 1]] }
|
||||
0x1b4e568: {[features Value[2 x *1]] }
|
||||
0x1b502a8: {[MeanOfFeatures Value[2]] }
|
||||
0x1b50cd8: {[Prior Value[2]] }
|
||||
0x1b514f8: {[W0 Value[50 x 2]] }
|
||||
0x1b53938: {[B1 Value[50 x 1]] }
|
||||
0x1c0fd98: {[EvalErrorPrediction Value[1]] }
|
||||
0x1c0fef8: {[CrossEntropyWithSoftmax Value[1]] }
|
||||
0x1c10438: {[LogOfPrior Value[2]] }
|
||||
0x1c11f48: {[MVNormalizedFeatures Value[2 x *1]] }
|
||||
0x1c122f8: {[W0*features Value[50 x *1]] }
|
||||
0x1c124b8: {[W0*features+B0 Value[50 x 1 x *1]] }
|
||||
0x1c12678: {[H1 Value[50 x 1 x *1]] }
|
||||
0x1c12838: {[W1*H1 Value[50 x 1 x *1]] }
|
||||
0x1c129f8: {[W1*H1+B1 Value[50 x 1 x *1]] }
|
||||
0x1c12bb8: {[H2 Value[50 x 1 x *1]] }
|
||||
0x1c12d78: {[W2*H1 Value[2 x 1 x *1]] }
|
||||
0x1c12f38: {[HLast Value[2 x 1 x *1]] }
|
||||
{ PosteriorProb : [2 x 1 x *1]
|
||||
ScaledLogLikelihood : [2 x 1 x *1] }
|
||||
|
||||
05/03/2016 15:21:55: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.05804312 * 603; CrossEntropyWithSoftmax = 0.12790061 * 603; perplexity = 1.13644005
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..603] (first sequence at sample 0), data subset 0 of 1
|
||||
Actual gradient aggregation time: 0.000192
|
||||
08/16/2016 10:01:30: Minibatch[1-1]: EvalErrorPrediction = 0.05140962 * 603; CrossEntropyWithSoftmax = 0.10665885 * 603
|
||||
08/16/2016 10:01:30: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.05140962 * 603; CrossEntropyWithSoftmax = 0.10665885 * 603; perplexity = 1.11255464
|
||||
|
||||
05/03/2016 15:21:55: Action "test" complete.
|
||||
08/16/2016 10:01:30: Action "test" complete.
|
||||
|
||||
05/03/2016 15:21:55: __COMPLETED__
|
||||
08/16/2016 10:01:30: __COMPLETED__
|
||||
~MPIWrapper
|
||||
|
|
@ -1,22 +1,27 @@
|
|||
=== Running /home/alrezni/src/cntk_git/build/release/bin/cntk configFile=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/MultiGpu/../Config/Multigpu.cntk currentDirectory=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data RunDir=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu DataDir=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data ConfigDir=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/MultiGpu/../Config OutputDir=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu DeviceId=0 timestamping=true Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 264172964 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/1bitsgd/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/MultiGpu/../../../../../../Examples/Other/Simple2d/Config/Multigpu.cntk currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_gpu DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/MultiGpu/../../../../../../Examples/Other/Simple2d/Config OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_gpu DeviceId=0 timestamping=true Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 3 2016 15:08:09
|
||||
Last modified date: Tue Apr 5 16:01:37 2016
|
||||
Built time: Aug 16 2016 09:41:57
|
||||
Last modified date: Mon Aug 15 23:39:17 2016
|
||||
Build type: release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: yes
|
||||
Math lib: acml
|
||||
CUDA_PATH: /usr/local/cuda-7.0
|
||||
Math lib: mkl
|
||||
CUDA_PATH: /usr/local/cuda-7.5
|
||||
CUB_PATH: /usr/local/cub-1.4.1
|
||||
CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
Build Branch: alrezni/examples_text
|
||||
Build SHA1: e80dab7d66009531806ce70b4842146e0da00516
|
||||
Built by alrezni on atleneu04
|
||||
Build Path: /home/alrezni/src/cntk_git
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by philly on 643085f7f8c2
|
||||
Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to /home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
Changed current directory to /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
MPIWrapper: initializing MPI
|
||||
ping [requestnodes (before change)]: 1 nodes pinging each other
|
||||
ping [requestnodes (before change)]: all 1 nodes responded
|
||||
|
|
@ -26,32 +31,40 @@ ping [requestnodes (after change)]: all 1 nodes responded
|
|||
mpihelper: only one MPI process: MPI operation will be boring
|
||||
ping [mpihelper]: 1 nodes pinging each other
|
||||
ping [mpihelper]: all 1 nodes responded
|
||||
05/03/2016 15:21:55: -------------------------------------------------------------------
|
||||
05/03/2016 15:21:55: Build info:
|
||||
08/16/2016 10:01:31: -------------------------------------------------------------------
|
||||
08/16/2016 10:01:31: Build info:
|
||||
|
||||
05/03/2016 15:21:55: Built time: May 3 2016 15:08:09
|
||||
05/03/2016 15:21:55: Last modified date: Tue Apr 5 16:01:37 2016
|
||||
05/03/2016 15:21:55: Build type: release
|
||||
05/03/2016 15:21:55: Build target: GPU
|
||||
05/03/2016 15:21:55: With 1bit-SGD: yes
|
||||
05/03/2016 15:21:55: Math lib: acml
|
||||
05/03/2016 15:21:55: CUDA_PATH: /usr/local/cuda-7.0
|
||||
05/03/2016 15:21:55: CUB_PATH: /usr/local/cub-1.4.1
|
||||
05/03/2016 15:21:55: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
05/03/2016 15:21:55: Build Branch: alrezni/examples_text
|
||||
05/03/2016 15:21:55: Build SHA1: e80dab7d66009531806ce70b4842146e0da00516
|
||||
05/03/2016 15:21:55: Built by alrezni on atleneu04
|
||||
05/03/2016 15:21:55: Build Path: /home/alrezni/src/cntk_git
|
||||
05/03/2016 15:21:55: -------------------------------------------------------------------
|
||||
08/16/2016 10:01:31: Built time: Aug 16 2016 09:41:57
|
||||
08/16/2016 10:01:31: Last modified date: Mon Aug 15 23:39:17 2016
|
||||
08/16/2016 10:01:31: Build type: release
|
||||
08/16/2016 10:01:31: Build target: GPU
|
||||
08/16/2016 10:01:31: With 1bit-SGD: yes
|
||||
08/16/2016 10:01:31: Math lib: mkl
|
||||
08/16/2016 10:01:31: CUDA_PATH: /usr/local/cuda-7.5
|
||||
08/16/2016 10:01:31: CUB_PATH: /usr/local/cub-1.4.1
|
||||
08/16/2016 10:01:31: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
08/16/2016 10:01:31: Build Branch: HEAD
|
||||
08/16/2016 10:01:31: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 10:01:31: Built by philly on 643085f7f8c2
|
||||
08/16/2016 10:01:31: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
08/16/2016 10:01:31: -------------------------------------------------------------------
|
||||
08/16/2016 10:01:32: -------------------------------------------------------------------
|
||||
08/16/2016 10:01:32: GPU info:
|
||||
|
||||
05/03/2016 15:21:55: Running on localhost at 2016/05/03 15:21:55
|
||||
05/03/2016 15:21:55: Command line:
|
||||
/home/alrezni/src/cntk_git/build/release/bin/cntk configFile=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/MultiGpu/../Config/Multigpu.cntk currentDirectory=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data RunDir=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu DataDir=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data ConfigDir=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/MultiGpu/../Config OutputDir=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu DeviceId=0 timestamping=true Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
08/16/2016 10:01:32: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:01:32: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:01:32: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:01:32: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:01:32: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 10:01:32: Running on localhost at 2016/08/16 10:01:32
|
||||
08/16/2016 10:01:32: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/1bitsgd/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/MultiGpu/../../../../../../Examples/Other/Simple2d/Config/Multigpu.cntk currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_gpu DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/MultiGpu/../../../../../../Examples/Other/Simple2d/Config OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_gpu DeviceId=0 timestamping=true Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
|
||||
|
||||
05/03/2016 15:21:55: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 15:21:55: RootDir = ".."
|
||||
08/16/2016 10:01:32: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:01:32: RootDir = ".."
|
||||
ConfigDir = "$RootDir$/Config"
|
||||
DataDir = "$RootDir$/Data"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
|
|
@ -140,28 +153,28 @@ dim = 2
|
|||
]
|
||||
outputPath = "$OutputDir$/MultigpuOutput"
|
||||
]
|
||||
currentDirectory=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
RunDir=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu
|
||||
DataDir=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
ConfigDir=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/MultiGpu/../Config
|
||||
OutputDir=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu
|
||||
currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_gpu
|
||||
DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/MultiGpu/../../../../../../Examples/Other/Simple2d/Config
|
||||
OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
05/03/2016 15:21:55: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:01:32: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 15:21:55: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 15:21:55: RootDir = ".."
|
||||
08/16/2016 10:01:32: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:01:32: RootDir = ".."
|
||||
ConfigDir = "../Config"
|
||||
DataDir = "../Data"
|
||||
OutputDir = "../Output"
|
||||
ModelDir = "/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu/Models"
|
||||
ModelDir = "/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_gpu/Models"
|
||||
deviceId = "auto"
|
||||
command = Multigpu_Demo_Train:Multigpu_Demo_Test
|
||||
precision = "float"
|
||||
traceLevel = 1
|
||||
modelPath = "/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn"
|
||||
modelPath = "/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn"
|
||||
outputNodeNames = ScaledLogLikelihood
|
||||
parallelTrain = true
|
||||
Multigpu_Demo_Train=[
|
||||
|
|
@ -193,7 +206,7 @@ Multigpu_Demo_Train=[
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTrain_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTrain_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -210,7 +223,7 @@ Multigpu_Demo_Test=[
|
|||
action = "test"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -227,7 +240,7 @@ Multigpu_Demo_Output=[
|
|||
action = "write"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -239,32 +252,32 @@ dim = 2
|
|||
]
|
||||
]
|
||||
]
|
||||
outputPath = "/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu/MultigpuOutput"
|
||||
outputPath = "/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_gpu/MultigpuOutput"
|
||||
]
|
||||
currentDirectory=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
RunDir=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu
|
||||
DataDir=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
ConfigDir=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/MultiGpu/../Config
|
||||
OutputDir=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu
|
||||
currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_gpu
|
||||
DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/MultiGpu/../../../../../../Examples/Other/Simple2d/Config
|
||||
OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
05/03/2016 15:21:55: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:01:32: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 15:21:55: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:01:32: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: Multigpu.cntk:command=Multigpu_Demo_Train:Multigpu_Demo_Test
|
||||
configparameters: Multigpu.cntk:ConfigDir=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/MultiGpu/../Config
|
||||
configparameters: Multigpu.cntk:currentDirectory=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
configparameters: Multigpu.cntk:DataDir=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
configparameters: Multigpu.cntk:ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/MultiGpu/../../../../../../Examples/Other/Simple2d/Config
|
||||
configparameters: Multigpu.cntk:currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
configparameters: Multigpu.cntk:DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
configparameters: Multigpu.cntk:deviceId=0
|
||||
configparameters: Multigpu.cntk:ModelDir=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu/Models
|
||||
configparameters: Multigpu.cntk:modelPath=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn
|
||||
configparameters: Multigpu.cntk:ModelDir=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_gpu/Models
|
||||
configparameters: Multigpu.cntk:modelPath=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn
|
||||
configparameters: Multigpu.cntk:Multigpu_Demo_Output=[
|
||||
action = "write"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -276,14 +289,14 @@ dim = 2
|
|||
]
|
||||
]
|
||||
]
|
||||
outputPath = "/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu/MultigpuOutput"
|
||||
outputPath = "/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_gpu/MultigpuOutput"
|
||||
]
|
||||
|
||||
configparameters: Multigpu.cntk:Multigpu_Demo_Test=[
|
||||
action = "test"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -326,7 +339,7 @@ configparameters: Multigpu.cntk:Multigpu_Demo_Train=[
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTrain_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTrain_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -340,32 +353,44 @@ dim = 2
|
|||
]
|
||||
] [SGD=[maxEpochs=3]]
|
||||
|
||||
configparameters: Multigpu.cntk:OutputDir=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu
|
||||
configparameters: Multigpu.cntk:OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_gpu
|
||||
configparameters: Multigpu.cntk:outputNodeNames=ScaledLogLikelihood
|
||||
configparameters: Multigpu.cntk:parallelTrain=true
|
||||
configparameters: Multigpu.cntk:precision=float
|
||||
configparameters: Multigpu.cntk:RootDir=..
|
||||
configparameters: Multigpu.cntk:RunDir=/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu
|
||||
configparameters: Multigpu.cntk:RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_gpu
|
||||
configparameters: Multigpu.cntk:timestamping=true
|
||||
configparameters: Multigpu.cntk:traceLevel=1
|
||||
05/03/2016 15:21:55: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/03/2016 15:21:55: Commands: Multigpu_Demo_Train Multigpu_Demo_Test
|
||||
05/03/2016 15:21:55: Precision = "float"
|
||||
05/03/2016 15:21:55: CNTKModelPath: /tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn
|
||||
05/03/2016 15:21:55: CNTKCommandTrainInfo: Multigpu_Demo_Train : 3
|
||||
05/03/2016 15:21:55: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
08/16/2016 10:01:32: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:01:32: Commands: Multigpu_Demo_Train Multigpu_Demo_Test
|
||||
08/16/2016 10:01:32: Precision = "float"
|
||||
08/16/2016 10:01:32: CNTKModelPath: /tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn
|
||||
08/16/2016 10:01:32: CNTKCommandTrainInfo: Multigpu_Demo_Train : 3
|
||||
08/16/2016 10:01:32: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
|
||||
05/03/2016 15:21:55: ##############################################################################
|
||||
05/03/2016 15:21:55: # #
|
||||
05/03/2016 15:21:55: # Action "train" #
|
||||
05/03/2016 15:21:55: # #
|
||||
05/03/2016 15:21:55: ##############################################################################
|
||||
08/16/2016 10:01:32: ##############################################################################
|
||||
08/16/2016 10:01:32: # #
|
||||
08/16/2016 10:01:32: # Action "train" #
|
||||
08/16/2016 10:01:32: # #
|
||||
08/16/2016 10:01:32: ##############################################################################
|
||||
|
||||
05/03/2016 15:21:55: CNTKCommandTrainBegin: Multigpu_Demo_Train
|
||||
08/16/2016 10:01:32: CNTKCommandTrainBegin: Multigpu_Demo_Train
|
||||
SimpleNetworkBuilder Using GPU 0
|
||||
|
||||
05/03/2016 15:21:55: Creating virgin network.
|
||||
08/16/2016 10:01:32: Creating virgin network.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[50 x 2] <- 0.000000.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[50 x 2] <- uniform(seed=1, range=0.050000*1.000000, onCPU=false).
|
||||
SetUniformRandomValue (GPU): creating curand object with seed 1, sizeof(ElemType)==4
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[50 x 50] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[50 x 50] <- uniform(seed=2, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[2 x 50] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[2 x 50] <- uniform(seed=3, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[2 x 1] <- 0.000000.
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[2 x 1] <- 0.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -417,207 +442,210 @@ Validating network, final pass.
|
|||
|
||||
Post-processing network complete.
|
||||
|
||||
05/03/2016 15:21:55: Created model with 25 nodes on GPU 0.
|
||||
08/16/2016 10:01:32: Created model with 25 nodes on GPU 0.
|
||||
|
||||
05/03/2016 15:21:55: Training criterion node(s):
|
||||
05/03/2016 15:21:55: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
|
||||
08/16/2016 10:01:32: Training criterion node(s):
|
||||
08/16/2016 10:01:32: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
|
||||
|
||||
05/03/2016 15:21:55: Evaluation criterion node(s):
|
||||
|
||||
05/03/2016 15:21:55: EvalErrorPrediction = ErrorPrediction
|
||||
08/16/2016 10:01:32: Evaluation criterion node(s):
|
||||
08/16/2016 10:01:32: EvalErrorPrediction = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 40 matrices, 19 are shared as 8, and 21 are not shared.
|
||||
|
||||
(nil): {[EvalErrorPrediction Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
|
||||
0x12a62e8: {[features Value[2 x *]] }
|
||||
0x20202b8: {[MeanOfFeatures Value[2]] }
|
||||
0x20207c8: {[InvStdOfFeatures Value[2]] }
|
||||
0x2021538: {[W0 Value[50 x 2]] }
|
||||
0x2786378: {[W1 Value[50 x 50]] }
|
||||
0x2787248: {[B1 Value[50 x 1]] }
|
||||
0x2788348: {[W2 Value[2 x 50]] }
|
||||
0x2788de8: {[B2 Value[2 x 1]] }
|
||||
0x2789cc8: {[labels Value[2 x *]] }
|
||||
0x278ae18: {[Prior Value[2]] }
|
||||
0x278c158: {[LogOfPrior Value[2]] }
|
||||
0x27908f8: {[H1 Value[50 x 1 x *]] [W0*features Gradient[50 x *]] }
|
||||
0x2790a18: {[EvalErrorPrediction Value[1]] }
|
||||
0x2790d18: {[ScaledLogLikelihood Value[2 x 1 x *]] }
|
||||
0x2790e78: {[CrossEntropyWithSoftmax Value[1]] }
|
||||
0x27966e8: {[B0 Value[50 x 1]] }
|
||||
0x2adb168: {[W0 Gradient[50 x 2]] [W0*features+B0 Value[50 x 1 x *]] }
|
||||
0x2adb378: {[MVNormalizedFeatures Value[2 x *]] }
|
||||
0x2adb698: {[W0*features Value[50 x *]] }
|
||||
0x2adb738: {[W0*features+B0 Gradient[50 x 1 x *]] [W1*H1 Value[50 x 1 x *]] }
|
||||
0x2adb898: {[W1 Gradient[50 x 50]] [W1*H1+B1 Value[50 x 1 x *]] }
|
||||
0x2adb9f8: {[H2 Value[50 x 1 x *]] [W1*H1 Gradient[50 x 1 x *]] }
|
||||
0x2adbb58: {[B0 Gradient[50 x 1]] [H1 Gradient[50 x 1 x *]] [W1*H1+B1 Gradient[50 x 1 x *]] [W2*H1 Value[2 x 1 x *]] }
|
||||
0x2adbcb8: {[HLast Value[2 x 1 x *]] [W2 Gradient[2 x 50]] }
|
||||
0x2adc6f8: {[CrossEntropyWithSoftmax Gradient[1]] }
|
||||
0x2adc8b8: {[B1 Gradient[50 x 1]] [H2 Gradient[50 x 1 x *]] [HLast Gradient[2 x 1 x *]] }
|
||||
0x2adca78: {[W2*H1 Gradient[2 x 1 x *]] }
|
||||
0x2adcc38: {[B2 Gradient[2 x 1]] }
|
||||
{ W0 : [50 x 2] (gradient)
|
||||
W0*features+B0 : [50 x 1 x *] }
|
||||
{ H1 : [50 x 1 x *]
|
||||
W0*features : [50 x *] (gradient) }
|
||||
{ W0*features+B0 : [50 x 1 x *] (gradient)
|
||||
W1*H1 : [50 x 1 x *] }
|
||||
{ W1 : [50 x 50] (gradient)
|
||||
W1*H1+B1 : [50 x 1 x *] }
|
||||
{ H2 : [50 x 1 x *]
|
||||
W1*H1 : [50 x 1 x *] (gradient) }
|
||||
{ B0 : [50 x 1] (gradient)
|
||||
H1 : [50 x 1 x *] (gradient)
|
||||
W1*H1+B1 : [50 x 1 x *] (gradient)
|
||||
W2*H1 : [2 x 1 x *] }
|
||||
{ HLast : [2 x 1 x *]
|
||||
W2 : [2 x 50] (gradient) }
|
||||
{ B1 : [50 x 1] (gradient)
|
||||
H2 : [50 x 1 x *] (gradient)
|
||||
HLast : [2 x 1 x *] (gradient) }
|
||||
|
||||
|
||||
05/03/2016 15:21:55: Precomputing --> 3 PreCompute nodes found.
|
||||
08/16/2016 10:01:32: Training 2802 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:
|
||||
|
||||
05/03/2016 15:21:55: MeanOfFeatures = Mean()
|
||||
05/03/2016 15:21:55: InvStdOfFeatures = InvStdDev()
|
||||
05/03/2016 15:21:55: Prior = Mean()
|
||||
|
||||
05/03/2016 15:21:56: Precomputing --> Completed.
|
||||
08/16/2016 10:01:32: Node 'B0' (LearnableParameter operation) : [50 x 1]
|
||||
08/16/2016 10:01:32: Node 'B1' (LearnableParameter operation) : [50 x 1]
|
||||
08/16/2016 10:01:32: Node 'B2' (LearnableParameter operation) : [2 x 1]
|
||||
08/16/2016 10:01:32: Node 'W0' (LearnableParameter operation) : [50 x 2]
|
||||
08/16/2016 10:01:32: Node 'W1' (LearnableParameter operation) : [50 x 50]
|
||||
08/16/2016 10:01:32: Node 'W2' (LearnableParameter operation) : [2 x 50]
|
||||
|
||||
|
||||
05/03/2016 15:21:56: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
08/16/2016 10:01:32: Precomputing --> 3 PreCompute nodes found.
|
||||
|
||||
05/03/2016 15:21:56: Starting minibatch loop.
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70004456 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0059s; samplesPerSecond = 42038.0
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.70309900 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0049s; samplesPerSecond = 50525.5
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.70606104 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0050s; samplesPerSecond = 50423.6
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.69845532 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0049s; samplesPerSecond = 50689.4
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.73496533 * 250; EvalErrorPrediction = 0.57600000 * 250; time = 0.0050s; samplesPerSecond = 50261.4
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72522827 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0050s; samplesPerSecond = 50454.1
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.73287500 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.0049s; samplesPerSecond = 50576.6
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.70135547 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0049s; samplesPerSecond = 50566.3
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.72466504 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0049s; samplesPerSecond = 50515.3
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.72187500 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0049s; samplesPerSecond = 50730.5
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.69799023 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0049s; samplesPerSecond = 50751.1
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.70696387 * 250; EvalErrorPrediction = 0.54800000 * 250; time = 0.0050s; samplesPerSecond = 50454.1
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.69863965 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0050s; samplesPerSecond = 50393.1
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.71772461 * 250; EvalErrorPrediction = 0.54800000 * 250; time = 0.0048s; samplesPerSecond = 51899.5
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.69526270 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0047s; samplesPerSecond = 53544.7
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.71436426 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0047s; samplesPerSecond = 53498.8
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70399316 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0047s; samplesPerSecond = 53694.2
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.71745508 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.0046s; samplesPerSecond = 53879.3
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.71963184 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0047s; samplesPerSecond = 53521.7
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.70689941 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0047s; samplesPerSecond = 53602.1
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.70425098 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0046s; samplesPerSecond = 53890.9
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70622754 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0047s; samplesPerSecond = 53728.8
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69729492 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0046s; samplesPerSecond = 53786.6
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.75974219 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0046s; samplesPerSecond = 54265.2
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.70631250 * 250; EvalErrorPrediction = 0.43600000 * 250; time = 0.0047s; samplesPerSecond = 53659.6
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.70705664 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0047s; samplesPerSecond = 53602.1
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.72660352 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0046s; samplesPerSecond = 54124.3
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.71369727 * 250; EvalErrorPrediction = 0.55600000 * 250; time = 0.0047s; samplesPerSecond = 53441.6
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.68916602 * 250; EvalErrorPrediction = 0.47200000 * 250; time = 0.0047s; samplesPerSecond = 53659.6
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.69964844 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0047s; samplesPerSecond = 53339.0
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.69387891 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0046s; samplesPerSecond = 53832.9
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.68885742 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0047s; samplesPerSecond = 53350.4
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.69388867 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0047s; samplesPerSecond = 53430.2
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.70363867 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0046s; samplesPerSecond = 53960.7
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.65449219 * 250; EvalErrorPrediction = 0.44400000 * 250; time = 0.0047s; samplesPerSecond = 53544.7
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.64607031 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0047s; samplesPerSecond = 53453.1
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.59492969 * 250; EvalErrorPrediction = 0.12400000 * 250; time = 0.0046s; samplesPerSecond = 53972.4
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.53965820 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0047s; samplesPerSecond = 53636.6
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.43681445 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0047s; samplesPerSecond = 52854.1
|
||||
05/03/2016 15:21:56: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.37407422 * 250; EvalErrorPrediction = 0.12000000 * 250; time = 0.0047s; samplesPerSecond = 53521.7
|
||||
05/03/2016 15:21:56: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.68409629 * 10000; EvalErrorPrediction = 0.45780000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=0.194983s
|
||||
05/03/2016 15:21:56: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn.1'
|
||||
08/16/2016 10:01:32: MeanOfFeatures = Mean()
|
||||
08/16/2016 10:01:32: InvStdOfFeatures = InvStdDev()
|
||||
08/16/2016 10:01:32: Prior = Mean()
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
05/03/2016 15:21:56: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
|
||||
05/03/2016 15:21:56: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.27919647 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0093s; samplesPerSecond = 26818.3
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.24468611 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0080s; samplesPerSecond = 31063.6
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.19639892 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0081s; samplesPerSecond = 30982.8
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.16397861 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0080s; samplesPerSecond = 31222.7
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.19745002 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0081s; samplesPerSecond = 30944.4
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.19548896 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0081s; samplesPerSecond = 30871.8
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.18230148 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0081s; samplesPerSecond = 30910.0
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.17531255 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0080s; samplesPerSecond = 31059.8
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.20166559 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0081s; samplesPerSecond = 30944.4
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.19749058 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0081s; samplesPerSecond = 31055.9
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.13463336 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0081s; samplesPerSecond = 30963.6
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.19006259 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0080s; samplesPerSecond = 31063.6
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.12234776 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0079s; samplesPerSecond = 31605.6
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16962922 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0077s; samplesPerSecond = 32649.9
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.16091639 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0076s; samplesPerSecond = 32743.9
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.18624030 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32748.2
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.18465726 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0076s; samplesPerSecond = 32899.1
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18514518 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0077s; samplesPerSecond = 32620.0
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.20127224 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0076s; samplesPerSecond = 32791.2
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.13418547 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0076s; samplesPerSecond = 32701.1
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13995001 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0076s; samplesPerSecond = 32838.6
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.15602538 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0076s; samplesPerSecond = 32907.7
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.15448171 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32864.5
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.14780067 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0076s; samplesPerSecond = 32894.7
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.12361633 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0077s; samplesPerSecond = 32628.6
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.14079766 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0077s; samplesPerSecond = 32632.8
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12624363 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0076s; samplesPerSecond = 32899.1
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.18913222 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32894.7
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.17952681 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0076s; samplesPerSecond = 32786.9
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.18825452 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0076s; samplesPerSecond = 32825.6
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.17517656 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0076s; samplesPerSecond = 32942.4
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.14744161 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32791.2
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.13888184 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0076s; samplesPerSecond = 32795.5
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.14156678 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0076s; samplesPerSecond = 32855.8
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13990591 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0077s; samplesPerSecond = 32607.3
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.15059729 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0076s; samplesPerSecond = 32855.8
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.14720846 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0076s; samplesPerSecond = 32799.8
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.13021243 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0076s; samplesPerSecond = 32912.1
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.19704037 * 250; EvalErrorPrediction = 0.11600000 * 250; time = 0.0076s; samplesPerSecond = 33029.5
|
||||
05/03/2016 15:21:56: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15858146 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0076s; samplesPerSecond = 32860.1
|
||||
05/03/2016 15:21:56: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.16938752 * 10000; EvalErrorPrediction = 0.07430000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.313881s
|
||||
05/03/2016 15:21:56: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn.2'
|
||||
|
||||
05/03/2016 15:21:56: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
|
||||
05/03/2016 15:21:56: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.18888809 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0078s; samplesPerSecond = 32129.5
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.14084978 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0076s; samplesPerSecond = 32756.8
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14561895 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0077s; samplesPerSecond = 32666.9
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.13238169 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0076s; samplesPerSecond = 32752.5
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.17465335 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0076s; samplesPerSecond = 32765.4
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.17752616 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0076s; samplesPerSecond = 32821.3
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.15030556 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0077s; samplesPerSecond = 32645.6
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.17118019 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0077s; samplesPerSecond = 32611.5
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.10379908 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0077s; samplesPerSecond = 32637.1
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.20636150 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0076s; samplesPerSecond = 32782.6
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.16606704 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0077s; samplesPerSecond = 32543.6
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.14937580 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0077s; samplesPerSecond = 32446.5
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.19161901 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0076s; samplesPerSecond = 32731.1
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13684752 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0076s; samplesPerSecond = 32696.8
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.21095939 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0076s; samplesPerSecond = 32688.3
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.13216461 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0076s; samplesPerSecond = 32769.7
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17341094 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0077s; samplesPerSecond = 32586.0
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16532641 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0076s; samplesPerSecond = 32868.8
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.14614740 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0076s; samplesPerSecond = 32696.8
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.12551177 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0076s; samplesPerSecond = 32705.4
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13419939 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32782.6
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.17050096 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0076s; samplesPerSecond = 32899.1
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.22579789 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0076s; samplesPerSecond = 32838.6
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.18219666 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0078s; samplesPerSecond = 32220.6
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.20347898 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32791.2
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.22972656 * 250; EvalErrorPrediction = 0.12000000 * 250; time = 0.0076s; samplesPerSecond = 32825.6
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12621914 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0076s; samplesPerSecond = 32890.4
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.15674728 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0076s; samplesPerSecond = 32808.4
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.11517532 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0077s; samplesPerSecond = 32658.4
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.14187870 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32860.1
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18496784 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0076s; samplesPerSecond = 32929.4
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.15026403 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0076s; samplesPerSecond = 32942.4
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.12862609 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32925.1
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.17651362 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0076s; samplesPerSecond = 32778.3
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.14975908 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0076s; samplesPerSecond = 32981.5
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.11465866 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0076s; samplesPerSecond = 32838.6
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16513610 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0076s; samplesPerSecond = 32808.4
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.14972374 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0076s; samplesPerSecond = 32977.2
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.15995582 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0076s; samplesPerSecond = 32825.6
|
||||
05/03/2016 15:21:56: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.17898927 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0076s; samplesPerSecond = 32756.8
|
||||
05/03/2016 15:21:56: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.16083773 * 10000; EvalErrorPrediction = 0.07760000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.307973s
|
||||
05/03/2016 15:21:56: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152142.598996/CNTKTextFormatReader/Examples/Other/Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn'
|
||||
05/03/2016 15:21:56: CNTKCommandTrainEnd: Multigpu_Demo_Train
|
||||
|
||||
05/03/2016 15:21:56: Action "train" complete.
|
||||
08/16/2016 10:01:32: Precomputing --> Completed.
|
||||
|
||||
|
||||
05/03/2016 15:21:56: ##############################################################################
|
||||
05/03/2016 15:21:56: # #
|
||||
05/03/2016 15:21:56: # Action "test" #
|
||||
05/03/2016 15:21:56: # #
|
||||
05/03/2016 15:21:56: ##############################################################################
|
||||
08/16/2016 10:01:32: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:01:32: Starting minibatch loop.
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70124231 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0076s; samplesPerSecond = 32761.1
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.76372424 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0062s; samplesPerSecond = 40374.7
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.72703027 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0061s; samplesPerSecond = 40836.3
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.73895923 * 250; EvalErrorPrediction = 0.56800000 * 250; time = 0.0061s; samplesPerSecond = 41077.9
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.70621924 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0061s; samplesPerSecond = 41010.5
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.74767041 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0061s; samplesPerSecond = 41308.7
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.75094434 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0061s; samplesPerSecond = 40690.1
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.78058936 * 250; EvalErrorPrediction = 0.48400000 * 250; time = 0.0061s; samplesPerSecond = 40990.3
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.70407129 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0061s; samplesPerSecond = 40763.1
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.69555762 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0061s; samplesPerSecond = 41247.3
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.70626123 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0061s; samplesPerSecond = 40976.9
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.74540430 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0061s; samplesPerSecond = 41179.4
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.70824414 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0060s; samplesPerSecond = 41480.0
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.69895020 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0060s; samplesPerSecond = 41397.6
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.70353223 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0061s; samplesPerSecond = 40763.1
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.69346387 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0061s; samplesPerSecond = 41186.2
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.74449902 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0062s; samplesPerSecond = 40643.8
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.73767969 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0060s; samplesPerSecond = 41820.0
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.71876855 * 250; EvalErrorPrediction = 0.48400000 * 250; time = 0.0060s; samplesPerSecond = 41862.0
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.71509473 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0061s; samplesPerSecond = 41138.7
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.69956152 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0059s; samplesPerSecond = 42108.8
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.69785937 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0059s; samplesPerSecond = 42337.0
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.70736035 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0059s; samplesPerSecond = 42030.9
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.69820508 * 250; EvalErrorPrediction = 0.56800000 * 250; time = 0.0059s; samplesPerSecond = 42430.4
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.69537109 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0059s; samplesPerSecond = 42286.9
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.69347266 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0059s; samplesPerSecond = 42387.2
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.70801172 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0060s; samplesPerSecond = 41652.8
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.69131641 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0059s; samplesPerSecond = 42294.0
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.70370312 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0058s; samplesPerSecond = 42771.6
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.71200195 * 250; EvalErrorPrediction = 0.43600000 * 250; time = 0.0058s; samplesPerSecond = 42808.2
|
||||
08/16/2016 10:01:32: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.69506836 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0058s; samplesPerSecond = 42800.9
|
||||
08/16/2016 10:01:33: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.69935352 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0058s; samplesPerSecond = 43305.0
|
||||
08/16/2016 10:01:33: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.69887109 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0058s; samplesPerSecond = 42764.3
|
||||
08/16/2016 10:01:33: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.69604492 * 250; EvalErrorPrediction = 0.49200000 * 250; time = 0.0058s; samplesPerSecond = 43110.9
|
||||
08/16/2016 10:01:33: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.69011719 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.0065s; samplesPerSecond = 38302.4
|
||||
08/16/2016 10:01:33: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.68419531 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0058s; samplesPerSecond = 43148.1
|
||||
08/16/2016 10:01:33: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.67551367 * 250; EvalErrorPrediction = 0.32400000 * 250; time = 0.0059s; samplesPerSecond = 42094.6
|
||||
08/16/2016 10:01:33: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.67028516 * 250; EvalErrorPrediction = 0.40000000 * 250; time = 0.0059s; samplesPerSecond = 42294.0
|
||||
08/16/2016 10:01:33: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.65152734 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0059s; samplesPerSecond = 42329.8
|
||||
08/16/2016 10:01:33: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.63594727 * 250; EvalErrorPrediction = 0.22000000 * 250; time = 0.0060s; samplesPerSecond = 41666.7
|
||||
08/16/2016 10:01:33: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.70729233 * 10000; EvalErrorPrediction = 0.47740000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=0.245257s
|
||||
08/16/2016 10:01:33: SGD: Saving checkpoint model '/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn.1'
|
||||
|
||||
08/16/2016 10:01:33: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [10000..20000] (first sequence at sample 10000), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:01:33: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.61550018 * 250; EvalErrorPrediction = 0.27600000 * 250; time = 0.0108s; samplesPerSecond = 23111.8
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.59409242 * 250; EvalErrorPrediction = 0.28800000 * 250; time = 0.0094s; samplesPerSecond = 26612.7
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.53884306 * 250; EvalErrorPrediction = 0.20400000 * 250; time = 0.0093s; samplesPerSecond = 26890.4
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.52450125 * 250; EvalErrorPrediction = 0.15200000 * 250; time = 0.0093s; samplesPerSecond = 26942.6
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.49237463 * 250; EvalErrorPrediction = 0.16400000 * 250; time = 0.0092s; samplesPerSecond = 27038.7
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.44029644 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0093s; samplesPerSecond = 26847.1
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.40029475 * 250; EvalErrorPrediction = 0.13200000 * 250; time = 0.0092s; samplesPerSecond = 27059.2
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.34001918 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0093s; samplesPerSecond = 26957.1
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.31615756 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0096s; samplesPerSecond = 26172.5
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.27277486 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0094s; samplesPerSecond = 26635.4
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.24557418 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0092s; samplesPerSecond = 27185.7
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.21023629 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0092s; samplesPerSecond = 27218.3
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.22380673 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0092s; samplesPerSecond = 27115.0
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.20455512 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0092s; samplesPerSecond = 27068.0
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.20168480 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0091s; samplesPerSecond = 27400.3
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.19212741 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0091s; samplesPerSecond = 27397.3
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.19324124 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0091s; samplesPerSecond = 27343.3
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.21777418 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0094s; samplesPerSecond = 26477.4
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.17514209 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0093s; samplesPerSecond = 26948.4
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.17993773 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0091s; samplesPerSecond = 27334.4
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13968032 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0093s; samplesPerSecond = 26989.1
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.17727753 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0095s; samplesPerSecond = 26452.2
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.12898624 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0095s; samplesPerSecond = 26438.2
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.21880105 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0095s; samplesPerSecond = 26340.7
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.21850111 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0097s; samplesPerSecond = 25805.1
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.18102491 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0096s; samplesPerSecond = 26082.4
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.16393427 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0095s; samplesPerSecond = 26235.7
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.13832267 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0095s; samplesPerSecond = 26241.2
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.16506280 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0096s; samplesPerSecond = 25995.6
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.14733234 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0095s; samplesPerSecond = 26452.2
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.15041138 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0095s; samplesPerSecond = 26189.0
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.12665836 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0095s; samplesPerSecond = 26296.4
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.16643186 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0095s; samplesPerSecond = 26249.5
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.14422443 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0096s; samplesPerSecond = 26147.9
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13888039 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0094s; samplesPerSecond = 26474.6
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.14108686 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0095s; samplesPerSecond = 26249.5
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.15887684 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0097s; samplesPerSecond = 25738.7
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.16247402 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0094s; samplesPerSecond = 26505.5
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.13586729 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0096s; samplesPerSecond = 26109.7
|
||||
08/16/2016 10:01:33: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15528679 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0094s; samplesPerSecond = 26626.9
|
||||
08/16/2016 10:01:33: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.24345139 * 10000; EvalErrorPrediction = 0.09720000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.379525s
|
||||
08/16/2016 10:01:33: SGD: Saving checkpoint model '/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn.2'
|
||||
|
||||
08/16/2016 10:01:33: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [20000..30000] (first sequence at sample 20000), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:01:33: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.18398525 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0097s; samplesPerSecond = 25685.8
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.12825686 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0095s; samplesPerSecond = 26374.1
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.17547006 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0095s; samplesPerSecond = 26318.6
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.14044644 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0095s; samplesPerSecond = 26321.3
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.16673170 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0094s; samplesPerSecond = 26615.6
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.19317383 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0095s; samplesPerSecond = 26202.7
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.12349199 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0093s; samplesPerSecond = 26778.1
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.16427535 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0095s; samplesPerSecond = 26346.3
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.12350212 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0093s; samplesPerSecond = 26746.5
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.19958846 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0096s; samplesPerSecond = 26028.1
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.14269741 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0095s; samplesPerSecond = 26189.0
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.12369058 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0095s; samplesPerSecond = 26219.2
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.16638059 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0093s; samplesPerSecond = 26847.1
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.20047975 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0098s; samplesPerSecond = 25401.3
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.16963457 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0099s; samplesPerSecond = 25204.2
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.13367401 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0098s; samplesPerSecond = 25518.0
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.14477143 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0097s; samplesPerSecond = 25805.1
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.21046366 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0097s; samplesPerSecond = 25791.8
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.19247125 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0096s; samplesPerSecond = 26047.1
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.15027023 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0097s; samplesPerSecond = 25670.0
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.15612870 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0098s; samplesPerSecond = 25528.4
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.13684548 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0097s; samplesPerSecond = 25725.5
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.17217344 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0096s; samplesPerSecond = 25939.0
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.14419519 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0097s; samplesPerSecond = 25807.8
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.13803181 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0097s; samplesPerSecond = 25866.5
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.14209585 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0097s; samplesPerSecond = 25730.8
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.16967141 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0097s; samplesPerSecond = 25730.8
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.18647515 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0097s; samplesPerSecond = 25813.1
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.16511327 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0098s; samplesPerSecond = 25541.5
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.15550174 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0097s; samplesPerSecond = 25752.0
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18759246 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0098s; samplesPerSecond = 25525.8
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.13178152 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0097s; samplesPerSecond = 25677.9
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.14624311 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0098s; samplesPerSecond = 25583.3
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.13930281 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0098s; samplesPerSecond = 25575.4
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.20110083 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0099s; samplesPerSecond = 25319.0
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.12558937 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0099s; samplesPerSecond = 25378.1
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.18612014 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0097s; samplesPerSecond = 25821.1
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15336297 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0096s; samplesPerSecond = 25998.3
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.11885079 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0097s; samplesPerSecond = 25850.5
|
||||
08/16/2016 10:01:33: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.12974982 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0096s; samplesPerSecond = 25979.4
|
||||
08/16/2016 10:01:33: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15798453 * 10000; EvalErrorPrediction = 0.07300000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.388464s
|
||||
08/16/2016 10:01:33: SGD: Saving checkpoint model '/tmp/cntk-test-20160816100054.995555/Examples/Other/Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn'
|
||||
08/16/2016 10:01:33: CNTKCommandTrainEnd: Multigpu_Demo_Train
|
||||
|
||||
08/16/2016 10:01:33: Action "train" complete.
|
||||
|
||||
|
||||
08/16/2016 10:01:33: ##############################################################################
|
||||
08/16/2016 10:01:33: # #
|
||||
08/16/2016 10:01:33: # Action "test" #
|
||||
08/16/2016 10:01:33: # #
|
||||
08/16/2016 10:01:33: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
|
@ -675,35 +703,17 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 25 matrices, 2 are shared as 1, and 23 are not shared.
|
||||
|
||||
(nil): {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalErrorPrediction Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
|
||||
0x1222268: {[InvStdOfFeatures Value[2]] }
|
||||
0x1223258: {[W2 Value[2 x 50]] }
|
||||
0x12a56c8: {[B0 Value[50 x 1]] }
|
||||
0x201fc78: {[W0*features+B0 Value[50 x 1 x *1]] }
|
||||
0x201fe38: {[H1 Value[50 x 1 x *1]] }
|
||||
0x201fff8: {[W1*H1 Value[50 x 1 x *1]] }
|
||||
0x20201b8: {[W1*H1+B1 Value[50 x 1 x *1]] }
|
||||
0x2020378: {[H2 Value[50 x 1 x *1]] }
|
||||
0x2020538: {[W2*H1 Value[2 x 1 x *1]] }
|
||||
0x20206f8: {[HLast Value[2 x 1 x *1]] }
|
||||
0x278a218: {[MeanOfFeatures Value[2]] }
|
||||
0x278b058: {[Prior Value[2]] }
|
||||
0x278d338: {[labels Value[2 x *1]] }
|
||||
0x27966e8: {[B1 Value[50 x 1]] }
|
||||
0x2ad9af8: {[B2 Value[2 x 1]] }
|
||||
0x2adcaa8: {[MVNormalizedFeatures Value[2 x *1]] }
|
||||
0x2adcc08: {[W0*features Value[50 x *1]] }
|
||||
0x2add0a8: {[W0 Value[50 x 2]] }
|
||||
0x2ae0518: {[W1 Value[50 x 50]] }
|
||||
0x68bf228: {[EvalErrorPrediction Value[1]] }
|
||||
0x68bf388: {[CrossEntropyWithSoftmax Value[1]] }
|
||||
0x68bf988: {[LogOfPrior Value[2]] }
|
||||
0x68d0438: {[features Value[2 x *1]] }
|
||||
{ PosteriorProb : [2 x 1 x *1]
|
||||
ScaledLogLikelihood : [2 x 1 x *1] }
|
||||
|
||||
05/03/2016 15:21:57: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.05804312 * 603; CrossEntropyWithSoftmax = 0.12736577 * 603; perplexity = 1.13583240
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..603] (first sequence at sample 0), data subset 0 of 1
|
||||
Actual gradient aggregation time: 0.000128
|
||||
08/16/2016 10:01:33: Minibatch[1-1]: EvalErrorPrediction = 0.05140962 * 603; CrossEntropyWithSoftmax = 0.10723887 * 603
|
||||
08/16/2016 10:01:33: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.05140962 * 603; CrossEntropyWithSoftmax = 0.10723887 * 603; perplexity = 1.11320013
|
||||
|
||||
05/03/2016 15:21:57: Action "test" complete.
|
||||
08/16/2016 10:01:33: Action "test" complete.
|
||||
|
||||
05/03/2016 15:21:57: __COMPLETED__
|
||||
08/16/2016 10:01:33: __COMPLETED__
|
||||
~MPIWrapper
|
||||
|
|
@ -1,21 +1,27 @@
|
|||
=== Running /cygdrive/c/src/cntk_github/x64/release/cntk.exe configFile=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config/Multigpu.cntk currentDirectory=C:\src\cntk_github\Examples\Other\Simple2d\Data RunDir=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu DataDir=C:\src\cntk_github\Examples\Other\Simple2d\Data ConfigDir=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config OutputDir=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu DeviceId=-1 timestamping=true Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 268381192 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config/Multigpu.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_cpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_cpu DeviceId=-1 timestamping=true Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 3 2016 16:22:10
|
||||
Last modified date: Thu Apr 7 11:05:47 2016
|
||||
Built time: Aug 16 2016 03:09:16
|
||||
Last modified date: Fri Aug 12 05:28:23 2016
|
||||
Build type: Release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: yes
|
||||
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0
|
||||
CUB_PATH: E:\lib\cub-1.4.1
|
||||
CUDNN_PATH: E:\lib\cuDNN_v4
|
||||
Build Branch: alrezni/examples_text
|
||||
Build SHA1: e80dab7d66009531806ce70b4842146e0da00516
|
||||
Built by alrezni on DIFFENG
|
||||
Build Path: C:\src\cntk_github\Source\CNTK\
|
||||
Math lib: mkl
|
||||
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
CUB_PATH: c:\src\cub-1.4.1
|
||||
CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by svcphil on Philly-Pool1
|
||||
Build Path: c:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
Changed current directory to C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
MPIWrapper: initializing MPI
|
||||
ping [requestnodes (before change)]: 1 nodes pinging each other
|
||||
ping [requestnodes (before change)]: all 1 nodes responded
|
||||
|
|
@ -25,31 +31,39 @@ ping [requestnodes (after change)]: all 1 nodes responded
|
|||
mpihelper: only one MPI process: MPI operation will be boring
|
||||
ping [mpihelper]: 1 nodes pinging each other
|
||||
ping [mpihelper]: all 1 nodes responded
|
||||
05/03/2016 15:29:48: -------------------------------------------------------------------
|
||||
05/03/2016 15:29:48: Build info:
|
||||
08/16/2016 03:19:45: -------------------------------------------------------------------
|
||||
08/16/2016 03:19:45: Build info:
|
||||
|
||||
05/03/2016 15:29:48: Built time: May 3 2016 16:22:10
|
||||
05/03/2016 15:29:48: Last modified date: Thu Apr 7 11:05:47 2016
|
||||
05/03/2016 15:29:48: Build type: Release
|
||||
05/03/2016 15:29:48: Build target: GPU
|
||||
05/03/2016 15:29:48: With 1bit-SGD: yes
|
||||
05/03/2016 15:29:48: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0
|
||||
05/03/2016 15:29:48: CUB_PATH: E:\lib\cub-1.4.1
|
||||
05/03/2016 15:29:48: CUDNN_PATH: E:\lib\cuDNN_v4
|
||||
05/03/2016 15:29:48: Build Branch: alrezni/examples_text
|
||||
05/03/2016 15:29:48: Build SHA1: e80dab7d66009531806ce70b4842146e0da00516
|
||||
05/03/2016 15:29:48: Built by alrezni on DIFFENG
|
||||
05/03/2016 15:29:48: Build Path: C:\src\cntk_github\Source\CNTK\
|
||||
05/03/2016 15:29:48: -------------------------------------------------------------------
|
||||
08/16/2016 03:19:45: Built time: Aug 16 2016 03:09:16
|
||||
08/16/2016 03:19:45: Last modified date: Fri Aug 12 05:28:23 2016
|
||||
08/16/2016 03:19:45: Build type: Release
|
||||
08/16/2016 03:19:45: Build target: GPU
|
||||
08/16/2016 03:19:45: With 1bit-SGD: yes
|
||||
08/16/2016 03:19:45: Math lib: mkl
|
||||
08/16/2016 03:19:45: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
08/16/2016 03:19:45: CUB_PATH: c:\src\cub-1.4.1
|
||||
08/16/2016 03:19:45: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
08/16/2016 03:19:45: Build Branch: HEAD
|
||||
08/16/2016 03:19:45: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 03:19:45: Built by svcphil on Philly-Pool1
|
||||
08/16/2016 03:19:45: Build Path: c:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
08/16/2016 03:19:45: -------------------------------------------------------------------
|
||||
08/16/2016 03:19:46: -------------------------------------------------------------------
|
||||
08/16/2016 03:19:46: GPU info:
|
||||
|
||||
05/03/2016 15:29:48: Running on DIFFENG at 2016/05/03 15:29:48
|
||||
05/03/2016 15:29:48: Command line:
|
||||
C:\src\cntk_github\x64\release\cntk.exe configFile=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config/Multigpu.cntk currentDirectory=C:\src\cntk_github\Examples\Other\Simple2d\Data RunDir=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu DataDir=C:\src\cntk_github\Examples\Other\Simple2d\Data ConfigDir=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config OutputDir=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu DeviceId=-1 timestamping=true Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
08/16/2016 03:19:46: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:19:46: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:19:46: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:19:46: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 03:19:46: Running on DPHAIM-25 at 2016/08/16 03:19:46
|
||||
08/16/2016 03:19:46: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config/Multigpu.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_cpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_cpu DeviceId=-1 timestamping=true Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
|
||||
|
||||
05/03/2016 15:29:48: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 15:29:48: RootDir = ".."
|
||||
08/16/2016 03:19:46: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:19:46: RootDir = ".."
|
||||
ConfigDir = "$RootDir$/Config"
|
||||
DataDir = "$RootDir$/Data"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
|
|
@ -138,28 +152,28 @@ dim = 2
|
|||
]
|
||||
outputPath = "$OutputDir$/MultigpuOutput"
|
||||
]
|
||||
currentDirectory=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
RunDir=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu
|
||||
DataDir=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
ConfigDir=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config
|
||||
OutputDir=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu
|
||||
currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_cpu
|
||||
DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_cpu
|
||||
DeviceId=-1
|
||||
timestamping=true
|
||||
Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
05/03/2016 15:29:48: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:19:46: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 15:29:48: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 15:29:48: RootDir = ".."
|
||||
08/16/2016 03:19:46: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:19:46: RootDir = ".."
|
||||
ConfigDir = "../Config"
|
||||
DataDir = "../Data"
|
||||
OutputDir = "../Output"
|
||||
ModelDir = "E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu/Models"
|
||||
ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_cpu/Models"
|
||||
deviceId = "auto"
|
||||
command = Multigpu_Demo_Train:Multigpu_Demo_Test
|
||||
precision = "float"
|
||||
traceLevel = 1
|
||||
modelPath = "E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn"
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn"
|
||||
outputNodeNames = ScaledLogLikelihood
|
||||
parallelTrain = true
|
||||
Multigpu_Demo_Train=[
|
||||
|
|
@ -191,7 +205,7 @@ Multigpu_Demo_Train=[
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTrain_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTrain_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -208,7 +222,7 @@ Multigpu_Demo_Test=[
|
|||
action = "test"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -225,7 +239,7 @@ Multigpu_Demo_Output=[
|
|||
action = "write"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -237,32 +251,32 @@ dim = 2
|
|||
]
|
||||
]
|
||||
]
|
||||
outputPath = "E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu/MultigpuOutput"
|
||||
outputPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_cpu/MultigpuOutput"
|
||||
]
|
||||
currentDirectory=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
RunDir=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu
|
||||
DataDir=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
ConfigDir=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config
|
||||
OutputDir=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu
|
||||
currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_cpu
|
||||
DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_cpu
|
||||
DeviceId=-1
|
||||
timestamping=true
|
||||
Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
05/03/2016 15:29:48: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:19:46: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 15:29:48: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:19:46: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: Multigpu.cntk:command=Multigpu_Demo_Train:Multigpu_Demo_Test
|
||||
configparameters: Multigpu.cntk:ConfigDir=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config
|
||||
configparameters: Multigpu.cntk:currentDirectory=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
configparameters: Multigpu.cntk:DataDir=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
configparameters: Multigpu.cntk:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config
|
||||
configparameters: Multigpu.cntk:currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
configparameters: Multigpu.cntk:DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
configparameters: Multigpu.cntk:deviceId=-1
|
||||
configparameters: Multigpu.cntk:ModelDir=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu/Models
|
||||
configparameters: Multigpu.cntk:modelPath=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn
|
||||
configparameters: Multigpu.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_cpu/Models
|
||||
configparameters: Multigpu.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn
|
||||
configparameters: Multigpu.cntk:Multigpu_Demo_Output=[
|
||||
action = "write"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -274,14 +288,14 @@ dim = 2
|
|||
]
|
||||
]
|
||||
]
|
||||
outputPath = "E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu/MultigpuOutput"
|
||||
outputPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_cpu/MultigpuOutput"
|
||||
]
|
||||
|
||||
configparameters: Multigpu.cntk:Multigpu_Demo_Test=[
|
||||
action = "test"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -324,7 +338,7 @@ configparameters: Multigpu.cntk:Multigpu_Demo_Train=[
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTrain_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTrain_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -338,31 +352,43 @@ dim = 2
|
|||
]
|
||||
] [SGD=[maxEpochs=3]]
|
||||
|
||||
configparameters: Multigpu.cntk:OutputDir=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu
|
||||
configparameters: Multigpu.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_cpu
|
||||
configparameters: Multigpu.cntk:outputNodeNames=ScaledLogLikelihood
|
||||
configparameters: Multigpu.cntk:parallelTrain=true
|
||||
configparameters: Multigpu.cntk:precision=float
|
||||
configparameters: Multigpu.cntk:RootDir=..
|
||||
configparameters: Multigpu.cntk:RunDir=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu
|
||||
configparameters: Multigpu.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_cpu
|
||||
configparameters: Multigpu.cntk:timestamping=true
|
||||
configparameters: Multigpu.cntk:traceLevel=1
|
||||
05/03/2016 15:29:48: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/03/2016 15:29:48: Commands: Multigpu_Demo_Train Multigpu_Demo_Test
|
||||
05/03/2016 15:29:48: Precision = "float"
|
||||
05/03/2016 15:29:48: CNTKModelPath: E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn
|
||||
05/03/2016 15:29:48: CNTKCommandTrainInfo: Multigpu_Demo_Train : 3
|
||||
05/03/2016 15:29:48: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
08/16/2016 03:19:46: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:19:46: Commands: Multigpu_Demo_Train Multigpu_Demo_Test
|
||||
08/16/2016 03:19:46: Precision = "float"
|
||||
08/16/2016 03:19:46: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn
|
||||
08/16/2016 03:19:46: CNTKCommandTrainInfo: Multigpu_Demo_Train : 3
|
||||
08/16/2016 03:19:46: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
|
||||
05/03/2016 15:29:48: ##############################################################################
|
||||
05/03/2016 15:29:48: # #
|
||||
05/03/2016 15:29:48: # Action "train" #
|
||||
05/03/2016 15:29:48: # #
|
||||
05/03/2016 15:29:48: ##############################################################################
|
||||
08/16/2016 03:19:46: ##############################################################################
|
||||
08/16/2016 03:19:46: # #
|
||||
08/16/2016 03:19:46: # Action "train" #
|
||||
08/16/2016 03:19:46: # #
|
||||
08/16/2016 03:19:46: ##############################################################################
|
||||
|
||||
05/03/2016 15:29:48: CNTKCommandTrainBegin: Multigpu_Demo_Train
|
||||
08/16/2016 03:19:46: CNTKCommandTrainBegin: Multigpu_Demo_Train
|
||||
SimpleNetworkBuilder Using CPU
|
||||
|
||||
05/03/2016 15:29:48: Creating virgin network.
|
||||
08/16/2016 03:19:46: Creating virgin network.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[50 x 2] <- 0.000000.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[50 x 2] <- uniform(seed=1, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[50 x 50] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[50 x 50] <- uniform(seed=2, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[2 x 50] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[2 x 50] <- uniform(seed=3, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[2 x 1] <- 0.000000.
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[2 x 1] <- 0.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -414,207 +440,210 @@ Validating network, final pass.
|
|||
|
||||
Post-processing network complete.
|
||||
|
||||
05/03/2016 15:29:48: Created model with 25 nodes on CPU.
|
||||
08/16/2016 03:19:47: Created model with 25 nodes on CPU.
|
||||
|
||||
05/03/2016 15:29:48: Training criterion node(s):
|
||||
05/03/2016 15:29:48: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
|
||||
08/16/2016 03:19:47: Training criterion node(s):
|
||||
08/16/2016 03:19:47: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
|
||||
|
||||
05/03/2016 15:29:48: Evaluation criterion node(s):
|
||||
|
||||
05/03/2016 15:29:48: EvalErrorPrediction = ErrorPrediction
|
||||
08/16/2016 03:19:47: Evaluation criterion node(s):
|
||||
08/16/2016 03:19:47: EvalErrorPrediction = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 40 matrices, 19 are shared as 8, and 21 are not shared.
|
||||
|
||||
0000000000000000: {[EvalErrorPrediction Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
|
||||
000000CDDFBEECA0: {[features Value[2 x *]] }
|
||||
000000CDDFC7B170: {[W0*features+B0 Gradient[50 x 1 x *]] [W1*H1 Value[50 x 1 x *]] }
|
||||
000000CDDFC7B490: {[HLast Value[2 x 1 x *]] [W2 Gradient[2 x 50]] }
|
||||
000000CDDFC7B530: {[W2*H1 Gradient[2 x 1 x *]] }
|
||||
000000CDDFC7B670: {[labels Value[2 x *]] }
|
||||
000000CDDFC7B8F0: {[W0*features Value[50 x *]] }
|
||||
000000CDDFC7B990: {[H1 Value[50 x 1 x *]] [W0*features Gradient[50 x *]] }
|
||||
000000CDDFC7BC10: {[LogOfPrior Value[2]] }
|
||||
000000CDDFC7BCB0: {[MVNormalizedFeatures Value[2 x *]] }
|
||||
000000CDDFC7BD50: {[EvalErrorPrediction Value[1]] }
|
||||
000000CDDFC7BDF0: {[W0 Gradient[50 x 2]] [W0*features+B0 Value[50 x 1 x *]] }
|
||||
000000CDDFC7BF30: {[ScaledLogLikelihood Value[2 x 1 x *]] }
|
||||
000000CDDFC7C070: {[H2 Value[50 x 1 x *]] [W1*H1 Gradient[50 x 1 x *]] }
|
||||
000000CDDFC7C250: {[B0 Gradient[50 x 1]] [H1 Gradient[50 x 1 x *]] [W1*H1+B1 Gradient[50 x 1 x *]] [W2*H1 Value[2 x 1 x *]] }
|
||||
000000CDDFC7C390: {[W1 Gradient[50 x 50]] [W1*H1+B1 Value[50 x 1 x *]] }
|
||||
000000CDDFC7C610: {[Prior Value[2]] }
|
||||
000000CDDFC7C930: {[CrossEntropyWithSoftmax Value[1]] }
|
||||
000000CDDFC7CBB0: {[B1 Gradient[50 x 1]] [H2 Gradient[50 x 1 x *]] [HLast Gradient[2 x 1 x *]] }
|
||||
000000CDDFC7CC50: {[CrossEntropyWithSoftmax Gradient[1]] }
|
||||
000000CDDFC7CCF0: {[B2 Gradient[2 x 1]] }
|
||||
000000CDE2DCDD50: {[W1 Value[50 x 50]] }
|
||||
000000CDE2DCDDF0: {[B1 Value[50 x 1]] }
|
||||
000000CDE2DCDF30: {[B2 Value[2 x 1]] }
|
||||
000000CDE2DCE110: {[W2 Value[2 x 50]] }
|
||||
000000CDE2DCE2F0: {[W0 Value[50 x 2]] }
|
||||
000000CDE2DCE930: {[B0 Value[50 x 1]] }
|
||||
000000CDE2DCEA70: {[InvStdOfFeatures Value[2]] }
|
||||
000000CDE2DCEFD0: {[MeanOfFeatures Value[2]] }
|
||||
{ H2 : [50 x 1 x *]
|
||||
W1*H1 : [50 x 1 x *] (gradient) }
|
||||
{ W0 : [50 x 2] (gradient)
|
||||
W0*features+B0 : [50 x 1 x *] }
|
||||
{ W1 : [50 x 50] (gradient)
|
||||
W1*H1+B1 : [50 x 1 x *] }
|
||||
{ H1 : [50 x 1 x *]
|
||||
W0*features : [50 x *] (gradient) }
|
||||
{ B1 : [50 x 1] (gradient)
|
||||
H2 : [50 x 1 x *] (gradient)
|
||||
HLast : [2 x 1 x *] (gradient) }
|
||||
{ B0 : [50 x 1] (gradient)
|
||||
H1 : [50 x 1 x *] (gradient)
|
||||
W1*H1+B1 : [50 x 1 x *] (gradient)
|
||||
W2*H1 : [2 x 1 x *] }
|
||||
{ W0*features+B0 : [50 x 1 x *] (gradient)
|
||||
W1*H1 : [50 x 1 x *] }
|
||||
{ HLast : [2 x 1 x *]
|
||||
W2 : [2 x 50] (gradient) }
|
||||
|
||||
|
||||
05/03/2016 15:29:48: Precomputing --> 3 PreCompute nodes found.
|
||||
08/16/2016 03:19:47: Training 2802 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:
|
||||
|
||||
05/03/2016 15:29:48: MeanOfFeatures = Mean()
|
||||
05/03/2016 15:29:48: InvStdOfFeatures = InvStdDev()
|
||||
05/03/2016 15:29:48: Prior = Mean()
|
||||
|
||||
05/03/2016 15:29:48: Precomputing --> Completed.
|
||||
08/16/2016 03:19:47: Node 'B0' (LearnableParameter operation) : [50 x 1]
|
||||
08/16/2016 03:19:47: Node 'B1' (LearnableParameter operation) : [50 x 1]
|
||||
08/16/2016 03:19:47: Node 'B2' (LearnableParameter operation) : [2 x 1]
|
||||
08/16/2016 03:19:47: Node 'W0' (LearnableParameter operation) : [50 x 2]
|
||||
08/16/2016 03:19:47: Node 'W1' (LearnableParameter operation) : [50 x 50]
|
||||
08/16/2016 03:19:47: Node 'W2' (LearnableParameter operation) : [2 x 50]
|
||||
|
||||
|
||||
05/03/2016 15:29:48: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
08/16/2016 03:19:47: Precomputing --> 3 PreCompute nodes found.
|
||||
|
||||
05/03/2016 15:29:48: Starting minibatch loop.
|
||||
05/03/2016 15:29:48: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70511987 * 250; EvalErrorPrediction = 0.55200000 * 250; time = 0.0377s; samplesPerSecond = 6637.8
|
||||
05/03/2016 15:29:48: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.69754895 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0300s; samplesPerSecond = 8341.4
|
||||
05/03/2016 15:29:48: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.71056921 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0285s; samplesPerSecond = 8758.7
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.72951074 * 250; EvalErrorPrediction = 0.56000000 * 250; time = 0.0290s; samplesPerSecond = 8610.3
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.70946655 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.0285s; samplesPerSecond = 8776.9
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72656787 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0289s; samplesPerSecond = 8652.6
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.69337402 * 250; EvalErrorPrediction = 0.43200000 * 250; time = 0.0288s; samplesPerSecond = 8670.9
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.73605176 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0277s; samplesPerSecond = 9033.4
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.71453076 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0271s; samplesPerSecond = 9209.5
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.75191992 * 250; EvalErrorPrediction = 0.47200000 * 250; time = 0.0247s; samplesPerSecond = 10134.6
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.75975146 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0270s; samplesPerSecond = 9243.5
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.73172168 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0268s; samplesPerSecond = 9333.9
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.76840820 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0265s; samplesPerSecond = 9435.7
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.70464746 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0269s; samplesPerSecond = 9309.3
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.70557227 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0253s; samplesPerSecond = 9880.3
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.72711816 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0267s; samplesPerSecond = 9357.7
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70076660 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0270s; samplesPerSecond = 9264.1
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.69409766 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0257s; samplesPerSecond = 9716.3
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.69139941 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0257s; samplesPerSecond = 9742.4
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.73361621 * 250; EvalErrorPrediction = 0.55200000 * 250; time = 0.0295s; samplesPerSecond = 8477.4
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.72225879 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0273s; samplesPerSecond = 9161.9
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70356348 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0261s; samplesPerSecond = 9562.8
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69928613 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0254s; samplesPerSecond = 9848.7
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.72360938 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0252s; samplesPerSecond = 9924.6
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.69871875 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0262s; samplesPerSecond = 9530.7
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.69114844 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0257s; samplesPerSecond = 9720.1
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.68648047 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0273s; samplesPerSecond = 9161.9
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.69657227 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0270s; samplesPerSecond = 9259.9
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.71585547 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0264s; samplesPerSecond = 9486.2
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.69730664 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0261s; samplesPerSecond = 9595.1
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.70432422 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0244s; samplesPerSecond = 10248.8
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.69991797 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0220s; samplesPerSecond = 11388.0
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.68696875 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0222s; samplesPerSecond = 11277.0
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.67331445 * 250; EvalErrorPrediction = 0.37200000 * 250; time = 0.0245s; samplesPerSecond = 10192.4
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.65711328 * 250; EvalErrorPrediction = 0.43200000 * 250; time = 0.0240s; samplesPerSecond = 10429.3
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.64534375 * 250; EvalErrorPrediction = 0.44800000 * 250; time = 0.0243s; samplesPerSecond = 10305.0
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.61021875 * 250; EvalErrorPrediction = 0.36400000 * 250; time = 0.0236s; samplesPerSecond = 10606.3
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.54191016 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0236s; samplesPerSecond = 10578.4
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.45624414 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0232s; samplesPerSecond = 10762.4
|
||||
05/03/2016 15:29:49: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.37636133 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0235s; samplesPerSecond = 10623.8
|
||||
05/03/2016 15:29:49: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.68695688 * 10000; EvalErrorPrediction = 0.45550000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=1.06166s
|
||||
05/03/2016 15:29:49: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn.1'
|
||||
08/16/2016 03:19:47: MeanOfFeatures = Mean()
|
||||
08/16/2016 03:19:47: InvStdOfFeatures = InvStdDev()
|
||||
08/16/2016 03:19:47: Prior = Mean()
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
05/03/2016 15:29:49: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
|
||||
05/03/2016 15:29:49: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
|
||||
05/03/2016 15:29:49: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.28780429 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0246s; samplesPerSecond = 10181.2
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.28222478 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0246s; samplesPerSecond = 10178.3
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.23589864 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0255s; samplesPerSecond = 9796.2
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.21209458 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0242s; samplesPerSecond = 10312.3
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.20285913 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0243s; samplesPerSecond = 10283.0
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.21300948 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0252s; samplesPerSecond = 9928.5
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.17835594 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0256s; samplesPerSecond = 9753.8
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.18830077 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0257s; samplesPerSecond = 9740.1
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.14198478 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0250s; samplesPerSecond = 10019.2
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.15895022 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0237s; samplesPerSecond = 10566.8
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.21062646 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0238s; samplesPerSecond = 10517.9
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.16081948 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0223s; samplesPerSecond = 11186.7
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.15635713 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0234s; samplesPerSecond = 10700.2
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13008516 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0239s; samplesPerSecond = 10453.7
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.16625347 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0234s; samplesPerSecond = 10674.2
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.15001793 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0245s; samplesPerSecond = 10223.7
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.22343917 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0234s; samplesPerSecond = 10692.4
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18006735 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0245s; samplesPerSecond = 10194.5
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.15361620 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0235s; samplesPerSecond = 10636.9
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.17039588 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0246s; samplesPerSecond = 10177.1
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.15516786 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0237s; samplesPerSecond = 10544.1
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.15969617 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0225s; samplesPerSecond = 11102.2
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.15939439 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0234s; samplesPerSecond = 10697.9
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.15300194 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0233s; samplesPerSecond = 10729.2
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.14902476 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0231s; samplesPerSecond = 10811.7
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.15043256 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0231s; samplesPerSecond = 10823.4
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.15531360 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0229s; samplesPerSecond = 10936.1
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.17990796 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0248s; samplesPerSecond = 10088.4
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.22925668 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0229s; samplesPerSecond = 10913.7
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16843626 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0234s; samplesPerSecond = 10682.8
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18045325 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0236s; samplesPerSecond = 10585.6
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.13337526 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0221s; samplesPerSecond = 11308.6
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.14332977 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0245s; samplesPerSecond = 10219.9
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.18749446 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0242s; samplesPerSecond = 10326.7
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.15505967 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0236s; samplesPerSecond = 10587.8
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.19616616 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0228s; samplesPerSecond = 10980.3
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.17305907 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0236s; samplesPerSecond = 10610.3
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15197365 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0249s; samplesPerSecond = 10033.3
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.12102416 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0238s; samplesPerSecond = 10483.5
|
||||
05/03/2016 15:29:50: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15278496 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0235s; samplesPerSecond = 10646.9
|
||||
05/03/2016 15:29:50: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.17643784 * 10000; EvalErrorPrediction = 0.07560000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.957696s
|
||||
05/03/2016 15:29:50: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn.2'
|
||||
|
||||
05/03/2016 15:29:50: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
|
||||
05/03/2016 15:29:50: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
|
||||
05/03/2016 15:29:50: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.10623312 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0235s; samplesPerSecond = 10637.4
|
||||
05/03/2016 15:29:50: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.17519442 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0236s; samplesPerSecond = 10608.5
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14133983 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0240s; samplesPerSecond = 10404.5
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.16278491 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0233s; samplesPerSecond = 10749.0
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.11783558 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0232s; samplesPerSecond = 10780.0
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.16342188 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0243s; samplesPerSecond = 10305.9
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.16272195 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0239s; samplesPerSecond = 10476.9
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.19401477 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0241s; samplesPerSecond = 10370.0
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.20186661 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0229s; samplesPerSecond = 10903.2
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.13672539 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0235s; samplesPerSecond = 10631.1
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.20069212 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0234s; samplesPerSecond = 10681.5
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.17729039 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0252s; samplesPerSecond = 9928.1
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.15906107 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0251s; samplesPerSecond = 9941.5
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16281632 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0247s; samplesPerSecond = 10121.5
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.19834981 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0248s; samplesPerSecond = 10067.7
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.10217642 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0247s; samplesPerSecond = 10105.1
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17011383 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0258s; samplesPerSecond = 9692.2
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16599137 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0252s; samplesPerSecond = 9911.6
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.12648996 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0254s; samplesPerSecond = 9848.7
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.11920298 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0248s; samplesPerSecond = 10091.2
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.12883164 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0272s; samplesPerSecond = 9205.1
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.18222479 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0250s; samplesPerSecond = 9988.0
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.13443351 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0246s; samplesPerSecond = 10149.4
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.19720325 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0244s; samplesPerSecond = 10230.8
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.15586137 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0254s; samplesPerSecond = 9860.4
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.11854887 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0250s; samplesPerSecond = 9991.6
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.13705285 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0249s; samplesPerSecond = 10050.7
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.20009941 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0240s; samplesPerSecond = 10411.5
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.19078680 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0233s; samplesPerSecond = 10741.6
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16505705 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0238s; samplesPerSecond = 10507.7
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.12232722 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0239s; samplesPerSecond = 10472.1
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.16342047 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0238s; samplesPerSecond = 10514.4
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.15875107 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0234s; samplesPerSecond = 10688.3
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.12248772 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0232s; samplesPerSecond = 10793.5
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13457009 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0238s; samplesPerSecond = 10521.4
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.20976565 * 250; EvalErrorPrediction = 0.11600000 * 250; time = 0.0238s; samplesPerSecond = 10494.9
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16519102 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0230s; samplesPerSecond = 10862.5
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.14971420 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0247s; samplesPerSecond = 10106.3
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.16456633 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0230s; samplesPerSecond = 10858.2
|
||||
05/03/2016 15:29:51: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.16971407 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0239s; samplesPerSecond = 10473.0
|
||||
05/03/2016 15:29:51: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15787325 * 10000; EvalErrorPrediction = 0.07430000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.972052s
|
||||
05/03/2016 15:29:51: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn'
|
||||
05/03/2016 15:29:51: CNTKCommandTrainEnd: Multigpu_Demo_Train
|
||||
|
||||
05/03/2016 15:29:51: Action "train" complete.
|
||||
08/16/2016 03:19:47: Precomputing --> Completed.
|
||||
|
||||
|
||||
05/03/2016 15:29:51: ##############################################################################
|
||||
05/03/2016 15:29:51: # #
|
||||
05/03/2016 15:29:51: # Action "test" #
|
||||
05/03/2016 15:29:51: # #
|
||||
05/03/2016 15:29:51: ##############################################################################
|
||||
08/16/2016 03:19:47: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:19:47: Starting minibatch loop.
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70264496 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0285s; samplesPerSecond = 8786.4
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.76483063 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0246s; samplesPerSecond = 10182.5
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.72648584 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0240s; samplesPerSecond = 10421.9
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.73860254 * 250; EvalErrorPrediction = 0.56800000 * 250; time = 0.0238s; samplesPerSecond = 10525.4
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.70622803 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0238s; samplesPerSecond = 10488.3
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.74772852 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0242s; samplesPerSecond = 10327.6
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.75092773 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0238s; samplesPerSecond = 10486.1
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.78004932 * 250; EvalErrorPrediction = 0.48400000 * 250; time = 0.0240s; samplesPerSecond = 10434.5
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.70444336 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0241s; samplesPerSecond = 10391.1
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.69544189 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0240s; samplesPerSecond = 10398.5
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.70595947 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0242s; samplesPerSecond = 10316.5
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.74544189 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0234s; samplesPerSecond = 10662.8
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.70809961 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0241s; samplesPerSecond = 10364.4
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.69884375 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0241s; samplesPerSecond = 10356.3
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.70363086 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0239s; samplesPerSecond = 10441.9
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.69351758 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0239s; samplesPerSecond = 10447.6
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.74453613 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0244s; samplesPerSecond = 10240.9
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.73761426 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0242s; samplesPerSecond = 10330.6
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.71868652 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.0240s; samplesPerSecond = 10417.5
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.71496484 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0236s; samplesPerSecond = 10595.0
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.69961230 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0237s; samplesPerSecond = 10566.4
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.69760645 * 250; EvalErrorPrediction = 0.49200000 * 250; time = 0.0238s; samplesPerSecond = 10503.8
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.70748047 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0237s; samplesPerSecond = 10531.6
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.69785937 * 250; EvalErrorPrediction = 0.56800000 * 250; time = 0.0236s; samplesPerSecond = 10608.1
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.69483203 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0246s; samplesPerSecond = 10162.6
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.69258203 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0232s; samplesPerSecond = 10776.8
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.70665625 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0239s; samplesPerSecond = 10480.4
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.69031445 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0238s; samplesPerSecond = 10502.4
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.70169531 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0240s; samplesPerSecond = 10434.5
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.71008398 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0239s; samplesPerSecond = 10462.0
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.69152930 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0238s; samplesPerSecond = 10514.4
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.69522656 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0240s; samplesPerSecond = 10419.7
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.69347070 * 250; EvalErrorPrediction = 0.49200000 * 250; time = 0.0238s; samplesPerSecond = 10490.5
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.68888281 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0238s; samplesPerSecond = 10499.8
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.68067578 * 250; EvalErrorPrediction = 0.48400000 * 250; time = 0.0237s; samplesPerSecond = 10557.4
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.66932227 * 250; EvalErrorPrediction = 0.44400000 * 250; time = 0.0242s; samplesPerSecond = 10314.8
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.65398437 * 250; EvalErrorPrediction = 0.24800000 * 250; time = 0.0235s; samplesPerSecond = 10638.8
|
||||
08/16/2016 03:19:47: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.63662500 * 250; EvalErrorPrediction = 0.32400000 * 250; time = 0.0234s; samplesPerSecond = 10692.4
|
||||
08/16/2016 03:19:48: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.59652344 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0236s; samplesPerSecond = 10595.5
|
||||
08/16/2016 03:19:48: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.55820898 * 250; EvalErrorPrediction = 0.12000000 * 250; time = 0.0245s; samplesPerSecond = 10215.8
|
||||
08/16/2016 03:19:48: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.70129624 * 10000; EvalErrorPrediction = 0.46850000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=0.964546s
|
||||
08/16/2016 03:19:48: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn.1'
|
||||
|
||||
08/16/2016 03:19:48: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [10000..20000] (first sequence at sample 10000), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:19:48: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.50509082 * 250; EvalErrorPrediction = 0.14400000 * 250; time = 0.0250s; samplesPerSecond = 9991.2
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.45891377 * 250; EvalErrorPrediction = 0.13200000 * 250; time = 0.0251s; samplesPerSecond = 9958.6
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.38371187 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0247s; samplesPerSecond = 10117.4
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.35526704 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0254s; samplesPerSecond = 9837.5
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.31361566 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0249s; samplesPerSecond = 10049.0
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.29756372 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0254s; samplesPerSecond = 9831.3
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.27214716 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0245s; samplesPerSecond = 10219.1
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.23149490 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0244s; samplesPerSecond = 10231.2
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.23825536 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0247s; samplesPerSecond = 10102.6
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.21847410 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0251s; samplesPerSecond = 9945.5
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.19974600 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0248s; samplesPerSecond = 10088.4
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.18213383 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0252s; samplesPerSecond = 9934.0
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.19621664 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0250s; samplesPerSecond = 10018.4
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.18917135 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0266s; samplesPerSecond = 9390.4
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.18997701 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0272s; samplesPerSecond = 9179.0
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.18456273 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0293s; samplesPerSecond = 8534.2
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.18678577 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0274s; samplesPerSecond = 9126.8
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.21314113 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0270s; samplesPerSecond = 9242.5
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.16860178 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0281s; samplesPerSecond = 8903.8
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.17451651 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0292s; samplesPerSecond = 8561.1
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13649532 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0291s; samplesPerSecond = 8585.8
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.17557703 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0271s; samplesPerSecond = 9213.2
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.12777527 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0266s; samplesPerSecond = 9414.8
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.21833707 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0245s; samplesPerSecond = 10188.7
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.21788590 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0251s; samplesPerSecond = 9969.7
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.18130830 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0250s; samplesPerSecond = 9987.6
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.16267770 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0249s; samplesPerSecond = 10056.7
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.13704118 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0247s; samplesPerSecond = 10125.1
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.16545012 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0242s; samplesPerSecond = 10321.6
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.14842740 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0252s; samplesPerSecond = 9932.1
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.15099778 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0250s; samplesPerSecond = 9988.0
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.12730237 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0256s; samplesPerSecond = 9775.2
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.16464377 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0257s; samplesPerSecond = 9723.5
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.14324668 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0248s; samplesPerSecond = 10096.5
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13824633 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0254s; samplesPerSecond = 9853.8
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.14128747 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0248s; samplesPerSecond = 10079.8
|
||||
08/16/2016 03:19:48: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.15910150 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0253s; samplesPerSecond = 9863.1
|
||||
08/16/2016 03:19:49: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.16253611 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0251s; samplesPerSecond = 9950.6
|
||||
08/16/2016 03:19:49: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.13535163 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0256s; samplesPerSecond = 9772.1
|
||||
08/16/2016 03:19:49: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15552570 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0249s; samplesPerSecond = 10044.2
|
||||
08/16/2016 03:19:49: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.20771504 * 10000; EvalErrorPrediction = 0.08060000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=1.02956s
|
||||
08/16/2016 03:19:49: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn.2'
|
||||
|
||||
08/16/2016 03:19:49: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [20000..30000] (first sequence at sample 20000), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:19:49: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.18436522 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0246s; samplesPerSecond = 10145.7
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.12821186 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0251s; samplesPerSecond = 9945.1
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.17512306 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0248s; samplesPerSecond = 10084.3
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.13980331 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0246s; samplesPerSecond = 10172.5
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.16538291 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0247s; samplesPerSecond = 10124.3
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.19375913 * 250; EvalErrorPrediction = 0.11600000 * 250; time = 0.0256s; samplesPerSecond = 9764.1
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.12331922 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0254s; samplesPerSecond = 9851.8
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.16604588 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0258s; samplesPerSecond = 9702.7
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.12468993 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0249s; samplesPerSecond = 10048.6
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.20005103 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0253s; samplesPerSecond = 9889.2
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.14282824 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0242s; samplesPerSecond = 10340.0
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.12364929 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0243s; samplesPerSecond = 10295.7
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.16738214 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0252s; samplesPerSecond = 9906.5
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.19934515 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0266s; samplesPerSecond = 9392.5
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.16932168 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0247s; samplesPerSecond = 10128.4
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.13332017 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0247s; samplesPerSecond = 10125.6
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.14351372 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0248s; samplesPerSecond = 10100.6
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.20938709 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0247s; samplesPerSecond = 10107.5
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.19203984 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0252s; samplesPerSecond = 9921.0
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.15014813 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0250s; samplesPerSecond = 10010.0
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.15581546 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0249s; samplesPerSecond = 10054.3
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.13716517 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0249s; samplesPerSecond = 10047.8
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.17233280 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0262s; samplesPerSecond = 9559.1
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.14434328 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0253s; samplesPerSecond = 9878.3
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.13849430 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0246s; samplesPerSecond = 10182.9
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.14141637 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0242s; samplesPerSecond = 10331.0
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.16967658 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0252s; samplesPerSecond = 9932.9
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.18536492 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0248s; samplesPerSecond = 10077.0
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.16547838 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0248s; samplesPerSecond = 10073.7
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.15382617 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0250s; samplesPerSecond = 9985.2
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18866317 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0250s; samplesPerSecond = 9980.0
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.13254335 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0249s; samplesPerSecond = 10049.8
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.14548822 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0245s; samplesPerSecond = 10191.2
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.13912198 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0245s; samplesPerSecond = 10194.1
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.20068190 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0257s; samplesPerSecond = 9729.9
|
||||
08/16/2016 03:19:49: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.12564777 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0245s; samplesPerSecond = 10190.8
|
||||
08/16/2016 03:19:50: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.18466509 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0251s; samplesPerSecond = 9966.1
|
||||
08/16/2016 03:19:50: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15248240 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0243s; samplesPerSecond = 10290.2
|
||||
08/16/2016 03:19:50: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.11889087 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0245s; samplesPerSecond = 10185.0
|
||||
08/16/2016 03:19:50: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.12990310 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0252s; samplesPerSecond = 9902.2
|
||||
08/16/2016 03:19:50: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15784221 * 10000; EvalErrorPrediction = 0.07350000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=1.00011s
|
||||
08/16/2016 03:19:50: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_cpu/Models/multigpu.dnn'
|
||||
08/16/2016 03:19:50: CNTKCommandTrainEnd: Multigpu_Demo_Train
|
||||
|
||||
08/16/2016 03:19:50: Action "train" complete.
|
||||
|
||||
|
||||
08/16/2016 03:19:50: ##############################################################################
|
||||
08/16/2016 03:19:50: # #
|
||||
08/16/2016 03:19:50: # Action "test" #
|
||||
08/16/2016 03:19:50: # #
|
||||
08/16/2016 03:19:50: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
|
@ -672,35 +701,17 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 25 matrices, 2 are shared as 1, and 23 are not shared.
|
||||
|
||||
0000000000000000: {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalErrorPrediction Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
|
||||
000000CDDFC7B490: {[W0 Value[50 x 2]] }
|
||||
000000CDDFC7B530: {[features Value[2 x *1]] }
|
||||
000000CDDFC7B710: {[W1 Value[50 x 50]] }
|
||||
000000CDDFC7BB70: {[Prior Value[2]] }
|
||||
000000CDDFC7BCB0: {[InvStdOfFeatures Value[2]] }
|
||||
000000CDDFC7BE90: {[MeanOfFeatures Value[2]] }
|
||||
000000CDDFC7C9D0: {[B2 Value[2 x 1]] }
|
||||
000000CDDFC7CC50: {[B0 Value[50 x 1]] }
|
||||
000000CDDFC7CCF0: {[W2 Value[2 x 50]] }
|
||||
000000CDDFC7CD90: {[labels Value[2 x *1]] }
|
||||
000000CDDFC7CF70: {[B1 Value[50 x 1]] }
|
||||
000000CDDFC8BC70: {[W0*features Value[50 x *1]] }
|
||||
000000CDDFC8C2B0: {[W1*H1+B1 Value[50 x 1 x *1]] }
|
||||
000000CDDFC8C490: {[CrossEntropyWithSoftmax Value[1]] }
|
||||
000000CDDFC8C5D0: {[LogOfPrior Value[2]] }
|
||||
000000CDDFC8C670: {[EvalErrorPrediction Value[1]] }
|
||||
000000CDDFC8C990: {[MVNormalizedFeatures Value[2 x *1]] }
|
||||
000000CDDFC8CA30: {[H2 Value[50 x 1 x *1]] }
|
||||
000000CDDFC8CC10: {[W1*H1 Value[50 x 1 x *1]] }
|
||||
000000CDDFC8CD50: {[W2*H1 Value[2 x 1 x *1]] }
|
||||
000000CDDFC8D2F0: {[H1 Value[50 x 1 x *1]] }
|
||||
000000CDDFC8D610: {[HLast Value[2 x 1 x *1]] }
|
||||
000000CDDFC8D750: {[W0*features+B0 Value[50 x 1 x *1]] }
|
||||
{ PosteriorProb : [2 x 1 x *1]
|
||||
ScaledLogLikelihood : [2 x 1 x *1] }
|
||||
|
||||
05/03/2016 15:29:52: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.05306799 * 603; CrossEntropyWithSoftmax = 0.11782631 * 603; perplexity = 1.12504868
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..603] (first sequence at sample 0), data subset 0 of 1
|
||||
Actual gradient aggregation time: 0.000128
|
||||
08/16/2016 03:19:50: Minibatch[1-1]: EvalErrorPrediction = 0.04975124 * 603; CrossEntropyWithSoftmax = 0.10640968 * 603
|
||||
08/16/2016 03:19:50: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.04975124 * 603; CrossEntropyWithSoftmax = 0.10640968 * 603; perplexity = 1.11227746
|
||||
|
||||
05/03/2016 15:29:52: Action "test" complete.
|
||||
08/16/2016 03:19:50: Action "test" complete.
|
||||
|
||||
05/03/2016 15:29:52: __COMPLETED__
|
||||
08/16/2016 03:19:50: __COMPLETED__
|
||||
~MPIWrapper
|
||||
|
|
@ -1,21 +1,27 @@
|
|||
=== Running /cygdrive/c/src/cntk_github/x64/release/cntk.exe configFile=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config/Multigpu.cntk currentDirectory=C:\src\cntk_github\Examples\Other\Simple2d\Data RunDir=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu DataDir=C:\src\cntk_github\Examples\Other\Simple2d\Data ConfigDir=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config OutputDir=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu DeviceId=0 timestamping=true Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 268381192 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config/Multigpu.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_gpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_gpu DeviceId=0 timestamping=true Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 3 2016 16:22:10
|
||||
Last modified date: Thu Apr 7 11:05:47 2016
|
||||
Built time: Aug 16 2016 03:09:16
|
||||
Last modified date: Fri Aug 12 05:28:23 2016
|
||||
Build type: Release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: yes
|
||||
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0
|
||||
CUB_PATH: E:\lib\cub-1.4.1
|
||||
CUDNN_PATH: E:\lib\cuDNN_v4
|
||||
Build Branch: alrezni/examples_text
|
||||
Build SHA1: e80dab7d66009531806ce70b4842146e0da00516
|
||||
Built by alrezni on DIFFENG
|
||||
Build Path: C:\src\cntk_github\Source\CNTK\
|
||||
Math lib: mkl
|
||||
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
CUB_PATH: c:\src\cub-1.4.1
|
||||
CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by svcphil on Philly-Pool1
|
||||
Build Path: c:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
Changed current directory to C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
MPIWrapper: initializing MPI
|
||||
ping [requestnodes (before change)]: 1 nodes pinging each other
|
||||
ping [requestnodes (before change)]: all 1 nodes responded
|
||||
|
|
@ -25,31 +31,39 @@ ping [requestnodes (after change)]: all 1 nodes responded
|
|||
mpihelper: only one MPI process: MPI operation will be boring
|
||||
ping [mpihelper]: 1 nodes pinging each other
|
||||
ping [mpihelper]: all 1 nodes responded
|
||||
05/03/2016 15:29:53: -------------------------------------------------------------------
|
||||
05/03/2016 15:29:53: Build info:
|
||||
08/16/2016 03:19:52: -------------------------------------------------------------------
|
||||
08/16/2016 03:19:52: Build info:
|
||||
|
||||
05/03/2016 15:29:53: Built time: May 3 2016 16:22:10
|
||||
05/03/2016 15:29:53: Last modified date: Thu Apr 7 11:05:47 2016
|
||||
05/03/2016 15:29:53: Build type: Release
|
||||
05/03/2016 15:29:53: Build target: GPU
|
||||
05/03/2016 15:29:53: With 1bit-SGD: yes
|
||||
05/03/2016 15:29:53: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0
|
||||
05/03/2016 15:29:53: CUB_PATH: E:\lib\cub-1.4.1
|
||||
05/03/2016 15:29:53: CUDNN_PATH: E:\lib\cuDNN_v4
|
||||
05/03/2016 15:29:53: Build Branch: alrezni/examples_text
|
||||
05/03/2016 15:29:53: Build SHA1: e80dab7d66009531806ce70b4842146e0da00516
|
||||
05/03/2016 15:29:53: Built by alrezni on DIFFENG
|
||||
05/03/2016 15:29:53: Build Path: C:\src\cntk_github\Source\CNTK\
|
||||
05/03/2016 15:29:53: -------------------------------------------------------------------
|
||||
08/16/2016 03:19:52: Built time: Aug 16 2016 03:09:16
|
||||
08/16/2016 03:19:52: Last modified date: Fri Aug 12 05:28:23 2016
|
||||
08/16/2016 03:19:52: Build type: Release
|
||||
08/16/2016 03:19:52: Build target: GPU
|
||||
08/16/2016 03:19:52: With 1bit-SGD: yes
|
||||
08/16/2016 03:19:52: Math lib: mkl
|
||||
08/16/2016 03:19:52: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
08/16/2016 03:19:52: CUB_PATH: c:\src\cub-1.4.1
|
||||
08/16/2016 03:19:52: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
08/16/2016 03:19:52: Build Branch: HEAD
|
||||
08/16/2016 03:19:52: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 03:19:52: Built by svcphil on Philly-Pool1
|
||||
08/16/2016 03:19:52: Build Path: c:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
08/16/2016 03:19:52: -------------------------------------------------------------------
|
||||
08/16/2016 03:19:53: -------------------------------------------------------------------
|
||||
08/16/2016 03:19:53: GPU info:
|
||||
|
||||
05/03/2016 15:29:53: Running on DIFFENG at 2016/05/03 15:29:53
|
||||
05/03/2016 15:29:53: Command line:
|
||||
C:\src\cntk_github\x64\release\cntk.exe configFile=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config/Multigpu.cntk currentDirectory=C:\src\cntk_github\Examples\Other\Simple2d\Data RunDir=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu DataDir=C:\src\cntk_github\Examples\Other\Simple2d\Data ConfigDir=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config OutputDir=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu DeviceId=0 timestamping=true Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
08/16/2016 03:19:53: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:19:53: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:19:53: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:19:53: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 03:19:53: Running on DPHAIM-25 at 2016/08/16 03:19:53
|
||||
08/16/2016 03:19:53: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config/Multigpu.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_gpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_gpu DeviceId=0 timestamping=true Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
|
||||
|
||||
05/03/2016 15:29:53: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 15:29:53: RootDir = ".."
|
||||
08/16/2016 03:19:53: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:19:53: RootDir = ".."
|
||||
ConfigDir = "$RootDir$/Config"
|
||||
DataDir = "$RootDir$/Data"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
|
|
@ -138,28 +152,28 @@ dim = 2
|
|||
]
|
||||
outputPath = "$OutputDir$/MultigpuOutput"
|
||||
]
|
||||
currentDirectory=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
RunDir=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu
|
||||
DataDir=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
ConfigDir=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config
|
||||
OutputDir=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu
|
||||
currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_gpu
|
||||
DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
05/03/2016 15:29:53: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:19:53: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 15:29:53: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 15:29:53: RootDir = ".."
|
||||
08/16/2016 03:19:53: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:19:53: RootDir = ".."
|
||||
ConfigDir = "../Config"
|
||||
DataDir = "../Data"
|
||||
OutputDir = "../Output"
|
||||
ModelDir = "E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu/Models"
|
||||
ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_gpu/Models"
|
||||
deviceId = "auto"
|
||||
command = Multigpu_Demo_Train:Multigpu_Demo_Test
|
||||
precision = "float"
|
||||
traceLevel = 1
|
||||
modelPath = "E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn"
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn"
|
||||
outputNodeNames = ScaledLogLikelihood
|
||||
parallelTrain = true
|
||||
Multigpu_Demo_Train=[
|
||||
|
|
@ -191,7 +205,7 @@ Multigpu_Demo_Train=[
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTrain_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTrain_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -208,7 +222,7 @@ Multigpu_Demo_Test=[
|
|||
action = "test"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -225,7 +239,7 @@ Multigpu_Demo_Output=[
|
|||
action = "write"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -237,32 +251,32 @@ dim = 2
|
|||
]
|
||||
]
|
||||
]
|
||||
outputPath = "E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu/MultigpuOutput"
|
||||
outputPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_gpu/MultigpuOutput"
|
||||
]
|
||||
currentDirectory=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
RunDir=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu
|
||||
DataDir=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
ConfigDir=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config
|
||||
OutputDir=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu
|
||||
currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_gpu
|
||||
DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Multigpu_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
05/03/2016 15:29:53: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:19:53: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 15:29:53: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:19:53: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: Multigpu.cntk:command=Multigpu_Demo_Train:Multigpu_Demo_Test
|
||||
configparameters: Multigpu.cntk:ConfigDir=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config
|
||||
configparameters: Multigpu.cntk:currentDirectory=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
configparameters: Multigpu.cntk:DataDir=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
configparameters: Multigpu.cntk:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config
|
||||
configparameters: Multigpu.cntk:currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
configparameters: Multigpu.cntk:DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
configparameters: Multigpu.cntk:deviceId=0
|
||||
configparameters: Multigpu.cntk:ModelDir=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu/Models
|
||||
configparameters: Multigpu.cntk:modelPath=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn
|
||||
configparameters: Multigpu.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_gpu/Models
|
||||
configparameters: Multigpu.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn
|
||||
configparameters: Multigpu.cntk:Multigpu_Demo_Output=[
|
||||
action = "write"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -274,14 +288,14 @@ dim = 2
|
|||
]
|
||||
]
|
||||
]
|
||||
outputPath = "E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu/MultigpuOutput"
|
||||
outputPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_gpu/MultigpuOutput"
|
||||
]
|
||||
|
||||
configparameters: Multigpu.cntk:Multigpu_Demo_Test=[
|
||||
action = "test"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -324,7 +338,7 @@ configparameters: Multigpu.cntk:Multigpu_Demo_Train=[
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTrain_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTrain_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -338,32 +352,44 @@ dim = 2
|
|||
]
|
||||
] [SGD=[maxEpochs=3]]
|
||||
|
||||
configparameters: Multigpu.cntk:OutputDir=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu
|
||||
configparameters: Multigpu.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_gpu
|
||||
configparameters: Multigpu.cntk:outputNodeNames=ScaledLogLikelihood
|
||||
configparameters: Multigpu.cntk:parallelTrain=true
|
||||
configparameters: Multigpu.cntk:precision=float
|
||||
configparameters: Multigpu.cntk:RootDir=..
|
||||
configparameters: Multigpu.cntk:RunDir=E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu
|
||||
configparameters: Multigpu.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_gpu
|
||||
configparameters: Multigpu.cntk:timestamping=true
|
||||
configparameters: Multigpu.cntk:traceLevel=1
|
||||
05/03/2016 15:29:53: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/03/2016 15:29:53: Commands: Multigpu_Demo_Train Multigpu_Demo_Test
|
||||
05/03/2016 15:29:53: Precision = "float"
|
||||
05/03/2016 15:29:53: CNTKModelPath: E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn
|
||||
05/03/2016 15:29:53: CNTKCommandTrainInfo: Multigpu_Demo_Train : 3
|
||||
05/03/2016 15:29:53: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
08/16/2016 03:19:53: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:19:53: Commands: Multigpu_Demo_Train Multigpu_Demo_Test
|
||||
08/16/2016 03:19:53: Precision = "float"
|
||||
08/16/2016 03:19:53: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn
|
||||
08/16/2016 03:19:53: CNTKCommandTrainInfo: Multigpu_Demo_Train : 3
|
||||
08/16/2016 03:19:53: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
|
||||
05/03/2016 15:29:53: ##############################################################################
|
||||
05/03/2016 15:29:53: # #
|
||||
05/03/2016 15:29:53: # Action "train" #
|
||||
05/03/2016 15:29:53: # #
|
||||
05/03/2016 15:29:53: ##############################################################################
|
||||
08/16/2016 03:19:53: ##############################################################################
|
||||
08/16/2016 03:19:53: # #
|
||||
08/16/2016 03:19:53: # Action "train" #
|
||||
08/16/2016 03:19:53: # #
|
||||
08/16/2016 03:19:53: ##############################################################################
|
||||
|
||||
05/03/2016 15:29:53: CNTKCommandTrainBegin: Multigpu_Demo_Train
|
||||
08/16/2016 03:19:53: CNTKCommandTrainBegin: Multigpu_Demo_Train
|
||||
SimpleNetworkBuilder Using GPU 0
|
||||
|
||||
05/03/2016 15:29:53: Creating virgin network.
|
||||
08/16/2016 03:19:53: Creating virgin network.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[50 x 2] <- 0.000000.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[50 x 2] <- uniform(seed=1, range=0.050000*1.000000, onCPU=false).
|
||||
Microsoft::MSR::CNTK::GPUMatrix<ElemType>::SetUniformRandomValue (GPU): creating curand object with seed 1, sizeof(ElemType)==4
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[50 x 50] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[50 x 50] <- uniform(seed=2, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[2 x 50] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[2 x 50] <- uniform(seed=3, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[2 x 1] <- 0.000000.
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[2 x 1] <- 0.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -415,207 +441,210 @@ Validating network, final pass.
|
|||
|
||||
Post-processing network complete.
|
||||
|
||||
05/03/2016 15:29:53: Created model with 25 nodes on GPU 0.
|
||||
08/16/2016 03:19:54: Created model with 25 nodes on GPU 0.
|
||||
|
||||
05/03/2016 15:29:53: Training criterion node(s):
|
||||
05/03/2016 15:29:53: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
|
||||
08/16/2016 03:19:54: Training criterion node(s):
|
||||
08/16/2016 03:19:54: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
|
||||
|
||||
05/03/2016 15:29:53: Evaluation criterion node(s):
|
||||
|
||||
05/03/2016 15:29:53: EvalErrorPrediction = ErrorPrediction
|
||||
08/16/2016 03:19:54: Evaluation criterion node(s):
|
||||
08/16/2016 03:19:54: EvalErrorPrediction = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 40 matrices, 19 are shared as 8, and 21 are not shared.
|
||||
|
||||
0000000000000000: {[EvalErrorPrediction Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
|
||||
000000572B66ECA0: {[features Value[2 x *]] }
|
||||
00000057420A1700: {[W1 Value[50 x 50]] }
|
||||
00000057420A1980: {[MeanOfFeatures Value[2]] }
|
||||
00000057420A1AC0: {[B2 Value[2 x 1]] }
|
||||
00000057420A1E80: {[W0 Value[50 x 2]] }
|
||||
00000057420A1F20: {[labels Value[2 x *]] }
|
||||
00000057420A22E0: {[Prior Value[2]] }
|
||||
00000057420A2560: {[InvStdOfFeatures Value[2]] }
|
||||
00000057420A2880: {[W2 Value[2 x 50]] }
|
||||
00000057420A2920: {[B1 Value[50 x 1]] }
|
||||
00000057420A2B00: {[B0 Value[50 x 1]] }
|
||||
0000005743927E40: {[CrossEntropyWithSoftmax Gradient[1]] }
|
||||
0000005743927EE0: {[W2*H1 Gradient[2 x 1 x *]] }
|
||||
0000005743928200: {[ScaledLogLikelihood Value[2 x 1 x *]] }
|
||||
00000057439282A0: {[W0*features+B0 Gradient[50 x 1 x *]] [W1*H1 Value[50 x 1 x *]] }
|
||||
00000057439283E0: {[LogOfPrior Value[2]] }
|
||||
00000057439285C0: {[W0 Gradient[50 x 2]] [W0*features+B0 Value[50 x 1 x *]] }
|
||||
0000005743928660: {[B1 Gradient[50 x 1]] [H2 Gradient[50 x 1 x *]] [HLast Gradient[2 x 1 x *]] }
|
||||
00000057439287A0: {[EvalErrorPrediction Value[1]] }
|
||||
0000005743928980: {[CrossEntropyWithSoftmax Value[1]] }
|
||||
0000005743928A20: {[B2 Gradient[2 x 1]] }
|
||||
0000005743928E80: {[H1 Value[50 x 1 x *]] [W0*features Gradient[50 x *]] }
|
||||
0000005743928FC0: {[W1 Gradient[50 x 50]] [W1*H1+B1 Value[50 x 1 x *]] }
|
||||
00000057439291A0: {[HLast Value[2 x 1 x *]] [W2 Gradient[2 x 50]] }
|
||||
0000005743929240: {[MVNormalizedFeatures Value[2 x *]] }
|
||||
00000057439292E0: {[H2 Value[50 x 1 x *]] [W1*H1 Gradient[50 x 1 x *]] }
|
||||
0000005743929420: {[B0 Gradient[50 x 1]] [H1 Gradient[50 x 1 x *]] [W1*H1+B1 Gradient[50 x 1 x *]] [W2*H1 Value[2 x 1 x *]] }
|
||||
00000057439297E0: {[W0*features Value[50 x *]] }
|
||||
{ B1 : [50 x 1] (gradient)
|
||||
H2 : [50 x 1 x *] (gradient)
|
||||
HLast : [2 x 1 x *] (gradient) }
|
||||
{ W1 : [50 x 50] (gradient)
|
||||
W1*H1+B1 : [50 x 1 x *] }
|
||||
{ B0 : [50 x 1] (gradient)
|
||||
H1 : [50 x 1 x *] (gradient)
|
||||
W1*H1+B1 : [50 x 1 x *] (gradient)
|
||||
W2*H1 : [2 x 1 x *] }
|
||||
{ H1 : [50 x 1 x *]
|
||||
W0*features : [50 x *] (gradient) }
|
||||
{ H2 : [50 x 1 x *]
|
||||
W1*H1 : [50 x 1 x *] (gradient) }
|
||||
{ HLast : [2 x 1 x *]
|
||||
W2 : [2 x 50] (gradient) }
|
||||
{ W0 : [50 x 2] (gradient)
|
||||
W0*features+B0 : [50 x 1 x *] }
|
||||
{ W0*features+B0 : [50 x 1 x *] (gradient)
|
||||
W1*H1 : [50 x 1 x *] }
|
||||
|
||||
|
||||
05/03/2016 15:29:53: Precomputing --> 3 PreCompute nodes found.
|
||||
08/16/2016 03:19:54: Training 2802 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:
|
||||
|
||||
05/03/2016 15:29:53: MeanOfFeatures = Mean()
|
||||
05/03/2016 15:29:53: InvStdOfFeatures = InvStdDev()
|
||||
05/03/2016 15:29:53: Prior = Mean()
|
||||
|
||||
05/03/2016 15:29:54: Precomputing --> Completed.
|
||||
08/16/2016 03:19:54: Node 'B0' (LearnableParameter operation) : [50 x 1]
|
||||
08/16/2016 03:19:54: Node 'B1' (LearnableParameter operation) : [50 x 1]
|
||||
08/16/2016 03:19:54: Node 'B2' (LearnableParameter operation) : [2 x 1]
|
||||
08/16/2016 03:19:54: Node 'W0' (LearnableParameter operation) : [50 x 2]
|
||||
08/16/2016 03:19:54: Node 'W1' (LearnableParameter operation) : [50 x 50]
|
||||
08/16/2016 03:19:54: Node 'W2' (LearnableParameter operation) : [2 x 50]
|
||||
|
||||
|
||||
05/03/2016 15:29:54: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
08/16/2016 03:19:54: Precomputing --> 3 PreCompute nodes found.
|
||||
|
||||
05/03/2016 15:29:54: Starting minibatch loop.
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70650452 * 250; EvalErrorPrediction = 0.55200000 * 250; time = 0.0115s; samplesPerSecond = 21832.2
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.69701831 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0095s; samplesPerSecond = 26326.9
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.71089587 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0100s; samplesPerSecond = 25067.7
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.72980273 * 250; EvalErrorPrediction = 0.56000000 * 250; time = 0.0096s; samplesPerSecond = 26079.7
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.70902783 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0115s; samplesPerSecond = 21692.0
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72657300 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0124s; samplesPerSecond = 20127.2
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.69319678 * 250; EvalErrorPrediction = 0.43200000 * 250; time = 0.0091s; samplesPerSecond = 27439.4
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.73563477 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0112s; samplesPerSecond = 22246.0
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.71463281 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0115s; samplesPerSecond = 21739.1
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.75213428 * 250; EvalErrorPrediction = 0.47200000 * 250; time = 0.0105s; samplesPerSecond = 23814.1
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.75931445 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0115s; samplesPerSecond = 21763.7
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.73075293 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0120s; samplesPerSecond = 20835.1
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.76701953 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0130s; samplesPerSecond = 19305.0
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.70451270 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0108s; samplesPerSecond = 23184.6
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.70539941 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0117s; samplesPerSecond = 21385.8
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.72700293 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0120s; samplesPerSecond = 20917.0
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70096191 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0112s; samplesPerSecond = 22301.5
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.69437305 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0113s; samplesPerSecond = 22079.0
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.69161621 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0116s; samplesPerSecond = 21514.6
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.73388281 * 250; EvalErrorPrediction = 0.55200000 * 250; time = 0.0107s; samplesPerSecond = 23406.0
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.72255664 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0116s; samplesPerSecond = 21546.2
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70414551 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0115s; samplesPerSecond = 21756.2
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69976758 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0113s; samplesPerSecond = 22065.3
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.72419141 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0114s; samplesPerSecond = 22018.7
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.69943945 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0111s; samplesPerSecond = 22604.0
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.69206445 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0111s; samplesPerSecond = 22504.3
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.68771680 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0113s; samplesPerSecond = 22118.0
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.69878516 * 250; EvalErrorPrediction = 0.44000000 * 250; time = 0.0130s; samplesPerSecond = 19278.2
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.71889844 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0127s; samplesPerSecond = 19632.5
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.70086523 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0095s; samplesPerSecond = 26329.6
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.70878320 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0112s; samplesPerSecond = 22361.4
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.70674414 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0130s; samplesPerSecond = 19168.8
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.69707422 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0094s; samplesPerSecond = 26729.4
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.68588281 * 250; EvalErrorPrediction = 0.40800000 * 250; time = 0.0112s; samplesPerSecond = 22365.4
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.67734766 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0128s; samplesPerSecond = 19583.3
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.67958008 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0092s; samplesPerSecond = 27144.4
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.66424805 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0114s; samplesPerSecond = 21864.6
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.62412500 * 250; EvalErrorPrediction = 0.20400000 * 250; time = 0.0116s; samplesPerSecond = 21475.8
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.58007422 * 250; EvalErrorPrediction = 0.16000000 * 250; time = 0.0094s; samplesPerSecond = 26567.5
|
||||
05/03/2016 15:29:54: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.52764648 * 250; EvalErrorPrediction = 0.19200000 * 250; time = 0.0132s; samplesPerSecond = 18988.3
|
||||
05/03/2016 15:29:54: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.69975483 * 10000; EvalErrorPrediction = 0.46850000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=0.453807s
|
||||
05/03/2016 15:29:54: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn.1'
|
||||
08/16/2016 03:19:54: MeanOfFeatures = Mean()
|
||||
08/16/2016 03:19:54: InvStdOfFeatures = InvStdDev()
|
||||
08/16/2016 03:19:54: Prior = Mean()
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
05/03/2016 15:29:54: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
|
||||
05/03/2016 15:29:54: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
|
||||
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.45075654 * 250; EvalErrorPrediction = 0.15200000 * 250; time = 0.0250s; samplesPerSecond = 10002.4
|
||||
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.40775497 * 250; EvalErrorPrediction = 0.14400000 * 250; time = 0.0219s; samplesPerSecond = 11420.2
|
||||
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.34165228 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0230s; samplesPerSecond = 10859.6
|
||||
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.29708900 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0198s; samplesPerSecond = 12604.0
|
||||
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.26669365 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0211s; samplesPerSecond = 11860.7
|
||||
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.25328680 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0212s; samplesPerSecond = 11817.0
|
||||
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.21017820 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0237s; samplesPerSecond = 10540.1
|
||||
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.21483054 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0214s; samplesPerSecond = 11699.7
|
||||
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.16626513 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0213s; samplesPerSecond = 11757.5
|
||||
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.17672434 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0239s; samplesPerSecond = 10454.6
|
||||
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.22140190 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0208s; samplesPerSecond = 12033.1
|
||||
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.17048554 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0237s; samplesPerSecond = 10553.4
|
||||
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.16438517 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0234s; samplesPerSecond = 10662.3
|
||||
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13782141 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0218s; samplesPerSecond = 11449.0
|
||||
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.16909663 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0244s; samplesPerSecond = 10228.7
|
||||
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.15419129 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0229s; samplesPerSecond = 10924.7
|
||||
05/03/2016 15:29:54: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.22229924 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0242s; samplesPerSecond = 10340.4
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18134995 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0236s; samplesPerSecond = 10579.3
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.15616904 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0236s; samplesPerSecond = 10594.6
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.17162733 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0262s; samplesPerSecond = 9530.3
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.15676289 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0262s; samplesPerSecond = 9554.4
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.16159542 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0262s; samplesPerSecond = 9558.8
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.16102246 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0284s; samplesPerSecond = 8800.3
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.15392923 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0248s; samplesPerSecond = 10089.6
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.14898334 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0269s; samplesPerSecond = 9279.5
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.15087969 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0285s; samplesPerSecond = 8785.2
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.15494578 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0247s; samplesPerSecond = 10101.4
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.17878713 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0250s; samplesPerSecond = 9986.0
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.22845049 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0249s; samplesPerSecond = 10045.4
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16884430 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0241s; samplesPerSecond = 10376.5
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.17970282 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0237s; samplesPerSecond = 10533.9
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.13292468 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0257s; samplesPerSecond = 9721.6
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.14167778 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0226s; samplesPerSecond = 11048.3
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.18716852 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0237s; samplesPerSecond = 10534.7
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.15480385 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0258s; samplesPerSecond = 9705.0
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.19482328 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0247s; samplesPerSecond = 10115.7
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.17488171 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0249s; samplesPerSecond = 10048.2
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15164433 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0281s; samplesPerSecond = 8901.2
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.12142463 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0222s; samplesPerSecond = 11279.0
|
||||
05/03/2016 15:29:55: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15287631 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0238s; samplesPerSecond = 10489.7
|
||||
05/03/2016 15:29:55: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.19475469 * 10000; EvalErrorPrediction = 0.07830000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.964496s
|
||||
05/03/2016 15:29:55: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn.2'
|
||||
|
||||
05/03/2016 15:29:55: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
|
||||
05/03/2016 15:29:55: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
|
||||
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.10717578 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0253s; samplesPerSecond = 9869.7
|
||||
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.17521929 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0234s; samplesPerSecond = 10701.1
|
||||
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14088211 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0250s; samplesPerSecond = 9986.8
|
||||
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.16281337 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0243s; samplesPerSecond = 10287.6
|
||||
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.11778386 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0234s; samplesPerSecond = 10666.9
|
||||
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.16295400 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0266s; samplesPerSecond = 9385.8
|
||||
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.16287201 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0233s; samplesPerSecond = 10746.2
|
||||
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.19482140 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0242s; samplesPerSecond = 10312.3
|
||||
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.20113689 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0235s; samplesPerSecond = 10643.3
|
||||
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.13748570 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0238s; samplesPerSecond = 10484.4
|
||||
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.20080420 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0236s; samplesPerSecond = 10600.9
|
||||
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.17730590 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0268s; samplesPerSecond = 9342.3
|
||||
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.15851029 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0233s; samplesPerSecond = 10743.0
|
||||
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16257260 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0250s; samplesPerSecond = 10012.8
|
||||
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.19772537 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0224s; samplesPerSecond = 11143.3
|
||||
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.10259204 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0235s; samplesPerSecond = 10626.1
|
||||
05/03/2016 15:29:55: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17093073 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0244s; samplesPerSecond = 10230.0
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16628544 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0252s; samplesPerSecond = 9936.8
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.12690716 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0246s; samplesPerSecond = 10171.7
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.11894288 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0233s; samplesPerSecond = 10718.1
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.12815907 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0246s; samplesPerSecond = 10151.0
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.18265773 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0225s; samplesPerSecond = 11131.9
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.13388730 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0231s; samplesPerSecond = 10807.5
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.19787903 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0251s; samplesPerSecond = 9951.4
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.15563315 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0241s; samplesPerSecond = 10373.0
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.11837055 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0240s; samplesPerSecond = 10429.3
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.13732942 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0234s; samplesPerSecond = 10689.7
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.20012115 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0253s; samplesPerSecond = 9872.4
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.19086846 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0238s; samplesPerSecond = 10525.4
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16492589 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0243s; samplesPerSecond = 10272.8
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.12141157 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0238s; samplesPerSecond = 10509.5
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.16335481 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0236s; samplesPerSecond = 10579.3
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.15923900 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0241s; samplesPerSecond = 10358.0
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.12315803 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0235s; samplesPerSecond = 10617.1
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13481532 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0260s; samplesPerSecond = 9612.4
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.20958008 * 250; EvalErrorPrediction = 0.11600000 * 250; time = 0.0223s; samplesPerSecond = 11232.4
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16519713 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0255s; samplesPerSecond = 9814.3
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.14990733 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0239s; samplesPerSecond = 10481.3
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.16508552 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0255s; samplesPerSecond = 9789.3
|
||||
05/03/2016 15:29:56: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.16941540 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0240s; samplesPerSecond = 10435.4
|
||||
05/03/2016 15:29:56: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15791792 * 10000; EvalErrorPrediction = 0.07460000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.970059s
|
||||
05/03/2016 15:29:56: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503162947.903093\CNTKTextFormatReader\Examples\Other\Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn'
|
||||
05/03/2016 15:29:56: CNTKCommandTrainEnd: Multigpu_Demo_Train
|
||||
|
||||
05/03/2016 15:29:56: Action "train" complete.
|
||||
08/16/2016 03:19:54: Precomputing --> Completed.
|
||||
|
||||
|
||||
05/03/2016 15:29:56: ##############################################################################
|
||||
05/03/2016 15:29:56: # #
|
||||
05/03/2016 15:29:56: # Action "test" #
|
||||
05/03/2016 15:29:56: # #
|
||||
05/03/2016 15:29:56: ##############################################################################
|
||||
08/16/2016 03:19:54: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:19:54: Starting minibatch loop.
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70124231 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0171s; samplesPerSecond = 14629.3
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.76372424 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0156s; samplesPerSecond = 15976.5
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.72703027 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0158s; samplesPerSecond = 15853.9
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.73895923 * 250; EvalErrorPrediction = 0.56800000 * 250; time = 0.0157s; samplesPerSecond = 15952.0
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.70621924 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0157s; samplesPerSecond = 15907.4
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.74767041 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0158s; samplesPerSecond = 15831.8
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.75094434 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0158s; samplesPerSecond = 15822.8
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.78058936 * 250; EvalErrorPrediction = 0.48400000 * 250; time = 0.0157s; samplesPerSecond = 15880.1
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.70407129 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0157s; samplesPerSecond = 15927.6
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.69555762 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0157s; samplesPerSecond = 15926.6
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.70626123 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0158s; samplesPerSecond = 15816.8
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.74540430 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0157s; samplesPerSecond = 15884.1
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.70824414 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0158s; samplesPerSecond = 15815.8
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.69895020 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0157s; samplesPerSecond = 15895.2
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.70353223 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0157s; samplesPerSecond = 15937.8
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.69346387 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0158s; samplesPerSecond = 15825.8
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.74449902 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0157s; samplesPerSecond = 15903.3
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.73767969 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0157s; samplesPerSecond = 15895.2
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.71876855 * 250; EvalErrorPrediction = 0.48400000 * 250; time = 0.0157s; samplesPerSecond = 15889.2
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.71509473 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0158s; samplesPerSecond = 15836.8
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.69956152 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0157s; samplesPerSecond = 15888.1
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.69785937 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0157s; samplesPerSecond = 15917.5
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.70736035 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0157s; samplesPerSecond = 15923.6
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.69820508 * 250; EvalErrorPrediction = 0.56800000 * 250; time = 0.0158s; samplesPerSecond = 15839.8
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.69537109 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0156s; samplesPerSecond = 15981.6
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.69347266 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0162s; samplesPerSecond = 15477.0
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.70801172 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0157s; samplesPerSecond = 15921.5
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.69131641 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0158s; samplesPerSecond = 15823.8
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.70370312 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0157s; samplesPerSecond = 15923.6
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.71200195 * 250; EvalErrorPrediction = 0.43600000 * 250; time = 0.0157s; samplesPerSecond = 15900.3
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.69506836 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0158s; samplesPerSecond = 15838.8
|
||||
08/16/2016 03:19:54: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.69935352 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0158s; samplesPerSecond = 15830.8
|
||||
08/16/2016 03:19:55: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.69887109 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0158s; samplesPerSecond = 15833.8
|
||||
08/16/2016 03:19:55: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.69604492 * 250; EvalErrorPrediction = 0.49200000 * 250; time = 0.0156s; samplesPerSecond = 15991.8
|
||||
08/16/2016 03:19:55: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.69011719 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.0158s; samplesPerSecond = 15794.8
|
||||
08/16/2016 03:19:55: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.68419531 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0158s; samplesPerSecond = 15850.9
|
||||
08/16/2016 03:19:55: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.67551367 * 250; EvalErrorPrediction = 0.32400000 * 250; time = 0.0158s; samplesPerSecond = 15859.9
|
||||
08/16/2016 03:19:55: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.67028516 * 250; EvalErrorPrediction = 0.40000000 * 250; time = 0.0157s; samplesPerSecond = 15940.8
|
||||
08/16/2016 03:19:55: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.65152734 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0158s; samplesPerSecond = 15841.8
|
||||
08/16/2016 03:19:55: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.63594727 * 250; EvalErrorPrediction = 0.22000000 * 250; time = 0.0157s; samplesPerSecond = 15917.5
|
||||
08/16/2016 03:19:55: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.70729233 * 10000; EvalErrorPrediction = 0.47740000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=0.633908s
|
||||
08/16/2016 03:19:55: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn.1'
|
||||
|
||||
08/16/2016 03:19:55: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [10000..20000] (first sequence at sample 10000), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:19:55: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.61550018 * 250; EvalErrorPrediction = 0.27600000 * 250; time = 0.0399s; samplesPerSecond = 6268.0
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.59409242 * 250; EvalErrorPrediction = 0.28800000 * 250; time = 0.0380s; samplesPerSecond = 6577.0
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.53884306 * 250; EvalErrorPrediction = 0.20400000 * 250; time = 0.0379s; samplesPerSecond = 6604.0
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.52450125 * 250; EvalErrorPrediction = 0.15200000 * 250; time = 0.0374s; samplesPerSecond = 6683.4
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.49237463 * 250; EvalErrorPrediction = 0.16400000 * 250; time = 0.0374s; samplesPerSecond = 6678.4
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.44029644 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0376s; samplesPerSecond = 6645.4
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.40029475 * 250; EvalErrorPrediction = 0.13200000 * 250; time = 0.0370s; samplesPerSecond = 6763.7
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.34001918 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0378s; samplesPerSecond = 6611.8
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.31615756 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0380s; samplesPerSecond = 6582.1
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.27277486 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0375s; samplesPerSecond = 6672.4
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.24557418 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0375s; samplesPerSecond = 6662.2
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.21023629 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0372s; samplesPerSecond = 6712.5
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.22380673 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0376s; samplesPerSecond = 6640.6
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.20455512 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0375s; samplesPerSecond = 6666.1
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.20168480 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0377s; samplesPerSecond = 6623.4
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.19212741 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0373s; samplesPerSecond = 6699.0
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.19324124 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0374s; samplesPerSecond = 6679.5
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.21777418 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0372s; samplesPerSecond = 6729.3
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.17514209 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0376s; samplesPerSecond = 6644.0
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.17993773 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0376s; samplesPerSecond = 6649.8
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13968032 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0376s; samplesPerSecond = 6641.3
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.17727753 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0373s; samplesPerSecond = 6699.0
|
||||
08/16/2016 03:19:55: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.12898624 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0370s; samplesPerSecond = 6749.8
|
||||
08/16/2016 03:19:56: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.21880105 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0373s; samplesPerSecond = 6708.2
|
||||
08/16/2016 03:19:56: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.21850111 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0377s; samplesPerSecond = 6622.9
|
||||
08/16/2016 03:19:56: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.18102491 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0377s; samplesPerSecond = 6636.6
|
||||
08/16/2016 03:19:56: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.16393427 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0372s; samplesPerSecond = 6714.3
|
||||
08/16/2016 03:19:56: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.13832267 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0375s; samplesPerSecond = 6659.7
|
||||
08/16/2016 03:19:56: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.16506280 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0377s; samplesPerSecond = 6634.6
|
||||
08/16/2016 03:19:56: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.14733234 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0376s; samplesPerSecond = 6644.7
|
||||
08/16/2016 03:19:56: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.15041138 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0379s; samplesPerSecond = 6600.5
|
||||
08/16/2016 03:19:56: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.12665836 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0376s; samplesPerSecond = 6641.3
|
||||
08/16/2016 03:19:56: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.16643186 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0373s; samplesPerSecond = 6699.5
|
||||
08/16/2016 03:19:56: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.14422443 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0360s; samplesPerSecond = 6946.8
|
||||
08/16/2016 03:19:56: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13888039 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0364s; samplesPerSecond = 6860.0
|
||||
08/16/2016 03:19:56: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.14108686 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0377s; samplesPerSecond = 6629.0
|
||||
08/16/2016 03:19:56: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.15887684 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0375s; samplesPerSecond = 6662.6
|
||||
08/16/2016 03:19:56: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.16247402 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0377s; samplesPerSecond = 6630.8
|
||||
08/16/2016 03:19:56: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.13586729 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0377s; samplesPerSecond = 6631.1
|
||||
08/16/2016 03:19:56: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15528679 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0376s; samplesPerSecond = 6642.8
|
||||
08/16/2016 03:19:56: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.24345139 * 10000; EvalErrorPrediction = 0.09720000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=1.50329s
|
||||
08/16/2016 03:19:56: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn.2'
|
||||
|
||||
08/16/2016 03:19:56: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [20000..30000] (first sequence at sample 20000), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:19:56: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1).
|
||||
08/16/2016 03:19:56: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.18398525 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0376s; samplesPerSecond = 6641.3
|
||||
08/16/2016 03:19:56: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.12825686 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0376s; samplesPerSecond = 6653.0
|
||||
08/16/2016 03:19:56: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.17547006 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0374s; samplesPerSecond = 6692.7
|
||||
08/16/2016 03:19:56: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.14044644 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0373s; samplesPerSecond = 6703.9
|
||||
08/16/2016 03:19:56: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.16673170 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0379s; samplesPerSecond = 6591.3
|
||||
08/16/2016 03:19:56: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.19317383 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0374s; samplesPerSecond = 6678.2
|
||||
08/16/2016 03:19:56: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.12349199 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0381s; samplesPerSecond = 6555.0
|
||||
08/16/2016 03:19:56: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.16427535 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0373s; samplesPerSecond = 6693.6
|
||||
08/16/2016 03:19:56: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.12350212 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0376s; samplesPerSecond = 6652.3
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.19958846 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0377s; samplesPerSecond = 6625.1
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.14269741 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0378s; samplesPerSecond = 6615.7
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.12369058 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0370s; samplesPerSecond = 6755.8
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.16638059 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0375s; samplesPerSecond = 6669.5
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.20047975 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0373s; samplesPerSecond = 6704.2
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.16963457 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0371s; samplesPerSecond = 6744.7
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.13367401 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0372s; samplesPerSecond = 6724.0
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.14477143 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0369s; samplesPerSecond = 6775.3
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.21046366 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0373s; samplesPerSecond = 6702.8
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.19247125 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0374s; samplesPerSecond = 6679.8
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.15027023 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0371s; samplesPerSecond = 6747.5
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.15612870 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0377s; samplesPerSecond = 6635.9
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.13684548 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0373s; samplesPerSecond = 6697.7
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.17217344 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0377s; samplesPerSecond = 6638.7
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.14419519 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0375s; samplesPerSecond = 6666.8
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.13803181 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0375s; samplesPerSecond = 6659.6
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.14209585 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0374s; samplesPerSecond = 6680.2
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.16967141 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0373s; samplesPerSecond = 6710.0
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.18647515 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0377s; samplesPerSecond = 6630.2
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.16511327 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0363s; samplesPerSecond = 6885.7
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.15550174 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0376s; samplesPerSecond = 6646.5
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18759246 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0373s; samplesPerSecond = 6695.4
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.13178152 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0376s; samplesPerSecond = 6657.3
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.14624311 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0372s; samplesPerSecond = 6714.7
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.13930281 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0374s; samplesPerSecond = 6682.3
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.20110083 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0377s; samplesPerSecond = 6632.4
|
||||
08/16/2016 03:19:57: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.12558937 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0369s; samplesPerSecond = 6776.5
|
||||
08/16/2016 03:19:58: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.18612014 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0376s; samplesPerSecond = 6647.2
|
||||
08/16/2016 03:19:58: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15336297 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0374s; samplesPerSecond = 6684.3
|
||||
08/16/2016 03:19:58: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.11885079 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0375s; samplesPerSecond = 6668.4
|
||||
08/16/2016 03:19:58: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.12974982 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0381s; samplesPerSecond = 6568.7
|
||||
08/16/2016 03:19:58: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15798453 * 10000; EvalErrorPrediction = 0.07300000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=1.49905s
|
||||
08/16/2016 03:19:58: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Other\Simple2d_MultiGpu@release_gpu/Models/multigpu.dnn'
|
||||
08/16/2016 03:19:58: CNTKCommandTrainEnd: Multigpu_Demo_Train
|
||||
|
||||
08/16/2016 03:19:58: Action "train" complete.
|
||||
|
||||
|
||||
08/16/2016 03:19:58: ##############################################################################
|
||||
08/16/2016 03:19:58: # #
|
||||
08/16/2016 03:19:58: # Action "test" #
|
||||
08/16/2016 03:19:58: # #
|
||||
08/16/2016 03:19:58: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
|
@ -673,35 +702,17 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 25 matrices, 2 are shared as 1, and 23 are not shared.
|
||||
|
||||
0000000000000000: {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalErrorPrediction Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
|
||||
0000005743925BB0: {[HLast Value[2 x 1 x *1]] }
|
||||
0000005743925D90: {[MVNormalizedFeatures Value[2 x *1]] }
|
||||
0000005743925E30: {[CrossEntropyWithSoftmax Value[1]] }
|
||||
0000005743925F70: {[W2 Value[2 x 50]] }
|
||||
0000005743926150: {[W0*features Value[50 x *1]] }
|
||||
00000057439261F0: {[H1 Value[50 x 1 x *1]] }
|
||||
0000005743926290: {[LogOfPrior Value[2]] }
|
||||
00000057439263D0: {[W1*H1+B1 Value[50 x 1 x *1]] }
|
||||
0000005743926470: {[W2*H1 Value[2 x 1 x *1]] }
|
||||
00000057439265B0: {[W0*features+B0 Value[50 x 1 x *1]] }
|
||||
0000005743926650: {[W1*H1 Value[50 x 1 x *1]] }
|
||||
0000005743926970: {[H2 Value[50 x 1 x *1]] }
|
||||
0000005743926AB0: {[EvalErrorPrediction Value[1]] }
|
||||
000000574B7FAD10: {[W0 Value[50 x 2]] }
|
||||
000000574B7FB170: {[InvStdOfFeatures Value[2]] }
|
||||
000000574B7FB210: {[MeanOfFeatures Value[2]] }
|
||||
000000574B7FB530: {[W1 Value[50 x 50]] }
|
||||
000000574B7FB7B0: {[labels Value[2 x *1]] }
|
||||
000000574B7FBA30: {[Prior Value[2]] }
|
||||
000000574C9F1D40: {[features Value[2 x *1]] }
|
||||
000000574D960D10: {[B1 Value[50 x 1]] }
|
||||
000000574D960E50: {[B2 Value[2 x 1]] }
|
||||
000000574D9610D0: {[B0 Value[50 x 1]] }
|
||||
{ PosteriorProb : [2 x 1 x *1]
|
||||
ScaledLogLikelihood : [2 x 1 x *1] }
|
||||
|
||||
05/03/2016 15:29:56: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.05638474 * 603; CrossEntropyWithSoftmax = 0.12022919 * 603; perplexity = 1.12775529
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..603] (first sequence at sample 0), data subset 0 of 1
|
||||
Actual gradient aggregation time: 5.7e-005
|
||||
08/16/2016 03:19:58: Minibatch[1-1]: EvalErrorPrediction = 0.05140962 * 603; CrossEntropyWithSoftmax = 0.10723887 * 603
|
||||
08/16/2016 03:19:58: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.05140962 * 603; CrossEntropyWithSoftmax = 0.10723887 * 603; perplexity = 1.11320013
|
||||
|
||||
05/03/2016 15:29:56: Action "test" complete.
|
||||
08/16/2016 03:19:58: Action "test" complete.
|
||||
|
||||
05/03/2016 15:29:56: __COMPLETED__
|
||||
08/16/2016 03:19:58: __COMPLETED__
|
||||
~MPIWrapper
|
||||
|
|
@ -1,48 +1,61 @@
|
|||
=== Running /home/alrezni/src/cntk_git/build/release/bin/cntk configFile=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/Simple/../Config/Simple.cntk currentDirectory=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data RunDir=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu DataDir=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data ConfigDir=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/Simple/../Config OutputDir=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu DeviceId=-1 timestamping=true Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 264172964 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/Simple/../../../../../../Examples/Other/Simple2d/Config/Simple.cntk currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_cpu DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/Simple/../../../../../../Examples/Other/Simple2d/Config OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_cpu DeviceId=-1 timestamping=true Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 3 2016 15:08:09
|
||||
Last modified date: Tue Apr 5 16:01:37 2016
|
||||
Built time: Aug 16 2016 09:41:56
|
||||
Last modified date: Fri Aug 12 07:32:43 2016
|
||||
Build type: release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: yes
|
||||
Math lib: acml
|
||||
CUDA_PATH: /usr/local/cuda-7.0
|
||||
With 1bit-SGD: no
|
||||
Math lib: mkl
|
||||
CUDA_PATH: /usr/local/cuda-7.5
|
||||
CUB_PATH: /usr/local/cub-1.4.1
|
||||
CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
Build Branch: alrezni/examples_text
|
||||
Build SHA1: e80dab7d66009531806ce70b4842146e0da00516
|
||||
Built by alrezni on atleneu04
|
||||
Build Path: /home/alrezni/src/cntk_git
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by philly on f67b30a647de
|
||||
Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to /home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
05/03/2016 15:21:15: -------------------------------------------------------------------
|
||||
05/03/2016 15:21:15: Build info:
|
||||
Changed current directory to /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
08/16/2016 10:51:34: -------------------------------------------------------------------
|
||||
08/16/2016 10:51:34: Build info:
|
||||
|
||||
05/03/2016 15:21:15: Built time: May 3 2016 15:08:09
|
||||
05/03/2016 15:21:15: Last modified date: Tue Apr 5 16:01:37 2016
|
||||
05/03/2016 15:21:15: Build type: release
|
||||
05/03/2016 15:21:15: Build target: GPU
|
||||
05/03/2016 15:21:15: With 1bit-SGD: yes
|
||||
05/03/2016 15:21:15: Math lib: acml
|
||||
05/03/2016 15:21:15: CUDA_PATH: /usr/local/cuda-7.0
|
||||
05/03/2016 15:21:15: CUB_PATH: /usr/local/cub-1.4.1
|
||||
05/03/2016 15:21:15: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
05/03/2016 15:21:15: Build Branch: alrezni/examples_text
|
||||
05/03/2016 15:21:15: Build SHA1: e80dab7d66009531806ce70b4842146e0da00516
|
||||
05/03/2016 15:21:15: Built by alrezni on atleneu04
|
||||
05/03/2016 15:21:15: Build Path: /home/alrezni/src/cntk_git
|
||||
05/03/2016 15:21:15: -------------------------------------------------------------------
|
||||
08/16/2016 10:51:34: Built time: Aug 16 2016 09:41:56
|
||||
08/16/2016 10:51:34: Last modified date: Fri Aug 12 07:32:43 2016
|
||||
08/16/2016 10:51:34: Build type: release
|
||||
08/16/2016 10:51:34: Build target: GPU
|
||||
08/16/2016 10:51:34: With 1bit-SGD: no
|
||||
08/16/2016 10:51:34: Math lib: mkl
|
||||
08/16/2016 10:51:34: CUDA_PATH: /usr/local/cuda-7.5
|
||||
08/16/2016 10:51:34: CUB_PATH: /usr/local/cub-1.4.1
|
||||
08/16/2016 10:51:34: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
08/16/2016 10:51:34: Build Branch: HEAD
|
||||
08/16/2016 10:51:34: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 10:51:34: Built by philly on f67b30a647de
|
||||
08/16/2016 10:51:34: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
08/16/2016 10:51:34: -------------------------------------------------------------------
|
||||
08/16/2016 10:51:35: -------------------------------------------------------------------
|
||||
08/16/2016 10:51:35: GPU info:
|
||||
|
||||
05/03/2016 15:21:15: Running on localhost at 2016/05/03 15:21:15
|
||||
05/03/2016 15:21:15: Command line:
|
||||
/home/alrezni/src/cntk_git/build/release/bin/cntk configFile=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/Simple/../Config/Simple.cntk currentDirectory=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data RunDir=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu DataDir=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data ConfigDir=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/Simple/../Config OutputDir=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu DeviceId=-1 timestamping=true Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
08/16/2016 10:51:35: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:51:35: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:51:35: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:51:35: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:51:35: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 10:51:35: Running on localhost at 2016/08/16 10:51:35
|
||||
08/16/2016 10:51:35: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/Simple/../../../../../../Examples/Other/Simple2d/Config/Simple.cntk currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_cpu DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/Simple/../../../../../../Examples/Other/Simple2d/Config OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_cpu DeviceId=-1 timestamping=true Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
|
||||
|
||||
05/03/2016 15:21:15: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 15:21:15: RootDir = ".."
|
||||
08/16/2016 10:51:35: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:51:35: RootDir = ".."
|
||||
ConfigDir = "$RootDir$/Config"
|
||||
DataDir = "$RootDir$/Data"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
|
|
@ -129,28 +142,28 @@ labelMappingFile = "$DataDir$/SimpleMapping.txt"
|
|||
sequenceEpilogue = "\t// %s\n"
|
||||
]
|
||||
]
|
||||
currentDirectory=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
RunDir=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu
|
||||
DataDir=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
ConfigDir=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/Simple/../Config
|
||||
OutputDir=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu
|
||||
currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_cpu
|
||||
DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/Simple/../../../../../../Examples/Other/Simple2d/Config
|
||||
OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_cpu
|
||||
DeviceId=-1
|
||||
timestamping=true
|
||||
Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
05/03/2016 15:21:15: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:51:35: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 15:21:15: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 15:21:15: RootDir = ".."
|
||||
08/16/2016 10:51:35: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:51:35: RootDir = ".."
|
||||
ConfigDir = "../Config"
|
||||
DataDir = "../Data"
|
||||
OutputDir = "../Output"
|
||||
ModelDir = "/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu/Models"
|
||||
ModelDir = "/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_cpu/Models"
|
||||
deviceId = -1
|
||||
command = Simple_Demo_Train:Simple_Demo_Test:Simple_Demo_Output
|
||||
precision = "float"
|
||||
traceLevel = 1
|
||||
modelPath = "/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu/Models/simple.dnn"
|
||||
modelPath = "/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_cpu/Models/simple.dnn"
|
||||
outputNodeNames = ScaledLogLikelihood
|
||||
Simple_Demo_Train = [
|
||||
action = "train"
|
||||
|
|
@ -174,7 +187,7 @@ Simple_Demo_Train = [
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTrain_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTrain_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -191,7 +204,7 @@ Simple_Demo_Test = [
|
|||
action = "test"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -208,7 +221,7 @@ Simple_Demo_Output=[
|
|||
action = "write"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -221,42 +234,42 @@ dim = 2
|
|||
]
|
||||
]
|
||||
outputNodeNames = PosteriorProb : labels
|
||||
outputPath = "/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu/SimpleOutput"
|
||||
outputPath = "/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_cpu/SimpleOutput"
|
||||
format = [
|
||||
type = "category"
|
||||
labelMappingFile = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleMapping.txt"
|
||||
labelMappingFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleMapping.txt"
|
||||
sequenceEpilogue = "\t// %s\n"
|
||||
]
|
||||
]
|
||||
currentDirectory=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
RunDir=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu
|
||||
DataDir=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
ConfigDir=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/Simple/../Config
|
||||
OutputDir=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu
|
||||
currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_cpu
|
||||
DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/Simple/../../../../../../Examples/Other/Simple2d/Config
|
||||
OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_cpu
|
||||
DeviceId=-1
|
||||
timestamping=true
|
||||
Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
05/03/2016 15:21:15: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:51:35: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 15:21:15: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:51:35: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: Simple.cntk:command=Simple_Demo_Train:Simple_Demo_Test:Simple_Demo_Output
|
||||
configparameters: Simple.cntk:ConfigDir=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/Simple/../Config
|
||||
configparameters: Simple.cntk:currentDirectory=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
configparameters: Simple.cntk:DataDir=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
configparameters: Simple.cntk:ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/Simple/../../../../../../Examples/Other/Simple2d/Config
|
||||
configparameters: Simple.cntk:currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
configparameters: Simple.cntk:DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
configparameters: Simple.cntk:deviceId=-1
|
||||
configparameters: Simple.cntk:ModelDir=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu/Models
|
||||
configparameters: Simple.cntk:modelPath=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu/Models/simple.dnn
|
||||
configparameters: Simple.cntk:OutputDir=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu
|
||||
configparameters: Simple.cntk:ModelDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_cpu/Models
|
||||
configparameters: Simple.cntk:modelPath=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_cpu/Models/simple.dnn
|
||||
configparameters: Simple.cntk:OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_cpu
|
||||
configparameters: Simple.cntk:outputNodeNames=ScaledLogLikelihood
|
||||
configparameters: Simple.cntk:precision=float
|
||||
configparameters: Simple.cntk:RootDir=..
|
||||
configparameters: Simple.cntk:RunDir=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu
|
||||
configparameters: Simple.cntk:RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_cpu
|
||||
configparameters: Simple.cntk:Simple_Demo_Output=[
|
||||
action = "write"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -269,10 +282,10 @@ dim = 2
|
|||
]
|
||||
]
|
||||
outputNodeNames = PosteriorProb : labels
|
||||
outputPath = "/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu/SimpleOutput"
|
||||
outputPath = "/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_cpu/SimpleOutput"
|
||||
format = [
|
||||
type = "category"
|
||||
labelMappingFile = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleMapping.txt"
|
||||
labelMappingFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleMapping.txt"
|
||||
sequenceEpilogue = "\t// %s\n"
|
||||
]
|
||||
]
|
||||
|
|
@ -281,7 +294,7 @@ configparameters: Simple.cntk:Simple_Demo_Test=[
|
|||
action = "test"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -317,7 +330,7 @@ configparameters: Simple.cntk:Simple_Demo_Train=[
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTrain_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTrain_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -333,23 +346,35 @@ dim = 2
|
|||
|
||||
configparameters: Simple.cntk:timestamping=true
|
||||
configparameters: Simple.cntk:traceLevel=1
|
||||
05/03/2016 15:21:15: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/03/2016 15:21:15: Commands: Simple_Demo_Train Simple_Demo_Test Simple_Demo_Output
|
||||
05/03/2016 15:21:15: Precision = "float"
|
||||
05/03/2016 15:21:15: CNTKModelPath: /tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu/Models/simple.dnn
|
||||
05/03/2016 15:21:15: CNTKCommandTrainInfo: Simple_Demo_Train : 3
|
||||
05/03/2016 15:21:15: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
08/16/2016 10:51:35: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:51:35: Commands: Simple_Demo_Train Simple_Demo_Test Simple_Demo_Output
|
||||
08/16/2016 10:51:35: Precision = "float"
|
||||
08/16/2016 10:51:35: CNTKModelPath: /tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_cpu/Models/simple.dnn
|
||||
08/16/2016 10:51:35: CNTKCommandTrainInfo: Simple_Demo_Train : 3
|
||||
08/16/2016 10:51:35: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
|
||||
05/03/2016 15:21:15: ##############################################################################
|
||||
05/03/2016 15:21:15: # #
|
||||
05/03/2016 15:21:15: # Action "train" #
|
||||
05/03/2016 15:21:15: # #
|
||||
05/03/2016 15:21:15: ##############################################################################
|
||||
08/16/2016 10:51:35: ##############################################################################
|
||||
08/16/2016 10:51:35: # #
|
||||
08/16/2016 10:51:35: # Action "train" #
|
||||
08/16/2016 10:51:35: # #
|
||||
08/16/2016 10:51:35: ##############################################################################
|
||||
|
||||
05/03/2016 15:21:15: CNTKCommandTrainBegin: Simple_Demo_Train
|
||||
08/16/2016 10:51:35: CNTKCommandTrainBegin: Simple_Demo_Train
|
||||
SimpleNetworkBuilder Using CPU
|
||||
|
||||
05/03/2016 15:21:15: Creating virgin network.
|
||||
08/16/2016 10:51:35: Creating virgin network.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[50 x 2] <- 0.000000.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[50 x 2] <- uniform(seed=1, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[50 x 50] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[50 x 50] <- uniform(seed=2, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[2 x 50] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[2 x 50] <- uniform(seed=3, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[2 x 1] <- 0.000000.
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[2 x 1] <- 0.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -401,207 +426,210 @@ Validating network, final pass.
|
|||
|
||||
Post-processing network complete.
|
||||
|
||||
05/03/2016 15:21:15: Created model with 25 nodes on CPU.
|
||||
08/16/2016 10:51:35: Created model with 25 nodes on CPU.
|
||||
|
||||
05/03/2016 15:21:15: Training criterion node(s):
|
||||
05/03/2016 15:21:15: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
|
||||
08/16/2016 10:51:35: Training criterion node(s):
|
||||
08/16/2016 10:51:35: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
|
||||
|
||||
05/03/2016 15:21:15: Evaluation criterion node(s):
|
||||
|
||||
05/03/2016 15:21:15: EvalErrorPrediction = ErrorPrediction
|
||||
08/16/2016 10:51:35: Evaluation criterion node(s):
|
||||
08/16/2016 10:51:35: EvalErrorPrediction = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 40 matrices, 19 are shared as 8, and 21 are not shared.
|
||||
|
||||
(nil): {[EvalErrorPrediction Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
|
||||
0x2e7f338: {[features Value[2 x *]] }
|
||||
0x2e82908: {[MeanOfFeatures Value[2]] }
|
||||
0x2e84f08: {[InvStdOfFeatures Value[2]] }
|
||||
0x2e861f8: {[W0 Value[50 x 2]] }
|
||||
0x2e867b8: {[B0 Value[50 x 1]] }
|
||||
0x2e87718: {[W1 Value[50 x 50]] }
|
||||
0x2e8a298: {[B1 Value[50 x 1]] }
|
||||
0x2e8b158: {[W2 Value[2 x 50]] }
|
||||
0x2e8b718: {[B2 Value[2 x 1]] }
|
||||
0x2e8c1e8: {[labels Value[2 x *]] }
|
||||
0x2e8cf38: {[Prior Value[2]] }
|
||||
0x2e926f8: {[EvalErrorPrediction Value[1]] }
|
||||
0x2e92858: {[ScaledLogLikelihood Value[2 x 1 x *]] }
|
||||
0x2e929b8: {[CrossEntropyWithSoftmax Value[1]] }
|
||||
0x2e93218: {[LogOfPrior Value[2]] }
|
||||
0x2e95498: {[MVNormalizedFeatures Value[2 x *]] }
|
||||
0x2e957b8: {[W0*features Value[50 x *]] }
|
||||
0x2e95918: {[W0 Gradient[50 x 2]] [W0*features+B0 Value[50 x 1 x *]] }
|
||||
0x2e95a78: {[H1 Value[50 x 1 x *]] [W0*features Gradient[50 x *]] }
|
||||
0x2e95c38: {[W0*features+B0 Gradient[50 x 1 x *]] [W1*H1 Value[50 x 1 x *]] }
|
||||
0x2e95df8: {[W1 Gradient[50 x 50]] [W1*H1+B1 Value[50 x 1 x *]] }
|
||||
0x2e95fb8: {[H2 Value[50 x 1 x *]] [W1*H1 Gradient[50 x 1 x *]] }
|
||||
0x2e96178: {[B0 Gradient[50 x 1]] [H1 Gradient[50 x 1 x *]] [W1*H1+B1 Gradient[50 x 1 x *]] [W2*H1 Value[2 x 1 x *]] }
|
||||
0x2e96338: {[HLast Value[2 x 1 x *]] [W2 Gradient[2 x 50]] }
|
||||
0x2e96ef8: {[CrossEntropyWithSoftmax Gradient[1]] }
|
||||
0x2e970b8: {[B1 Gradient[50 x 1]] [H2 Gradient[50 x 1 x *]] [HLast Gradient[2 x 1 x *]] }
|
||||
0x2e97278: {[W2*H1 Gradient[2 x 1 x *]] }
|
||||
0x2e97438: {[B2 Gradient[2 x 1]] }
|
||||
{ W0 : [50 x 2] (gradient)
|
||||
W0*features+B0 : [50 x 1 x *] }
|
||||
{ H1 : [50 x 1 x *]
|
||||
W0*features : [50 x *] (gradient) }
|
||||
{ W0*features+B0 : [50 x 1 x *] (gradient)
|
||||
W1*H1 : [50 x 1 x *] }
|
||||
{ W1 : [50 x 50] (gradient)
|
||||
W1*H1+B1 : [50 x 1 x *] }
|
||||
{ H2 : [50 x 1 x *]
|
||||
W1*H1 : [50 x 1 x *] (gradient) }
|
||||
{ B0 : [50 x 1] (gradient)
|
||||
H1 : [50 x 1 x *] (gradient)
|
||||
W1*H1+B1 : [50 x 1 x *] (gradient)
|
||||
W2*H1 : [2 x 1 x *] }
|
||||
{ HLast : [2 x 1 x *]
|
||||
W2 : [2 x 50] (gradient) }
|
||||
{ B1 : [50 x 1] (gradient)
|
||||
H2 : [50 x 1 x *] (gradient)
|
||||
HLast : [2 x 1 x *] (gradient) }
|
||||
|
||||
|
||||
05/03/2016 15:21:15: Precomputing --> 3 PreCompute nodes found.
|
||||
08/16/2016 10:51:35: Training 2802 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:
|
||||
|
||||
05/03/2016 15:21:15: MeanOfFeatures = Mean()
|
||||
05/03/2016 15:21:15: InvStdOfFeatures = InvStdDev()
|
||||
05/03/2016 15:21:15: Prior = Mean()
|
||||
|
||||
05/03/2016 15:21:17: Precomputing --> Completed.
|
||||
08/16/2016 10:51:35: Node 'B0' (LearnableParameter operation) : [50 x 1]
|
||||
08/16/2016 10:51:35: Node 'B1' (LearnableParameter operation) : [50 x 1]
|
||||
08/16/2016 10:51:35: Node 'B2' (LearnableParameter operation) : [2 x 1]
|
||||
08/16/2016 10:51:35: Node 'W0' (LearnableParameter operation) : [50 x 2]
|
||||
08/16/2016 10:51:35: Node 'W1' (LearnableParameter operation) : [50 x 50]
|
||||
08/16/2016 10:51:35: Node 'W2' (LearnableParameter operation) : [2 x 50]
|
||||
|
||||
|
||||
05/03/2016 15:21:17: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
08/16/2016 10:51:35: Precomputing --> 3 PreCompute nodes found.
|
||||
|
||||
05/03/2016 15:21:17: Starting minibatch loop.
|
||||
05/03/2016 15:21:17: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.69966235 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0806s; samplesPerSecond = 3103.4
|
||||
05/03/2016 15:21:17: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.70639648 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0489s; samplesPerSecond = 5107.5
|
||||
05/03/2016 15:21:17: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.70470264 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0598s; samplesPerSecond = 4180.0
|
||||
05/03/2016 15:21:17: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.69813501 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0581s; samplesPerSecond = 4306.3
|
||||
05/03/2016 15:21:17: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.73551416 * 250; EvalErrorPrediction = 0.57600000 * 250; time = 0.0618s; samplesPerSecond = 4045.4
|
||||
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72432324 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0579s; samplesPerSecond = 4314.7
|
||||
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.73327588 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.2699s; samplesPerSecond = 926.3
|
||||
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.70092627 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0620s; samplesPerSecond = 4035.0
|
||||
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.72354980 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0826s; samplesPerSecond = 3027.2
|
||||
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.72148096 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0811s; samplesPerSecond = 3082.2
|
||||
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.69814941 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0895s; samplesPerSecond = 2793.1
|
||||
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.70699121 * 250; EvalErrorPrediction = 0.54800000 * 250; time = 0.0482s; samplesPerSecond = 5187.9
|
||||
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.69898437 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0567s; samplesPerSecond = 4408.3
|
||||
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.71712695 * 250; EvalErrorPrediction = 0.54000000 * 250; time = 0.0586s; samplesPerSecond = 4266.7
|
||||
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.69470703 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0546s; samplesPerSecond = 4575.3
|
||||
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.71375879 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0640s; samplesPerSecond = 3907.4
|
||||
05/03/2016 15:21:18: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70381641 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0756s; samplesPerSecond = 3307.9
|
||||
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.71748633 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.0598s; samplesPerSecond = 4178.1
|
||||
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.71863281 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0813s; samplesPerSecond = 3075.3
|
||||
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.70715234 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0811s; samplesPerSecond = 3082.9
|
||||
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.70401074 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0673s; samplesPerSecond = 3717.1
|
||||
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70599414 * 250; EvalErrorPrediction = 0.48400000 * 250; time = 0.0819s; samplesPerSecond = 3052.5
|
||||
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69628711 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0909s; samplesPerSecond = 2749.3
|
||||
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.75920898 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0752s; samplesPerSecond = 3323.1
|
||||
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.70542578 * 250; EvalErrorPrediction = 0.43600000 * 250; time = 0.0734s; samplesPerSecond = 3406.2
|
||||
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.70643945 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0869s; samplesPerSecond = 2875.4
|
||||
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.72481641 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0893s; samplesPerSecond = 2798.7
|
||||
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.71133594 * 250; EvalErrorPrediction = 0.55600000 * 250; time = 0.0814s; samplesPerSecond = 3072.2
|
||||
05/03/2016 15:21:19: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.68605664 * 250; EvalErrorPrediction = 0.47200000 * 250; time = 0.0812s; samplesPerSecond = 3077.4
|
||||
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.69535352 * 250; EvalErrorPrediction = 0.47200000 * 250; time = 0.0895s; samplesPerSecond = 2792.1
|
||||
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.68741797 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0831s; samplesPerSecond = 3008.7
|
||||
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.67916406 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0818s; samplesPerSecond = 3056.5
|
||||
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.67841992 * 250; EvalErrorPrediction = 0.44800000 * 250; time = 0.2681s; samplesPerSecond = 932.5
|
||||
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.68038477 * 250; EvalErrorPrediction = 0.49200000 * 250; time = 0.0513s; samplesPerSecond = 4869.4
|
||||
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.61937109 * 250; EvalErrorPrediction = 0.30400000 * 250; time = 0.0680s; samplesPerSecond = 3678.3
|
||||
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.57844141 * 250; EvalErrorPrediction = 0.27200000 * 250; time = 0.0758s; samplesPerSecond = 3296.3
|
||||
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.49124023 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0664s; samplesPerSecond = 3763.4
|
||||
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.39071289 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0505s; samplesPerSecond = 4955.3
|
||||
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.27650586 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0515s; samplesPerSecond = 4855.7
|
||||
05/03/2016 15:21:20: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.26430078 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0517s; samplesPerSecond = 4834.4
|
||||
05/03/2016 15:21:20: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.66664150 * 10000; EvalErrorPrediction = 0.44430000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=3.21314s
|
||||
05/03/2016 15:21:20: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu/Models/simple.dnn.1'
|
||||
08/16/2016 10:51:35: MeanOfFeatures = Mean()
|
||||
08/16/2016 10:51:35: InvStdOfFeatures = InvStdDev()
|
||||
08/16/2016 10:51:35: Prior = Mean()
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
05/03/2016 15:21:20: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
|
||||
05/03/2016 15:21:20: Starting minibatch loop.
|
||||
05/03/2016 15:21:20: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.20732678 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0782s; samplesPerSecond = 3196.0
|
||||
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.19684015 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0812s; samplesPerSecond = 3079.4
|
||||
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.16083588 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0796s; samplesPerSecond = 3141.3
|
||||
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.13558752 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0811s; samplesPerSecond = 3083.5
|
||||
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.17992950 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0814s; samplesPerSecond = 3070.9
|
||||
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.17858063 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0812s; samplesPerSecond = 3079.3
|
||||
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.16847546 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0688s; samplesPerSecond = 3631.6
|
||||
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.16359399 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0547s; samplesPerSecond = 4572.7
|
||||
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.19534705 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0521s; samplesPerSecond = 4796.2
|
||||
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.19363660 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0758s; samplesPerSecond = 3297.5
|
||||
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.12703638 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0682s; samplesPerSecond = 3667.7
|
||||
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.18622827 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0576s; samplesPerSecond = 4344.0
|
||||
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.11595044 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0599s; samplesPerSecond = 4171.2
|
||||
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16689380 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0650s; samplesPerSecond = 3845.2
|
||||
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.15822559 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0631s; samplesPerSecond = 3964.2
|
||||
05/03/2016 15:21:21: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.18381909 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0638s; samplesPerSecond = 3920.5
|
||||
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.18274048 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0642s; samplesPerSecond = 3893.2
|
||||
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18638428 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0564s; samplesPerSecond = 4431.5
|
||||
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.20111572 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0528s; samplesPerSecond = 4733.8
|
||||
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.13185034 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0504s; samplesPerSecond = 4962.1
|
||||
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13692554 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0559s; samplesPerSecond = 4468.8
|
||||
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.15396802 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0672s; samplesPerSecond = 3719.4
|
||||
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.15347241 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0818s; samplesPerSecond = 3057.6
|
||||
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.14583887 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.2662s; samplesPerSecond = 939.1
|
||||
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.12333276 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0738s; samplesPerSecond = 3389.0
|
||||
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.13958154 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0778s; samplesPerSecond = 3211.3
|
||||
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12539844 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0772s; samplesPerSecond = 3239.1
|
||||
05/03/2016 15:21:22: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.19014404 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0475s; samplesPerSecond = 5259.1
|
||||
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.17959521 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0780s; samplesPerSecond = 3206.4
|
||||
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.18899121 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0469s; samplesPerSecond = 5333.6
|
||||
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.17525586 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0625s; samplesPerSecond = 4003.1
|
||||
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.14735645 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0940s; samplesPerSecond = 2658.9
|
||||
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.13705518 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0543s; samplesPerSecond = 4600.2
|
||||
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.13610693 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0752s; samplesPerSecond = 3324.2
|
||||
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13555811 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0583s; samplesPerSecond = 4291.1
|
||||
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.14883594 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0598s; samplesPerSecond = 4180.7
|
||||
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.14724707 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0599s; samplesPerSecond = 4172.4
|
||||
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.13130469 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0664s; samplesPerSecond = 3764.2
|
||||
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.19636084 * 250; EvalErrorPrediction = 0.11600000 * 250; time = 0.0644s; samplesPerSecond = 3884.1
|
||||
05/03/2016 15:21:23: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15681836 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0651s; samplesPerSecond = 3841.0
|
||||
05/03/2016 15:21:23: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.16173864 * 10000; EvalErrorPrediction = 0.07520000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=2.87283s
|
||||
05/03/2016 15:21:23: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu/Models/simple.dnn.2'
|
||||
|
||||
05/03/2016 15:21:23: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
|
||||
05/03/2016 15:21:23: Starting minibatch loop.
|
||||
05/03/2016 15:21:23: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.18214960 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0604s; samplesPerSecond = 4138.7
|
||||
05/03/2016 15:21:23: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.13526825 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0622s; samplesPerSecond = 4020.6
|
||||
05/03/2016 15:21:23: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14344995 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0640s; samplesPerSecond = 3906.0
|
||||
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.12557471 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0628s; samplesPerSecond = 3978.7
|
||||
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.17627924 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0639s; samplesPerSecond = 3914.6
|
||||
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.17585291 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0644s; samplesPerSecond = 3884.2
|
||||
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.14716791 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0628s; samplesPerSecond = 3979.1
|
||||
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.16757751 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0643s; samplesPerSecond = 3885.5
|
||||
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.10314917 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0642s; samplesPerSecond = 3895.3
|
||||
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.20322217 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0650s; samplesPerSecond = 3848.0
|
||||
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.16604797 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0642s; samplesPerSecond = 3892.3
|
||||
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.15105725 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0651s; samplesPerSecond = 3839.4
|
||||
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.19206934 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0640s; samplesPerSecond = 3903.9
|
||||
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13667065 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.2688s; samplesPerSecond = 930.0
|
||||
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.20713037 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0472s; samplesPerSecond = 5299.3
|
||||
05/03/2016 15:21:24: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.12862158 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0625s; samplesPerSecond = 3998.5
|
||||
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17174683 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0465s; samplesPerSecond = 5381.7
|
||||
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16493628 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0526s; samplesPerSecond = 4753.8
|
||||
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.14843726 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0505s; samplesPerSecond = 4952.5
|
||||
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.12574292 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0505s; samplesPerSecond = 4951.4
|
||||
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13455151 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0614s; samplesPerSecond = 4072.8
|
||||
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.16762988 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0495s; samplesPerSecond = 5055.0
|
||||
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.22347461 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0523s; samplesPerSecond = 4780.1
|
||||
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.18213623 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0542s; samplesPerSecond = 4611.6
|
||||
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.19970923 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0539s; samplesPerSecond = 4638.8
|
||||
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.22695947 * 250; EvalErrorPrediction = 0.12800000 * 250; time = 0.0542s; samplesPerSecond = 4609.7
|
||||
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12664502 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0541s; samplesPerSecond = 4625.3
|
||||
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.15838037 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0538s; samplesPerSecond = 4648.8
|
||||
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.11555566 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0581s; samplesPerSecond = 4305.4
|
||||
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.14157520 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0544s; samplesPerSecond = 4595.2
|
||||
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18558350 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0541s; samplesPerSecond = 4622.4
|
||||
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.15083594 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0540s; samplesPerSecond = 4632.9
|
||||
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.12831787 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0541s; samplesPerSecond = 4624.1
|
||||
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.17656494 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0545s; samplesPerSecond = 4587.6
|
||||
05/03/2016 15:21:25: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.14956396 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0625s; samplesPerSecond = 4000.3
|
||||
05/03/2016 15:21:26: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.11451660 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0496s; samplesPerSecond = 5040.3
|
||||
05/03/2016 15:21:26: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16392383 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0496s; samplesPerSecond = 5036.0
|
||||
05/03/2016 15:21:26: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.14811230 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0505s; samplesPerSecond = 4955.0
|
||||
05/03/2016 15:21:26: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.16003760 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0588s; samplesPerSecond = 4255.2
|
||||
05/03/2016 15:21:26: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.17969775 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0482s; samplesPerSecond = 5185.4
|
||||
05/03/2016 15:21:26: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15964808 * 10000; EvalErrorPrediction = 0.07750000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=2.49695s
|
||||
05/03/2016 15:21:26: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu/Models/simple.dnn'
|
||||
05/03/2016 15:21:26: CNTKCommandTrainEnd: Simple_Demo_Train
|
||||
|
||||
05/03/2016 15:21:26: Action "train" complete.
|
||||
08/16/2016 10:51:35: Precomputing --> Completed.
|
||||
|
||||
|
||||
05/03/2016 15:21:26: ##############################################################################
|
||||
05/03/2016 15:21:26: # #
|
||||
05/03/2016 15:21:26: # Action "test" #
|
||||
05/03/2016 15:21:26: # #
|
||||
05/03/2016 15:21:26: ##############################################################################
|
||||
08/16/2016 10:51:35: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:51:35: Starting minibatch loop.
|
||||
08/16/2016 10:51:35: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.69846765 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0606s; samplesPerSecond = 4125.1
|
||||
08/16/2016 10:51:35: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.76129944 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0177s; samplesPerSecond = 14150.7
|
||||
08/16/2016 10:51:35: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.72963208 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0497s; samplesPerSecond = 5028.9
|
||||
08/16/2016 10:51:35: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.74041528 * 250; EvalErrorPrediction = 0.56800000 * 250; time = 0.0333s; samplesPerSecond = 7501.9
|
||||
08/16/2016 10:51:35: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.70611035 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0223s; samplesPerSecond = 11225.9
|
||||
08/16/2016 10:51:35: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.74740723 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0168s; samplesPerSecond = 14876.5
|
||||
08/16/2016 10:51:35: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.75085840 * 250; EvalErrorPrediction = 0.40400000 * 250; time = 0.0169s; samplesPerSecond = 14758.8
|
||||
08/16/2016 10:51:35: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.78210742 * 250; EvalErrorPrediction = 0.48400000 * 250; time = 0.0170s; samplesPerSecond = 14729.3
|
||||
08/16/2016 10:51:35: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.70286572 * 250; EvalErrorPrediction = 0.47200000 * 250; time = 0.0263s; samplesPerSecond = 9508.6
|
||||
08/16/2016 10:51:35: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.69580322 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0247s; samplesPerSecond = 10135.4
|
||||
08/16/2016 10:51:35: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.70703613 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0170s; samplesPerSecond = 14700.7
|
||||
08/16/2016 10:51:35: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.74512988 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0169s; samplesPerSecond = 14772.8
|
||||
08/16/2016 10:51:35: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.70837598 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0168s; samplesPerSecond = 14850.9
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.69913086 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0173s; samplesPerSecond = 14456.7
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.70321875 * 250; EvalErrorPrediction = 0.53600000 * 250; time = 0.0168s; samplesPerSecond = 14899.6
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.69290918 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0198s; samplesPerSecond = 12597.0
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.74415527 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0141s; samplesPerSecond = 17694.1
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.73745117 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0172s; samplesPerSecond = 14513.8
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.71849609 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0200s; samplesPerSecond = 12484.4
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.71476953 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0284s; samplesPerSecond = 8813.1
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.69918457 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0212s; samplesPerSecond = 11786.9
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.69749512 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0243s; samplesPerSecond = 10267.4
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.70658887 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0140s; samplesPerSecond = 17871.2
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.69760742 * 250; EvalErrorPrediction = 0.56800000 * 250; time = 0.0170s; samplesPerSecond = 14747.5
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.69499219 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0169s; samplesPerSecond = 14768.4
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.69291211 * 250; EvalErrorPrediction = 0.49200000 * 250; time = 0.0294s; samplesPerSecond = 8497.9
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.70718945 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0336s; samplesPerSecond = 7433.2
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.69039453 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0157s; samplesPerSecond = 15957.1
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.70257422 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0222s; samplesPerSecond = 11244.0
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.71058984 * 250; EvalErrorPrediction = 0.42800000 * 250; time = 0.0151s; samplesPerSecond = 16568.4
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.69296875 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0177s; samplesPerSecond = 14113.1
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.69641211 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0251s; samplesPerSecond = 9974.1
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.69531055 * 250; EvalErrorPrediction = 0.49200000 * 250; time = 0.0164s; samplesPerSecond = 15214.2
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.69090430 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0172s; samplesPerSecond = 14501.2
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.68339063 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0170s; samplesPerSecond = 14691.2
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.67383984 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0170s; samplesPerSecond = 14691.2
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.65904102 * 250; EvalErrorPrediction = 0.26400000 * 250; time = 0.0239s; samplesPerSecond = 10454.6
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.64259766 * 250; EvalErrorPrediction = 0.36000000 * 250; time = 0.0186s; samplesPerSecond = 13465.5
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.60433398 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0196s; samplesPerSecond = 12787.7
|
||||
08/16/2016 10:51:36: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.56497070 * 250; EvalErrorPrediction = 0.11600000 * 250; time = 0.0142s; samplesPerSecond = 17556.2
|
||||
08/16/2016 10:51:36: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.70222344 * 10000; EvalErrorPrediction = 0.46820000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=0.873074s
|
||||
08/16/2016 10:51:36: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_cpu/Models/simple.dnn.1'
|
||||
|
||||
08/16/2016 10:51:36: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [10000..20000] (first sequence at sample 10000), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:51:36: Starting minibatch loop.
|
||||
08/16/2016 10:51:36: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.50663568 * 250; EvalErrorPrediction = 0.15200000 * 250; time = 0.0194s; samplesPerSecond = 12857.4
|
||||
08/16/2016 10:51:36: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.45398022 * 250; EvalErrorPrediction = 0.12000000 * 250; time = 0.0204s; samplesPerSecond = 12253.7
|
||||
08/16/2016 10:51:36: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.37457013 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0168s; samplesPerSecond = 14862.4
|
||||
08/16/2016 10:51:36: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.34124719 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0167s; samplesPerSecond = 14992.5
|
||||
08/16/2016 10:51:36: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.29298340 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0238s; samplesPerSecond = 10498.0
|
||||
08/16/2016 10:51:36: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.27701599 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0332s; samplesPerSecond = 7519.0
|
||||
08/16/2016 10:51:36: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.25128760 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0490s; samplesPerSecond = 5104.9
|
||||
08/16/2016 10:51:36: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.21941431 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0315s; samplesPerSecond = 7933.5
|
||||
08/16/2016 10:51:36: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.22864038 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0145s; samplesPerSecond = 17220.0
|
||||
08/16/2016 10:51:36: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.20533081 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0193s; samplesPerSecond = 12942.6
|
||||
08/16/2016 10:51:36: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.18820288 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0197s; samplesPerSecond = 12660.2
|
||||
08/16/2016 10:51:36: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.17363208 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0207s; samplesPerSecond = 12054.0
|
||||
08/16/2016 10:51:36: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.18979712 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0400s; samplesPerSecond = 6257.7
|
||||
08/16/2016 10:51:36: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.18266016 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0167s; samplesPerSecond = 15002.4
|
||||
08/16/2016 10:51:36: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.18476245 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0167s; samplesPerSecond = 14997.0
|
||||
08/16/2016 10:51:36: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.17951782 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0381s; samplesPerSecond = 6554.3
|
||||
08/16/2016 10:51:36: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.18190771 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0495s; samplesPerSecond = 5048.7
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.21016113 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0245s; samplesPerSecond = 10195.3
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.16539111 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0166s; samplesPerSecond = 15091.2
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.17295947 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0166s; samplesPerSecond = 15059.3
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13286475 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0134s; samplesPerSecond = 18714.0
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.17238135 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0370s; samplesPerSecond = 6753.5
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.12533740 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0166s; samplesPerSecond = 15029.5
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.21608838 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0306s; samplesPerSecond = 8160.1
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.21742236 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0164s; samplesPerSecond = 15279.3
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.17923486 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0174s; samplesPerSecond = 14330.8
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.16031250 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0165s; samplesPerSecond = 15119.4
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.13486084 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0199s; samplesPerSecond = 12574.8
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.16416699 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0162s; samplesPerSecond = 15386.5
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.14665625 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0172s; samplesPerSecond = 14556.9
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.14992627 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0190s; samplesPerSecond = 13191.2
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.12446338 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0165s; samplesPerSecond = 15123.1
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.16560303 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0169s; samplesPerSecond = 14759.7
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.14359863 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0163s; samplesPerSecond = 15295.2
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13723389 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0165s; samplesPerSecond = 15156.1
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.14104785 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0206s; samplesPerSecond = 12144.8
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.15801807 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0259s; samplesPerSecond = 9664.1
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.16213721 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0165s; samplesPerSecond = 15138.7
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.13545947 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0167s; samplesPerSecond = 15003.3
|
||||
08/16/2016 10:51:37: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15420410 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0197s; samplesPerSecond = 12690.4
|
||||
08/16/2016 10:51:37: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.20252788 * 10000; EvalErrorPrediction = 0.07960000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.894097s
|
||||
08/16/2016 10:51:37: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_cpu/Models/simple.dnn.2'
|
||||
|
||||
08/16/2016 10:51:37: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [20000..30000] (first sequence at sample 20000), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:51:37: Starting minibatch loop.
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.18365215 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0160s; samplesPerSecond = 15637.7
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.12863173 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0195s; samplesPerSecond = 12842.9
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.17736676 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0134s; samplesPerSecond = 18714.0
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.14110736 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0137s; samplesPerSecond = 18288.2
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.16524695 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0221s; samplesPerSecond = 11297.4
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.19137244 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0239s; samplesPerSecond = 10451.5
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.12233600 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0179s; samplesPerSecond = 13986.0
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.16686743 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0229s; samplesPerSecond = 10916.1
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.12411963 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0179s; samplesPerSecond = 13940.8
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.19959802 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0211s; samplesPerSecond = 11875.4
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.14190784 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0180s; samplesPerSecond = 13927.6
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.12357324 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0188s; samplesPerSecond = 13270.3
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.16388794 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0180s; samplesPerSecond = 13866.5
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.19857666 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0179s; samplesPerSecond = 13944.7
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.17161865 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0541s; samplesPerSecond = 4625.3
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.13291455 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0204s; samplesPerSecond = 12276.6
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.14355762 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0132s; samplesPerSecond = 18926.5
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.20757080 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0164s; samplesPerSecond = 15286.8
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.19119531 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0288s; samplesPerSecond = 8688.4
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.14750488 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0101s; samplesPerSecond = 24781.9
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.15454297 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0204s; samplesPerSecond = 12226.7
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.13628662 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0134s; samplesPerSecond = 18693.0
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.17363599 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0236s; samplesPerSecond = 10598.6
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.14413379 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0100s; samplesPerSecond = 24942.6
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.13718579 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0195s; samplesPerSecond = 12810.7
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.14220020 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0134s; samplesPerSecond = 18648.4
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.16849121 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0164s; samplesPerSecond = 15271.8
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.18580225 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0166s; samplesPerSecond = 15018.6
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.16339307 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0223s; samplesPerSecond = 11232.4
|
||||
08/16/2016 10:51:37: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.15557813 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0133s; samplesPerSecond = 18785.7
|
||||
08/16/2016 10:51:38: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18845215 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0163s; samplesPerSecond = 15311.1
|
||||
08/16/2016 10:51:38: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.13286035 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0170s; samplesPerSecond = 14677.4
|
||||
08/16/2016 10:51:38: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.14664014 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0164s; samplesPerSecond = 15248.6
|
||||
08/16/2016 10:51:38: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.13965381 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0143s; samplesPerSecond = 17474.0
|
||||
08/16/2016 10:51:38: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.20020557 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0196s; samplesPerSecond = 12779.2
|
||||
08/16/2016 10:51:38: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.12576953 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0197s; samplesPerSecond = 12707.1
|
||||
08/16/2016 10:51:38: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.18509766 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0422s; samplesPerSecond = 5925.9
|
||||
08/16/2016 10:51:38: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15134277 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0391s; samplesPerSecond = 6392.4
|
||||
08/16/2016 10:51:38: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.11977588 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0355s; samplesPerSecond = 7032.9
|
||||
08/16/2016 10:51:38: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.13046729 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0726s; samplesPerSecond = 3443.6
|
||||
08/16/2016 10:51:38: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15760303 * 10000; EvalErrorPrediction = 0.07280000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.876577s
|
||||
08/16/2016 10:51:38: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_cpu/Models/simple.dnn'
|
||||
08/16/2016 10:51:38: CNTKCommandTrainEnd: Simple_Demo_Train
|
||||
|
||||
08/16/2016 10:51:38: Action "train" complete.
|
||||
|
||||
|
||||
08/16/2016 10:51:38: ##############################################################################
|
||||
08/16/2016 10:51:38: # #
|
||||
08/16/2016 10:51:38: # Action "test" #
|
||||
08/16/2016 10:51:38: # #
|
||||
08/16/2016 10:51:38: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
|
@ -659,43 +687,23 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 25 matrices, 2 are shared as 1, and 23 are not shared.
|
||||
|
||||
(nil): {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalErrorPrediction Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
|
||||
0x2e83eb8: {[W2 Value[2 x 50]] }
|
||||
0x2e87ac8: {[MVNormalizedFeatures Value[2 x *1]] }
|
||||
0x2e87e78: {[W0*features Value[50 x *1]] }
|
||||
0x2e88038: {[W0*features+B0 Value[50 x 1 x *1]] }
|
||||
0x2e881f8: {[H1 Value[50 x 1 x *1]] }
|
||||
0x2e883b8: {[W1*H1 Value[50 x 1 x *1]] }
|
||||
0x2e88578: {[W1*H1+B1 Value[50 x 1 x *1]] }
|
||||
0x2e88738: {[H2 Value[50 x 1 x *1]] }
|
||||
0x2e888f8: {[W2*H1 Value[2 x 1 x *1]] }
|
||||
0x2e88ab8: {[HLast Value[2 x 1 x *1]] }
|
||||
0x2e8cec8: {[B1 Value[50 x 1]] }
|
||||
0x2e8d298: {[B2 Value[2 x 1]] }
|
||||
0x2e8f2c8: {[labels Value[2 x *1]] }
|
||||
0x2e8f8e8: {[MeanOfFeatures Value[2]] }
|
||||
0x2e91598: {[EvalErrorPrediction Value[1]] }
|
||||
0x2e916f8: {[CrossEntropyWithSoftmax Value[1]] }
|
||||
0x2e91bb8: {[LogOfPrior Value[2]] }
|
||||
0x2e93758: {[B0 Value[50 x 1]] }
|
||||
0x2e93da8: {[InvStdOfFeatures Value[2]] }
|
||||
0x2e94fe8: {[Prior Value[2]] }
|
||||
0x2e95508: {[W0 Value[50 x 2]] }
|
||||
0x2e985f8: {[W1 Value[50 x 50]] }
|
||||
0x2e99178: {[features Value[2 x *1]] }
|
||||
{ PosteriorProb : [2 x 1 x *1]
|
||||
ScaledLogLikelihood : [2 x 1 x *1] }
|
||||
|
||||
05/03/2016 15:21:26: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.05970149 * 603; CrossEntropyWithSoftmax = 0.13085309 * 603; perplexity = 1.13980032
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..603] (first sequence at sample 0), data subset 0 of 1
|
||||
08/16/2016 10:51:38: Minibatch[1-1]: EvalErrorPrediction = 0.04975124 * 603; CrossEntropyWithSoftmax = 0.10807832 * 603
|
||||
08/16/2016 10:51:38: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.04975124 * 603; CrossEntropyWithSoftmax = 0.10807832 * 603; perplexity = 1.11413500
|
||||
|
||||
05/03/2016 15:21:26: Action "test" complete.
|
||||
08/16/2016 10:51:38: Action "test" complete.
|
||||
|
||||
|
||||
05/03/2016 15:21:26: ##############################################################################
|
||||
05/03/2016 15:21:26: # #
|
||||
05/03/2016 15:21:26: # Action "write" #
|
||||
05/03/2016 15:21:26: # #
|
||||
05/03/2016 15:21:26: ##############################################################################
|
||||
08/16/2016 10:51:38: ##############################################################################
|
||||
08/16/2016 10:51:38: # #
|
||||
08/16/2016 10:51:38: # Action "write" #
|
||||
08/16/2016 10:51:38: # #
|
||||
08/16/2016 10:51:38: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
|
@ -753,36 +761,16 @@ Post-processing network complete.
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 25 matrices, 3 are shared as 1, and 22 are not shared.
|
||||
|
||||
(nil): {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [CrossEntropyWithSoftmax Value[1]] [EvalErrorPrediction Gradient[1]] [EvalErrorPrediction Value[1]] [H1 Gradient[50 x 1 x *2]] [H2 Gradient[50 x 1 x *2]] [HLast Gradient[2 x 1 x *2]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *2]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *2]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *2]] [ScaledLogLikelihood Value[2 x 1 x *2]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *2]] [W0*features+B0 Gradient[50 x 1 x *2]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *2]] [W1*H1+B1 Gradient[50 x 1 x *2]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *2]] [features Gradient[2 x *2]] [labels Gradient[2 x *2]] }
|
||||
0x2e82858: {[PosteriorProb Value[2 x 1 x *2]] }
|
||||
0x2e83b58: {[labels Value[2 x *2]] }
|
||||
0x2e84318: {[MeanOfFeatures Value[2]] }
|
||||
0x2e87878: {[LogOfPrior Value[2]] }
|
||||
0x2e89098: {[MVNormalizedFeatures Value[2 x *2]] }
|
||||
0x2e89448: {[W0*features Value[50 x *2]] }
|
||||
0x2e89608: {[W0*features+B0 Value[50 x 1 x *2]] }
|
||||
0x2e897c8: {[H1 Value[50 x 1 x *2]] }
|
||||
0x2e89988: {[W1*H1 Value[50 x 1 x *2]] }
|
||||
0x2e89b48: {[W1*H1+B1 Value[50 x 1 x *2]] }
|
||||
0x2e89d08: {[H2 Value[50 x 1 x *2]] }
|
||||
0x2e89ec8: {[W2*H1 Value[2 x 1 x *2]] }
|
||||
0x2e8a088: {[HLast Value[2 x 1 x *2]] }
|
||||
0x2e8f7c8: {[Prior Value[2]] }
|
||||
0x2e8fe88: {[W0 Value[50 x 2]] }
|
||||
0x2e93fa8: {[B0 Value[50 x 1]] }
|
||||
0x2e94378: {[B1 Value[50 x 1]] }
|
||||
0x2e94a78: {[B2 Value[2 x 1]] }
|
||||
0x2e953f8: {[features Value[2 x *2]] }
|
||||
0x2e96148: {[W1 Value[50 x 50]] }
|
||||
0x2e96a38: {[W2 Value[2 x 50]] }
|
||||
0x2e981b8: {[InvStdOfFeatures Value[2]] }
|
||||
{ CrossEntropyWithSoftmax : [1]
|
||||
EvalErrorPrediction : [1]
|
||||
ScaledLogLikelihood : [2 x 1 x *2] }
|
||||
|
||||
Minibatch[0]: ActualMBSize = 603
|
||||
Written to /tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_cpu/SimpleOutput*
|
||||
Written to /tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_cpu/SimpleOutput*
|
||||
Total Samples Evaluated = 603
|
||||
|
||||
05/03/2016 15:21:26: Action "write" complete.
|
||||
08/16/2016 10:51:38: Action "write" complete.
|
||||
|
||||
05/03/2016 15:21:26: __COMPLETED__
|
||||
08/16/2016 10:51:38: __COMPLETED__
|
||||
|
|
@ -1,48 +1,61 @@
|
|||
=== Running /home/alrezni/src/cntk_git/build/release/bin/cntk configFile=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/Simple/../Config/Simple.cntk currentDirectory=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data RunDir=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu DataDir=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data ConfigDir=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/Simple/../Config OutputDir=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu DeviceId=0 timestamping=true Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 264172964 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/Simple/../../../../../../Examples/Other/Simple2d/Config/Simple.cntk currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/Simple/../../../../../../Examples/Other/Simple2d/Config OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu DeviceId=0 timestamping=true Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 3 2016 15:08:09
|
||||
Last modified date: Tue Apr 5 16:01:37 2016
|
||||
Built time: Aug 16 2016 09:41:56
|
||||
Last modified date: Fri Aug 12 07:32:43 2016
|
||||
Build type: release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: yes
|
||||
Math lib: acml
|
||||
CUDA_PATH: /usr/local/cuda-7.0
|
||||
With 1bit-SGD: no
|
||||
Math lib: mkl
|
||||
CUDA_PATH: /usr/local/cuda-7.5
|
||||
CUB_PATH: /usr/local/cub-1.4.1
|
||||
CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
Build Branch: alrezni/examples_text
|
||||
Build SHA1: e80dab7d66009531806ce70b4842146e0da00516
|
||||
Built by alrezni on atleneu04
|
||||
Build Path: /home/alrezni/src/cntk_git
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by philly on f67b30a647de
|
||||
Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to /home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
05/03/2016 15:21:27: -------------------------------------------------------------------
|
||||
05/03/2016 15:21:27: Build info:
|
||||
Changed current directory to /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
08/16/2016 10:51:39: -------------------------------------------------------------------
|
||||
08/16/2016 10:51:39: Build info:
|
||||
|
||||
05/03/2016 15:21:27: Built time: May 3 2016 15:08:09
|
||||
05/03/2016 15:21:27: Last modified date: Tue Apr 5 16:01:37 2016
|
||||
05/03/2016 15:21:27: Build type: release
|
||||
05/03/2016 15:21:27: Build target: GPU
|
||||
05/03/2016 15:21:27: With 1bit-SGD: yes
|
||||
05/03/2016 15:21:27: Math lib: acml
|
||||
05/03/2016 15:21:27: CUDA_PATH: /usr/local/cuda-7.0
|
||||
05/03/2016 15:21:27: CUB_PATH: /usr/local/cub-1.4.1
|
||||
05/03/2016 15:21:27: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
05/03/2016 15:21:27: Build Branch: alrezni/examples_text
|
||||
05/03/2016 15:21:27: Build SHA1: e80dab7d66009531806ce70b4842146e0da00516
|
||||
05/03/2016 15:21:27: Built by alrezni on atleneu04
|
||||
05/03/2016 15:21:27: Build Path: /home/alrezni/src/cntk_git
|
||||
05/03/2016 15:21:27: -------------------------------------------------------------------
|
||||
08/16/2016 10:51:39: Built time: Aug 16 2016 09:41:56
|
||||
08/16/2016 10:51:39: Last modified date: Fri Aug 12 07:32:43 2016
|
||||
08/16/2016 10:51:39: Build type: release
|
||||
08/16/2016 10:51:39: Build target: GPU
|
||||
08/16/2016 10:51:39: With 1bit-SGD: no
|
||||
08/16/2016 10:51:39: Math lib: mkl
|
||||
08/16/2016 10:51:39: CUDA_PATH: /usr/local/cuda-7.5
|
||||
08/16/2016 10:51:39: CUB_PATH: /usr/local/cub-1.4.1
|
||||
08/16/2016 10:51:39: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
08/16/2016 10:51:39: Build Branch: HEAD
|
||||
08/16/2016 10:51:39: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 10:51:39: Built by philly on f67b30a647de
|
||||
08/16/2016 10:51:39: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
08/16/2016 10:51:39: -------------------------------------------------------------------
|
||||
08/16/2016 10:51:40: -------------------------------------------------------------------
|
||||
08/16/2016 10:51:40: GPU info:
|
||||
|
||||
05/03/2016 15:21:27: Running on localhost at 2016/05/03 15:21:27
|
||||
05/03/2016 15:21:27: Command line:
|
||||
/home/alrezni/src/cntk_git/build/release/bin/cntk configFile=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/Simple/../Config/Simple.cntk currentDirectory=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data RunDir=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu DataDir=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data ConfigDir=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/Simple/../Config OutputDir=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu DeviceId=0 timestamping=true Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
08/16/2016 10:51:40: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:51:40: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:51:40: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:51:40: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:51:40: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 10:51:40: Running on localhost at 2016/08/16 10:51:40
|
||||
08/16/2016 10:51:40: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/Simple/../../../../../../Examples/Other/Simple2d/Config/Simple.cntk currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/Simple/../../../../../../Examples/Other/Simple2d/Config OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu DeviceId=0 timestamping=true Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
|
||||
|
||||
05/03/2016 15:21:27: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 15:21:27: RootDir = ".."
|
||||
08/16/2016 10:51:40: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:51:40: RootDir = ".."
|
||||
ConfigDir = "$RootDir$/Config"
|
||||
DataDir = "$RootDir$/Data"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
|
|
@ -129,28 +142,28 @@ labelMappingFile = "$DataDir$/SimpleMapping.txt"
|
|||
sequenceEpilogue = "\t// %s\n"
|
||||
]
|
||||
]
|
||||
currentDirectory=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
RunDir=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu
|
||||
DataDir=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
ConfigDir=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/Simple/../Config
|
||||
OutputDir=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu
|
||||
currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu
|
||||
DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/Simple/../../../../../../Examples/Other/Simple2d/Config
|
||||
OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
05/03/2016 15:21:27: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:51:40: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 15:21:27: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 15:21:27: RootDir = ".."
|
||||
08/16/2016 10:51:40: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:51:40: RootDir = ".."
|
||||
ConfigDir = "../Config"
|
||||
DataDir = "../Data"
|
||||
OutputDir = "../Output"
|
||||
ModelDir = "/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu/Models"
|
||||
ModelDir = "/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu/Models"
|
||||
deviceId = -1
|
||||
command = Simple_Demo_Train:Simple_Demo_Test:Simple_Demo_Output
|
||||
precision = "float"
|
||||
traceLevel = 1
|
||||
modelPath = "/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu/Models/simple.dnn"
|
||||
modelPath = "/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu/Models/simple.dnn"
|
||||
outputNodeNames = ScaledLogLikelihood
|
||||
Simple_Demo_Train = [
|
||||
action = "train"
|
||||
|
|
@ -174,7 +187,7 @@ Simple_Demo_Train = [
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTrain_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTrain_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -191,7 +204,7 @@ Simple_Demo_Test = [
|
|||
action = "test"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -208,7 +221,7 @@ Simple_Demo_Output=[
|
|||
action = "write"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -221,42 +234,42 @@ dim = 2
|
|||
]
|
||||
]
|
||||
outputNodeNames = PosteriorProb : labels
|
||||
outputPath = "/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu/SimpleOutput"
|
||||
outputPath = "/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu/SimpleOutput"
|
||||
format = [
|
||||
type = "category"
|
||||
labelMappingFile = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleMapping.txt"
|
||||
labelMappingFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleMapping.txt"
|
||||
sequenceEpilogue = "\t// %s\n"
|
||||
]
|
||||
]
|
||||
currentDirectory=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
RunDir=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu
|
||||
DataDir=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
ConfigDir=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/Simple/../Config
|
||||
OutputDir=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu
|
||||
currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu
|
||||
DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/Simple/../../../../../../Examples/Other/Simple2d/Config
|
||||
OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
05/03/2016 15:21:27: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:51:40: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 15:21:27: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:51:40: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: Simple.cntk:command=Simple_Demo_Train:Simple_Demo_Test:Simple_Demo_Output
|
||||
configparameters: Simple.cntk:ConfigDir=/home/alrezni/src/cntk_git/Tests/EndToEndTests/CNTKTextFormatReader/Examples/Other/Simple2d/Simple/../Config
|
||||
configparameters: Simple.cntk:currentDirectory=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
configparameters: Simple.cntk:DataDir=/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data
|
||||
configparameters: Simple.cntk:ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/Simple/../../../../../../Examples/Other/Simple2d/Config
|
||||
configparameters: Simple.cntk:currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
configparameters: Simple.cntk:DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
|
||||
configparameters: Simple.cntk:deviceId=0
|
||||
configparameters: Simple.cntk:ModelDir=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu/Models
|
||||
configparameters: Simple.cntk:modelPath=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu/Models/simple.dnn
|
||||
configparameters: Simple.cntk:OutputDir=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu
|
||||
configparameters: Simple.cntk:ModelDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu/Models
|
||||
configparameters: Simple.cntk:modelPath=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu/Models/simple.dnn
|
||||
configparameters: Simple.cntk:OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu
|
||||
configparameters: Simple.cntk:outputNodeNames=ScaledLogLikelihood
|
||||
configparameters: Simple.cntk:precision=float
|
||||
configparameters: Simple.cntk:RootDir=..
|
||||
configparameters: Simple.cntk:RunDir=/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu
|
||||
configparameters: Simple.cntk:RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu
|
||||
configparameters: Simple.cntk:Simple_Demo_Output=[
|
||||
action = "write"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -269,10 +282,10 @@ dim = 2
|
|||
]
|
||||
]
|
||||
outputNodeNames = PosteriorProb : labels
|
||||
outputPath = "/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu/SimpleOutput"
|
||||
outputPath = "/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu/SimpleOutput"
|
||||
format = [
|
||||
type = "category"
|
||||
labelMappingFile = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleMapping.txt"
|
||||
labelMappingFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleMapping.txt"
|
||||
sequenceEpilogue = "\t// %s\n"
|
||||
]
|
||||
]
|
||||
|
|
@ -281,7 +294,7 @@ configparameters: Simple.cntk:Simple_Demo_Test=[
|
|||
action = "test"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -317,7 +330,7 @@ configparameters: Simple.cntk:Simple_Demo_Train=[
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "/home/alrezni/src/cntk_git/Examples/Other/Simple2d/Data/SimpleDataTrain_cntk_text.txt"
|
||||
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTrain_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -333,24 +346,36 @@ dim = 2
|
|||
|
||||
configparameters: Simple.cntk:timestamping=true
|
||||
configparameters: Simple.cntk:traceLevel=1
|
||||
05/03/2016 15:21:27: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/03/2016 15:21:27: Commands: Simple_Demo_Train Simple_Demo_Test Simple_Demo_Output
|
||||
05/03/2016 15:21:27: Precision = "float"
|
||||
05/03/2016 15:21:27: CNTKModelPath: /tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu/Models/simple.dnn
|
||||
05/03/2016 15:21:27: CNTKCommandTrainInfo: Simple_Demo_Train : 3
|
||||
05/03/2016 15:21:27: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
08/16/2016 10:51:40: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:51:40: Commands: Simple_Demo_Train Simple_Demo_Test Simple_Demo_Output
|
||||
08/16/2016 10:51:40: Precision = "float"
|
||||
08/16/2016 10:51:40: CNTKModelPath: /tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu/Models/simple.dnn
|
||||
08/16/2016 10:51:40: CNTKCommandTrainInfo: Simple_Demo_Train : 3
|
||||
08/16/2016 10:51:40: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
|
||||
05/03/2016 15:21:27: ##############################################################################
|
||||
05/03/2016 15:21:27: # #
|
||||
05/03/2016 15:21:27: # Action "train" #
|
||||
05/03/2016 15:21:27: # #
|
||||
05/03/2016 15:21:27: ##############################################################################
|
||||
08/16/2016 10:51:40: ##############################################################################
|
||||
08/16/2016 10:51:40: # #
|
||||
08/16/2016 10:51:40: # Action "train" #
|
||||
08/16/2016 10:51:40: # #
|
||||
08/16/2016 10:51:40: ##############################################################################
|
||||
|
||||
05/03/2016 15:21:27: CNTKCommandTrainBegin: Simple_Demo_Train
|
||||
08/16/2016 10:51:40: CNTKCommandTrainBegin: Simple_Demo_Train
|
||||
SimpleNetworkBuilder Using GPU 0
|
||||
|
||||
05/03/2016 15:21:27: Creating virgin network.
|
||||
08/16/2016 10:51:40: Creating virgin network.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[50 x 2] <- 0.000000.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[50 x 2] <- uniform(seed=1, range=0.050000*1.000000, onCPU=false).
|
||||
SetUniformRandomValue (GPU): creating curand object with seed 1, sizeof(ElemType)==4
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[50 x 50] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[50 x 50] <- uniform(seed=2, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[2 x 50] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[2 x 50] <- uniform(seed=3, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[2 x 1] <- 0.000000.
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[2 x 1] <- 0.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -402,207 +427,210 @@ Validating network, final pass.
|
|||
|
||||
Post-processing network complete.
|
||||
|
||||
05/03/2016 15:21:27: Created model with 25 nodes on GPU 0.
|
||||
08/16/2016 10:51:40: Created model with 25 nodes on GPU 0.
|
||||
|
||||
05/03/2016 15:21:27: Training criterion node(s):
|
||||
05/03/2016 15:21:27: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
|
||||
08/16/2016 10:51:40: Training criterion node(s):
|
||||
08/16/2016 10:51:40: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
|
||||
|
||||
05/03/2016 15:21:27: Evaluation criterion node(s):
|
||||
|
||||
05/03/2016 15:21:27: EvalErrorPrediction = ErrorPrediction
|
||||
08/16/2016 10:51:40: Evaluation criterion node(s):
|
||||
08/16/2016 10:51:40: EvalErrorPrediction = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 40 matrices, 19 are shared as 8, and 21 are not shared.
|
||||
|
||||
(nil): {[EvalErrorPrediction Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
|
||||
0x1ef9338: {[features Value[2 x *]] }
|
||||
0x2b32ad8: {[MeanOfFeatures Value[2]] }
|
||||
0x2b32fe8: {[InvStdOfFeatures Value[2]] }
|
||||
0x2b33cd8: {[W0 Value[50 x 2]] }
|
||||
0x3180df8: {[W1 Value[50 x 50]] }
|
||||
0x3181cc8: {[B1 Value[50 x 1]] }
|
||||
0x3182dc8: {[W2 Value[2 x 50]] }
|
||||
0x3183868: {[B2 Value[2 x 1]] }
|
||||
0x3184748: {[labels Value[2 x *]] }
|
||||
0x3185898: {[Prior Value[2]] }
|
||||
0x3186bd8: {[LogOfPrior Value[2]] }
|
||||
0x318b378: {[H1 Value[50 x 1 x *]] [W0*features Gradient[50 x *]] }
|
||||
0x318b498: {[EvalErrorPrediction Value[1]] }
|
||||
0x318b798: {[ScaledLogLikelihood Value[2 x 1 x *]] }
|
||||
0x318b8f8: {[CrossEntropyWithSoftmax Value[1]] }
|
||||
0x3191148: {[B0 Value[50 x 1]] }
|
||||
0x34d5bc8: {[W0 Gradient[50 x 2]] [W0*features+B0 Value[50 x 1 x *]] }
|
||||
0x34d5dd8: {[MVNormalizedFeatures Value[2 x *]] }
|
||||
0x34d60f8: {[W0*features Value[50 x *]] }
|
||||
0x34d6198: {[W0*features+B0 Gradient[50 x 1 x *]] [W1*H1 Value[50 x 1 x *]] }
|
||||
0x34d62f8: {[W1 Gradient[50 x 50]] [W1*H1+B1 Value[50 x 1 x *]] }
|
||||
0x34d6458: {[H2 Value[50 x 1 x *]] [W1*H1 Gradient[50 x 1 x *]] }
|
||||
0x34d65b8: {[B0 Gradient[50 x 1]] [H1 Gradient[50 x 1 x *]] [W1*H1+B1 Gradient[50 x 1 x *]] [W2*H1 Value[2 x 1 x *]] }
|
||||
0x34d6718: {[HLast Value[2 x 1 x *]] [W2 Gradient[2 x 50]] }
|
||||
0x34d7158: {[CrossEntropyWithSoftmax Gradient[1]] }
|
||||
0x34d7318: {[B1 Gradient[50 x 1]] [H2 Gradient[50 x 1 x *]] [HLast Gradient[2 x 1 x *]] }
|
||||
0x34d74d8: {[W2*H1 Gradient[2 x 1 x *]] }
|
||||
0x34d7698: {[B2 Gradient[2 x 1]] }
|
||||
{ W0 : [50 x 2] (gradient)
|
||||
W0*features+B0 : [50 x 1 x *] }
|
||||
{ H1 : [50 x 1 x *]
|
||||
W0*features : [50 x *] (gradient) }
|
||||
{ W0*features+B0 : [50 x 1 x *] (gradient)
|
||||
W1*H1 : [50 x 1 x *] }
|
||||
{ W1 : [50 x 50] (gradient)
|
||||
W1*H1+B1 : [50 x 1 x *] }
|
||||
{ H2 : [50 x 1 x *]
|
||||
W1*H1 : [50 x 1 x *] (gradient) }
|
||||
{ B0 : [50 x 1] (gradient)
|
||||
H1 : [50 x 1 x *] (gradient)
|
||||
W1*H1+B1 : [50 x 1 x *] (gradient)
|
||||
W2*H1 : [2 x 1 x *] }
|
||||
{ HLast : [2 x 1 x *]
|
||||
W2 : [2 x 50] (gradient) }
|
||||
{ B1 : [50 x 1] (gradient)
|
||||
H2 : [50 x 1 x *] (gradient)
|
||||
HLast : [2 x 1 x *] (gradient) }
|
||||
|
||||
|
||||
05/03/2016 15:21:27: Precomputing --> 3 PreCompute nodes found.
|
||||
08/16/2016 10:51:40: Training 2802 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:
|
||||
|
||||
05/03/2016 15:21:27: MeanOfFeatures = Mean()
|
||||
05/03/2016 15:21:27: InvStdOfFeatures = InvStdDev()
|
||||
05/03/2016 15:21:27: Prior = Mean()
|
||||
|
||||
05/03/2016 15:21:28: Precomputing --> Completed.
|
||||
08/16/2016 10:51:40: Node 'B0' (LearnableParameter operation) : [50 x 1]
|
||||
08/16/2016 10:51:40: Node 'B1' (LearnableParameter operation) : [50 x 1]
|
||||
08/16/2016 10:51:40: Node 'B2' (LearnableParameter operation) : [2 x 1]
|
||||
08/16/2016 10:51:40: Node 'W0' (LearnableParameter operation) : [50 x 2]
|
||||
08/16/2016 10:51:40: Node 'W1' (LearnableParameter operation) : [50 x 50]
|
||||
08/16/2016 10:51:40: Node 'W2' (LearnableParameter operation) : [2 x 50]
|
||||
|
||||
|
||||
05/03/2016 15:21:28: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
08/16/2016 10:51:40: Precomputing --> 3 PreCompute nodes found.
|
||||
|
||||
05/03/2016 15:21:28: Starting minibatch loop.
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70004456 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0055s; samplesPerSecond = 45495.9
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.70309900 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0046s; samplesPerSecond = 54347.8
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.70606104 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0046s; samplesPerSecond = 54241.7
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.69845532 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0046s; samplesPerSecond = 54549.4
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.73496533 * 250; EvalErrorPrediction = 0.57600000 * 250; time = 0.0046s; samplesPerSecond = 54136.0
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72522827 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0046s; samplesPerSecond = 54359.6
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.73287500 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.0046s; samplesPerSecond = 54466.2
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.70135547 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0046s; samplesPerSecond = 54872.7
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.72466504 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0046s; samplesPerSecond = 54194.7
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.72187500 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0046s; samplesPerSecond = 54501.9
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.69799023 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0046s; samplesPerSecond = 54788.5
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.70696387 * 250; EvalErrorPrediction = 0.54800000 * 250; time = 0.0046s; samplesPerSecond = 54371.5
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.69863965 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0046s; samplesPerSecond = 54300.6
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.71772461 * 250; EvalErrorPrediction = 0.54800000 * 250; time = 0.0046s; samplesPerSecond = 54644.8
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.69526270 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0046s; samplesPerSecond = 54525.6
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.71436426 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0046s; samplesPerSecond = 54561.3
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70399316 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0046s; samplesPerSecond = 54573.2
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.71745508 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.0046s; samplesPerSecond = 54716.6
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.71963184 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0046s; samplesPerSecond = 54537.5
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.70689941 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0046s; samplesPerSecond = 54336.0
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.70425098 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0046s; samplesPerSecond = 54692.6
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70622754 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0046s; samplesPerSecond = 54561.3
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69729492 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0046s; samplesPerSecond = 54537.5
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.75974219 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0046s; samplesPerSecond = 54680.7
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.70631250 * 250; EvalErrorPrediction = 0.43600000 * 250; time = 0.0046s; samplesPerSecond = 54288.8
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.70705664 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0046s; samplesPerSecond = 54561.3
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.72660352 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0046s; samplesPerSecond = 54824.6
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.71369727 * 250; EvalErrorPrediction = 0.55600000 * 250; time = 0.0046s; samplesPerSecond = 54537.5
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.68916602 * 250; EvalErrorPrediction = 0.47200000 * 250; time = 0.0046s; samplesPerSecond = 54371.5
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.69964844 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0046s; samplesPerSecond = 54218.2
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.69387891 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0045s; samplesPerSecond = 54969.2
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.68885742 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0046s; samplesPerSecond = 54573.2
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.69388867 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0046s; samplesPerSecond = 54454.4
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.70363867 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0046s; samplesPerSecond = 54824.6
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.65449219 * 250; EvalErrorPrediction = 0.44400000 * 250; time = 0.0046s; samplesPerSecond = 54561.3
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.64607031 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0046s; samplesPerSecond = 54347.8
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.59492969 * 250; EvalErrorPrediction = 0.12400000 * 250; time = 0.0046s; samplesPerSecond = 54764.5
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.53965820 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54609.0
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.43681445 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0046s; samplesPerSecond = 54525.6
|
||||
05/03/2016 15:21:28: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.37407422 * 250; EvalErrorPrediction = 0.12000000 * 250; time = 0.0046s; samplesPerSecond = 54466.2
|
||||
05/03/2016 15:21:28: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.68409629 * 10000; EvalErrorPrediction = 0.45780000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=0.1879s
|
||||
05/03/2016 15:21:28: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu/Models/simple.dnn.1'
|
||||
08/16/2016 10:51:40: MeanOfFeatures = Mean()
|
||||
08/16/2016 10:51:40: InvStdOfFeatures = InvStdDev()
|
||||
08/16/2016 10:51:40: Prior = Mean()
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
05/03/2016 15:21:28: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
|
||||
05/03/2016 15:21:28: Starting minibatch loop.
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.27895840 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0046s; samplesPerSecond = 53902.5
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.24395615 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54933.0
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.19587115 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0046s; samplesPerSecond = 54824.6
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.16368213 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0045s; samplesPerSecond = 55126.8
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.19700140 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0046s; samplesPerSecond = 54933.0
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.19580530 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54585.2
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.18257983 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0045s; samplesPerSecond = 55248.6
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.17520911 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0046s; samplesPerSecond = 54752.5
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.20164514 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0046s; samplesPerSecond = 54752.5
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.19787024 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0046s; samplesPerSecond = 54466.2
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.13437573 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0045s; samplesPerSecond = 55090.3
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.19004956 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0046s; samplesPerSecond = 54848.6
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.12287280 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0045s; samplesPerSecond = 54957.1
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16975903 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0045s; samplesPerSecond = 55175.5
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.16102686 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54513.7
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.18611646 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54800.5
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.18469507 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0045s; samplesPerSecond = 55334.2
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18472339 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54908.9
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.20064648 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0046s; samplesPerSecond = 54597.1
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.13324683 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0045s; samplesPerSecond = 54969.2
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13878418 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0045s; samplesPerSecond = 55078.2
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.15587354 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0046s; samplesPerSecond = 54920.9
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.15337378 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54812.5
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.14797070 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0045s; samplesPerSecond = 55199.8
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.12512891 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0046s; samplesPerSecond = 54383.3
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.14058545 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0045s; samplesPerSecond = 54993.4
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12611963 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0045s; samplesPerSecond = 54945.1
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.18970605 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0046s; samplesPerSecond = 54884.7
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.17965479 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0045s; samplesPerSecond = 54969.2
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.18866455 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0046s; samplesPerSecond = 54836.6
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.17539941 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0045s; samplesPerSecond = 54945.1
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.14742432 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54848.6
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.13789502 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0046s; samplesPerSecond = 54788.5
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.13652100 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0045s; samplesPerSecond = 55224.2
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13619336 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0046s; samplesPerSecond = 54920.9
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.14909424 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54478.1
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.14762256 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0045s; samplesPerSecond = 55139.0
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.13142578 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0046s; samplesPerSecond = 54860.7
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.19570459 * 250; EvalErrorPrediction = 0.11600000 * 250; time = 0.0046s; samplesPerSecond = 54764.5
|
||||
05/03/2016 15:21:28: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15718604 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0045s; samplesPerSecond = 55005.5
|
||||
05/03/2016 15:21:28: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.16901047 * 10000; EvalErrorPrediction = 0.07510000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.184798s
|
||||
05/03/2016 15:21:28: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu/Models/simple.dnn.2'
|
||||
|
||||
05/03/2016 15:21:28: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
|
||||
05/03/2016 15:21:28: Starting minibatch loop.
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.18133401 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54124.3
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.13605756 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0046s; samplesPerSecond = 54884.7
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14345651 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54668.7
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.12512610 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0045s; samplesPerSecond = 54969.2
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.17690991 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54800.5
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.17504150 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0046s; samplesPerSecond = 54740.5
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.14723834 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0045s; samplesPerSecond = 55224.2
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.16752893 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0045s; samplesPerSecond = 54993.4
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.10317773 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0046s; samplesPerSecond = 54800.5
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.20306372 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0045s; samplesPerSecond = 55248.6
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.16637036 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0045s; samplesPerSecond = 55066.1
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.15126868 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54824.6
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.19167224 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0046s; samplesPerSecond = 54884.7
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13687085 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0045s; samplesPerSecond = 55420.1
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.20709912 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0046s; samplesPerSecond = 54740.5
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.12918774 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0045s; samplesPerSecond = 54981.3
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17185107 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0045s; samplesPerSecond = 55322.0
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16523242 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54908.9
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.14880249 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0046s; samplesPerSecond = 54728.5
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.12590967 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0045s; samplesPerSecond = 54957.1
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13443018 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54872.7
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.16726147 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0046s; samplesPerSecond = 54836.6
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.22407422 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0045s; samplesPerSecond = 55041.8
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.18191553 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0045s; samplesPerSecond = 55078.2
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.19983057 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0046s; samplesPerSecond = 54680.7
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.22728223 * 250; EvalErrorPrediction = 0.12400000 * 250; time = 0.0046s; samplesPerSecond = 54692.6
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.12720459 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0045s; samplesPerSecond = 55151.1
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.15842871 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0045s; samplesPerSecond = 54945.1
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.11558691 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0045s; samplesPerSecond = 54945.1
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.14163428 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0045s; samplesPerSecond = 55248.6
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18560596 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0045s; samplesPerSecond = 54993.4
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.15099561 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0045s; samplesPerSecond = 55078.2
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.12822461 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0046s; samplesPerSecond = 54395.1
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.17662500 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0045s; samplesPerSecond = 55309.7
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.14950781 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0046s; samplesPerSecond = 54945.1
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.11450977 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0046s; samplesPerSecond = 54908.9
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16386768 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0045s; samplesPerSecond = 55260.8
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.14811523 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0045s; samplesPerSecond = 54981.3
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.16021143 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0046s; samplesPerSecond = 54764.5
|
||||
05/03/2016 15:21:28: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.17989551 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0045s; samplesPerSecond = 55151.1
|
||||
05/03/2016 15:21:28: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15971016 * 10000; EvalErrorPrediction = 0.07740000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.184406s
|
||||
05/03/2016 15:21:28: SGD: Saving checkpoint model '/tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu/Models/simple.dnn'
|
||||
05/03/2016 15:21:29: CNTKCommandTrainEnd: Simple_Demo_Train
|
||||
|
||||
05/03/2016 15:21:29: Action "train" complete.
|
||||
08/16/2016 10:51:40: Precomputing --> Completed.
|
||||
|
||||
|
||||
05/03/2016 15:21:29: ##############################################################################
|
||||
05/03/2016 15:21:29: # #
|
||||
05/03/2016 15:21:29: # Action "test" #
|
||||
05/03/2016 15:21:29: # #
|
||||
05/03/2016 15:21:29: ##############################################################################
|
||||
08/16/2016 10:51:40: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:51:40: Starting minibatch loop.
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70124231 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0078s; samplesPerSecond = 32034.9
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.76372424 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0064s; samplesPerSecond = 38892.3
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.72703027 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0064s; samplesPerSecond = 39166.5
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.73895923 * 250; EvalErrorPrediction = 0.56800000 * 250; time = 0.0064s; samplesPerSecond = 38886.3
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.70621924 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0065s; samplesPerSecond = 38759.7
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.74767041 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0065s; samplesPerSecond = 38753.7
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.75094434 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0064s; samplesPerSecond = 38989.4
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.78058936 * 250; EvalErrorPrediction = 0.48400000 * 250; time = 0.0064s; samplesPerSecond = 38922.6
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.70407129 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0064s; samplesPerSecond = 39265.0
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.69555762 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0064s; samplesPerSecond = 38922.6
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.70626123 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0064s; samplesPerSecond = 38844.0
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.74540430 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0064s; samplesPerSecond = 39178.8
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.70824414 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0064s; samplesPerSecond = 39209.5
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.69895020 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0064s; samplesPerSecond = 38886.3
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.70353223 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0065s; samplesPerSecond = 38669.8
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.69346387 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0064s; samplesPerSecond = 38989.4
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.74449902 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0064s; samplesPerSecond = 38886.3
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.73767969 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0064s; samplesPerSecond = 39025.9
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.71876855 * 250; EvalErrorPrediction = 0.48400000 * 250; time = 0.0063s; samplesPerSecond = 39594.6
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.71509473 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0064s; samplesPerSecond = 39271.1
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.69956152 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0064s; samplesPerSecond = 38886.3
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.69785937 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0062s; samplesPerSecond = 40303.1
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.70736035 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0063s; samplesPerSecond = 39563.2
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.69820508 * 250; EvalErrorPrediction = 0.56800000 * 250; time = 0.0062s; samplesPerSecond = 40512.1
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.69537109 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0063s; samplesPerSecond = 39432.2
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.69347266 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0062s; samplesPerSecond = 40492.4
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.70801172 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0061s; samplesPerSecond = 40909.8
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.69131641 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0062s; samplesPerSecond = 40257.6
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.70370312 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0062s; samplesPerSecond = 40270.6
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.71200195 * 250; EvalErrorPrediction = 0.43600000 * 250; time = 0.0061s; samplesPerSecond = 40909.8
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.69506836 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0062s; samplesPerSecond = 40577.8
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.69935352 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0061s; samplesPerSecond = 40889.8
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.69887109 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0062s; samplesPerSecond = 40440.0
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.69604492 * 250; EvalErrorPrediction = 0.49200000 * 250; time = 0.0062s; samplesPerSecond = 40512.1
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.69011719 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.0062s; samplesPerSecond = 40617.4
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.68419531 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0061s; samplesPerSecond = 40783.0
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.67551367 * 250; EvalErrorPrediction = 0.32400000 * 250; time = 0.0063s; samplesPerSecond = 39904.2
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.67028516 * 250; EvalErrorPrediction = 0.40000000 * 250; time = 0.0062s; samplesPerSecond = 40044.9
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.65152734 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0062s; samplesPerSecond = 40630.6
|
||||
08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.63594727 * 250; EvalErrorPrediction = 0.22000000 * 250; time = 0.0062s; samplesPerSecond = 40283.6
|
||||
08/16/2016 10:51:40: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.70729233 * 10000; EvalErrorPrediction = 0.47740000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=0.256818s
|
||||
08/16/2016 10:51:40: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu/Models/simple.dnn.1'
|
||||
|
||||
08/16/2016 10:51:40: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [10000..20000] (first sequence at sample 10000), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:51:40: Starting minibatch loop.
|
||||
08/16/2016 10:51:40: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.61492108 * 250; EvalErrorPrediction = 0.26800000 * 250; time = 0.0064s; samplesPerSecond = 38801.8
|
||||
08/16/2016 10:51:40: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.59171271 * 250; EvalErrorPrediction = 0.28400000 * 250; time = 0.0063s; samplesPerSecond = 39923.3
|
||||
08/16/2016 10:51:40: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.53591638 * 250; EvalErrorPrediction = 0.20000000 * 250; time = 0.0062s; samplesPerSecond = 40122.0
|
||||
08/16/2016 10:51:40: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.51872742 * 250; EvalErrorPrediction = 0.14000000 * 250; time = 0.0062s; samplesPerSecond = 40479.3
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.48384375 * 250; EvalErrorPrediction = 0.12400000 * 250; time = 0.0062s; samplesPerSecond = 40109.1
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.43163501 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0062s; samplesPerSecond = 40128.4
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.38970386 * 250; EvalErrorPrediction = 0.12400000 * 250; time = 0.0063s; samplesPerSecond = 39733.0
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.33681616 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0062s; samplesPerSecond = 40044.9
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.31352393 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0062s; samplesPerSecond = 40525.2
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.26829492 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0062s; samplesPerSecond = 40270.6
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.24240820 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0063s; samplesPerSecond = 39531.9
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.21015820 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0062s; samplesPerSecond = 40012.8
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.22358789 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0061s; samplesPerSecond = 40856.3
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.20496631 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0061s; samplesPerSecond = 40756.4
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.20070508 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0062s; samplesPerSecond = 40643.8
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.19224707 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0061s; samplesPerSecond = 40896.5
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.19326562 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0061s; samplesPerSecond = 40789.7
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.21712451 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0061s; samplesPerSecond = 40883.1
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.17562354 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0061s; samplesPerSecond = 40869.7
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.18186035 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0062s; samplesPerSecond = 40577.8
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.14065234 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0062s; samplesPerSecond = 40212.3
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.17710254 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0065s; samplesPerSecond = 38711.7
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.13001953 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0064s; samplesPerSecond = 38819.9
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.21622949 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0063s; samplesPerSecond = 39613.4
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.21902246 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0063s; samplesPerSecond = 39904.2
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.18068799 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0064s; samplesPerSecond = 39332.9
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.16232471 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0064s; samplesPerSecond = 39160.4
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.13792139 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0063s; samplesPerSecond = 39607.1
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.16526709 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0064s; samplesPerSecond = 39080.8
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.14743457 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0063s; samplesPerSecond = 39619.7
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.15089160 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0064s; samplesPerSecond = 39339.1
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.12636230 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0063s; samplesPerSecond = 39834.3
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.16735547 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0063s; samplesPerSecond = 39382.5
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.14530957 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0064s; samplesPerSecond = 39044.2
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13859570 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0063s; samplesPerSecond = 39638.5
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.14215234 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0064s; samplesPerSecond = 39351.5
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.15903027 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0064s; samplesPerSecond = 39203.4
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.16232520 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0064s; samplesPerSecond = 39191.1
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.13596484 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0064s; samplesPerSecond = 39099.2
|
||||
08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15469434 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0064s; samplesPerSecond = 38965.1
|
||||
08/16/2016 10:51:41: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.24215964 * 10000; EvalErrorPrediction = 0.09440000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.253663s
|
||||
08/16/2016 10:51:41: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu/Models/simple.dnn.2'
|
||||
|
||||
08/16/2016 10:51:41: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [20000..30000] (first sequence at sample 20000), data subset 0 of 1
|
||||
|
||||
08/16/2016 10:51:41: Starting minibatch loop.
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.18305315 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0064s; samplesPerSecond = 38880.2
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.12945729 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0063s; samplesPerSecond = 39980.8
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.17735931 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0065s; samplesPerSecond = 38729.7
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.14128339 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0064s; samplesPerSecond = 39013.7
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.16558209 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0064s; samplesPerSecond = 39080.8
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.19102692 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0065s; samplesPerSecond = 38627.9
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.12279083 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0064s; samplesPerSecond = 39001.6
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.16642798 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0065s; samplesPerSecond = 38314.2
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.12386572 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0064s; samplesPerSecond = 38844.0
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.19928418 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0065s; samplesPerSecond = 38681.7
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.14213635 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0064s; samplesPerSecond = 38898.4
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.12377087 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0062s; samplesPerSecond = 40032.0
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.16361621 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0063s; samplesPerSecond = 39789.9
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.19886914 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0063s; samplesPerSecond = 39821.6
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.17207544 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0063s; samplesPerSecond = 39968.0
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.13323437 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0063s; samplesPerSecond = 39663.7
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.14397510 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0063s; samplesPerSecond = 39866.1
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.20777515 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0063s; samplesPerSecond = 39980.8
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.19094092 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0062s; samplesPerSecond = 40057.7
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.14731372 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0062s; samplesPerSecond = 40038.4
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.15483569 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0064s; samplesPerSecond = 39252.6
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.13625415 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0065s; samplesPerSecond = 38491.1
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.17354004 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0063s; samplesPerSecond = 39942.5
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.14408350 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0064s; samplesPerSecond = 39013.7
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.13720044 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0062s; samplesPerSecond = 40025.6
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.14236426 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0062s; samplesPerSecond = 40019.2
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.16857861 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0063s; samplesPerSecond = 39847.0
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.18606982 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0062s; samplesPerSecond = 40381.2
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.16334619 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0062s; samplesPerSecond = 40199.4
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.15598535 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0063s; samplesPerSecond = 39827.9
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18848584 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0062s; samplesPerSecond = 40238.2
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.13281348 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0063s; samplesPerSecond = 39669.9
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.14679150 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0063s; samplesPerSecond = 39419.7
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.13977344 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0063s; samplesPerSecond = 39726.7
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.20015137 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0062s; samplesPerSecond = 40244.7
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.12582129 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0063s; samplesPerSecond = 39388.7
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.18500098 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0062s; samplesPerSecond = 40051.3
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15147754 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0062s; samplesPerSecond = 40057.7
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.11988379 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0063s; samplesPerSecond = 39827.9
|
||||
08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.13059082 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0064s; samplesPerSecond = 39345.3
|
||||
08/16/2016 10:51:41: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15767216 * 10000; EvalErrorPrediction = 0.07330000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.255461s
|
||||
08/16/2016 10:51:41: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu/Models/simple.dnn'
|
||||
08/16/2016 10:51:41: CNTKCommandTrainEnd: Simple_Demo_Train
|
||||
|
||||
08/16/2016 10:51:41: Action "train" complete.
|
||||
|
||||
|
||||
08/16/2016 10:51:41: ##############################################################################
|
||||
08/16/2016 10:51:41: # #
|
||||
08/16/2016 10:51:41: # Action "test" #
|
||||
08/16/2016 10:51:41: # #
|
||||
08/16/2016 10:51:41: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
|
@ -660,43 +688,23 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 25 matrices, 2 are shared as 1, and 23 are not shared.
|
||||
|
||||
(nil): {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalErrorPrediction Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
|
||||
0x1efcc08: {[B2 Value[2 x 1]] }
|
||||
0x1efd8c8: {[W0 Value[50 x 2]] }
|
||||
0x1efee68: {[InvStdOfFeatures Value[2]] }
|
||||
0x2b337e8: {[EvalErrorPrediction Value[1]] }
|
||||
0x2b33948: {[CrossEntropyWithSoftmax Value[1]] }
|
||||
0x2b33f08: {[LogOfPrior Value[2]] }
|
||||
0x31808e8: {[W2 Value[2 x 50]] }
|
||||
0x3182698: {[MVNormalizedFeatures Value[2 x *1]] }
|
||||
0x3182a48: {[W0*features Value[50 x *1]] }
|
||||
0x3182c08: {[W0*features+B0 Value[50 x 1 x *1]] }
|
||||
0x3182dc8: {[H1 Value[50 x 1 x *1]] }
|
||||
0x3182f88: {[W1*H1 Value[50 x 1 x *1]] }
|
||||
0x3183148: {[W1*H1+B1 Value[50 x 1 x *1]] }
|
||||
0x3183308: {[H2 Value[50 x 1 x *1]] }
|
||||
0x3191148: {[B0 Value[50 x 1]] }
|
||||
0x34d4158: {[Prior Value[2]] }
|
||||
0x34d5128: {[features Value[2 x *1]] }
|
||||
0x34d54a8: {[labels Value[2 x *1]] }
|
||||
0x34d8028: {[W1 Value[50 x 50]] }
|
||||
0x34d9e68: {[MeanOfFeatures Value[2]] }
|
||||
0x7272228: {[B1 Value[50 x 1]] }
|
||||
0x7273058: {[W2*H1 Value[2 x 1 x *1]] }
|
||||
0x7273218: {[HLast Value[2 x 1 x *1]] }
|
||||
{ PosteriorProb : [2 x 1 x *1]
|
||||
ScaledLogLikelihood : [2 x 1 x *1] }
|
||||
|
||||
05/03/2016 15:21:29: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.05970149 * 603; CrossEntropyWithSoftmax = 0.13093129 * 603; perplexity = 1.13988946
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..603] (first sequence at sample 0), data subset 0 of 1
|
||||
08/16/2016 10:51:41: Minibatch[1-1]: EvalErrorPrediction = 0.04975124 * 603; CrossEntropyWithSoftmax = 0.10845041 * 603
|
||||
08/16/2016 10:51:41: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.04975124 * 603; CrossEntropyWithSoftmax = 0.10845041 * 603; perplexity = 1.11454964
|
||||
|
||||
05/03/2016 15:21:29: Action "test" complete.
|
||||
08/16/2016 10:51:41: Action "test" complete.
|
||||
|
||||
|
||||
05/03/2016 15:21:29: ##############################################################################
|
||||
05/03/2016 15:21:29: # #
|
||||
05/03/2016 15:21:29: # Action "write" #
|
||||
05/03/2016 15:21:29: # #
|
||||
05/03/2016 15:21:29: ##############################################################################
|
||||
08/16/2016 10:51:41: ##############################################################################
|
||||
08/16/2016 10:51:41: # #
|
||||
08/16/2016 10:51:41: # Action "write" #
|
||||
08/16/2016 10:51:41: # #
|
||||
08/16/2016 10:51:41: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
|
@ -754,36 +762,16 @@ Post-processing network complete.
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 25 matrices, 3 are shared as 1, and 22 are not shared.
|
||||
|
||||
(nil): {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [CrossEntropyWithSoftmax Value[1]] [EvalErrorPrediction Gradient[1]] [EvalErrorPrediction Value[1]] [H1 Gradient[50 x 1 x *2]] [H2 Gradient[50 x 1 x *2]] [HLast Gradient[2 x 1 x *2]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *2]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *2]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *2]] [ScaledLogLikelihood Value[2 x 1 x *2]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *2]] [W0*features+B0 Gradient[50 x 1 x *2]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *2]] [W1*H1+B1 Gradient[50 x 1 x *2]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *2]] [features Gradient[2 x *2]] [labels Gradient[2 x *2]] }
|
||||
0x1efcef8: {[features Value[2 x *2]] }
|
||||
0x1efe2c8: {[labels Value[2 x *2]] }
|
||||
0x1eff188: {[PosteriorProb Value[2 x 1 x *2]] }
|
||||
0x3180ab8: {[MeanOfFeatures Value[2]] }
|
||||
0x31817c8: {[W0 Value[50 x 2]] }
|
||||
0x31839d8: {[LogOfPrior Value[2]] }
|
||||
0x3185228: {[MVNormalizedFeatures Value[2 x *2]] }
|
||||
0x31855d8: {[W0*features Value[50 x *2]] }
|
||||
0x3185798: {[W0*features+B0 Value[50 x 1 x *2]] }
|
||||
0x3185958: {[H1 Value[50 x 1 x *2]] }
|
||||
0x3185b18: {[W1*H1 Value[50 x 1 x *2]] }
|
||||
0x3185cd8: {[W1*H1+B1 Value[50 x 1 x *2]] }
|
||||
0x3185e98: {[H2 Value[50 x 1 x *2]] }
|
||||
0x3186058: {[W2*H1 Value[2 x 1 x *2]] }
|
||||
0x3186218: {[HLast Value[2 x 1 x *2]] }
|
||||
0x34d4108: {[B2 Value[2 x 1]] }
|
||||
0x34d4fe8: {[InvStdOfFeatures Value[2]] }
|
||||
0x34d8528: {[Prior Value[2]] }
|
||||
0x34da1c8: {[B0 Value[50 x 1]] }
|
||||
0x3596b08: {[B1 Value[50 x 1]] }
|
||||
0x72775d8: {[W1 Value[50 x 50]] }
|
||||
0x72788f8: {[W2 Value[2 x 50]] }
|
||||
{ CrossEntropyWithSoftmax : [1]
|
||||
EvalErrorPrediction : [1]
|
||||
ScaledLogLikelihood : [2 x 1 x *2] }
|
||||
|
||||
Minibatch[0]: ActualMBSize = 603
|
||||
Written to /tmp/cntk-test-20160503152115.267374/CNTKTextFormatReader/Examples/Other/Simple2d_Simple@release_gpu/SimpleOutput*
|
||||
Written to /tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu/SimpleOutput*
|
||||
Total Samples Evaluated = 603
|
||||
|
||||
05/03/2016 15:21:29: Action "write" complete.
|
||||
08/16/2016 10:51:41: Action "write" complete.
|
||||
|
||||
05/03/2016 15:21:29: __COMPLETED__
|
||||
08/16/2016 10:51:41: __COMPLETED__
|
||||
|
|
@ -1,46 +1,61 @@
|
|||
=== Running /cygdrive/c/src/cntk_github/x64/release/cntk.exe configFile=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config/Simple.cntk currentDirectory=C:\src\cntk_github\Examples\Other\Simple2d\Data RunDir=E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu DataDir=C:\src\cntk_github\Examples\Other\Simple2d\Data ConfigDir=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config OutputDir=E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu DeviceId=-1 timestamping=true Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 268381192 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config/Simple.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_cpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_cpu DeviceId=-1 timestamping=true Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 3 2016 12:19:59
|
||||
Last modified date: Thu Apr 7 11:05:47 2016
|
||||
Built time: Aug 16 2016 02:54:53
|
||||
Last modified date: Fri Aug 12 05:31:21 2016
|
||||
Build type: Release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: no
|
||||
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0
|
||||
CUB_PATH: E:\lib\cub-1.4.1
|
||||
CUDNN_PATH: E:\lib\cuDNN_v4
|
||||
Build Branch: alrezni/examples_text
|
||||
Build SHA1: d5e576046e2fa850c4296da155f15c2b08b7927a
|
||||
Built by alrezni on DIFFENG
|
||||
Build Path: C:\src\cntk_github\Source\CNTK\
|
||||
Math lib: mkl
|
||||
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
CUB_PATH: c:\src\cub-1.4.1
|
||||
CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by svcphil on Philly-Pool3
|
||||
Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
05/03/2016 13:12:46: -------------------------------------------------------------------
|
||||
05/03/2016 13:12:46: Build info:
|
||||
Changed current directory to C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
08/16/2016 03:04:13: -------------------------------------------------------------------
|
||||
08/16/2016 03:04:13: Build info:
|
||||
|
||||
05/03/2016 13:12:46: Built time: May 3 2016 12:19:59
|
||||
05/03/2016 13:12:46: Last modified date: Thu Apr 7 11:05:47 2016
|
||||
05/03/2016 13:12:46: Build type: Release
|
||||
05/03/2016 13:12:46: Build target: GPU
|
||||
05/03/2016 13:12:46: With 1bit-SGD: no
|
||||
05/03/2016 13:12:46: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0
|
||||
05/03/2016 13:12:46: CUB_PATH: E:\lib\cub-1.4.1
|
||||
05/03/2016 13:12:46: CUDNN_PATH: E:\lib\cuDNN_v4
|
||||
05/03/2016 13:12:46: Build Branch: alrezni/examples_text
|
||||
05/03/2016 13:12:46: Build SHA1: d5e576046e2fa850c4296da155f15c2b08b7927a
|
||||
05/03/2016 13:12:46: Built by alrezni on DIFFENG
|
||||
05/03/2016 13:12:46: Build Path: C:\src\cntk_github\Source\CNTK\
|
||||
05/03/2016 13:12:46: -------------------------------------------------------------------
|
||||
08/16/2016 03:04:13: Built time: Aug 16 2016 02:54:53
|
||||
08/16/2016 03:04:13: Last modified date: Fri Aug 12 05:31:21 2016
|
||||
08/16/2016 03:04:13: Build type: Release
|
||||
08/16/2016 03:04:13: Build target: GPU
|
||||
08/16/2016 03:04:13: With 1bit-SGD: no
|
||||
08/16/2016 03:04:13: Math lib: mkl
|
||||
08/16/2016 03:04:13: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
08/16/2016 03:04:13: CUB_PATH: c:\src\cub-1.4.1
|
||||
08/16/2016 03:04:13: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
08/16/2016 03:04:13: Build Branch: HEAD
|
||||
08/16/2016 03:04:13: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 03:04:13: Built by svcphil on Philly-Pool3
|
||||
08/16/2016 03:04:13: Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
08/16/2016 03:04:13: -------------------------------------------------------------------
|
||||
08/16/2016 03:04:16: -------------------------------------------------------------------
|
||||
08/16/2016 03:04:16: GPU info:
|
||||
|
||||
05/03/2016 13:12:46: Running on DIFFENG at 2016/05/03 13:12:46
|
||||
05/03/2016 13:12:46: Command line:
|
||||
C:\src\cntk_github\x64\release\cntk.exe configFile=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config/Simple.cntk currentDirectory=C:\src\cntk_github\Examples\Other\Simple2d\Data RunDir=E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu DataDir=C:\src\cntk_github\Examples\Other\Simple2d\Data ConfigDir=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config OutputDir=E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu DeviceId=-1 timestamping=true Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
08/16/2016 03:04:16: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:04:16: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:04:16: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:04:16: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:04:16: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 03:04:16: Running on DPHAIM-24 at 2016/08/16 03:04:16
|
||||
08/16/2016 03:04:16: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config/Simple.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_cpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_cpu DeviceId=-1 timestamping=true Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
|
||||
|
||||
05/03/2016 13:12:46: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 13:12:46: RootDir = ".."
|
||||
08/16/2016 03:04:16: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:04:16: RootDir = ".."
|
||||
ConfigDir = "$RootDir$/Config"
|
||||
DataDir = "$RootDir$/Data"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
|
|
@ -127,28 +142,28 @@ labelMappingFile = "$DataDir$/SimpleMapping.txt"
|
|||
sequenceEpilogue = "\t// %s\n"
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
RunDir=E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu
|
||||
DataDir=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
ConfigDir=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config
|
||||
OutputDir=E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu
|
||||
currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_cpu
|
||||
DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_cpu
|
||||
DeviceId=-1
|
||||
timestamping=true
|
||||
Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
05/03/2016 13:12:46: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:04:16: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 13:12:46: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 13:12:46: RootDir = ".."
|
||||
08/16/2016 03:04:16: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:04:16: RootDir = ".."
|
||||
ConfigDir = "../Config"
|
||||
DataDir = "../Data"
|
||||
OutputDir = "../Output"
|
||||
ModelDir = "E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu/Models"
|
||||
ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_cpu/Models"
|
||||
deviceId = -1
|
||||
command = Simple_Demo_Train:Simple_Demo_Test:Simple_Demo_Output
|
||||
precision = "float"
|
||||
traceLevel = 1
|
||||
modelPath = "E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu/Models/simple.dnn"
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_cpu/Models/simple.dnn"
|
||||
outputNodeNames = ScaledLogLikelihood
|
||||
Simple_Demo_Train = [
|
||||
action = "train"
|
||||
|
|
@ -172,7 +187,7 @@ Simple_Demo_Train = [
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTrain_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTrain_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -189,7 +204,7 @@ Simple_Demo_Test = [
|
|||
action = "test"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -206,7 +221,7 @@ Simple_Demo_Output=[
|
|||
action = "write"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -219,42 +234,42 @@ dim = 2
|
|||
]
|
||||
]
|
||||
outputNodeNames = PosteriorProb : labels
|
||||
outputPath = "E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu/SimpleOutput"
|
||||
outputPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_cpu/SimpleOutput"
|
||||
format = [
|
||||
type = "category"
|
||||
labelMappingFile = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleMapping.txt"
|
||||
labelMappingFile = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleMapping.txt"
|
||||
sequenceEpilogue = "\t// %s\n"
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
RunDir=E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu
|
||||
DataDir=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
ConfigDir=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config
|
||||
OutputDir=E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu
|
||||
currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_cpu
|
||||
DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_cpu
|
||||
DeviceId=-1
|
||||
timestamping=true
|
||||
Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
05/03/2016 13:12:46: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:04:16: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 13:12:46: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:04:16: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: Simple.cntk:command=Simple_Demo_Train:Simple_Demo_Test:Simple_Demo_Output
|
||||
configparameters: Simple.cntk:ConfigDir=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config
|
||||
configparameters: Simple.cntk:currentDirectory=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
configparameters: Simple.cntk:DataDir=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
configparameters: Simple.cntk:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config
|
||||
configparameters: Simple.cntk:currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
configparameters: Simple.cntk:DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
configparameters: Simple.cntk:deviceId=-1
|
||||
configparameters: Simple.cntk:ModelDir=E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu/Models
|
||||
configparameters: Simple.cntk:modelPath=E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu/Models/simple.dnn
|
||||
configparameters: Simple.cntk:OutputDir=E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu
|
||||
configparameters: Simple.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_cpu/Models
|
||||
configparameters: Simple.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_cpu/Models/simple.dnn
|
||||
configparameters: Simple.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_cpu
|
||||
configparameters: Simple.cntk:outputNodeNames=ScaledLogLikelihood
|
||||
configparameters: Simple.cntk:precision=float
|
||||
configparameters: Simple.cntk:RootDir=..
|
||||
configparameters: Simple.cntk:RunDir=E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu
|
||||
configparameters: Simple.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_cpu
|
||||
configparameters: Simple.cntk:Simple_Demo_Output=[
|
||||
action = "write"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -267,10 +282,10 @@ dim = 2
|
|||
]
|
||||
]
|
||||
outputNodeNames = PosteriorProb : labels
|
||||
outputPath = "E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu/SimpleOutput"
|
||||
outputPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_cpu/SimpleOutput"
|
||||
format = [
|
||||
type = "category"
|
||||
labelMappingFile = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleMapping.txt"
|
||||
labelMappingFile = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleMapping.txt"
|
||||
sequenceEpilogue = "\t// %s\n"
|
||||
]
|
||||
]
|
||||
|
|
@ -279,7 +294,7 @@ configparameters: Simple.cntk:Simple_Demo_Test=[
|
|||
action = "test"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -315,7 +330,7 @@ configparameters: Simple.cntk:Simple_Demo_Train=[
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTrain_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTrain_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -331,23 +346,35 @@ dim = 2
|
|||
|
||||
configparameters: Simple.cntk:timestamping=true
|
||||
configparameters: Simple.cntk:traceLevel=1
|
||||
05/03/2016 13:12:46: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/03/2016 13:12:46: Commands: Simple_Demo_Train Simple_Demo_Test Simple_Demo_Output
|
||||
05/03/2016 13:12:46: Precision = "float"
|
||||
05/03/2016 13:12:46: CNTKModelPath: E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu/Models/simple.dnn
|
||||
05/03/2016 13:12:46: CNTKCommandTrainInfo: Simple_Demo_Train : 3
|
||||
05/03/2016 13:12:46: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
08/16/2016 03:04:16: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:04:16: Commands: Simple_Demo_Train Simple_Demo_Test Simple_Demo_Output
|
||||
08/16/2016 03:04:16: Precision = "float"
|
||||
08/16/2016 03:04:16: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_cpu/Models/simple.dnn
|
||||
08/16/2016 03:04:16: CNTKCommandTrainInfo: Simple_Demo_Train : 3
|
||||
08/16/2016 03:04:16: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
|
||||
05/03/2016 13:12:46: ##############################################################################
|
||||
05/03/2016 13:12:46: # #
|
||||
05/03/2016 13:12:46: # Action "train" #
|
||||
05/03/2016 13:12:46: # #
|
||||
05/03/2016 13:12:46: ##############################################################################
|
||||
08/16/2016 03:04:16: ##############################################################################
|
||||
08/16/2016 03:04:16: # #
|
||||
08/16/2016 03:04:16: # Action "train" #
|
||||
08/16/2016 03:04:16: # #
|
||||
08/16/2016 03:04:16: ##############################################################################
|
||||
|
||||
05/03/2016 13:12:46: CNTKCommandTrainBegin: Simple_Demo_Train
|
||||
08/16/2016 03:04:16: CNTKCommandTrainBegin: Simple_Demo_Train
|
||||
SimpleNetworkBuilder Using CPU
|
||||
|
||||
05/03/2016 13:12:46: Creating virgin network.
|
||||
08/16/2016 03:04:16: Creating virgin network.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[50 x 2] <- 0.000000.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[50 x 2] <- uniform(seed=1, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[50 x 50] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[50 x 50] <- uniform(seed=2, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[2 x 50] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[2 x 50] <- uniform(seed=3, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[2 x 1] <- 0.000000.
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[2 x 1] <- 0.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -399,207 +426,210 @@ Validating network, final pass.
|
|||
|
||||
Post-processing network complete.
|
||||
|
||||
05/03/2016 13:12:46: Created model with 25 nodes on CPU.
|
||||
08/16/2016 03:04:16: Created model with 25 nodes on CPU.
|
||||
|
||||
05/03/2016 13:12:46: Training criterion node(s):
|
||||
05/03/2016 13:12:46: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
|
||||
08/16/2016 03:04:16: Training criterion node(s):
|
||||
08/16/2016 03:04:16: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
|
||||
|
||||
05/03/2016 13:12:46: Evaluation criterion node(s):
|
||||
|
||||
05/03/2016 13:12:46: EvalErrorPrediction = ErrorPrediction
|
||||
08/16/2016 03:04:16: Evaluation criterion node(s):
|
||||
08/16/2016 03:04:16: EvalErrorPrediction = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 40 matrices, 19 are shared as 8, and 21 are not shared.
|
||||
|
||||
0000000000000000: {[EvalErrorPrediction Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
|
||||
000000702B410E90: {[features Value[2 x *]] }
|
||||
000000702B44E0C0: {[W0 Value[50 x 2]] }
|
||||
000000702B4D76F0: {[H2 Value[50 x 1 x *]] [W1*H1 Gradient[50 x 1 x *]] }
|
||||
000000702B4D7970: {[W0 Gradient[50 x 2]] [W0*features+B0 Value[50 x 1 x *]] }
|
||||
000000702B4D7AB0: {[CrossEntropyWithSoftmax Gradient[1]] }
|
||||
000000702B4D7DD0: {[LogOfPrior Value[2]] }
|
||||
000000702B4D7F10: {[W1 Gradient[50 x 50]] [W1*H1+B1 Value[50 x 1 x *]] }
|
||||
000000702B4D7FB0: {[B1 Gradient[50 x 1]] [H2 Gradient[50 x 1 x *]] [HLast Gradient[2 x 1 x *]] }
|
||||
000000702B4D82D0: {[W0*features+B0 Gradient[50 x 1 x *]] [W1*H1 Value[50 x 1 x *]] }
|
||||
000000702B4D8370: {[W0*features Value[50 x *]] }
|
||||
000000702B4D84B0: {[MVNormalizedFeatures Value[2 x *]] }
|
||||
000000702B4D8690: {[B0 Gradient[50 x 1]] [H1 Gradient[50 x 1 x *]] [W1*H1+B1 Gradient[50 x 1 x *]] [W2*H1 Value[2 x 1 x *]] }
|
||||
000000702B4D8730: {[HLast Value[2 x 1 x *]] [W2 Gradient[2 x 50]] }
|
||||
000000702B4D89B0: {[CrossEntropyWithSoftmax Value[1]] }
|
||||
000000702B4D8AF0: {[EvalErrorPrediction Value[1]] }
|
||||
000000702B4D8B90: {[H1 Value[50 x 1 x *]] [W0*features Gradient[50 x *]] }
|
||||
000000702B4D8F50: {[B2 Gradient[2 x 1]] }
|
||||
000000702B4D91D0: {[ScaledLogLikelihood Value[2 x 1 x *]] }
|
||||
000000702B4D9270: {[W2*H1 Gradient[2 x 1 x *]] }
|
||||
000000702E1EDCB0: {[B2 Value[2 x 1]] }
|
||||
000000702E1EDDF0: {[B0 Value[50 x 1]] }
|
||||
000000702E1EDE90: {[B1 Value[50 x 1]] }
|
||||
000000702E1EE2F0: {[W2 Value[2 x 50]] }
|
||||
000000702E1EE6B0: {[labels Value[2 x *]] }
|
||||
000000702E1EE930: {[Prior Value[2]] }
|
||||
000000702E1EE9D0: {[W1 Value[50 x 50]] }
|
||||
000000702E1EEB30: {[MeanOfFeatures Value[2]] }
|
||||
000000702E1EEEE0: {[InvStdOfFeatures Value[2]] }
|
||||
{ H1 : [50 x 1 x *]
|
||||
W0*features : [50 x *] (gradient) }
|
||||
{ W0 : [50 x 2] (gradient)
|
||||
W0*features+B0 : [50 x 1 x *] }
|
||||
{ W0*features+B0 : [50 x 1 x *] (gradient)
|
||||
W1*H1 : [50 x 1 x *] }
|
||||
{ W1 : [50 x 50] (gradient)
|
||||
W1*H1+B1 : [50 x 1 x *] }
|
||||
{ H2 : [50 x 1 x *]
|
||||
W1*H1 : [50 x 1 x *] (gradient) }
|
||||
{ B0 : [50 x 1] (gradient)
|
||||
H1 : [50 x 1 x *] (gradient)
|
||||
W1*H1+B1 : [50 x 1 x *] (gradient)
|
||||
W2*H1 : [2 x 1 x *] }
|
||||
{ B1 : [50 x 1] (gradient)
|
||||
H2 : [50 x 1 x *] (gradient)
|
||||
HLast : [2 x 1 x *] (gradient) }
|
||||
{ HLast : [2 x 1 x *]
|
||||
W2 : [2 x 50] (gradient) }
|
||||
|
||||
|
||||
05/03/2016 13:12:46: Precomputing --> 3 PreCompute nodes found.
|
||||
08/16/2016 03:04:16: Training 2802 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:
|
||||
|
||||
05/03/2016 13:12:46: MeanOfFeatures = Mean()
|
||||
05/03/2016 13:12:46: InvStdOfFeatures = InvStdDev()
|
||||
05/03/2016 13:12:46: Prior = Mean()
|
||||
|
||||
05/03/2016 13:12:47: Precomputing --> Completed.
|
||||
08/16/2016 03:04:16: Node 'B0' (LearnableParameter operation) : [50 x 1]
|
||||
08/16/2016 03:04:16: Node 'B1' (LearnableParameter operation) : [50 x 1]
|
||||
08/16/2016 03:04:16: Node 'B2' (LearnableParameter operation) : [2 x 1]
|
||||
08/16/2016 03:04:16: Node 'W0' (LearnableParameter operation) : [50 x 2]
|
||||
08/16/2016 03:04:16: Node 'W1' (LearnableParameter operation) : [50 x 50]
|
||||
08/16/2016 03:04:16: Node 'W2' (LearnableParameter operation) : [2 x 50]
|
||||
|
||||
|
||||
05/03/2016 13:12:47: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
08/16/2016 03:04:16: Precomputing --> 3 PreCompute nodes found.
|
||||
|
||||
05/03/2016 13:12:47: Starting minibatch loop.
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70511987 * 250; EvalErrorPrediction = 0.55200000 * 250; time = 0.0327s; samplesPerSecond = 7657.0
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.69754895 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0257s; samplesPerSecond = 9726.5
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.71056921 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0248s; samplesPerSecond = 10096.1
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.72951074 * 250; EvalErrorPrediction = 0.56000000 * 250; time = 0.0245s; samplesPerSecond = 10210.3
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.70946655 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.0249s; samplesPerSecond = 10032.5
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72656787 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0248s; samplesPerSecond = 10065.2
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.69337402 * 250; EvalErrorPrediction = 0.43200000 * 250; time = 0.0256s; samplesPerSecond = 9766.8
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.73605176 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0259s; samplesPerSecond = 9662.6
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.71453076 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0239s; samplesPerSecond = 10469.0
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.75191992 * 250; EvalErrorPrediction = 0.47200000 * 250; time = 0.0255s; samplesPerSecond = 9802.0
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.75975146 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0248s; samplesPerSecond = 10100.6
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.73172168 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0255s; samplesPerSecond = 9808.5
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.76840820 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0261s; samplesPerSecond = 9593.2
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.70464746 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0255s; samplesPerSecond = 9807.4
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.70557227 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0242s; samplesPerSecond = 10340.4
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.72711816 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0249s; samplesPerSecond = 10049.8
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70076660 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0247s; samplesPerSecond = 10117.4
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.69409766 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0254s; samplesPerSecond = 9834.0
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.69139941 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0243s; samplesPerSecond = 10275.8
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.73361621 * 250; EvalErrorPrediction = 0.55200000 * 250; time = 0.0255s; samplesPerSecond = 9802.8
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.72225879 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0246s; samplesPerSecond = 10146.5
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70356348 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0243s; samplesPerSecond = 10286.8
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69928613 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0252s; samplesPerSecond = 9909.2
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.72360938 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0244s; samplesPerSecond = 10227.0
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.69871875 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0244s; samplesPerSecond = 10243.8
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.69114844 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0248s; samplesPerSecond = 10081.5
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.68648047 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0254s; samplesPerSecond = 9844.5
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.69657227 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0258s; samplesPerSecond = 9679.8
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.71585547 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0255s; samplesPerSecond = 9798.2
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.69730664 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0260s; samplesPerSecond = 9609.1
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.70432422 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0265s; samplesPerSecond = 9431.1
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.69991797 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0257s; samplesPerSecond = 9722.7
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.68696875 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0259s; samplesPerSecond = 9647.3
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.67331445 * 250; EvalErrorPrediction = 0.37200000 * 250; time = 0.0267s; samplesPerSecond = 9364.7
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.65711328 * 250; EvalErrorPrediction = 0.43200000 * 250; time = 0.0258s; samplesPerSecond = 9700.1
|
||||
05/03/2016 13:12:47: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.64534375 * 250; EvalErrorPrediction = 0.44800000 * 250; time = 0.0260s; samplesPerSecond = 9608.0
|
||||
05/03/2016 13:12:48: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.61021875 * 250; EvalErrorPrediction = 0.36400000 * 250; time = 0.0263s; samplesPerSecond = 9515.5
|
||||
05/03/2016 13:12:48: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.54191016 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0229s; samplesPerSecond = 10907.5
|
||||
05/03/2016 13:12:48: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.45624414 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0239s; samplesPerSecond = 10479.5
|
||||
05/03/2016 13:12:48: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.37636133 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0229s; samplesPerSecond = 10917.0
|
||||
05/03/2016 13:12:48: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.68695688 * 10000; EvalErrorPrediction = 0.45550000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=1.01718s
|
||||
05/03/2016 13:12:48: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu/Models/simple.dnn.1'
|
||||
08/16/2016 03:04:16: MeanOfFeatures = Mean()
|
||||
08/16/2016 03:04:16: InvStdOfFeatures = InvStdDev()
|
||||
08/16/2016 03:04:16: Prior = Mean()
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
05/03/2016 13:12:48: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
|
||||
05/03/2016 13:12:48: Starting minibatch loop.
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.28579105 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0228s; samplesPerSecond = 10943.3
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.27768619 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0230s; samplesPerSecond = 10860.1
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.23309790 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0223s; samplesPerSecond = 11187.2
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.20937585 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0221s; samplesPerSecond = 11327.1
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.20192059 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0225s; samplesPerSecond = 11116.5
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.21303992 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0232s; samplesPerSecond = 10762.9
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.17823340 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0247s; samplesPerSecond = 10120.6
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.18892688 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0231s; samplesPerSecond = 10816.4
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.14161328 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0225s; samplesPerSecond = 11100.8
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.15813574 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0226s; samplesPerSecond = 11077.1
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.21082446 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0233s; samplesPerSecond = 10728.2
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.16117041 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0229s; samplesPerSecond = 10928.0
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.15665234 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0223s; samplesPerSecond = 11195.2
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13067773 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0226s; samplesPerSecond = 11047.3
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.16602710 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0212s; samplesPerSecond = 11796.9
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.14975708 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0215s; samplesPerSecond = 11641.4
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.22351709 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0214s; samplesPerSecond = 11708.5
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18010474 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0207s; samplesPerSecond = 12085.5
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.15341577 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0207s; samplesPerSecond = 12072.6
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.17195337 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0209s; samplesPerSecond = 11976.6
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.15546069 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0217s; samplesPerSecond = 11534.6
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.16008325 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0214s; samplesPerSecond = 11689.3
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.15944043 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0209s; samplesPerSecond = 11981.2
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.15336865 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0207s; samplesPerSecond = 12102.4
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.14822266 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0212s; samplesPerSecond = 11766.4
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.14999512 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0211s; samplesPerSecond = 11833.2
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.15481982 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0208s; samplesPerSecond = 11992.7
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.17656738 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0204s; samplesPerSecond = 12229.1
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.22373242 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0213s; samplesPerSecond = 11738.7
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16403760 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0211s; samplesPerSecond = 11856.8
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.17322168 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0211s; samplesPerSecond = 11868.0
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.13165430 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0205s; samplesPerSecond = 12202.3
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.14016992 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0208s; samplesPerSecond = 11993.9
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.18369678 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0214s; samplesPerSecond = 11657.7
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.15161035 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0215s; samplesPerSecond = 11612.8
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.18919824 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0215s; samplesPerSecond = 11632.8
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.17373975 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0212s; samplesPerSecond = 11818.1
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15033740 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0208s; samplesPerSecond = 12036.6
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.12107568 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0207s; samplesPerSecond = 12075.5
|
||||
05/03/2016 13:12:48: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15386328 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0227s; samplesPerSecond = 10997.7
|
||||
05/03/2016 13:12:48: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.17515541 * 10000; EvalErrorPrediction = 0.07440000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.87149s
|
||||
05/03/2016 13:12:48: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu/Models/simple.dnn.2'
|
||||
|
||||
05/03/2016 13:12:48: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
|
||||
05/03/2016 13:12:48: Starting minibatch loop.
|
||||
05/03/2016 13:12:48: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.10671188 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0217s; samplesPerSecond = 11511.2
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.17609265 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0205s; samplesPerSecond = 12183.8
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14152701 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0208s; samplesPerSecond = 12001.9
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.16348053 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0213s; samplesPerSecond = 11748.1
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.11764551 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0219s; samplesPerSecond = 11435.4
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.16246954 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0212s; samplesPerSecond = 11811.4
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.16140149 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0207s; samplesPerSecond = 12078.5
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.19747632 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0202s; samplesPerSecond = 12391.0
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.20041309 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0214s; samplesPerSecond = 11659.9
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.13657080 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0208s; samplesPerSecond = 12033.7
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.20124377 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0203s; samplesPerSecond = 12293.5
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.17898120 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0206s; samplesPerSecond = 12144.2
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.16037830 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0232s; samplesPerSecond = 10779.1
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16276050 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0214s; samplesPerSecond = 11704.7
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.19882275 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0218s; samplesPerSecond = 11454.2
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.10263354 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0208s; samplesPerSecond = 12041.2
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17038770 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0213s; samplesPerSecond = 11725.5
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16624731 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0209s; samplesPerSecond = 11958.3
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.12664160 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0213s; samplesPerSecond = 11723.3
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.11944995 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0213s; samplesPerSecond = 11733.8
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.12949756 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0208s; samplesPerSecond = 11996.2
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.18147778 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0222s; samplesPerSecond = 11242.5
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.13172412 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0233s; samplesPerSecond = 10719.0
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.19600269 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0238s; samplesPerSecond = 10521.0
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.15840479 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0226s; samplesPerSecond = 11084.5
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.11888281 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0225s; samplesPerSecond = 11129.9
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.13710742 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0222s; samplesPerSecond = 11251.1
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.20026318 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0233s; samplesPerSecond = 10730.5
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.18824951 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0223s; samplesPerSecond = 11227.9
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16653223 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0225s; samplesPerSecond = 11096.3
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.11935254 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0229s; samplesPerSecond = 10918.5
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.16085400 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0225s; samplesPerSecond = 11132.9
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.16112646 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0219s; samplesPerSecond = 11439.6
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.12345313 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0229s; samplesPerSecond = 10904.6
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13502686 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0226s; samplesPerSecond = 11075.2
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.20874756 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0224s; samplesPerSecond = 11185.2
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16650537 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0227s; samplesPerSecond = 11009.3
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.14995752 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0206s; samplesPerSecond = 12134.7
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.16497070 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0209s; samplesPerSecond = 11953.7
|
||||
05/03/2016 13:12:49: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.16843018 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0210s; samplesPerSecond = 11912.1
|
||||
05/03/2016 13:12:49: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15794755 * 10000; EvalErrorPrediction = 0.07480000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.871499s
|
||||
05/03/2016 13:12:49: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu/Models/simple.dnn'
|
||||
05/03/2016 13:12:49: CNTKCommandTrainEnd: Simple_Demo_Train
|
||||
|
||||
05/03/2016 13:12:49: Action "train" complete.
|
||||
08/16/2016 03:04:16: Precomputing --> Completed.
|
||||
|
||||
|
||||
05/03/2016 13:12:49: ##############################################################################
|
||||
05/03/2016 13:12:49: # #
|
||||
05/03/2016 13:12:49: # Action "test" #
|
||||
05/03/2016 13:12:49: # #
|
||||
05/03/2016 13:12:49: ##############################################################################
|
||||
08/16/2016 03:04:16: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:04:16: Starting minibatch loop.
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70264496 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0355s; samplesPerSecond = 7041.1
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.76483063 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0281s; samplesPerSecond = 8903.5
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.72648584 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0269s; samplesPerSecond = 9307.5
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.73860254 * 250; EvalErrorPrediction = 0.56800000 * 250; time = 0.0254s; samplesPerSecond = 9858.4
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.70622803 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0248s; samplesPerSecond = 10062.8
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.74772852 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0246s; samplesPerSecond = 10142.8
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.75092773 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0253s; samplesPerSecond = 9869.3
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.78004932 * 250; EvalErrorPrediction = 0.48400000 * 250; time = 0.0250s; samplesPerSecond = 9983.2
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.70444336 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0257s; samplesPerSecond = 9745.8
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.69544189 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0253s; samplesPerSecond = 9889.6
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.70595947 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0255s; samplesPerSecond = 9823.2
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.74544189 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0250s; samplesPerSecond = 9994.4
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.70809961 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0253s; samplesPerSecond = 9888.5
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.69884375 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0252s; samplesPerSecond = 9917.5
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.70363086 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0257s; samplesPerSecond = 9717.8
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.69351758 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0250s; samplesPerSecond = 9998.8
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.74453613 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0252s; samplesPerSecond = 9901.8
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.73761426 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0247s; samplesPerSecond = 10133.8
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.71868652 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.0256s; samplesPerSecond = 9782.1
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.71496484 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0249s; samplesPerSecond = 10052.7
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.69961230 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0249s; samplesPerSecond = 10036.1
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.69760645 * 250; EvalErrorPrediction = 0.49200000 * 250; time = 0.0260s; samplesPerSecond = 9618.3
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.70748047 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0256s; samplesPerSecond = 9771.7
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.69785937 * 250; EvalErrorPrediction = 0.56800000 * 250; time = 0.0253s; samplesPerSecond = 9882.6
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.69483203 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0256s; samplesPerSecond = 9754.6
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.69258203 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0238s; samplesPerSecond = 10503.8
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.70665625 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0245s; samplesPerSecond = 10191.2
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.69031445 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0241s; samplesPerSecond = 10352.4
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.70169531 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0242s; samplesPerSecond = 10326.3
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.71008398 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0238s; samplesPerSecond = 10486.6
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.69152930 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0242s; samplesPerSecond = 10347.3
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.69522656 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0239s; samplesPerSecond = 10472.1
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.69347070 * 250; EvalErrorPrediction = 0.49200000 * 250; time = 0.0243s; samplesPerSecond = 10308.9
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.68888281 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0242s; samplesPerSecond = 10329.7
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.68067578 * 250; EvalErrorPrediction = 0.48400000 * 250; time = 0.0243s; samplesPerSecond = 10280.9
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.66932227 * 250; EvalErrorPrediction = 0.44400000 * 250; time = 0.0242s; samplesPerSecond = 10317.8
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.65398437 * 250; EvalErrorPrediction = 0.24800000 * 250; time = 0.0237s; samplesPerSecond = 10545.4
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.63662500 * 250; EvalErrorPrediction = 0.32400000 * 250; time = 0.0240s; samplesPerSecond = 10400.6
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.59652344 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0242s; samplesPerSecond = 10346.8
|
||||
08/16/2016 03:04:17: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.55820898 * 250; EvalErrorPrediction = 0.12000000 * 250; time = 0.0238s; samplesPerSecond = 10488.3
|
||||
08/16/2016 03:04:17: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.70129624 * 10000; EvalErrorPrediction = 0.46850000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=1.01068s
|
||||
08/16/2016 03:04:17: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_cpu/Models/simple.dnn.1'
|
||||
|
||||
08/16/2016 03:04:18: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [10000..20000] (first sequence at sample 10000), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:04:18: Starting minibatch loop.
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.50449603 * 250; EvalErrorPrediction = 0.14800000 * 250; time = 0.0230s; samplesPerSecond = 10862.5
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.45593445 * 250; EvalErrorPrediction = 0.12800000 * 250; time = 0.0229s; samplesPerSecond = 10916.6
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.38063666 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0230s; samplesPerSecond = 10880.0
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.35197192 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0227s; samplesPerSecond = 11005.0
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.30828760 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0229s; samplesPerSecond = 10918.0
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.29232886 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0228s; samplesPerSecond = 10979.4
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.26675781 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0230s; samplesPerSecond = 10878.6
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.23178394 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0230s; samplesPerSecond = 10857.3
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.23917383 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0228s; samplesPerSecond = 10954.3
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.21675732 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0227s; samplesPerSecond = 11001.6
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.19885376 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0230s; samplesPerSecond = 10854.5
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.18136646 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0232s; samplesPerSecond = 10786.6
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.19802368 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0231s; samplesPerSecond = 10826.7
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.18948218 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0227s; samplesPerSecond = 10990.0
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.18990088 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0230s; samplesPerSecond = 10861.1
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.18491504 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0232s; samplesPerSecond = 10772.1
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.18686621 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0232s; samplesPerSecond = 10788.0
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.21271729 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0232s; samplesPerSecond = 10780.5
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.16924951 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0225s; samplesPerSecond = 11127.4
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.17609473 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0231s; samplesPerSecond = 10845.0
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.13717920 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0229s; samplesPerSecond = 10921.8
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.17546387 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0233s; samplesPerSecond = 10708.0
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.12864746 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0228s; samplesPerSecond = 10944.8
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.21596680 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0231s; samplesPerSecond = 10832.8
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.21857666 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0228s; samplesPerSecond = 10946.7
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.18096436 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0239s; samplesPerSecond = 10463.8
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.16132373 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0230s; samplesPerSecond = 10881.4
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.13699268 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0228s; samplesPerSecond = 10960.6
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.16551953 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0229s; samplesPerSecond = 10909.4
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.14865527 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0238s; samplesPerSecond = 10483.1
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.15119824 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0226s; samplesPerSecond = 11060.0
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.12673340 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0230s; samplesPerSecond = 10887.1
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.16551514 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0231s; samplesPerSecond = 10808.9
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.14445264 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0233s; samplesPerSecond = 10734.2
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13810986 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0230s; samplesPerSecond = 10880.4
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.14219189 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0225s; samplesPerSecond = 11107.2
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.15920459 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0225s; samplesPerSecond = 11113.1
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.16245654 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0238s; samplesPerSecond = 10512.2
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.13554053 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0228s; samplesPerSecond = 10988.5
|
||||
08/16/2016 03:04:18: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15504346 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0228s; samplesPerSecond = 10968.3
|
||||
08/16/2016 03:04:18: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.20713335 * 10000; EvalErrorPrediction = 0.08030000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.921702s
|
||||
08/16/2016 03:04:18: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_cpu/Models/simple.dnn.2'
|
||||
|
||||
08/16/2016 03:04:18: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [20000..30000] (first sequence at sample 20000), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:04:18: Starting minibatch loop.
|
||||
08/16/2016 03:04:18: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.18297285 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0231s; samplesPerSecond = 10833.8
|
||||
08/16/2016 03:04:18: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.12934721 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0230s; samplesPerSecond = 10872.4
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.17702411 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0225s; samplesPerSecond = 11110.1
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.14030841 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0228s; samplesPerSecond = 10941.4
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.16429517 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0223s; samplesPerSecond = 11187.2
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.19154443 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0229s; samplesPerSecond = 10924.7
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.12275391 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0244s; samplesPerSecond = 10245.1
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.16801855 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0228s; samplesPerSecond = 10987.6
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.12472571 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0230s; samplesPerSecond = 10877.1
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.19939526 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0229s; samplesPerSecond = 10895.1
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.14222791 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0227s; samplesPerSecond = 10995.3
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.12374048 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0228s; samplesPerSecond = 10962.0
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.16442969 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0224s; samplesPerSecond = 11142.8
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.19837036 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0227s; samplesPerSecond = 11003.0
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.17180200 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0235s; samplesPerSecond = 10638.3
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.13326343 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0229s; samplesPerSecond = 10936.6
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.14289917 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0233s; samplesPerSecond = 10727.8
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.20692944 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0231s; samplesPerSecond = 10827.2
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.19077197 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0231s; samplesPerSecond = 10817.4
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.14746069 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0227s; samplesPerSecond = 11027.8
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.15464526 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0227s; samplesPerSecond = 11007.9
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.13673071 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0229s; samplesPerSecond = 10923.2
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.17348853 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0226s; samplesPerSecond = 11065.4
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.14420581 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0233s; samplesPerSecond = 10725.5
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.13774097 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0228s; samplesPerSecond = 10975.0
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.14177905 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0225s; samplesPerSecond = 11100.8
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.16864648 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0229s; samplesPerSecond = 10928.0
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.18513623 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0228s; samplesPerSecond = 10968.3
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.16393555 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0226s; samplesPerSecond = 11067.8
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.15467676 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0228s; samplesPerSecond = 10969.2
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18951318 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0223s; samplesPerSecond = 11207.2
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.13329639 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0232s; samplesPerSecond = 10793.1
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.14604785 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0229s; samplesPerSecond = 10894.2
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.13938086 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0229s; samplesPerSecond = 10896.6
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.19969873 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0226s; samplesPerSecond = 11040.9
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.12584180 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0228s; samplesPerSecond = 10949.1
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.18373438 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0232s; samplesPerSecond = 10780.0
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15064795 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0234s; samplesPerSecond = 10683.8
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.11991260 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0229s; samplesPerSecond = 10911.3
|
||||
08/16/2016 03:04:19: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.13070557 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0230s; samplesPerSecond = 10857.8
|
||||
08/16/2016 03:04:19: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15755114 * 10000; EvalErrorPrediction = 0.07370000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.918193s
|
||||
08/16/2016 03:04:19: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_cpu/Models/simple.dnn'
|
||||
08/16/2016 03:04:19: CNTKCommandTrainEnd: Simple_Demo_Train
|
||||
|
||||
08/16/2016 03:04:19: Action "train" complete.
|
||||
|
||||
|
||||
08/16/2016 03:04:19: ##############################################################################
|
||||
08/16/2016 03:04:19: # #
|
||||
08/16/2016 03:04:19: # Action "test" #
|
||||
08/16/2016 03:04:19: # #
|
||||
08/16/2016 03:04:19: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
|
@ -657,43 +687,23 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 25 matrices, 2 are shared as 1, and 23 are not shared.
|
||||
|
||||
0000000000000000: {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalErrorPrediction Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
|
||||
00000070343C5200: {[InvStdOfFeatures Value[2]] }
|
||||
00000070343C5340: {[Prior Value[2]] }
|
||||
00000070343C53E0: {[W0 Value[50 x 2]] }
|
||||
00000070343C5520: {[W1 Value[50 x 50]] }
|
||||
00000070343C5980: {[labels Value[2 x *1]] }
|
||||
00000070343C5AC0: {[MeanOfFeatures Value[2]] }
|
||||
000000703442CE50: {[MVNormalizedFeatures Value[2 x *1]] }
|
||||
000000703442CF90: {[W1*H1 Value[50 x 1 x *1]] }
|
||||
000000703442D030: {[HLast Value[2 x 1 x *1]] }
|
||||
000000703442D0D0: {[W0*features Value[50 x *1]] }
|
||||
000000703442D170: {[W1*H1+B1 Value[50 x 1 x *1]] }
|
||||
000000703442D2B0: {[EvalErrorPrediction Value[1]] }
|
||||
000000703442D530: {[CrossEntropyWithSoftmax Value[1]] }
|
||||
000000703442D5D0: {[W2 Value[2 x 50]] }
|
||||
000000703442D670: {[LogOfPrior Value[2]] }
|
||||
000000703442D7B0: {[W0*features+B0 Value[50 x 1 x *1]] }
|
||||
000000703442D850: {[W2*H1 Value[2 x 1 x *1]] }
|
||||
000000703442DAD0: {[H1 Value[50 x 1 x *1]] }
|
||||
000000703442DB70: {[H2 Value[50 x 1 x *1]] }
|
||||
0000007034431EE0: {[features Value[2 x *1]] }
|
||||
00000070344320C0: {[B1 Value[50 x 1]] }
|
||||
0000007034432340: {[B0 Value[50 x 1]] }
|
||||
0000007034432480: {[B2 Value[2 x 1]] }
|
||||
{ PosteriorProb : [2 x 1 x *1]
|
||||
ScaledLogLikelihood : [2 x 1 x *1] }
|
||||
|
||||
05/03/2016 13:12:50: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.05638474 * 603; CrossEntropyWithSoftmax = 0.12474995 * 603; perplexity = 1.13286515
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..603] (first sequence at sample 0), data subset 0 of 1
|
||||
08/16/2016 03:04:19: Minibatch[1-1]: EvalErrorPrediction = 0.05140962 * 603; CrossEntropyWithSoftmax = 0.10734609 * 603
|
||||
08/16/2016 03:04:19: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.05140962 * 603; CrossEntropyWithSoftmax = 0.10734609 * 603; perplexity = 1.11331949
|
||||
|
||||
05/03/2016 13:12:50: Action "test" complete.
|
||||
08/16/2016 03:04:19: Action "test" complete.
|
||||
|
||||
|
||||
05/03/2016 13:12:50: ##############################################################################
|
||||
05/03/2016 13:12:50: # #
|
||||
05/03/2016 13:12:50: # Action "write" #
|
||||
05/03/2016 13:12:50: # #
|
||||
05/03/2016 13:12:50: ##############################################################################
|
||||
08/16/2016 03:04:19: ##############################################################################
|
||||
08/16/2016 03:04:19: # #
|
||||
08/16/2016 03:04:19: # Action "write" #
|
||||
08/16/2016 03:04:19: # #
|
||||
08/16/2016 03:04:19: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
|
@ -751,36 +761,16 @@ Post-processing network complete.
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 25 matrices, 3 are shared as 1, and 22 are not shared.
|
||||
|
||||
0000000000000000: {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [CrossEntropyWithSoftmax Value[1]] [EvalErrorPrediction Gradient[1]] [EvalErrorPrediction Value[1]] [H1 Gradient[50 x 1 x *2]] [H2 Gradient[50 x 1 x *2]] [HLast Gradient[2 x 1 x *2]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *2]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *2]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *2]] [ScaledLogLikelihood Value[2 x 1 x *2]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *2]] [W0*features+B0 Gradient[50 x 1 x *2]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *2]] [W1*H1+B1 Gradient[50 x 1 x *2]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *2]] [features Gradient[2 x *2]] [labels Gradient[2 x *2]] }
|
||||
000000702E3275E0: {[H2 Value[50 x 1 x *2]] }
|
||||
000000702E327680: {[W2*H1 Value[2 x 1 x *2]] }
|
||||
000000702E3277C0: {[LogOfPrior Value[2]] }
|
||||
000000702E327860: {[HLast Value[2 x 1 x *2]] }
|
||||
000000702E327A40: {[W2 Value[2 x 50]] }
|
||||
000000702E327CC0: {[W0*features Value[50 x *2]] }
|
||||
000000702E327D60: {[W0*features+B0 Value[50 x 1 x *2]] }
|
||||
000000702E327E00: {[H1 Value[50 x 1 x *2]] }
|
||||
000000702E327FE0: {[PosteriorProb Value[2 x 1 x *2]] }
|
||||
000000702E328120: {[MVNormalizedFeatures Value[2 x *2]] }
|
||||
000000702E328260: {[W1*H1 Value[50 x 1 x *2]] }
|
||||
000000702E3283A0: {[W1*H1+B1 Value[50 x 1 x *2]] }
|
||||
00000070343C4E40: {[labels Value[2 x *2]] }
|
||||
00000070343C4EE0: {[Prior Value[2]] }
|
||||
00000070343C52A0: {[InvStdOfFeatures Value[2]] }
|
||||
00000070343C53E0: {[W1 Value[50 x 50]] }
|
||||
00000070343C58E0: {[W0 Value[50 x 2]] }
|
||||
00000070343C5980: {[MeanOfFeatures Value[2]] }
|
||||
0000007034431770: {[features Value[2 x *2]] }
|
||||
0000007034431A90: {[B1 Value[50 x 1]] }
|
||||
0000007034431B30: {[B2 Value[2 x 1]] }
|
||||
0000007034431C70: {[B0 Value[50 x 1]] }
|
||||
{ CrossEntropyWithSoftmax : [1]
|
||||
EvalErrorPrediction : [1]
|
||||
ScaledLogLikelihood : [2 x 1 x *2] }
|
||||
|
||||
Minibatch[0]: ActualMBSize = 603
|
||||
Written to E:\cygwin64\tmp\cntk-test-20160503141245.787579\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_cpu/SimpleOutput*
|
||||
Written to C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_cpu/SimpleOutput*
|
||||
Total Samples Evaluated = 603
|
||||
|
||||
05/03/2016 13:12:50: Action "write" complete.
|
||||
08/16/2016 03:04:19: Action "write" complete.
|
||||
|
||||
05/03/2016 13:12:50: __COMPLETED__
|
||||
08/16/2016 03:04:19: __COMPLETED__
|
||||
|
|
@ -1,46 +1,61 @@
|
|||
=== Running /cygdrive/c/src/cntk_github/x64/release/cntk.exe configFile=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config/Simple.cntk currentDirectory=C:\src\cntk_github\Examples\Other\Simple2d\Data RunDir=E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu DataDir=C:\src\cntk_github\Examples\Other\Simple2d\Data ConfigDir=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config OutputDir=E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu DeviceId=0 timestamping=true Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 268381192 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config/Simple.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_gpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_gpu DeviceId=0 timestamping=true Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 3 2016 12:19:59
|
||||
Last modified date: Thu Apr 7 11:05:47 2016
|
||||
Built time: Aug 16 2016 02:54:53
|
||||
Last modified date: Fri Aug 12 05:31:21 2016
|
||||
Build type: Release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: no
|
||||
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0
|
||||
CUB_PATH: E:\lib\cub-1.4.1
|
||||
CUDNN_PATH: E:\lib\cuDNN_v4
|
||||
Build Branch: alrezni/examples_text
|
||||
Build SHA1: d5e576046e2fa850c4296da155f15c2b08b7927a
|
||||
Built by alrezni on DIFFENG
|
||||
Build Path: C:\src\cntk_github\Source\CNTK\
|
||||
Math lib: mkl
|
||||
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
CUB_PATH: c:\src\cub-1.4.1
|
||||
CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by svcphil on Philly-Pool3
|
||||
Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
05/03/2016 13:01:58: -------------------------------------------------------------------
|
||||
05/03/2016 13:01:58: Build info:
|
||||
Changed current directory to C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
08/16/2016 03:04:23: -------------------------------------------------------------------
|
||||
08/16/2016 03:04:23: Build info:
|
||||
|
||||
05/03/2016 13:01:58: Built time: May 3 2016 12:19:59
|
||||
05/03/2016 13:01:58: Last modified date: Thu Apr 7 11:05:47 2016
|
||||
05/03/2016 13:01:58: Build type: Release
|
||||
05/03/2016 13:01:58: Build target: GPU
|
||||
05/03/2016 13:01:58: With 1bit-SGD: no
|
||||
05/03/2016 13:01:58: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0
|
||||
05/03/2016 13:01:58: CUB_PATH: E:\lib\cub-1.4.1
|
||||
05/03/2016 13:01:58: CUDNN_PATH: E:\lib\cuDNN_v4
|
||||
05/03/2016 13:01:58: Build Branch: alrezni/examples_text
|
||||
05/03/2016 13:01:58: Build SHA1: d5e576046e2fa850c4296da155f15c2b08b7927a
|
||||
05/03/2016 13:01:58: Built by alrezni on DIFFENG
|
||||
05/03/2016 13:01:58: Build Path: C:\src\cntk_github\Source\CNTK\
|
||||
05/03/2016 13:01:58: -------------------------------------------------------------------
|
||||
08/16/2016 03:04:23: Built time: Aug 16 2016 02:54:53
|
||||
08/16/2016 03:04:23: Last modified date: Fri Aug 12 05:31:21 2016
|
||||
08/16/2016 03:04:23: Build type: Release
|
||||
08/16/2016 03:04:23: Build target: GPU
|
||||
08/16/2016 03:04:23: With 1bit-SGD: no
|
||||
08/16/2016 03:04:23: Math lib: mkl
|
||||
08/16/2016 03:04:23: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
08/16/2016 03:04:23: CUB_PATH: c:\src\cub-1.4.1
|
||||
08/16/2016 03:04:23: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
08/16/2016 03:04:23: Build Branch: HEAD
|
||||
08/16/2016 03:04:23: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 03:04:23: Built by svcphil on Philly-Pool3
|
||||
08/16/2016 03:04:23: Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
08/16/2016 03:04:23: -------------------------------------------------------------------
|
||||
08/16/2016 03:04:26: -------------------------------------------------------------------
|
||||
08/16/2016 03:04:26: GPU info:
|
||||
|
||||
05/03/2016 13:01:58: Running on DIFFENG at 2016/05/03 13:01:58
|
||||
05/03/2016 13:01:58: Command line:
|
||||
C:\src\cntk_github\x64\release\cntk.exe configFile=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config/Simple.cntk currentDirectory=C:\src\cntk_github\Examples\Other\Simple2d\Data RunDir=E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu DataDir=C:\src\cntk_github\Examples\Other\Simple2d\Data ConfigDir=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config OutputDir=E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu DeviceId=0 timestamping=true Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
08/16/2016 03:04:26: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:04:26: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:04:26: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:04:26: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:04:26: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 03:04:26: Running on DPHAIM-24 at 2016/08/16 03:04:26
|
||||
08/16/2016 03:04:26: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config/Simple.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_gpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_gpu DeviceId=0 timestamping=true Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
|
||||
|
||||
05/03/2016 13:01:58: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 13:01:58: RootDir = ".."
|
||||
08/16/2016 03:04:26: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:04:26: RootDir = ".."
|
||||
ConfigDir = "$RootDir$/Config"
|
||||
DataDir = "$RootDir$/Data"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
|
|
@ -127,28 +142,28 @@ labelMappingFile = "$DataDir$/SimpleMapping.txt"
|
|||
sequenceEpilogue = "\t// %s\n"
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
RunDir=E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu
|
||||
DataDir=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
ConfigDir=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config
|
||||
OutputDir=E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu
|
||||
currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_gpu
|
||||
DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
05/03/2016 13:01:58: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:04:26: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 13:01:58: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 13:01:58: RootDir = ".."
|
||||
08/16/2016 03:04:26: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:04:26: RootDir = ".."
|
||||
ConfigDir = "../Config"
|
||||
DataDir = "../Data"
|
||||
OutputDir = "../Output"
|
||||
ModelDir = "E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu/Models"
|
||||
ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_gpu/Models"
|
||||
deviceId = -1
|
||||
command = Simple_Demo_Train:Simple_Demo_Test:Simple_Demo_Output
|
||||
precision = "float"
|
||||
traceLevel = 1
|
||||
modelPath = "E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu/Models/simple.dnn"
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_gpu/Models/simple.dnn"
|
||||
outputNodeNames = ScaledLogLikelihood
|
||||
Simple_Demo_Train = [
|
||||
action = "train"
|
||||
|
|
@ -172,7 +187,7 @@ Simple_Demo_Train = [
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTrain_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTrain_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -189,7 +204,7 @@ Simple_Demo_Test = [
|
|||
action = "test"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -206,7 +221,7 @@ Simple_Demo_Output=[
|
|||
action = "write"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -219,42 +234,42 @@ dim = 2
|
|||
]
|
||||
]
|
||||
outputNodeNames = PosteriorProb : labels
|
||||
outputPath = "E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu/SimpleOutput"
|
||||
outputPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_gpu/SimpleOutput"
|
||||
format = [
|
||||
type = "category"
|
||||
labelMappingFile = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleMapping.txt"
|
||||
labelMappingFile = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleMapping.txt"
|
||||
sequenceEpilogue = "\t// %s\n"
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
RunDir=E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu
|
||||
DataDir=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
ConfigDir=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config
|
||||
OutputDir=E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu
|
||||
currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_gpu
|
||||
DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Simple_Demo_Train=[SGD=[maxEpochs=3]]
|
||||
|
||||
05/03/2016 13:01:58: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:04:26: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 13:01:58: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:04:26: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: Simple.cntk:command=Simple_Demo_Train:Simple_Demo_Test:Simple_Demo_Output
|
||||
configparameters: Simple.cntk:ConfigDir=C:\src\cntk_github\Tests\EndToEndTests\CNTKTextFormatReader\Examples\Other\Simple2d\Config
|
||||
configparameters: Simple.cntk:currentDirectory=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
configparameters: Simple.cntk:DataDir=C:\src\cntk_github\Examples\Other\Simple2d\Data
|
||||
configparameters: Simple.cntk:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Config
|
||||
configparameters: Simple.cntk:currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
configparameters: Simple.cntk:DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data
|
||||
configparameters: Simple.cntk:deviceId=0
|
||||
configparameters: Simple.cntk:ModelDir=E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu/Models
|
||||
configparameters: Simple.cntk:modelPath=E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu/Models/simple.dnn
|
||||
configparameters: Simple.cntk:OutputDir=E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu
|
||||
configparameters: Simple.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_gpu/Models
|
||||
configparameters: Simple.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_gpu/Models/simple.dnn
|
||||
configparameters: Simple.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_gpu
|
||||
configparameters: Simple.cntk:outputNodeNames=ScaledLogLikelihood
|
||||
configparameters: Simple.cntk:precision=float
|
||||
configparameters: Simple.cntk:RootDir=..
|
||||
configparameters: Simple.cntk:RunDir=E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu
|
||||
configparameters: Simple.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_gpu
|
||||
configparameters: Simple.cntk:Simple_Demo_Output=[
|
||||
action = "write"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -267,10 +282,10 @@ dim = 2
|
|||
]
|
||||
]
|
||||
outputNodeNames = PosteriorProb : labels
|
||||
outputPath = "E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu/SimpleOutput"
|
||||
outputPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_gpu/SimpleOutput"
|
||||
format = [
|
||||
type = "category"
|
||||
labelMappingFile = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleMapping.txt"
|
||||
labelMappingFile = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleMapping.txt"
|
||||
sequenceEpilogue = "\t// %s\n"
|
||||
]
|
||||
]
|
||||
|
|
@ -279,7 +294,7 @@ configparameters: Simple.cntk:Simple_Demo_Test=[
|
|||
action = "test"
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTest_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -315,7 +330,7 @@ configparameters: Simple.cntk:Simple_Demo_Train=[
|
|||
]
|
||||
reader = [
|
||||
readerType = "CNTKTextFormatReader"
|
||||
file = "C:\src\cntk_github\Examples\Other\Simple2d\Data/SimpleDataTrain_cntk_text.txt"
|
||||
file = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Other\Simple2d\Data/SimpleDataTrain_cntk_text.txt"
|
||||
input = [
|
||||
features = [
|
||||
dim = 2
|
||||
|
|
@ -331,24 +346,36 @@ dim = 2
|
|||
|
||||
configparameters: Simple.cntk:timestamping=true
|
||||
configparameters: Simple.cntk:traceLevel=1
|
||||
05/03/2016 13:01:58: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/03/2016 13:01:58: Commands: Simple_Demo_Train Simple_Demo_Test Simple_Demo_Output
|
||||
05/03/2016 13:01:58: Precision = "float"
|
||||
05/03/2016 13:01:58: CNTKModelPath: E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu/Models/simple.dnn
|
||||
05/03/2016 13:01:58: CNTKCommandTrainInfo: Simple_Demo_Train : 3
|
||||
05/03/2016 13:01:58: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
08/16/2016 03:04:26: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:04:26: Commands: Simple_Demo_Train Simple_Demo_Test Simple_Demo_Output
|
||||
08/16/2016 03:04:26: Precision = "float"
|
||||
08/16/2016 03:04:26: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_gpu/Models/simple.dnn
|
||||
08/16/2016 03:04:26: CNTKCommandTrainInfo: Simple_Demo_Train : 3
|
||||
08/16/2016 03:04:26: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
|
||||
05/03/2016 13:01:58: ##############################################################################
|
||||
05/03/2016 13:01:58: # #
|
||||
05/03/2016 13:01:58: # Action "train" #
|
||||
05/03/2016 13:01:58: # #
|
||||
05/03/2016 13:01:58: ##############################################################################
|
||||
08/16/2016 03:04:26: ##############################################################################
|
||||
08/16/2016 03:04:26: # #
|
||||
08/16/2016 03:04:26: # Action "train" #
|
||||
08/16/2016 03:04:26: # #
|
||||
08/16/2016 03:04:26: ##############################################################################
|
||||
|
||||
05/03/2016 13:01:58: CNTKCommandTrainBegin: Simple_Demo_Train
|
||||
08/16/2016 03:04:26: CNTKCommandTrainBegin: Simple_Demo_Train
|
||||
SimpleNetworkBuilder Using GPU 0
|
||||
|
||||
05/03/2016 13:01:58: Creating virgin network.
|
||||
08/16/2016 03:04:26: Creating virgin network.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[50 x 2] <- 0.000000.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[50 x 2] <- uniform(seed=1, range=0.050000*1.000000, onCPU=false).
|
||||
Microsoft::MSR::CNTK::GPUMatrix<ElemType>::SetUniformRandomValue (GPU): creating curand object with seed 1, sizeof(ElemType)==4
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[50 x 50] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[50 x 50] <- uniform(seed=2, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[2 x 50] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[2 x 50] <- uniform(seed=3, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[2 x 1] <- 0.000000.
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[2 x 1] <- 0.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -400,207 +427,210 @@ Validating network, final pass.
|
|||
|
||||
Post-processing network complete.
|
||||
|
||||
05/03/2016 13:01:59: Created model with 25 nodes on GPU 0.
|
||||
08/16/2016 03:04:26: Created model with 25 nodes on GPU 0.
|
||||
|
||||
05/03/2016 13:01:59: Training criterion node(s):
|
||||
05/03/2016 13:01:59: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
|
||||
08/16/2016 03:04:26: Training criterion node(s):
|
||||
08/16/2016 03:04:26: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
|
||||
|
||||
05/03/2016 13:01:59: Evaluation criterion node(s):
|
||||
|
||||
05/03/2016 13:01:59: EvalErrorPrediction = ErrorPrediction
|
||||
08/16/2016 03:04:26: Evaluation criterion node(s):
|
||||
08/16/2016 03:04:26: EvalErrorPrediction = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 40 matrices, 19 are shared as 8, and 21 are not shared.
|
||||
|
||||
0000000000000000: {[EvalErrorPrediction Gradient[1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *]] [PosteriorProb Value[2 x 1 x *]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *]] [features Gradient[2 x *]] [labels Gradient[2 x *]] }
|
||||
000000501A590FF0: {[W2 Value[2 x 50]] }
|
||||
000000501A591090: {[W0 Value[50 x 2]] }
|
||||
000000501A5919F0: {[B1 Value[50 x 1]] }
|
||||
000000501A591A90: {[InvStdOfFeatures Value[2]] }
|
||||
000000501A591E50: {[B0 Value[50 x 1]] }
|
||||
000000501A591EF0: {[W1 Value[50 x 50]] }
|
||||
000000501A592350: {[B2 Value[2 x 1]] }
|
||||
000000501A592530: {[labels Value[2 x *]] }
|
||||
000000501A592670: {[Prior Value[2]] }
|
||||
000000501A5A1180: {[ScaledLogLikelihood Value[2 x 1 x *]] }
|
||||
000000501A5A1220: {[B0 Gradient[50 x 1]] [H1 Gradient[50 x 1 x *]] [W1*H1+B1 Gradient[50 x 1 x *]] [W2*H1 Value[2 x 1 x *]] }
|
||||
000000501A5A17C0: {[W0 Gradient[50 x 2]] [W0*features+B0 Value[50 x 1 x *]] }
|
||||
000000501A5A1900: {[EvalErrorPrediction Value[1]] }
|
||||
000000501A5A19A0: {[W0*features Value[50 x *]] }
|
||||
000000501A5A1A40: {[W2*H1 Gradient[2 x 1 x *]] }
|
||||
000000501A5A1F40: {[MVNormalizedFeatures Value[2 x *]] }
|
||||
000000501A5A2080: {[H1 Value[50 x 1 x *]] [W0*features Gradient[50 x *]] }
|
||||
000000501A5A2120: {[W1 Gradient[50 x 50]] [W1*H1+B1 Value[50 x 1 x *]] }
|
||||
000000501A5A21C0: {[W0*features+B0 Gradient[50 x 1 x *]] [W1*H1 Value[50 x 1 x *]] }
|
||||
000000501A5A2260: {[LogOfPrior Value[2]] }
|
||||
000000501A5A2300: {[HLast Value[2 x 1 x *]] [W2 Gradient[2 x 50]] }
|
||||
000000501A5A2800: {[H2 Value[50 x 1 x *]] [W1*H1 Gradient[50 x 1 x *]] }
|
||||
000000501A5A2940: {[CrossEntropyWithSoftmax Gradient[1]] }
|
||||
000000501A5A2A80: {[B2 Gradient[2 x 1]] }
|
||||
000000501A5A2B20: {[CrossEntropyWithSoftmax Value[1]] }
|
||||
000000501A5A2C60: {[B1 Gradient[50 x 1]] [H2 Gradient[50 x 1 x *]] [HLast Gradient[2 x 1 x *]] }
|
||||
000000507C5F0E90: {[features Value[2 x *]] }
|
||||
000000507F44EB10: {[MeanOfFeatures Value[2]] }
|
||||
{ W1 : [50 x 50] (gradient)
|
||||
W1*H1+B1 : [50 x 1 x *] }
|
||||
{ W0*features+B0 : [50 x 1 x *] (gradient)
|
||||
W1*H1 : [50 x 1 x *] }
|
||||
{ B0 : [50 x 1] (gradient)
|
||||
H1 : [50 x 1 x *] (gradient)
|
||||
W1*H1+B1 : [50 x 1 x *] (gradient)
|
||||
W2*H1 : [2 x 1 x *] }
|
||||
{ H2 : [50 x 1 x *]
|
||||
W1*H1 : [50 x 1 x *] (gradient) }
|
||||
{ B1 : [50 x 1] (gradient)
|
||||
H2 : [50 x 1 x *] (gradient)
|
||||
HLast : [2 x 1 x *] (gradient) }
|
||||
{ H1 : [50 x 1 x *]
|
||||
W0*features : [50 x *] (gradient) }
|
||||
{ W0 : [50 x 2] (gradient)
|
||||
W0*features+B0 : [50 x 1 x *] }
|
||||
{ HLast : [2 x 1 x *]
|
||||
W2 : [2 x 50] (gradient) }
|
||||
|
||||
|
||||
05/03/2016 13:01:59: Precomputing --> 3 PreCompute nodes found.
|
||||
08/16/2016 03:04:26: Training 2802 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:
|
||||
|
||||
05/03/2016 13:01:59: MeanOfFeatures = Mean()
|
||||
05/03/2016 13:01:59: InvStdOfFeatures = InvStdDev()
|
||||
05/03/2016 13:01:59: Prior = Mean()
|
||||
|
||||
05/03/2016 13:01:59: Precomputing --> Completed.
|
||||
08/16/2016 03:04:26: Node 'B0' (LearnableParameter operation) : [50 x 1]
|
||||
08/16/2016 03:04:26: Node 'B1' (LearnableParameter operation) : [50 x 1]
|
||||
08/16/2016 03:04:26: Node 'B2' (LearnableParameter operation) : [2 x 1]
|
||||
08/16/2016 03:04:26: Node 'W0' (LearnableParameter operation) : [50 x 2]
|
||||
08/16/2016 03:04:26: Node 'W1' (LearnableParameter operation) : [50 x 50]
|
||||
08/16/2016 03:04:26: Node 'W2' (LearnableParameter operation) : [2 x 50]
|
||||
|
||||
|
||||
05/03/2016 13:01:59: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
08/16/2016 03:04:26: Precomputing --> 3 PreCompute nodes found.
|
||||
|
||||
05/03/2016 13:01:59: Starting minibatch loop.
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70650452 * 250; EvalErrorPrediction = 0.55200000 * 250; time = 0.0123s; samplesPerSecond = 20247.8
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.69701831 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0112s; samplesPerSecond = 22393.4
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.71089587 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0126s; samplesPerSecond = 19907.6
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.72980273 * 250; EvalErrorPrediction = 0.56000000 * 250; time = 0.0113s; samplesPerSecond = 22042.0
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.70902783 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0131s; samplesPerSecond = 19088.3
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.72657300 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0138s; samplesPerSecond = 18059.7
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.69319678 * 250; EvalErrorPrediction = 0.43200000 * 250; time = 0.0148s; samplesPerSecond = 16917.0
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.73563477 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0164s; samplesPerSecond = 15236.5
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.71463281 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0123s; samplesPerSecond = 20321.9
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.75213428 * 250; EvalErrorPrediction = 0.47200000 * 250; time = 0.0167s; samplesPerSecond = 14944.1
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.75931445 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0131s; samplesPerSecond = 19105.8
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.73075293 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0132s; samplesPerSecond = 18886.5
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.76701953 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0128s; samplesPerSecond = 19574.1
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.70451270 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0128s; samplesPerSecond = 19467.4
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.70539941 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0143s; samplesPerSecond = 17444.7
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.72700293 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0123s; samplesPerSecond = 20391.5
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.70096191 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0143s; samplesPerSecond = 17465.4
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.69437305 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0117s; samplesPerSecond = 21367.5
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.69161621 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0137s; samplesPerSecond = 18200.3
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.73388281 * 250; EvalErrorPrediction = 0.55200000 * 250; time = 0.0115s; samplesPerSecond = 21782.7
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.72255664 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0127s; samplesPerSecond = 19745.7
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.70414551 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0131s; samplesPerSecond = 19017.2
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.69976758 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0137s; samplesPerSecond = 18191.1
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.72419141 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0143s; samplesPerSecond = 17444.7
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.69943945 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0109s; samplesPerSecond = 22891.7
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.69206445 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0133s; samplesPerSecond = 18739.2
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.68771680 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0130s; samplesPerSecond = 19291.6
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.69878516 * 250; EvalErrorPrediction = 0.44000000 * 250; time = 0.0130s; samplesPerSecond = 19230.8
|
||||
05/03/2016 13:01:59: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.71889844 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0118s; samplesPerSecond = 21168.5
|
||||
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.70086523 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0128s; samplesPerSecond = 19577.1
|
||||
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.70878320 * 250; EvalErrorPrediction = 0.53200000 * 250; time = 0.0129s; samplesPerSecond = 19432.6
|
||||
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.70674414 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0126s; samplesPerSecond = 19767.5
|
||||
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.69707422 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0121s; samplesPerSecond = 20736.6
|
||||
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.68588281 * 250; EvalErrorPrediction = 0.40800000 * 250; time = 0.0124s; samplesPerSecond = 20109.4
|
||||
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.67734766 * 250; EvalErrorPrediction = 0.45600000 * 250; time = 0.0127s; samplesPerSecond = 19727.0
|
||||
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.67958008 * 250; EvalErrorPrediction = 0.48000000 * 250; time = 0.0127s; samplesPerSecond = 19615.5
|
||||
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.66424805 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0117s; samplesPerSecond = 21292.9
|
||||
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.62412500 * 250; EvalErrorPrediction = 0.20400000 * 250; time = 0.0127s; samplesPerSecond = 19624.8
|
||||
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.58007422 * 250; EvalErrorPrediction = 0.16000000 * 250; time = 0.0130s; samplesPerSecond = 19157.1
|
||||
05/03/2016 13:02:00: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.52764648 * 250; EvalErrorPrediction = 0.19200000 * 250; time = 0.0143s; samplesPerSecond = 17521.7
|
||||
05/03/2016 13:02:00: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.69975483 * 10000; EvalErrorPrediction = 0.46850000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=0.526194s
|
||||
05/03/2016 13:02:00: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu/Models/simple.dnn.1'
|
||||
08/16/2016 03:04:26: MeanOfFeatures = Mean()
|
||||
08/16/2016 03:04:26: InvStdOfFeatures = InvStdDev()
|
||||
08/16/2016 03:04:26: Prior = Mean()
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
05/03/2016 13:02:00: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
|
||||
05/03/2016 13:02:00: Starting minibatch loop.
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.44832977 * 250; EvalErrorPrediction = 0.15200000 * 250; time = 0.0124s; samplesPerSecond = 20205.3
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.40085291 * 250; EvalErrorPrediction = 0.12400000 * 250; time = 0.0142s; samplesPerSecond = 17631.7
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.33487201 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0129s; samplesPerSecond = 19405.4
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.29081885 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0125s; samplesPerSecond = 20016.0
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.26279236 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0118s; samplesPerSecond = 21188.2
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.25220630 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0138s; samplesPerSecond = 18158.0
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.20988293 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0129s; samplesPerSecond = 19447.7
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.21577441 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0148s; samplesPerSecond = 16846.4
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.16622900 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0157s; samplesPerSecond = 15967.3
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.17637866 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0144s; samplesPerSecond = 17315.4
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.22185278 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0123s; samplesPerSecond = 20366.6
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.17055811 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0151s; samplesPerSecond = 16564.0
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.16481055 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0140s; samplesPerSecond = 17910.9
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.13871704 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0156s; samplesPerSecond = 16005.1
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.16922363 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0143s; samplesPerSecond = 17454.4
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.15403345 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0135s; samplesPerSecond = 18485.7
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.22255859 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0108s; samplesPerSecond = 23079.8
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.18146851 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0133s; samplesPerSecond = 18843.7
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.15611523 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0131s; samplesPerSecond = 19081.1
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.17320215 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0137s; samplesPerSecond = 18192.4
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.15727930 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0117s; samplesPerSecond = 21404.1
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.16195410 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0119s; samplesPerSecond = 21088.1
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.16121338 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0128s; samplesPerSecond = 19546.5
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.15427100 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0125s; samplesPerSecond = 20011.2
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.14844775 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0141s; samplesPerSecond = 17743.1
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.15055713 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0108s; samplesPerSecond = 23067.0
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.15467627 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0132s; samplesPerSecond = 18965.3
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.17615869 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0140s; samplesPerSecond = 17872.5
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.22356104 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0121s; samplesPerSecond = 20650.9
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16514209 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0109s; samplesPerSecond = 22946.3
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.17355859 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0129s; samplesPerSecond = 19372.3
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.13117578 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0138s; samplesPerSecond = 18151.5
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.13956104 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0121s; samplesPerSecond = 20743.4
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.18397363 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0105s; samplesPerSecond = 23741.7
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.15222656 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0126s; samplesPerSecond = 19909.2
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.18856396 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0145s; samplesPerSecond = 17207.0
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.17513330 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0130s; samplesPerSecond = 19199.8
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15008252 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0108s; samplesPerSecond = 23043.6
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.12125342 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0127s; samplesPerSecond = 19668.0
|
||||
05/03/2016 13:02:00: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15408496 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0141s; samplesPerSecond = 17788.5
|
||||
05/03/2016 13:02:00: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.19333879 * 10000; EvalErrorPrediction = 0.07700000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.525411s
|
||||
05/03/2016 13:02:00: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu/Models/simple.dnn.2'
|
||||
|
||||
05/03/2016 13:02:00: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
|
||||
05/03/2016 13:02:00: Starting minibatch loop.
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.10746781 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0126s; samplesPerSecond = 19806.7
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.17648278 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0122s; samplesPerSecond = 20429.8
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.14106094 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0126s; samplesPerSecond = 19838.1
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.16348077 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0127s; samplesPerSecond = 19745.7
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.11767151 * 250; EvalErrorPrediction = 0.04000000 * 250; time = 0.0110s; samplesPerSecond = 22787.3
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.16217944 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0137s; samplesPerSecond = 18292.2
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.16171204 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0147s; samplesPerSecond = 16977.9
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.19844067 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0130s; samplesPerSecond = 19285.7
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.19984509 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0116s; samplesPerSecond = 21585.2
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.13727051 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0133s; samplesPerSecond = 18839.5
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.20126648 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0150s; samplesPerSecond = 16709.0
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.17913672 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0138s; samplesPerSecond = 18066.2
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.15983582 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0138s; samplesPerSecond = 18131.7
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.16260010 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0126s; samplesPerSecond = 19798.8
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.19813428 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0122s; samplesPerSecond = 20453.2
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.10295117 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0124s; samplesPerSecond = 20091.6
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.17117065 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0127s; samplesPerSecond = 19762.8
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.16661938 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0127s; samplesPerSecond = 19620.2
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.12718042 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0108s; samplesPerSecond = 23156.7
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.11923853 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0139s; samplesPerSecond = 17989.5
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.12890332 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0129s; samplesPerSecond = 19340.9
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.18205469 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0124s; samplesPerSecond = 20182.4
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.13154199 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0111s; samplesPerSecond = 22599.9
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.19668359 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0139s; samplesPerSecond = 17922.4
|
||||
05/03/2016 13:02:00: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.15817578 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0126s; samplesPerSecond = 19915.6
|
||||
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.11871240 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0136s; samplesPerSecond = 18378.3
|
||||
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.13730908 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0107s; samplesPerSecond = 23384.2
|
||||
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.20024854 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0134s; samplesPerSecond = 18719.6
|
||||
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.18850244 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0131s; samplesPerSecond = 19151.2
|
||||
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.16640479 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0108s; samplesPerSecond = 23086.2
|
||||
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.11872168 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0107s; samplesPerSecond = 23347.0
|
||||
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.16090430 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0127s; samplesPerSecond = 19730.1
|
||||
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.16162939 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0137s; samplesPerSecond = 18205.7
|
||||
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.12408594 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0109s; samplesPerSecond = 22839.4
|
||||
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13544434 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0126s; samplesPerSecond = 19893.4
|
||||
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.20890771 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0129s; samplesPerSecond = 19366.3
|
||||
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.16674365 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0146s; samplesPerSecond = 17116.3
|
||||
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15033398 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0131s; samplesPerSecond = 19152.7
|
||||
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.16547705 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0120s; samplesPerSecond = 20752.1
|
||||
05/03/2016 13:02:01: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.16792480 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0129s; samplesPerSecond = 19450.7
|
||||
05/03/2016 13:02:01: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15806136 * 10000; EvalErrorPrediction = 0.07470000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.511151s
|
||||
05/03/2016 13:02:01: SGD: Saving checkpoint model 'E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu/Models/simple.dnn'
|
||||
05/03/2016 13:02:01: CNTKCommandTrainEnd: Simple_Demo_Train
|
||||
|
||||
05/03/2016 13:02:01: Action "train" complete.
|
||||
08/16/2016 03:04:27: Precomputing --> Completed.
|
||||
|
||||
|
||||
05/03/2016 13:02:01: ##############################################################################
|
||||
05/03/2016 13:02:01: # #
|
||||
05/03/2016 13:02:01: # Action "test" #
|
||||
05/03/2016 13:02:01: # #
|
||||
05/03/2016 13:02:01: ##############################################################################
|
||||
08/16/2016 03:04:27: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:04:27: Starting minibatch loop.
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70124231 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0194s; samplesPerSecond = 12887.9
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.76372424 * 250; EvalErrorPrediction = 0.46400000 * 250; time = 0.0179s; samplesPerSecond = 13952.5
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.72703027 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0181s; samplesPerSecond = 13829.0
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.73895923 * 250; EvalErrorPrediction = 0.56800000 * 250; time = 0.0178s; samplesPerSecond = 14035.5
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.70621924 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0178s; samplesPerSecond = 14078.2
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.74767041 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0177s; samplesPerSecond = 14152.3
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.75094434 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0181s; samplesPerSecond = 13803.8
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.78058936 * 250; EvalErrorPrediction = 0.48400000 * 250; time = 0.0176s; samplesPerSecond = 14213.4
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.70407129 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0180s; samplesPerSecond = 13910.5
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.69555762 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0178s; samplesPerSecond = 14074.2
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.70626123 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0178s; samplesPerSecond = 14061.5
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.74540430 * 250; EvalErrorPrediction = 0.50800000 * 250; time = 0.0178s; samplesPerSecond = 14030.8
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.70824414 * 250; EvalErrorPrediction = 0.47600000 * 250; time = 0.0179s; samplesPerSecond = 14003.2
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.69895020 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0179s; samplesPerSecond = 13995.4
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.70353223 * 250; EvalErrorPrediction = 0.52400000 * 250; time = 0.0176s; samplesPerSecond = 14198.1
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.69346387 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0177s; samplesPerSecond = 14153.9
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.74449902 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0177s; samplesPerSecond = 14157.1
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.73767969 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0176s; samplesPerSecond = 14175.6
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.71876855 * 250; EvalErrorPrediction = 0.48400000 * 250; time = 0.0179s; samplesPerSecond = 13987.6
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.71509473 * 250; EvalErrorPrediction = 0.50400000 * 250; time = 0.0180s; samplesPerSecond = 13914.4
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.69956152 * 250; EvalErrorPrediction = 0.52000000 * 250; time = 0.0179s; samplesPerSecond = 13953.2
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.69785937 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0179s; samplesPerSecond = 13960.2
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.70736035 * 250; EvalErrorPrediction = 0.54400000 * 250; time = 0.0177s; samplesPerSecond = 14094.8
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.69820508 * 250; EvalErrorPrediction = 0.56800000 * 250; time = 0.0176s; samplesPerSecond = 14205.4
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.69537109 * 250; EvalErrorPrediction = 0.49600000 * 250; time = 0.0178s; samplesPerSecond = 14067.1
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.69347266 * 250; EvalErrorPrediction = 0.51200000 * 250; time = 0.0179s; samplesPerSecond = 13982.1
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.70801172 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0178s; samplesPerSecond = 14023.7
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.69131641 * 250; EvalErrorPrediction = 0.46800000 * 250; time = 0.0177s; samplesPerSecond = 14152.3
|
||||
08/16/2016 03:04:27: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.70370312 * 250; EvalErrorPrediction = 0.52800000 * 250; time = 0.0178s; samplesPerSecond = 14023.7
|
||||
08/16/2016 03:04:28: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.71200195 * 250; EvalErrorPrediction = 0.43600000 * 250; time = 0.0177s; samplesPerSecond = 14133.1
|
||||
08/16/2016 03:04:28: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.69506836 * 250; EvalErrorPrediction = 0.45200000 * 250; time = 0.0178s; samplesPerSecond = 14056.0
|
||||
08/16/2016 03:04:28: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.69935352 * 250; EvalErrorPrediction = 0.51600000 * 250; time = 0.0179s; samplesPerSecond = 13976.6
|
||||
08/16/2016 03:04:28: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.69887109 * 250; EvalErrorPrediction = 0.50000000 * 250; time = 0.0178s; samplesPerSecond = 14018.2
|
||||
08/16/2016 03:04:28: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.69604492 * 250; EvalErrorPrediction = 0.49200000 * 250; time = 0.0180s; samplesPerSecond = 13874.2
|
||||
08/16/2016 03:04:28: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.69011719 * 250; EvalErrorPrediction = 0.48800000 * 250; time = 0.0181s; samplesPerSecond = 13820.6
|
||||
08/16/2016 03:04:28: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.68419531 * 250; EvalErrorPrediction = 0.46000000 * 250; time = 0.0181s; samplesPerSecond = 13831.3
|
||||
08/16/2016 03:04:28: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.67551367 * 250; EvalErrorPrediction = 0.32400000 * 250; time = 0.0177s; samplesPerSecond = 14140.3
|
||||
08/16/2016 03:04:28: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.67028516 * 250; EvalErrorPrediction = 0.40000000 * 250; time = 0.0180s; samplesPerSecond = 13868.1
|
||||
08/16/2016 03:04:28: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.65152734 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0179s; samplesPerSecond = 13937.7
|
||||
08/16/2016 03:04:28: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.63594727 * 250; EvalErrorPrediction = 0.22000000 * 250; time = 0.0178s; samplesPerSecond = 14028.4
|
||||
08/16/2016 03:04:28: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.70729233 * 10000; EvalErrorPrediction = 0.47740000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=0.717672s
|
||||
08/16/2016 03:04:28: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_gpu/Models/simple.dnn.1'
|
||||
|
||||
08/16/2016 03:04:28: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [10000..20000] (first sequence at sample 10000), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:04:28: Starting minibatch loop.
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.61492108 * 250; EvalErrorPrediction = 0.26800000 * 250; time = 0.0183s; samplesPerSecond = 13687.4
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.59171271 * 250; EvalErrorPrediction = 0.28400000 * 250; time = 0.0180s; samplesPerSecond = 13905.9
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.53591638 * 250; EvalErrorPrediction = 0.20000000 * 250; time = 0.0178s; samplesPerSecond = 14045.7
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.51872742 * 250; EvalErrorPrediction = 0.14000000 * 250; time = 0.0181s; samplesPerSecond = 13821.3
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.48384375 * 250; EvalErrorPrediction = 0.12400000 * 250; time = 0.0177s; samplesPerSecond = 14094.0
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.43163501 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0181s; samplesPerSecond = 13790.8
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.38970386 * 250; EvalErrorPrediction = 0.12400000 * 250; time = 0.0180s; samplesPerSecond = 13915.9
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.33681616 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0180s; samplesPerSecond = 13862.7
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.31352393 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0178s; samplesPerSecond = 14010.3
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.26829492 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0179s; samplesPerSecond = 13966.5
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.24240820 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0177s; samplesPerSecond = 14094.0
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.21015820 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0179s; samplesPerSecond = 13976.6
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.22358789 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0177s; samplesPerSecond = 14153.1
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.20496631 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0181s; samplesPerSecond = 13776.4
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.20070508 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0175s; samplesPerSecond = 14307.0
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.19224707 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0180s; samplesPerSecond = 13886.6
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.19326563 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0176s; samplesPerSecond = 14189.2
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.21712451 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0177s; samplesPerSecond = 14109.1
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.17562354 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0177s; samplesPerSecond = 14125.9
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.18186035 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0177s; samplesPerSecond = 14111.5
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.14065234 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0179s; samplesPerSecond = 13957.9
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.17710254 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0177s; samplesPerSecond = 14107.6
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.13001953 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0176s; samplesPerSecond = 14178.0
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.21622949 * 250; EvalErrorPrediction = 0.10000000 * 250; time = 0.0179s; samplesPerSecond = 13949.3
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.21902246 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0182s; samplesPerSecond = 13726.5
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.18068799 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0179s; samplesPerSecond = 13998.5
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.16232471 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0176s; samplesPerSecond = 14165.1
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.13792139 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0177s; samplesPerSecond = 14102.8
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.16526709 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0181s; samplesPerSecond = 13800.7
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.14743457 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0177s; samplesPerSecond = 14108.4
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.15089160 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0178s; samplesPerSecond = 14053.6
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.12636230 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0179s; samplesPerSecond = 13932.2
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.16735547 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0176s; samplesPerSecond = 14164.3
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.14530957 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0178s; samplesPerSecond = 14006.4
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13859570 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0176s; samplesPerSecond = 14166.7
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.14215234 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0179s; samplesPerSecond = 13940.0
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.15903027 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0178s; samplesPerSecond = 14069.4
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.16232520 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0177s; samplesPerSecond = 14160.3
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.13596484 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0176s; samplesPerSecond = 14198.9
|
||||
08/16/2016 03:04:28: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15469434 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0176s; samplesPerSecond = 14185.2
|
||||
08/16/2016 03:04:28: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.24215964 * 10000; EvalErrorPrediction = 0.09440000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.71504s
|
||||
08/16/2016 03:04:28: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_gpu/Models/simple.dnn.2'
|
||||
|
||||
08/16/2016 03:04:28: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [20000..30000] (first sequence at sample 20000), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:04:28: Starting minibatch loop.
|
||||
08/16/2016 03:04:28: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.18305315 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0183s; samplesPerSecond = 13632.2
|
||||
08/16/2016 03:04:28: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.12945729 * 250; EvalErrorPrediction = 0.04800000 * 250; time = 0.0177s; samplesPerSecond = 14137.9
|
||||
08/16/2016 03:04:28: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.17735931 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0180s; samplesPerSecond = 13886.6
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.14128339 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0180s; samplesPerSecond = 13903.6
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.16558209 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0176s; samplesPerSecond = 14173.9
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.19102692 * 250; EvalErrorPrediction = 0.10800000 * 250; time = 0.0178s; samplesPerSecond = 14036.3
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.12279083 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0181s; samplesPerSecond = 13844.3
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.16642798 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0178s; samplesPerSecond = 14033.9
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.12386572 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0177s; samplesPerSecond = 14110.7
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.19928418 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0177s; samplesPerSecond = 14102.8
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.14213635 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0179s; samplesPerSecond = 13957.9
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.12377087 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0177s; samplesPerSecond = 14088.5
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.16361621 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0178s; samplesPerSecond = 14026.0
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.19886914 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0178s; samplesPerSecond = 14015.8
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.17207544 * 250; EvalErrorPrediction = 0.09200000 * 250; time = 0.0179s; samplesPerSecond = 13935.3
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.13323437 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0180s; samplesPerSecond = 13901.2
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.14397510 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0180s; samplesPerSecond = 13905.9
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.20777515 * 250; EvalErrorPrediction = 0.10400000 * 250; time = 0.0179s; samplesPerSecond = 13964.1
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.19094092 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0180s; samplesPerSecond = 13874.2
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.14731372 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0179s; samplesPerSecond = 13942.3
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.15483569 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0177s; samplesPerSecond = 14117.1
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.13625415 * 250; EvalErrorPrediction = 0.04400000 * 250; time = 0.0177s; samplesPerSecond = 14162.7
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.17354004 * 250; EvalErrorPrediction = 0.08800000 * 250; time = 0.0177s; samplesPerSecond = 14094.0
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.14408350 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0179s; samplesPerSecond = 13929.9
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.13720044 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0180s; samplesPerSecond = 13895.8
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.14236426 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0178s; samplesPerSecond = 14027.6
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.16857861 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0179s; samplesPerSecond = 13968.8
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.18606982 * 250; EvalErrorPrediction = 0.08400000 * 250; time = 0.0180s; samplesPerSecond = 13861.9
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.16334619 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0177s; samplesPerSecond = 14094.8
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.15598535 * 250; EvalErrorPrediction = 0.07200000 * 250; time = 0.0179s; samplesPerSecond = 13979.0
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18848584 * 250; EvalErrorPrediction = 0.09600000 * 250; time = 0.0178s; samplesPerSecond = 14073.4
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.13281348 * 250; EvalErrorPrediction = 0.05200000 * 250; time = 0.0178s; samplesPerSecond = 14067.1
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.14679150 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0178s; samplesPerSecond = 14047.3
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.13977344 * 250; EvalErrorPrediction = 0.06800000 * 250; time = 0.0178s; samplesPerSecond = 14027.6
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.20015137 * 250; EvalErrorPrediction = 0.11200000 * 250; time = 0.0181s; samplesPerSecond = 13831.3
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.12582129 * 250; EvalErrorPrediction = 0.06000000 * 250; time = 0.0178s; samplesPerSecond = 14022.1
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.18500098 * 250; EvalErrorPrediction = 0.07600000 * 250; time = 0.0180s; samplesPerSecond = 13907.4
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15147754 * 250; EvalErrorPrediction = 0.08000000 * 250; time = 0.0181s; samplesPerSecond = 13800.0
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.11988379 * 250; EvalErrorPrediction = 0.05600000 * 250; time = 0.0177s; samplesPerSecond = 14089.3
|
||||
08/16/2016 03:04:29: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.13059082 * 250; EvalErrorPrediction = 0.06400000 * 250; time = 0.0181s; samplesPerSecond = 13797.7
|
||||
08/16/2016 03:04:29: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15767216 * 10000; EvalErrorPrediction = 0.07330000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.716967s
|
||||
08/16/2016 03:04:29: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_gpu/Models/simple.dnn'
|
||||
08/16/2016 03:04:29: CNTKCommandTrainEnd: Simple_Demo_Train
|
||||
|
||||
08/16/2016 03:04:29: Action "train" complete.
|
||||
|
||||
|
||||
08/16/2016 03:04:29: ##############################################################################
|
||||
08/16/2016 03:04:29: # #
|
||||
08/16/2016 03:04:29: # Action "test" #
|
||||
08/16/2016 03:04:29: # #
|
||||
08/16/2016 03:04:29: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
|
@ -658,43 +688,23 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 25 matrices, 2 are shared as 1, and 23 are not shared.
|
||||
|
||||
0000000000000000: {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [EvalErrorPrediction Gradient[1]] [H1 Gradient[50 x 1 x *1]] [H2 Gradient[50 x 1 x *1]] [HLast Gradient[2 x 1 x *1]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *1]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *1]] [PosteriorProb Value[2 x 1 x *1]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *1]] [ScaledLogLikelihood Value[2 x 1 x *1]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *1]] [W0*features+B0 Gradient[50 x 1 x *1]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *1]] [W1*H1+B1 Gradient[50 x 1 x *1]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *1]] [features Gradient[2 x *1]] [labels Gradient[2 x *1]] }
|
||||
000000501A591090: {[W0*features+B0 Value[50 x 1 x *1]] }
|
||||
000000501A591130: {[W1*H1 Value[50 x 1 x *1]] }
|
||||
000000501A5916D0: {[W1*H1+B1 Value[50 x 1 x *1]] }
|
||||
000000501A591770: {[W2*H1 Value[2 x 1 x *1]] }
|
||||
000000501A5919F0: {[MVNormalizedFeatures Value[2 x *1]] }
|
||||
000000501A591E50: {[W0*features Value[50 x *1]] }
|
||||
000000501A592030: {[H1 Value[50 x 1 x *1]] }
|
||||
000000501A592170: {[HLast Value[2 x 1 x *1]] }
|
||||
000000501A592850: {[LogOfPrior Value[2]] }
|
||||
000000501A5928F0: {[H2 Value[50 x 1 x *1]] }
|
||||
000000501A592B70: {[W2 Value[2 x 50]] }
|
||||
000000501A592D50: {[EvalErrorPrediction Value[1]] }
|
||||
000000501A592DF0: {[CrossEntropyWithSoftmax Value[1]] }
|
||||
0000005024E60C70: {[W1 Value[50 x 50]] }
|
||||
0000005024E613F0: {[W0 Value[50 x 2]] }
|
||||
0000005024E61490: {[Prior Value[2]] }
|
||||
0000005024E615D0: {[MeanOfFeatures Value[2]] }
|
||||
0000005024E61C10: {[B0 Value[50 x 1]] }
|
||||
0000005024E61CB0: {[B2 Value[2 x 1]] }
|
||||
0000005024E622F0: {[InvStdOfFeatures Value[2]] }
|
||||
0000005024E62390: {[labels Value[2 x *1]] }
|
||||
0000005024E62430: {[features Value[2 x *1]] }
|
||||
0000005024E624D0: {[B1 Value[50 x 1]] }
|
||||
{ PosteriorProb : [2 x 1 x *1]
|
||||
ScaledLogLikelihood : [2 x 1 x *1] }
|
||||
|
||||
05/03/2016 13:02:01: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.05638474 * 603; CrossEntropyWithSoftmax = 0.12740351 * 603; perplexity = 1.13587526
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..603] (first sequence at sample 0), data subset 0 of 1
|
||||
08/16/2016 03:04:29: Minibatch[1-1]: EvalErrorPrediction = 0.04975124 * 603; CrossEntropyWithSoftmax = 0.10845041 * 603
|
||||
08/16/2016 03:04:29: Final Results: Minibatch[1-1]: EvalErrorPrediction = 0.04975124 * 603; CrossEntropyWithSoftmax = 0.10845041 * 603; perplexity = 1.11454964
|
||||
|
||||
05/03/2016 13:02:01: Action "test" complete.
|
||||
08/16/2016 03:04:29: Action "test" complete.
|
||||
|
||||
|
||||
05/03/2016 13:02:01: ##############################################################################
|
||||
05/03/2016 13:02:01: # #
|
||||
05/03/2016 13:02:01: # Action "write" #
|
||||
05/03/2016 13:02:01: # #
|
||||
05/03/2016 13:02:01: ##############################################################################
|
||||
08/16/2016 03:04:29: ##############################################################################
|
||||
08/16/2016 03:04:29: # #
|
||||
08/16/2016 03:04:29: # Action "write" #
|
||||
08/16/2016 03:04:29: # #
|
||||
08/16/2016 03:04:29: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
|
@ -752,36 +762,16 @@ Post-processing network complete.
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 25 matrices, 3 are shared as 1, and 22 are not shared.
|
||||
|
||||
0000000000000000: {[B0 Gradient[50 x 1]] [B1 Gradient[50 x 1]] [B2 Gradient[2 x 1]] [CrossEntropyWithSoftmax Gradient[1]] [CrossEntropyWithSoftmax Value[1]] [EvalErrorPrediction Gradient[1]] [EvalErrorPrediction Value[1]] [H1 Gradient[50 x 1 x *2]] [H2 Gradient[50 x 1 x *2]] [HLast Gradient[2 x 1 x *2]] [InvStdOfFeatures Gradient[2]] [LogOfPrior Gradient[2]] [MVNormalizedFeatures Gradient[2 x *2]] [MeanOfFeatures Gradient[2]] [PosteriorProb Gradient[2 x 1 x *2]] [Prior Gradient[2]] [ScaledLogLikelihood Gradient[2 x 1 x *2]] [ScaledLogLikelihood Value[2 x 1 x *2]] [W0 Gradient[50 x 2]] [W0*features Gradient[50 x *2]] [W0*features+B0 Gradient[50 x 1 x *2]] [W1 Gradient[50 x 50]] [W1*H1 Gradient[50 x 1 x *2]] [W1*H1+B1 Gradient[50 x 1 x *2]] [W2 Gradient[2 x 50]] [W2*H1 Gradient[2 x 1 x *2]] [features Gradient[2 x *2]] [labels Gradient[2 x *2]] }
|
||||
000000501A5914F0: {[InvStdOfFeatures Value[2]] }
|
||||
000000501A591590: {[MeanOfFeatures Value[2]] }
|
||||
000000501A5916D0: {[labels Value[2 x *2]] }
|
||||
000000501A591810: {[B2 Value[2 x 1]] }
|
||||
000000501A591B30: {[B1 Value[50 x 1]] }
|
||||
000000501A592030: {[Prior Value[2]] }
|
||||
000000501A592170: {[W0 Value[50 x 2]] }
|
||||
000000501A5922B0: {[W1 Value[50 x 50]] }
|
||||
000000501A592490: {[features Value[2 x *2]] }
|
||||
000000501A592C10: {[B0 Value[50 x 1]] }
|
||||
0000005024E60950: {[PosteriorProb Value[2 x 1 x *2]] }
|
||||
0000005024E609F0: {[W0*features+B0 Value[50 x 1 x *2]] }
|
||||
0000005024E60A90: {[W2*H1 Value[2 x 1 x *2]] }
|
||||
0000005024E60BD0: {[W2 Value[2 x 50]] }
|
||||
0000005024E60C70: {[W0*features Value[50 x *2]] }
|
||||
0000005024E60DB0: {[MVNormalizedFeatures Value[2 x *2]] }
|
||||
0000005024E60EF0: {[HLast Value[2 x 1 x *2]] }
|
||||
0000005024E61990: {[LogOfPrior Value[2]] }
|
||||
0000005024E61D50: {[H1 Value[50 x 1 x *2]] }
|
||||
0000005024E62110: {[W1*H1+B1 Value[50 x 1 x *2]] }
|
||||
0000005024E62390: {[W1*H1 Value[50 x 1 x *2]] }
|
||||
0000005024E62430: {[H2 Value[50 x 1 x *2]] }
|
||||
{ CrossEntropyWithSoftmax : [1]
|
||||
EvalErrorPrediction : [1]
|
||||
ScaledLogLikelihood : [2 x 1 x *2] }
|
||||
|
||||
Minibatch[0]: ActualMBSize = 603
|
||||
Written to E:\cygwin64\tmp\cntk-test-20160503140157.802427\CNTKTextFormatReader\Examples\Other\Simple2d_Simple@release_gpu/SimpleOutput*
|
||||
Written to C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Other\Simple2d_Simple@release_gpu/SimpleOutput*
|
||||
Total Samples Evaluated = 603
|
||||
|
||||
05/03/2016 13:02:01: Action "write" complete.
|
||||
08/16/2016 03:04:29: Action "write" complete.
|
||||
|
||||
05/03/2016 13:02:01: __COMPLETED__
|
||||
08/16/2016 03:04:29: __COMPLETED__
|
||||
|
|
@ -0,0 +1,434 @@
|
|||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 264172964 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/1bitsgd/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/FeedForward/../../../../../../Examples/Speech/AN4/Config/FeedForward.cntk currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_cpu DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/FeedForward/../../../../../../Examples/Speech/AN4/Config OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_cpu DeviceId=-1 timestamping=true speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=2048]] speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: Aug 16 2016 09:41:57
|
||||
Last modified date: Mon Aug 15 23:39:17 2016
|
||||
Build type: release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: yes
|
||||
Math lib: mkl
|
||||
CUDA_PATH: /usr/local/cuda-7.5
|
||||
CUB_PATH: /usr/local/cub-1.4.1
|
||||
CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by philly on 643085f7f8c2
|
||||
Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
MPIWrapper: initializing MPI
|
||||
ping [requestnodes (before change)]: 1 nodes pinging each other
|
||||
ping [requestnodes (before change)]: all 1 nodes responded
|
||||
requestnodes [MPIWrapper]: using 1 out of 1 MPI nodes (1 requested); we (0) are in (participating)
|
||||
ping [requestnodes (after change)]: 1 nodes pinging each other
|
||||
ping [requestnodes (after change)]: all 1 nodes responded
|
||||
mpihelper: only one MPI process: MPI operation will be boring
|
||||
ping [mpihelper]: 1 nodes pinging each other
|
||||
ping [mpihelper]: all 1 nodes responded
|
||||
08/16/2016 10:01:41: -------------------------------------------------------------------
|
||||
08/16/2016 10:01:41: Build info:
|
||||
|
||||
08/16/2016 10:01:41: Built time: Aug 16 2016 09:41:57
|
||||
08/16/2016 10:01:41: Last modified date: Mon Aug 15 23:39:17 2016
|
||||
08/16/2016 10:01:41: Build type: release
|
||||
08/16/2016 10:01:41: Build target: GPU
|
||||
08/16/2016 10:01:41: With 1bit-SGD: yes
|
||||
08/16/2016 10:01:41: Math lib: mkl
|
||||
08/16/2016 10:01:41: CUDA_PATH: /usr/local/cuda-7.5
|
||||
08/16/2016 10:01:41: CUB_PATH: /usr/local/cub-1.4.1
|
||||
08/16/2016 10:01:41: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
08/16/2016 10:01:41: Build Branch: HEAD
|
||||
08/16/2016 10:01:41: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 10:01:41: Built by philly on 643085f7f8c2
|
||||
08/16/2016 10:01:41: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
08/16/2016 10:01:41: -------------------------------------------------------------------
|
||||
08/16/2016 10:01:42: -------------------------------------------------------------------
|
||||
08/16/2016 10:01:42: GPU info:
|
||||
|
||||
08/16/2016 10:01:42: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:01:42: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:01:42: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:01:42: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:01:42: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 10:01:42: Running on localhost at 2016/08/16 10:01:42
|
||||
08/16/2016 10:01:42: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/1bitsgd/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/FeedForward/../../../../../../Examples/Speech/AN4/Config/FeedForward.cntk currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_cpu DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/FeedForward/../../../../../../Examples/Speech/AN4/Config OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_cpu DeviceId=-1 timestamping=true speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=2048]] speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
|
||||
|
||||
|
||||
08/16/2016 10:01:42: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:01:42: RootDir = ".."
|
||||
ConfigDir = "$RootDir$/Config"
|
||||
DataDir = "$RootDir$/Data"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
ModelDir = "$OutputDir$/Models"
|
||||
deviceId = -1
|
||||
command = speechTrain
|
||||
precision = "float"
|
||||
traceLevel = "1"
|
||||
modelPath = "$ModelDir$/cntkSpeechFF.dnn"
|
||||
parallelTrain = true
|
||||
speechTrain = [
|
||||
action = "train"
|
||||
SimpleNetworkBuilder = [
|
||||
layerSizes = 363:512:512:132
|
||||
trainingCriterion = "CrossEntropyWithSoftmax"
|
||||
evalCriterion = "ErrorPrediction"
|
||||
layerTypes = "Sigmoid"
|
||||
applyMeanVarNorm = true
|
||||
needPrior = true
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 20480
|
||||
minibatchSize = 256:1024:2048
|
||||
learningRatesPerMB = 1.0:0.5:0.1
|
||||
numMBsToShowResult = 10
|
||||
momentumPerMB = 0.9:0.656119
|
||||
maxEpochs = 3
|
||||
keepCheckPointFiles = true
|
||||
parallelTrain = [
|
||||
parallelizationMethod = "DataParallelSGD"
|
||||
distributedMBReading = true
|
||||
dataParallelSGD = [
|
||||
gradientBits = 1
|
||||
]
|
||||
]
|
||||
autoAdjust=[
|
||||
autoAdjustMinibatch = true
|
||||
minibatchSizeTuningFrequency = 1
|
||||
minibatchSearchCriterionErrorMargin = 2
|
||||
]
|
||||
]
|
||||
reader = [
|
||||
readerType = "HTKMLFReader"
|
||||
readMethod = "blockRandomize"
|
||||
miniBatchMode = "partial"
|
||||
randomize = "auto"
|
||||
verbosity = 0
|
||||
features = [
|
||||
dim = 363
|
||||
type = "real"
|
||||
scpFile = "$DataDir$/glob_0000.scp"
|
||||
]
|
||||
labels = [
|
||||
mlfFile = "$DataDir$/glob_0000.mlf"
|
||||
labelMappingFile = "$DataDir$/state.list"
|
||||
labelDim = 132
|
||||
labelType = "category"
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_cpu
|
||||
DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/FeedForward/../../../../../../Examples/Speech/AN4/Config
|
||||
OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_cpu
|
||||
DeviceId=-1
|
||||
timestamping=true
|
||||
speechTrain=[SGD=[maxEpochs=1]]
|
||||
speechTrain=[SGD=[epochSize=2048]]
|
||||
speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
|
||||
08/16/2016 10:01:42: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
08/16/2016 10:01:42: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:01:42: RootDir = ".."
|
||||
ConfigDir = "../Config"
|
||||
DataDir = "../Data"
|
||||
OutputDir = "../Output"
|
||||
ModelDir = "/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_cpu/Models"
|
||||
deviceId = -1
|
||||
command = speechTrain
|
||||
precision = "float"
|
||||
traceLevel = "1"
|
||||
modelPath = "/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_cpu/Models/cntkSpeechFF.dnn"
|
||||
parallelTrain = true
|
||||
speechTrain = [
|
||||
action = "train"
|
||||
SimpleNetworkBuilder = [
|
||||
layerSizes = 363:512:512:132
|
||||
trainingCriterion = "CrossEntropyWithSoftmax"
|
||||
evalCriterion = "ErrorPrediction"
|
||||
layerTypes = "Sigmoid"
|
||||
applyMeanVarNorm = true
|
||||
needPrior = true
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 20480
|
||||
minibatchSize = 256:1024:2048
|
||||
learningRatesPerMB = 1.0:0.5:0.1
|
||||
numMBsToShowResult = 10
|
||||
momentumPerMB = 0.9:0.656119
|
||||
maxEpochs = 3
|
||||
keepCheckPointFiles = true
|
||||
parallelTrain = [
|
||||
parallelizationMethod = "DataParallelSGD"
|
||||
distributedMBReading = true
|
||||
dataParallelSGD = [
|
||||
gradientBits = 1
|
||||
]
|
||||
]
|
||||
autoAdjust=[
|
||||
autoAdjustMinibatch = true
|
||||
minibatchSizeTuningFrequency = 1
|
||||
minibatchSearchCriterionErrorMargin = 2
|
||||
]
|
||||
]
|
||||
reader = [
|
||||
readerType = "HTKMLFReader"
|
||||
readMethod = "blockRandomize"
|
||||
miniBatchMode = "partial"
|
||||
randomize = "auto"
|
||||
verbosity = 0
|
||||
features = [
|
||||
dim = 363
|
||||
type = "real"
|
||||
scpFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.scp"
|
||||
]
|
||||
labels = [
|
||||
mlfFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.mlf"
|
||||
labelMappingFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/state.list"
|
||||
labelDim = 132
|
||||
labelType = "category"
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_cpu
|
||||
DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/FeedForward/../../../../../../Examples/Speech/AN4/Config
|
||||
OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_cpu
|
||||
DeviceId=-1
|
||||
timestamping=true
|
||||
speechTrain=[SGD=[maxEpochs=1]]
|
||||
speechTrain=[SGD=[epochSize=2048]]
|
||||
speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
|
||||
08/16/2016 10:01:42: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
08/16/2016 10:01:42: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: FeedForward.cntk:command=speechTrain
|
||||
configparameters: FeedForward.cntk:ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/FeedForward/../../../../../../Examples/Speech/AN4/Config
|
||||
configparameters: FeedForward.cntk:currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
configparameters: FeedForward.cntk:DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
configparameters: FeedForward.cntk:deviceId=-1
|
||||
configparameters: FeedForward.cntk:ModelDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_cpu/Models
|
||||
configparameters: FeedForward.cntk:modelPath=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_cpu/Models/cntkSpeechFF.dnn
|
||||
configparameters: FeedForward.cntk:OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_cpu
|
||||
configparameters: FeedForward.cntk:parallelTrain=true
|
||||
configparameters: FeedForward.cntk:precision=float
|
||||
configparameters: FeedForward.cntk:RootDir=..
|
||||
configparameters: FeedForward.cntk:RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_cpu
|
||||
configparameters: FeedForward.cntk:speechTrain=[
|
||||
action = "train"
|
||||
SimpleNetworkBuilder = [
|
||||
layerSizes = 363:512:512:132
|
||||
trainingCriterion = "CrossEntropyWithSoftmax"
|
||||
evalCriterion = "ErrorPrediction"
|
||||
layerTypes = "Sigmoid"
|
||||
applyMeanVarNorm = true
|
||||
needPrior = true
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 20480
|
||||
minibatchSize = 256:1024:2048
|
||||
learningRatesPerMB = 1.0:0.5:0.1
|
||||
numMBsToShowResult = 10
|
||||
momentumPerMB = 0.9:0.656119
|
||||
maxEpochs = 3
|
||||
keepCheckPointFiles = true
|
||||
parallelTrain = [
|
||||
parallelizationMethod = "DataParallelSGD"
|
||||
distributedMBReading = true
|
||||
dataParallelSGD = [
|
||||
gradientBits = 1
|
||||
]
|
||||
]
|
||||
autoAdjust=[
|
||||
autoAdjustMinibatch = true
|
||||
minibatchSizeTuningFrequency = 1
|
||||
minibatchSearchCriterionErrorMargin = 2
|
||||
]
|
||||
]
|
||||
reader = [
|
||||
readerType = "HTKMLFReader"
|
||||
readMethod = "blockRandomize"
|
||||
miniBatchMode = "partial"
|
||||
randomize = "auto"
|
||||
verbosity = 0
|
||||
features = [
|
||||
dim = 363
|
||||
type = "real"
|
||||
scpFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.scp"
|
||||
]
|
||||
labels = [
|
||||
mlfFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.mlf"
|
||||
labelMappingFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/state.list"
|
||||
labelDim = 132
|
||||
labelType = "category"
|
||||
]
|
||||
]
|
||||
] [SGD=[maxEpochs=1]] [SGD=[epochSize=2048]] [reader=[useMersenneTwisterRand=true]]
|
||||
|
||||
configparameters: FeedForward.cntk:timestamping=true
|
||||
configparameters: FeedForward.cntk:traceLevel=1
|
||||
08/16/2016 10:01:42: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:01:42: Commands: speechTrain
|
||||
08/16/2016 10:01:42: Precision = "float"
|
||||
08/16/2016 10:01:42: CNTKModelPath: /tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_cpu/Models/cntkSpeechFF.dnn
|
||||
08/16/2016 10:01:42: CNTKCommandTrainInfo: speechTrain : 1
|
||||
08/16/2016 10:01:42: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 1
|
||||
|
||||
08/16/2016 10:01:42: ##############################################################################
|
||||
08/16/2016 10:01:42: # #
|
||||
08/16/2016 10:01:42: # Action "train" #
|
||||
08/16/2016 10:01:42: # #
|
||||
08/16/2016 10:01:42: ##############################################################################
|
||||
|
||||
08/16/2016 10:01:42: CNTKCommandTrainBegin: speechTrain
|
||||
SimpleNetworkBuilder Using CPU
|
||||
reading script file /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.scp ... 948 entries
|
||||
total 132 state names in state list /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/state.list
|
||||
htkmlfreader: reading MLF file /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.mlf ... total 948 entries
|
||||
...............................................................................................feature set 0: 252734 frames in 948 out of 948 utterances
|
||||
label set 0: 129 classes
|
||||
minibatchutterancesource: 948 utterances grouped into 3 chunks, av. chunk size: 316.0 utterances, 84244.7 frames
|
||||
|
||||
08/16/2016 10:01:42: Creating virgin network.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[512 x 363] <- 0.000000.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[512 x 363] <- uniform(seed=1, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[512 x 1] <- 0.000000.
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[512 x 1] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[512 x 512] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[512 x 512] <- uniform(seed=2, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[512 x 1] <- 0.000000.
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[512 x 1] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[132 x 512] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[132 x 512] <- uniform(seed=3, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[132 x 1] <- 0.000000.
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[132 x 1] <- 0.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
7 roots:
|
||||
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
|
||||
EvalErrorPrediction = ErrorPrediction()
|
||||
InvStdOfFeatures = InvStdDev()
|
||||
MeanOfFeatures = Mean()
|
||||
PosteriorProb = Softmax()
|
||||
Prior = Mean()
|
||||
ScaledLogLikelihood = Minus()
|
||||
|
||||
Validating network. 25 nodes to process in pass 1.
|
||||
|
||||
Validating --> labels = InputValue() : -> [132 x *]
|
||||
Validating --> W2 = LearnableParameter() : -> [132 x 512]
|
||||
Validating --> W1 = LearnableParameter() : -> [512 x 512]
|
||||
Validating --> W0 = LearnableParameter() : -> [512 x 363]
|
||||
Validating --> features = InputValue() : -> [363 x *]
|
||||
Validating --> MeanOfFeatures = Mean (features) : [363 x *] -> [363]
|
||||
Validating --> InvStdOfFeatures = InvStdDev (features) : [363 x *] -> [363]
|
||||
Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization (features, MeanOfFeatures, InvStdOfFeatures) : [363 x *], [363], [363] -> [363 x *]
|
||||
Validating --> W0*features = Times (W0, MVNormalizedFeatures) : [512 x 363], [363 x *] -> [512 x *]
|
||||
Validating --> B0 = LearnableParameter() : -> [512 x 1]
|
||||
Validating --> W0*features+B0 = Plus (W0*features, B0) : [512 x *], [512 x 1] -> [512 x 1 x *]
|
||||
Validating --> H1 = Sigmoid (W0*features+B0) : [512 x 1 x *] -> [512 x 1 x *]
|
||||
Validating --> W1*H1 = Times (W1, H1) : [512 x 512], [512 x 1 x *] -> [512 x 1 x *]
|
||||
Validating --> B1 = LearnableParameter() : -> [512 x 1]
|
||||
Validating --> W1*H1+B1 = Plus (W1*H1, B1) : [512 x 1 x *], [512 x 1] -> [512 x 1 x *]
|
||||
Validating --> H2 = Sigmoid (W1*H1+B1) : [512 x 1 x *] -> [512 x 1 x *]
|
||||
Validating --> W2*H1 = Times (W2, H2) : [132 x 512], [512 x 1 x *] -> [132 x 1 x *]
|
||||
Validating --> B2 = LearnableParameter() : -> [132 x 1]
|
||||
Validating --> HLast = Plus (W2*H1, B2) : [132 x 1 x *], [132 x 1] -> [132 x 1 x *]
|
||||
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [132 x *], [132 x 1 x *] -> [1]
|
||||
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [132 x *], [132 x 1 x *] -> [1]
|
||||
Validating --> PosteriorProb = Softmax (HLast) : [132 x 1 x *] -> [132 x 1 x *]
|
||||
Validating --> Prior = Mean (labels) : [132 x *] -> [132]
|
||||
Validating --> LogOfPrior = Log (Prior) : [132] -> [132]
|
||||
Validating --> ScaledLogLikelihood = Minus (HLast, LogOfPrior) : [132 x 1 x *], [132] -> [132 x 1 x *]
|
||||
|
||||
Validating network. 17 nodes to process in pass 2.
|
||||
|
||||
|
||||
Validating network, final pass.
|
||||
|
||||
|
||||
|
||||
12 out of 25 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
08/16/2016 10:01:42: Created model with 25 nodes on CPU.
|
||||
|
||||
08/16/2016 10:01:42: Training criterion node(s):
|
||||
08/16/2016 10:01:42: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
|
||||
|
||||
08/16/2016 10:01:42: Evaluation criterion node(s):
|
||||
08/16/2016 10:01:42: EvalErrorPrediction = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing: Out of 40 matrices, 19 are shared as 8, and 21 are not shared.
|
||||
|
||||
{ W1 : [512 x 512] (gradient)
|
||||
W1*H1+B1 : [512 x 1 x *] }
|
||||
{ H2 : [512 x 1 x *]
|
||||
W1*H1 : [512 x 1 x *] (gradient) }
|
||||
{ B0 : [512 x 1] (gradient)
|
||||
H1 : [512 x 1 x *] (gradient)
|
||||
W1*H1+B1 : [512 x 1 x *] (gradient)
|
||||
W2*H1 : [132 x 1 x *] }
|
||||
{ HLast : [132 x 1 x *]
|
||||
W2 : [132 x 512] (gradient) }
|
||||
{ B1 : [512 x 1] (gradient)
|
||||
H2 : [512 x 1 x *] (gradient)
|
||||
HLast : [132 x 1 x *] (gradient) }
|
||||
{ W0 : [512 x 363] (gradient)
|
||||
W0*features+B0 : [512 x 1 x *] }
|
||||
{ H1 : [512 x 1 x *]
|
||||
W0*features : [512 x *] (gradient) }
|
||||
{ W0*features+B0 : [512 x 1 x *] (gradient)
|
||||
W1*H1 : [512 x 1 x *] }
|
||||
|
||||
|
||||
08/16/2016 10:01:42: Training 516740 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:
|
||||
|
||||
08/16/2016 10:01:42: Node 'B0' (LearnableParameter operation) : [512 x 1]
|
||||
08/16/2016 10:01:42: Node 'B1' (LearnableParameter operation) : [512 x 1]
|
||||
08/16/2016 10:01:42: Node 'B2' (LearnableParameter operation) : [132 x 1]
|
||||
08/16/2016 10:01:42: Node 'W0' (LearnableParameter operation) : [512 x 363]
|
||||
08/16/2016 10:01:42: Node 'W1' (LearnableParameter operation) : [512 x 512]
|
||||
08/16/2016 10:01:42: Node 'W2' (LearnableParameter operation) : [132 x 512]
|
||||
|
||||
|
||||
08/16/2016 10:01:42: Precomputing --> 3 PreCompute nodes found.
|
||||
|
||||
08/16/2016 10:01:42: MeanOfFeatures = Mean()
|
||||
08/16/2016 10:01:42: InvStdOfFeatures = InvStdDev()
|
||||
08/16/2016 10:01:42: Prior = Mean()
|
||||
minibatchiterator: epoch 0: frames [0..252734] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses
|
||||
requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms
|
||||
|
||||
08/16/2016 10:01:43: Precomputing --> Completed.
|
||||
|
||||
|
||||
08/16/2016 10:01:43: Starting Epoch 1: learning rate per sample = 0.003906 effective momentum = 0.900000 momentum as time constant = 2429.8 samples
|
||||
minibatchiterator: epoch 0: frames [0..2048] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses
|
||||
|
||||
08/16/2016 10:01:43: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1), distributed reading is ENABLED.
|
||||
08/16/2016 10:01:44: Finished Epoch[ 1 of 1]: [Training] CrossEntropyWithSoftmax = 4.45117986 * 2048; EvalErrorPrediction = 0.92187500 * 2048; totalSamplesSeen = 2048; learningRatePerSample = 0.00390625; epochTime=0.209966s
|
||||
08/16/2016 10:01:44: SGD: Saving checkpoint model '/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_cpu/Models/cntkSpeechFF.dnn'
|
||||
08/16/2016 10:01:44: CNTKCommandTrainEnd: speechTrain
|
||||
|
||||
08/16/2016 10:01:44: Action "train" complete.
|
||||
|
||||
08/16/2016 10:01:44: __COMPLETED__
|
||||
~MPIWrapper
|
||||
|
|
@ -1 +0,0 @@
|
|||
__COMPLETED__
|
||||
|
|
@ -1 +0,0 @@
|
|||
__COMPLETED__
|
||||
|
|
@ -0,0 +1,435 @@
|
|||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 264172964 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/1bitsgd/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/FeedForward/../../../../../../Examples/Speech/AN4/Config/FeedForward.cntk currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_gpu DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/FeedForward/../../../../../../Examples/Speech/AN4/Config OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_gpu DeviceId=0 timestamping=true speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=2048]] speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: Aug 16 2016 09:41:57
|
||||
Last modified date: Mon Aug 15 23:39:17 2016
|
||||
Build type: release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: yes
|
||||
Math lib: mkl
|
||||
CUDA_PATH: /usr/local/cuda-7.5
|
||||
CUB_PATH: /usr/local/cub-1.4.1
|
||||
CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by philly on 643085f7f8c2
|
||||
Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
MPIWrapper: initializing MPI
|
||||
ping [requestnodes (before change)]: 1 nodes pinging each other
|
||||
ping [requestnodes (before change)]: all 1 nodes responded
|
||||
requestnodes [MPIWrapper]: using 1 out of 1 MPI nodes (1 requested); we (0) are in (participating)
|
||||
ping [requestnodes (after change)]: 1 nodes pinging each other
|
||||
ping [requestnodes (after change)]: all 1 nodes responded
|
||||
mpihelper: only one MPI process: MPI operation will be boring
|
||||
ping [mpihelper]: 1 nodes pinging each other
|
||||
ping [mpihelper]: all 1 nodes responded
|
||||
08/16/2016 10:01:45: -------------------------------------------------------------------
|
||||
08/16/2016 10:01:45: Build info:
|
||||
|
||||
08/16/2016 10:01:45: Built time: Aug 16 2016 09:41:57
|
||||
08/16/2016 10:01:45: Last modified date: Mon Aug 15 23:39:17 2016
|
||||
08/16/2016 10:01:45: Build type: release
|
||||
08/16/2016 10:01:45: Build target: GPU
|
||||
08/16/2016 10:01:45: With 1bit-SGD: yes
|
||||
08/16/2016 10:01:45: Math lib: mkl
|
||||
08/16/2016 10:01:45: CUDA_PATH: /usr/local/cuda-7.5
|
||||
08/16/2016 10:01:45: CUB_PATH: /usr/local/cub-1.4.1
|
||||
08/16/2016 10:01:45: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
08/16/2016 10:01:45: Build Branch: HEAD
|
||||
08/16/2016 10:01:45: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 10:01:45: Built by philly on 643085f7f8c2
|
||||
08/16/2016 10:01:45: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
08/16/2016 10:01:45: -------------------------------------------------------------------
|
||||
08/16/2016 10:01:46: -------------------------------------------------------------------
|
||||
08/16/2016 10:01:46: GPU info:
|
||||
|
||||
08/16/2016 10:01:46: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:01:46: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:01:46: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:01:46: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:01:46: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 10:01:46: Running on localhost at 2016/08/16 10:01:46
|
||||
08/16/2016 10:01:46: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/1bitsgd/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/FeedForward/../../../../../../Examples/Speech/AN4/Config/FeedForward.cntk currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_gpu DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/FeedForward/../../../../../../Examples/Speech/AN4/Config OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_gpu DeviceId=0 timestamping=true speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=2048]] speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
|
||||
|
||||
|
||||
08/16/2016 10:01:46: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:01:46: RootDir = ".."
|
||||
ConfigDir = "$RootDir$/Config"
|
||||
DataDir = "$RootDir$/Data"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
ModelDir = "$OutputDir$/Models"
|
||||
deviceId = -1
|
||||
command = speechTrain
|
||||
precision = "float"
|
||||
traceLevel = "1"
|
||||
modelPath = "$ModelDir$/cntkSpeechFF.dnn"
|
||||
parallelTrain = true
|
||||
speechTrain = [
|
||||
action = "train"
|
||||
SimpleNetworkBuilder = [
|
||||
layerSizes = 363:512:512:132
|
||||
trainingCriterion = "CrossEntropyWithSoftmax"
|
||||
evalCriterion = "ErrorPrediction"
|
||||
layerTypes = "Sigmoid"
|
||||
applyMeanVarNorm = true
|
||||
needPrior = true
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 20480
|
||||
minibatchSize = 256:1024:2048
|
||||
learningRatesPerMB = 1.0:0.5:0.1
|
||||
numMBsToShowResult = 10
|
||||
momentumPerMB = 0.9:0.656119
|
||||
maxEpochs = 3
|
||||
keepCheckPointFiles = true
|
||||
parallelTrain = [
|
||||
parallelizationMethod = "DataParallelSGD"
|
||||
distributedMBReading = true
|
||||
dataParallelSGD = [
|
||||
gradientBits = 1
|
||||
]
|
||||
]
|
||||
autoAdjust=[
|
||||
autoAdjustMinibatch = true
|
||||
minibatchSizeTuningFrequency = 1
|
||||
minibatchSearchCriterionErrorMargin = 2
|
||||
]
|
||||
]
|
||||
reader = [
|
||||
readerType = "HTKMLFReader"
|
||||
readMethod = "blockRandomize"
|
||||
miniBatchMode = "partial"
|
||||
randomize = "auto"
|
||||
verbosity = 0
|
||||
features = [
|
||||
dim = 363
|
||||
type = "real"
|
||||
scpFile = "$DataDir$/glob_0000.scp"
|
||||
]
|
||||
labels = [
|
||||
mlfFile = "$DataDir$/glob_0000.mlf"
|
||||
labelMappingFile = "$DataDir$/state.list"
|
||||
labelDim = 132
|
||||
labelType = "category"
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_gpu
|
||||
DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/FeedForward/../../../../../../Examples/Speech/AN4/Config
|
||||
OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
speechTrain=[SGD=[maxEpochs=1]]
|
||||
speechTrain=[SGD=[epochSize=2048]]
|
||||
speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
|
||||
08/16/2016 10:01:46: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
08/16/2016 10:01:46: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:01:46: RootDir = ".."
|
||||
ConfigDir = "../Config"
|
||||
DataDir = "../Data"
|
||||
OutputDir = "../Output"
|
||||
ModelDir = "/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_gpu/Models"
|
||||
deviceId = -1
|
||||
command = speechTrain
|
||||
precision = "float"
|
||||
traceLevel = "1"
|
||||
modelPath = "/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_gpu/Models/cntkSpeechFF.dnn"
|
||||
parallelTrain = true
|
||||
speechTrain = [
|
||||
action = "train"
|
||||
SimpleNetworkBuilder = [
|
||||
layerSizes = 363:512:512:132
|
||||
trainingCriterion = "CrossEntropyWithSoftmax"
|
||||
evalCriterion = "ErrorPrediction"
|
||||
layerTypes = "Sigmoid"
|
||||
applyMeanVarNorm = true
|
||||
needPrior = true
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 20480
|
||||
minibatchSize = 256:1024:2048
|
||||
learningRatesPerMB = 1.0:0.5:0.1
|
||||
numMBsToShowResult = 10
|
||||
momentumPerMB = 0.9:0.656119
|
||||
maxEpochs = 3
|
||||
keepCheckPointFiles = true
|
||||
parallelTrain = [
|
||||
parallelizationMethod = "DataParallelSGD"
|
||||
distributedMBReading = true
|
||||
dataParallelSGD = [
|
||||
gradientBits = 1
|
||||
]
|
||||
]
|
||||
autoAdjust=[
|
||||
autoAdjustMinibatch = true
|
||||
minibatchSizeTuningFrequency = 1
|
||||
minibatchSearchCriterionErrorMargin = 2
|
||||
]
|
||||
]
|
||||
reader = [
|
||||
readerType = "HTKMLFReader"
|
||||
readMethod = "blockRandomize"
|
||||
miniBatchMode = "partial"
|
||||
randomize = "auto"
|
||||
verbosity = 0
|
||||
features = [
|
||||
dim = 363
|
||||
type = "real"
|
||||
scpFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.scp"
|
||||
]
|
||||
labels = [
|
||||
mlfFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.mlf"
|
||||
labelMappingFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/state.list"
|
||||
labelDim = 132
|
||||
labelType = "category"
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_gpu
|
||||
DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/FeedForward/../../../../../../Examples/Speech/AN4/Config
|
||||
OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
speechTrain=[SGD=[maxEpochs=1]]
|
||||
speechTrain=[SGD=[epochSize=2048]]
|
||||
speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
|
||||
08/16/2016 10:01:46: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
08/16/2016 10:01:46: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: FeedForward.cntk:command=speechTrain
|
||||
configparameters: FeedForward.cntk:ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/FeedForward/../../../../../../Examples/Speech/AN4/Config
|
||||
configparameters: FeedForward.cntk:currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
configparameters: FeedForward.cntk:DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
configparameters: FeedForward.cntk:deviceId=0
|
||||
configparameters: FeedForward.cntk:ModelDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_gpu/Models
|
||||
configparameters: FeedForward.cntk:modelPath=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_gpu/Models/cntkSpeechFF.dnn
|
||||
configparameters: FeedForward.cntk:OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_gpu
|
||||
configparameters: FeedForward.cntk:parallelTrain=true
|
||||
configparameters: FeedForward.cntk:precision=float
|
||||
configparameters: FeedForward.cntk:RootDir=..
|
||||
configparameters: FeedForward.cntk:RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_gpu
|
||||
configparameters: FeedForward.cntk:speechTrain=[
|
||||
action = "train"
|
||||
SimpleNetworkBuilder = [
|
||||
layerSizes = 363:512:512:132
|
||||
trainingCriterion = "CrossEntropyWithSoftmax"
|
||||
evalCriterion = "ErrorPrediction"
|
||||
layerTypes = "Sigmoid"
|
||||
applyMeanVarNorm = true
|
||||
needPrior = true
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 20480
|
||||
minibatchSize = 256:1024:2048
|
||||
learningRatesPerMB = 1.0:0.5:0.1
|
||||
numMBsToShowResult = 10
|
||||
momentumPerMB = 0.9:0.656119
|
||||
maxEpochs = 3
|
||||
keepCheckPointFiles = true
|
||||
parallelTrain = [
|
||||
parallelizationMethod = "DataParallelSGD"
|
||||
distributedMBReading = true
|
||||
dataParallelSGD = [
|
||||
gradientBits = 1
|
||||
]
|
||||
]
|
||||
autoAdjust=[
|
||||
autoAdjustMinibatch = true
|
||||
minibatchSizeTuningFrequency = 1
|
||||
minibatchSearchCriterionErrorMargin = 2
|
||||
]
|
||||
]
|
||||
reader = [
|
||||
readerType = "HTKMLFReader"
|
||||
readMethod = "blockRandomize"
|
||||
miniBatchMode = "partial"
|
||||
randomize = "auto"
|
||||
verbosity = 0
|
||||
features = [
|
||||
dim = 363
|
||||
type = "real"
|
||||
scpFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.scp"
|
||||
]
|
||||
labels = [
|
||||
mlfFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.mlf"
|
||||
labelMappingFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/state.list"
|
||||
labelDim = 132
|
||||
labelType = "category"
|
||||
]
|
||||
]
|
||||
] [SGD=[maxEpochs=1]] [SGD=[epochSize=2048]] [reader=[useMersenneTwisterRand=true]]
|
||||
|
||||
configparameters: FeedForward.cntk:timestamping=true
|
||||
configparameters: FeedForward.cntk:traceLevel=1
|
||||
08/16/2016 10:01:46: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:01:46: Commands: speechTrain
|
||||
08/16/2016 10:01:46: Precision = "float"
|
||||
08/16/2016 10:01:46: CNTKModelPath: /tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_gpu/Models/cntkSpeechFF.dnn
|
||||
08/16/2016 10:01:46: CNTKCommandTrainInfo: speechTrain : 1
|
||||
08/16/2016 10:01:46: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 1
|
||||
|
||||
08/16/2016 10:01:46: ##############################################################################
|
||||
08/16/2016 10:01:46: # #
|
||||
08/16/2016 10:01:46: # Action "train" #
|
||||
08/16/2016 10:01:46: # #
|
||||
08/16/2016 10:01:46: ##############################################################################
|
||||
|
||||
08/16/2016 10:01:46: CNTKCommandTrainBegin: speechTrain
|
||||
SimpleNetworkBuilder Using GPU 0
|
||||
reading script file /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.scp ... 948 entries
|
||||
total 132 state names in state list /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/state.list
|
||||
htkmlfreader: reading MLF file /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.mlf ... total 948 entries
|
||||
...............................................................................................feature set 0: 252734 frames in 948 out of 948 utterances
|
||||
label set 0: 129 classes
|
||||
minibatchutterancesource: 948 utterances grouped into 3 chunks, av. chunk size: 316.0 utterances, 84244.7 frames
|
||||
|
||||
08/16/2016 10:01:46: Creating virgin network.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[512 x 363] <- 0.000000.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[512 x 363] <- uniform(seed=1, range=0.050000*1.000000, onCPU=false).
|
||||
SetUniformRandomValue (GPU): creating curand object with seed 1, sizeof(ElemType)==4
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[512 x 1] <- 0.000000.
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[512 x 1] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[512 x 512] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[512 x 512] <- uniform(seed=2, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[512 x 1] <- 0.000000.
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[512 x 1] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[132 x 512] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[132 x 512] <- uniform(seed=3, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[132 x 1] <- 0.000000.
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[132 x 1] <- 0.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
7 roots:
|
||||
CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
|
||||
EvalErrorPrediction = ErrorPrediction()
|
||||
InvStdOfFeatures = InvStdDev()
|
||||
MeanOfFeatures = Mean()
|
||||
PosteriorProb = Softmax()
|
||||
Prior = Mean()
|
||||
ScaledLogLikelihood = Minus()
|
||||
|
||||
Validating network. 25 nodes to process in pass 1.
|
||||
|
||||
Validating --> labels = InputValue() : -> [132 x *]
|
||||
Validating --> W2 = LearnableParameter() : -> [132 x 512]
|
||||
Validating --> W1 = LearnableParameter() : -> [512 x 512]
|
||||
Validating --> W0 = LearnableParameter() : -> [512 x 363]
|
||||
Validating --> features = InputValue() : -> [363 x *]
|
||||
Validating --> MeanOfFeatures = Mean (features) : [363 x *] -> [363]
|
||||
Validating --> InvStdOfFeatures = InvStdDev (features) : [363 x *] -> [363]
|
||||
Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization (features, MeanOfFeatures, InvStdOfFeatures) : [363 x *], [363], [363] -> [363 x *]
|
||||
Validating --> W0*features = Times (W0, MVNormalizedFeatures) : [512 x 363], [363 x *] -> [512 x *]
|
||||
Validating --> B0 = LearnableParameter() : -> [512 x 1]
|
||||
Validating --> W0*features+B0 = Plus (W0*features, B0) : [512 x *], [512 x 1] -> [512 x 1 x *]
|
||||
Validating --> H1 = Sigmoid (W0*features+B0) : [512 x 1 x *] -> [512 x 1 x *]
|
||||
Validating --> W1*H1 = Times (W1, H1) : [512 x 512], [512 x 1 x *] -> [512 x 1 x *]
|
||||
Validating --> B1 = LearnableParameter() : -> [512 x 1]
|
||||
Validating --> W1*H1+B1 = Plus (W1*H1, B1) : [512 x 1 x *], [512 x 1] -> [512 x 1 x *]
|
||||
Validating --> H2 = Sigmoid (W1*H1+B1) : [512 x 1 x *] -> [512 x 1 x *]
|
||||
Validating --> W2*H1 = Times (W2, H2) : [132 x 512], [512 x 1 x *] -> [132 x 1 x *]
|
||||
Validating --> B2 = LearnableParameter() : -> [132 x 1]
|
||||
Validating --> HLast = Plus (W2*H1, B2) : [132 x 1 x *], [132 x 1] -> [132 x 1 x *]
|
||||
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [132 x *], [132 x 1 x *] -> [1]
|
||||
Validating --> EvalErrorPrediction = ErrorPrediction (labels, HLast) : [132 x *], [132 x 1 x *] -> [1]
|
||||
Validating --> PosteriorProb = Softmax (HLast) : [132 x 1 x *] -> [132 x 1 x *]
|
||||
Validating --> Prior = Mean (labels) : [132 x *] -> [132]
|
||||
Validating --> LogOfPrior = Log (Prior) : [132] -> [132]
|
||||
Validating --> ScaledLogLikelihood = Minus (HLast, LogOfPrior) : [132 x 1 x *], [132] -> [132 x 1 x *]
|
||||
|
||||
Validating network. 17 nodes to process in pass 2.
|
||||
|
||||
|
||||
Validating network, final pass.
|
||||
|
||||
|
||||
|
||||
12 out of 25 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
08/16/2016 10:01:46: Created model with 25 nodes on GPU 0.
|
||||
|
||||
08/16/2016 10:01:46: Training criterion node(s):
|
||||
08/16/2016 10:01:46: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
|
||||
|
||||
08/16/2016 10:01:46: Evaluation criterion node(s):
|
||||
08/16/2016 10:01:46: EvalErrorPrediction = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing: Out of 40 matrices, 19 are shared as 8, and 21 are not shared.
|
||||
|
||||
{ W0 : [512 x 363] (gradient)
|
||||
W0*features+B0 : [512 x 1 x *] }
|
||||
{ H1 : [512 x 1 x *]
|
||||
W0*features : [512 x *] (gradient) }
|
||||
{ W0*features+B0 : [512 x 1 x *] (gradient)
|
||||
W1*H1 : [512 x 1 x *] }
|
||||
{ W1 : [512 x 512] (gradient)
|
||||
W1*H1+B1 : [512 x 1 x *] }
|
||||
{ H2 : [512 x 1 x *]
|
||||
W1*H1 : [512 x 1 x *] (gradient) }
|
||||
{ B0 : [512 x 1] (gradient)
|
||||
H1 : [512 x 1 x *] (gradient)
|
||||
W1*H1+B1 : [512 x 1 x *] (gradient)
|
||||
W2*H1 : [132 x 1 x *] }
|
||||
{ HLast : [132 x 1 x *]
|
||||
W2 : [132 x 512] (gradient) }
|
||||
{ B1 : [512 x 1] (gradient)
|
||||
H2 : [512 x 1 x *] (gradient)
|
||||
HLast : [132 x 1 x *] (gradient) }
|
||||
|
||||
|
||||
08/16/2016 10:01:46: Training 516740 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:
|
||||
|
||||
08/16/2016 10:01:46: Node 'B0' (LearnableParameter operation) : [512 x 1]
|
||||
08/16/2016 10:01:46: Node 'B1' (LearnableParameter operation) : [512 x 1]
|
||||
08/16/2016 10:01:46: Node 'B2' (LearnableParameter operation) : [132 x 1]
|
||||
08/16/2016 10:01:46: Node 'W0' (LearnableParameter operation) : [512 x 363]
|
||||
08/16/2016 10:01:46: Node 'W1' (LearnableParameter operation) : [512 x 512]
|
||||
08/16/2016 10:01:46: Node 'W2' (LearnableParameter operation) : [132 x 512]
|
||||
|
||||
|
||||
08/16/2016 10:01:46: Precomputing --> 3 PreCompute nodes found.
|
||||
|
||||
08/16/2016 10:01:46: MeanOfFeatures = Mean()
|
||||
08/16/2016 10:01:46: InvStdOfFeatures = InvStdDev()
|
||||
08/16/2016 10:01:46: Prior = Mean()
|
||||
minibatchiterator: epoch 0: frames [0..252734] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses
|
||||
requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms
|
||||
|
||||
08/16/2016 10:01:46: Precomputing --> Completed.
|
||||
|
||||
|
||||
08/16/2016 10:01:46: Starting Epoch 1: learning rate per sample = 0.003906 effective momentum = 0.900000 momentum as time constant = 2429.8 samples
|
||||
minibatchiterator: epoch 0: frames [0..2048] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses
|
||||
|
||||
08/16/2016 10:01:46: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1), distributed reading is ENABLED.
|
||||
08/16/2016 10:01:46: Finished Epoch[ 1 of 1]: [Training] CrossEntropyWithSoftmax = 4.41144794 * 2048; EvalErrorPrediction = 0.92773438 * 2048; totalSamplesSeen = 2048; learningRatePerSample = 0.00390625; epochTime=0.023072s
|
||||
08/16/2016 10:01:46: SGD: Saving checkpoint model '/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_FeedForward@release_gpu/Models/cntkSpeechFF.dnn'
|
||||
08/16/2016 10:01:46: CNTKCommandTrainEnd: speechTrain
|
||||
|
||||
08/16/2016 10:01:46: Action "train" complete.
|
||||
|
||||
08/16/2016 10:01:46: __COMPLETED__
|
||||
~MPIWrapper
|
||||
|
|
@ -1 +0,0 @@
|
|||
__COMPLETED__
|
||||
|
|
@ -1 +0,0 @@
|
|||
__COMPLETED__
|
||||
|
|
@ -1,18 +1,24 @@
|
|||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config/FeedForward.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_cpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_cpu DeviceId=-1 timestamping=true speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=2048]]
|
||||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 268381192 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config/FeedForward.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_cpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_cpu DeviceId=-1 timestamping=true speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=2048]] speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 3 2016 13:15:46
|
||||
Last modified date: Tue Apr 26 23:35:31 2016
|
||||
Built time: Aug 16 2016 03:09:16
|
||||
Last modified date: Fri Aug 12 05:28:23 2016
|
||||
Build type: Release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: no
|
||||
With 1bit-SGD: yes
|
||||
Math lib: mkl
|
||||
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
CUB_PATH: c:\src\cub-1.4.1
|
||||
CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
Build Branch: HEAD
|
||||
Build SHA1: af96f7cce6c3c78a4f1e9315e061291c79360e12
|
||||
Built by svcphil on cntk-muc01
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by svcphil on Philly-Pool1
|
||||
Build Path: c:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
|
|
@ -25,31 +31,39 @@ ping [requestnodes (after change)]: all 1 nodes responded
|
|||
mpihelper: only one MPI process: MPI operation will be boring
|
||||
ping [mpihelper]: 1 nodes pinging each other
|
||||
ping [mpihelper]: all 1 nodes responded
|
||||
05/03/2016 13:22:22: -------------------------------------------------------------------
|
||||
05/03/2016 13:22:22: Build info:
|
||||
08/16/2016 03:20:10: -------------------------------------------------------------------
|
||||
08/16/2016 03:20:10: Build info:
|
||||
|
||||
05/03/2016 13:22:22: Built time: May 3 2016 13:15:46
|
||||
05/03/2016 13:22:22: Last modified date: Tue Apr 26 23:35:31 2016
|
||||
05/03/2016 13:22:22: Build type: Release
|
||||
05/03/2016 13:22:22: Build target: GPU
|
||||
05/03/2016 13:22:22: With 1bit-SGD: no
|
||||
05/03/2016 13:22:22: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
05/03/2016 13:22:22: CUB_PATH: c:\src\cub-1.4.1
|
||||
05/03/2016 13:22:22: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
05/03/2016 13:22:22: Build Branch: HEAD
|
||||
05/03/2016 13:22:22: Build SHA1: af96f7cce6c3c78a4f1e9315e061291c79360e12
|
||||
05/03/2016 13:22:22: Built by svcphil on cntk-muc01
|
||||
05/03/2016 13:22:22: Build Path: c:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
05/03/2016 13:22:22: -------------------------------------------------------------------
|
||||
08/16/2016 03:20:10: Built time: Aug 16 2016 03:09:16
|
||||
08/16/2016 03:20:10: Last modified date: Fri Aug 12 05:28:23 2016
|
||||
08/16/2016 03:20:10: Build type: Release
|
||||
08/16/2016 03:20:10: Build target: GPU
|
||||
08/16/2016 03:20:10: With 1bit-SGD: yes
|
||||
08/16/2016 03:20:10: Math lib: mkl
|
||||
08/16/2016 03:20:10: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
08/16/2016 03:20:10: CUB_PATH: c:\src\cub-1.4.1
|
||||
08/16/2016 03:20:10: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
08/16/2016 03:20:10: Build Branch: HEAD
|
||||
08/16/2016 03:20:10: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 03:20:10: Built by svcphil on Philly-Pool1
|
||||
08/16/2016 03:20:10: Build Path: c:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
08/16/2016 03:20:10: -------------------------------------------------------------------
|
||||
08/16/2016 03:20:12: -------------------------------------------------------------------
|
||||
08/16/2016 03:20:12: GPU info:
|
||||
|
||||
05/03/2016 13:22:22: Running on DPHAIM-22 at 2016/05/03 13:22:22
|
||||
05/03/2016 13:22:22: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config/FeedForward.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_cpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_cpu DeviceId=-1 timestamping=true speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=2048]]
|
||||
08/16/2016 03:20:12: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:20:12: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:20:12: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:20:12: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 03:20:12: Running on DPHAIM-25 at 2016/08/16 03:20:12
|
||||
08/16/2016 03:20:12: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config/FeedForward.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_cpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_cpu DeviceId=-1 timestamping=true speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=2048]] speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
|
||||
|
||||
|
||||
05/03/2016 13:22:22: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 13:22:22: RootDir = ".."
|
||||
08/16/2016 03:20:12: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:20:12: RootDir = ".."
|
||||
ConfigDir = "$RootDir$/Config"
|
||||
DataDir = "$RootDir$/Data"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
|
|
@ -111,28 +125,29 @@ speechTrain = [
|
|||
]
|
||||
]
|
||||
currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_cpu
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_cpu
|
||||
DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_cpu
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_cpu
|
||||
DeviceId=-1
|
||||
timestamping=true
|
||||
speechTrain=[SGD=[maxEpochs=1]]
|
||||
speechTrain=[SGD=[epochSize=2048]]
|
||||
speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
|
||||
05/03/2016 13:22:22: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:20:12: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 13:22:22: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 13:22:22: RootDir = ".."
|
||||
08/16/2016 03:20:12: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:20:12: RootDir = ".."
|
||||
ConfigDir = "../Config"
|
||||
DataDir = "../Data"
|
||||
OutputDir = "../Output"
|
||||
ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_cpu/Models"
|
||||
ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_cpu/Models"
|
||||
deviceId = -1
|
||||
command = speechTrain
|
||||
precision = "float"
|
||||
traceLevel = "1"
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_cpu/Models/cntkSpeechFF.dnn"
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_cpu/Models/cntkSpeechFF.dnn"
|
||||
parallelTrain = true
|
||||
speechTrain = [
|
||||
action = "train"
|
||||
|
|
@ -185,30 +200,31 @@ speechTrain = [
|
|||
]
|
||||
]
|
||||
currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_cpu
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_cpu
|
||||
DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_cpu
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_cpu
|
||||
DeviceId=-1
|
||||
timestamping=true
|
||||
speechTrain=[SGD=[maxEpochs=1]]
|
||||
speechTrain=[SGD=[epochSize=2048]]
|
||||
speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
|
||||
05/03/2016 13:22:22: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:20:12: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 13:22:22: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:20:12: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: FeedForward.cntk:command=speechTrain
|
||||
configparameters: FeedForward.cntk:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config
|
||||
configparameters: FeedForward.cntk:currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
configparameters: FeedForward.cntk:DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
configparameters: FeedForward.cntk:deviceId=-1
|
||||
configparameters: FeedForward.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_cpu/Models
|
||||
configparameters: FeedForward.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_cpu/Models/cntkSpeechFF.dnn
|
||||
configparameters: FeedForward.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_cpu
|
||||
configparameters: FeedForward.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_cpu/Models
|
||||
configparameters: FeedForward.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_cpu/Models/cntkSpeechFF.dnn
|
||||
configparameters: FeedForward.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_cpu
|
||||
configparameters: FeedForward.cntk:parallelTrain=true
|
||||
configparameters: FeedForward.cntk:precision=float
|
||||
configparameters: FeedForward.cntk:RootDir=..
|
||||
configparameters: FeedForward.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_cpu
|
||||
configparameters: FeedForward.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_cpu
|
||||
configparameters: FeedForward.cntk:speechTrain=[
|
||||
action = "train"
|
||||
SimpleNetworkBuilder = [
|
||||
|
|
@ -258,24 +274,24 @@ configparameters: FeedForward.cntk:speechTrain=[
|
|||
labelType = "category"
|
||||
]
|
||||
]
|
||||
] [SGD=[maxEpochs=1]] [SGD=[epochSize=2048]]
|
||||
] [SGD=[maxEpochs=1]] [SGD=[epochSize=2048]] [reader=[useMersenneTwisterRand=true]]
|
||||
|
||||
configparameters: FeedForward.cntk:timestamping=true
|
||||
configparameters: FeedForward.cntk:traceLevel=1
|
||||
05/03/2016 13:22:22: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/03/2016 13:22:22: Commands: speechTrain
|
||||
05/03/2016 13:22:22: Precision = "float"
|
||||
05/03/2016 13:22:22: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_cpu/Models/cntkSpeechFF.dnn
|
||||
05/03/2016 13:22:22: CNTKCommandTrainInfo: speechTrain : 1
|
||||
05/03/2016 13:22:22: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 1
|
||||
08/16/2016 03:20:12: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:20:12: Commands: speechTrain
|
||||
08/16/2016 03:20:12: Precision = "float"
|
||||
08/16/2016 03:20:12: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_cpu/Models/cntkSpeechFF.dnn
|
||||
08/16/2016 03:20:12: CNTKCommandTrainInfo: speechTrain : 1
|
||||
08/16/2016 03:20:12: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 1
|
||||
|
||||
05/03/2016 13:22:22: ##############################################################################
|
||||
05/03/2016 13:22:22: # #
|
||||
05/03/2016 13:22:22: # Action "train" #
|
||||
05/03/2016 13:22:22: # #
|
||||
05/03/2016 13:22:22: ##############################################################################
|
||||
08/16/2016 03:20:12: ##############################################################################
|
||||
08/16/2016 03:20:12: # #
|
||||
08/16/2016 03:20:12: # Action "train" #
|
||||
08/16/2016 03:20:12: # #
|
||||
08/16/2016 03:20:12: ##############################################################################
|
||||
|
||||
05/03/2016 13:22:22: CNTKCommandTrainBegin: speechTrain
|
||||
08/16/2016 03:20:12: CNTKCommandTrainBegin: speechTrain
|
||||
SimpleNetworkBuilder Using CPU
|
||||
reading script file C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data/glob_0000.scp ... 948 entries
|
||||
total 132 state names in state list C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data/state.list
|
||||
|
|
@ -284,7 +300,19 @@ htkmlfreader: reading MLF file C:\jenkins\workspace\CNTK-Test-Windows-W1\Example
|
|||
label set 0: 129 classes
|
||||
minibatchutterancesource: 948 utterances grouped into 3 chunks, av. chunk size: 316.0 utterances, 84244.7 frames
|
||||
|
||||
05/03/2016 13:22:23: Creating virgin network.
|
||||
08/16/2016 03:20:12: Creating virgin network.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[512 x 363] <- 0.000000.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[512 x 363] <- uniform(seed=1, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[512 x 1] <- 0.000000.
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[512 x 1] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[512 x 512] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[512 x 512] <- uniform(seed=2, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[512 x 1] <- 0.000000.
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[512 x 1] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[132 x 512] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[132 x 512] <- uniform(seed=3, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[132 x 1] <- 0.000000.
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[132 x 1] <- 0.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -336,70 +364,70 @@ Validating network, final pass.
|
|||
|
||||
Post-processing network complete.
|
||||
|
||||
05/03/2016 13:22:23: Created model with 25 nodes on CPU.
|
||||
08/16/2016 03:20:12: Created model with 25 nodes on CPU.
|
||||
|
||||
05/03/2016 13:22:23: Training criterion node(s):
|
||||
05/03/2016 13:22:23: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
|
||||
08/16/2016 03:20:12: Training criterion node(s):
|
||||
08/16/2016 03:20:12: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
|
||||
|
||||
05/03/2016 13:22:23: Evaluation criterion node(s):
|
||||
|
||||
05/03/2016 13:22:23: EvalErrorPrediction = ErrorPrediction
|
||||
08/16/2016 03:20:12: Evaluation criterion node(s):
|
||||
08/16/2016 03:20:12: EvalErrorPrediction = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 40 matrices, 19 are shared as 8, and 21 are not shared.
|
||||
|
||||
0000000000000000: {[EvalErrorPrediction Gradient[1]] [InvStdOfFeatures Gradient[363]] [LogOfPrior Gradient[132]] [MVNormalizedFeatures Gradient[363 x *]] [MeanOfFeatures Gradient[363]] [PosteriorProb Gradient[132 x 1 x *]] [PosteriorProb Value[132 x 1 x *]] [Prior Gradient[132]] [ScaledLogLikelihood Gradient[132 x 1 x *]] [features Gradient[363 x *]] [labels Gradient[132 x *]] }
|
||||
000000BDD334C430: {[features Value[363 x *]] }
|
||||
000000BDD334C4D0: {[W0 Value[512 x 363]] }
|
||||
000000BDD334C610: {[MeanOfFeatures Value[363]] }
|
||||
000000BDD334C890: {[B0 Value[512 x 1]] }
|
||||
000000BDD334CCF0: {[W1 Value[512 x 512]] }
|
||||
000000BDD334CE30: {[B1 Value[512 x 1]] }
|
||||
000000BDD334D1F0: {[InvStdOfFeatures Value[363]] }
|
||||
000000BDD5BCA080: {[Prior Value[132]] }
|
||||
000000BDD5BCA120: {[EvalErrorPrediction Value[1]] }
|
||||
000000BDD5BCA260: {[W2 Value[132 x 512]] }
|
||||
000000BDD5BCA440: {[labels Value[132 x *]] }
|
||||
000000BDD5BCA6C0: {[MVNormalizedFeatures Value[363 x *]] }
|
||||
000000BDD5BCAE40: {[B0 Gradient[512 x 1]] [H1 Gradient[512 x 1 x *]] [W1*H1+B1 Gradient[512 x 1 x *]] [W2*H1 Value[132 x 1 x *]] }
|
||||
000000BDD5BCAEE0: {[CrossEntropyWithSoftmax Gradient[1]] }
|
||||
000000BDD5BCAF80: {[B1 Gradient[512 x 1]] [H2 Gradient[512 x 1 x *]] [HLast Gradient[132 x 1 x *]] }
|
||||
000000BDD5BCB0C0: {[H1 Value[512 x 1 x *]] [W0*features Gradient[512 x *]] }
|
||||
000000BDD5BCB160: {[ScaledLogLikelihood Value[132 x 1 x *]] }
|
||||
000000BDD5BCB340: {[W0 Gradient[512 x 363]] [W0*features+B0 Value[512 x 1 x *]] }
|
||||
000000BDD5BCB520: {[W1 Gradient[512 x 512]] [W1*H1+B1 Value[512 x 1 x *]] }
|
||||
000000BDD5BCB5C0: {[B2 Gradient[132 x 1]] }
|
||||
000000BDD5BCB700: {[W0*features Value[512 x *]] }
|
||||
000000BDD5BCB7A0: {[HLast Value[132 x 1 x *]] [W2 Gradient[132 x 512]] }
|
||||
000000BDD5BCB8E0: {[LogOfPrior Value[132]] }
|
||||
000000BDD5BCB980: {[H2 Value[512 x 1 x *]] [W1*H1 Gradient[512 x 1 x *]] }
|
||||
000000BDD5BCBAC0: {[B2 Value[132 x 1]] }
|
||||
000000BDD5BCBB60: {[CrossEntropyWithSoftmax Value[1]] }
|
||||
000000BDD5BCBC00: {[W0*features+B0 Gradient[512 x 1 x *]] [W1*H1 Value[512 x 1 x *]] }
|
||||
000000BDD5BCBCA0: {[W2*H1 Gradient[132 x 1 x *]] }
|
||||
{ W0*features+B0 : [512 x 1 x *] (gradient)
|
||||
W1*H1 : [512 x 1 x *] }
|
||||
{ W0 : [512 x 363] (gradient)
|
||||
W0*features+B0 : [512 x 1 x *] }
|
||||
{ H1 : [512 x 1 x *]
|
||||
W0*features : [512 x *] (gradient) }
|
||||
{ W1 : [512 x 512] (gradient)
|
||||
W1*H1+B1 : [512 x 1 x *] }
|
||||
{ H2 : [512 x 1 x *]
|
||||
W1*H1 : [512 x 1 x *] (gradient) }
|
||||
{ HLast : [132 x 1 x *]
|
||||
W2 : [132 x 512] (gradient) }
|
||||
{ B0 : [512 x 1] (gradient)
|
||||
H1 : [512 x 1 x *] (gradient)
|
||||
W1*H1+B1 : [512 x 1 x *] (gradient)
|
||||
W2*H1 : [132 x 1 x *] }
|
||||
{ B1 : [512 x 1] (gradient)
|
||||
H2 : [512 x 1 x *] (gradient)
|
||||
HLast : [132 x 1 x *] (gradient) }
|
||||
|
||||
|
||||
05/03/2016 13:22:23: Precomputing --> 3 PreCompute nodes found.
|
||||
08/16/2016 03:20:12: Training 516740 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:
|
||||
|
||||
05/03/2016 13:22:23: MeanOfFeatures = Mean()
|
||||
05/03/2016 13:22:23: InvStdOfFeatures = InvStdDev()
|
||||
05/03/2016 13:22:23: Prior = Mean()
|
||||
08/16/2016 03:20:12: Node 'B0' (LearnableParameter operation) : [512 x 1]
|
||||
08/16/2016 03:20:12: Node 'B1' (LearnableParameter operation) : [512 x 1]
|
||||
08/16/2016 03:20:12: Node 'B2' (LearnableParameter operation) : [132 x 1]
|
||||
08/16/2016 03:20:12: Node 'W0' (LearnableParameter operation) : [512 x 363]
|
||||
08/16/2016 03:20:12: Node 'W1' (LearnableParameter operation) : [512 x 512]
|
||||
08/16/2016 03:20:12: Node 'W2' (LearnableParameter operation) : [132 x 512]
|
||||
|
||||
|
||||
08/16/2016 03:20:12: Precomputing --> 3 PreCompute nodes found.
|
||||
|
||||
08/16/2016 03:20:12: MeanOfFeatures = Mean()
|
||||
08/16/2016 03:20:12: InvStdOfFeatures = InvStdDev()
|
||||
08/16/2016 03:20:12: Prior = Mean()
|
||||
minibatchiterator: epoch 0: frames [0..252734] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses
|
||||
requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms
|
||||
|
||||
05/03/2016 13:22:24: Precomputing --> Completed.
|
||||
08/16/2016 03:20:15: Precomputing --> Completed.
|
||||
|
||||
|
||||
05/03/2016 13:22:24: Starting Epoch 1: learning rate per sample = 0.003906 effective momentum = 0.900000 momentum as time constant = 2429.8 samples
|
||||
08/16/2016 03:20:15: Starting Epoch 1: learning rate per sample = 0.003906 effective momentum = 0.900000 momentum as time constant = 2429.8 samples
|
||||
minibatchiterator: epoch 0: frames [0..2048] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses
|
||||
|
||||
05/03/2016 13:22:24: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1), distributed reading is ENABLED.
|
||||
05/03/2016 13:22:25: Finished Epoch[ 1 of 1]: [Training] CrossEntropyWithSoftmax = 4.48531419 * 2048; EvalErrorPrediction = 0.90722656 * 2048; totalSamplesSeen = 2048; learningRatePerSample = 0.00390625; epochTime=0.288909s
|
||||
05/03/2016 13:22:25: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_cpu/Models/cntkSpeechFF.dnn'
|
||||
05/03/2016 13:22:25: CNTKCommandTrainEnd: speechTrain
|
||||
08/16/2016 03:20:15: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1), distributed reading is ENABLED.
|
||||
08/16/2016 03:20:15: Finished Epoch[ 1 of 1]: [Training] CrossEntropyWithSoftmax = 4.46427900 * 2048; EvalErrorPrediction = 0.91259766 * 2048; totalSamplesSeen = 2048; learningRatePerSample = 0.00390625; epochTime=0.28059s
|
||||
08/16/2016 03:20:15: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_cpu/Models/cntkSpeechFF.dnn'
|
||||
08/16/2016 03:20:15: CNTKCommandTrainEnd: speechTrain
|
||||
|
||||
05/03/2016 13:22:25: Action "train" complete.
|
||||
08/16/2016 03:20:15: Action "train" complete.
|
||||
|
||||
05/03/2016 13:22:25: __COMPLETED__
|
||||
08/16/2016 03:20:15: __COMPLETED__
|
||||
~MPIWrapper
|
||||
|
|
@ -1 +0,0 @@
|
|||
__COMPLETED__
|
||||
|
|
@ -1 +0,0 @@
|
|||
__COMPLETED__
|
||||
|
|
@ -1,18 +1,24 @@
|
|||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config/FeedForward.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_gpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_gpu DeviceId=0 timestamping=true speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=2048]]
|
||||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 268381192 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config/FeedForward.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_gpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_gpu DeviceId=0 timestamping=true speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=2048]] speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 3 2016 13:15:46
|
||||
Last modified date: Tue Apr 26 23:35:31 2016
|
||||
Built time: Aug 16 2016 03:09:16
|
||||
Last modified date: Fri Aug 12 05:28:23 2016
|
||||
Build type: Release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: no
|
||||
With 1bit-SGD: yes
|
||||
Math lib: mkl
|
||||
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
CUB_PATH: c:\src\cub-1.4.1
|
||||
CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
Build Branch: HEAD
|
||||
Build SHA1: af96f7cce6c3c78a4f1e9315e061291c79360e12
|
||||
Built by svcphil on cntk-muc01
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by svcphil on Philly-Pool1
|
||||
Build Path: c:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
|
|
@ -25,31 +31,39 @@ ping [requestnodes (after change)]: all 1 nodes responded
|
|||
mpihelper: only one MPI process: MPI operation will be boring
|
||||
ping [mpihelper]: 1 nodes pinging each other
|
||||
ping [mpihelper]: all 1 nodes responded
|
||||
05/03/2016 13:22:25: -------------------------------------------------------------------
|
||||
05/03/2016 13:22:25: Build info:
|
||||
08/16/2016 03:20:17: -------------------------------------------------------------------
|
||||
08/16/2016 03:20:17: Build info:
|
||||
|
||||
05/03/2016 13:22:25: Built time: May 3 2016 13:15:46
|
||||
05/03/2016 13:22:25: Last modified date: Tue Apr 26 23:35:31 2016
|
||||
05/03/2016 13:22:25: Build type: Release
|
||||
05/03/2016 13:22:25: Build target: GPU
|
||||
05/03/2016 13:22:25: With 1bit-SGD: no
|
||||
05/03/2016 13:22:25: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
05/03/2016 13:22:25: CUB_PATH: c:\src\cub-1.4.1
|
||||
05/03/2016 13:22:25: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
05/03/2016 13:22:25: Build Branch: HEAD
|
||||
05/03/2016 13:22:25: Build SHA1: af96f7cce6c3c78a4f1e9315e061291c79360e12
|
||||
05/03/2016 13:22:25: Built by svcphil on cntk-muc01
|
||||
05/03/2016 13:22:25: Build Path: c:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
05/03/2016 13:22:25: -------------------------------------------------------------------
|
||||
08/16/2016 03:20:17: Built time: Aug 16 2016 03:09:16
|
||||
08/16/2016 03:20:17: Last modified date: Fri Aug 12 05:28:23 2016
|
||||
08/16/2016 03:20:17: Build type: Release
|
||||
08/16/2016 03:20:17: Build target: GPU
|
||||
08/16/2016 03:20:17: With 1bit-SGD: yes
|
||||
08/16/2016 03:20:17: Math lib: mkl
|
||||
08/16/2016 03:20:17: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
08/16/2016 03:20:17: CUB_PATH: c:\src\cub-1.4.1
|
||||
08/16/2016 03:20:17: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
08/16/2016 03:20:17: Build Branch: HEAD
|
||||
08/16/2016 03:20:17: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 03:20:17: Built by svcphil on Philly-Pool1
|
||||
08/16/2016 03:20:17: Build Path: c:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
08/16/2016 03:20:17: -------------------------------------------------------------------
|
||||
08/16/2016 03:20:19: -------------------------------------------------------------------
|
||||
08/16/2016 03:20:19: GPU info:
|
||||
|
||||
05/03/2016 13:22:25: Running on DPHAIM-22 at 2016/05/03 13:22:25
|
||||
05/03/2016 13:22:25: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config/FeedForward.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_gpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_gpu DeviceId=0 timestamping=true speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=2048]]
|
||||
08/16/2016 03:20:19: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:20:19: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:20:19: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:20:19: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 03:20:19: Running on DPHAIM-25 at 2016/08/16 03:20:19
|
||||
08/16/2016 03:20:19: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config/FeedForward.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_gpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_gpu DeviceId=0 timestamping=true speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=2048]] speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
|
||||
|
||||
|
||||
05/03/2016 13:22:25: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 13:22:25: RootDir = ".."
|
||||
08/16/2016 03:20:19: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:20:19: RootDir = ".."
|
||||
ConfigDir = "$RootDir$/Config"
|
||||
DataDir = "$RootDir$/Data"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
|
|
@ -111,28 +125,29 @@ speechTrain = [
|
|||
]
|
||||
]
|
||||
currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_gpu
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_gpu
|
||||
DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_gpu
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
speechTrain=[SGD=[maxEpochs=1]]
|
||||
speechTrain=[SGD=[epochSize=2048]]
|
||||
speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
|
||||
05/03/2016 13:22:25: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:20:19: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 13:22:25: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 13:22:25: RootDir = ".."
|
||||
08/16/2016 03:20:19: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:20:19: RootDir = ".."
|
||||
ConfigDir = "../Config"
|
||||
DataDir = "../Data"
|
||||
OutputDir = "../Output"
|
||||
ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_gpu/Models"
|
||||
ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_gpu/Models"
|
||||
deviceId = -1
|
||||
command = speechTrain
|
||||
precision = "float"
|
||||
traceLevel = "1"
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_gpu/Models/cntkSpeechFF.dnn"
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_gpu/Models/cntkSpeechFF.dnn"
|
||||
parallelTrain = true
|
||||
speechTrain = [
|
||||
action = "train"
|
||||
|
|
@ -185,30 +200,31 @@ speechTrain = [
|
|||
]
|
||||
]
|
||||
currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_gpu
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_gpu
|
||||
DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_gpu
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
speechTrain=[SGD=[maxEpochs=1]]
|
||||
speechTrain=[SGD=[epochSize=2048]]
|
||||
speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
|
||||
05/03/2016 13:22:25: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:20:19: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 13:22:25: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:20:19: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: FeedForward.cntk:command=speechTrain
|
||||
configparameters: FeedForward.cntk:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config
|
||||
configparameters: FeedForward.cntk:currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
configparameters: FeedForward.cntk:DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
configparameters: FeedForward.cntk:deviceId=0
|
||||
configparameters: FeedForward.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_gpu/Models
|
||||
configparameters: FeedForward.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_gpu/Models/cntkSpeechFF.dnn
|
||||
configparameters: FeedForward.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_gpu
|
||||
configparameters: FeedForward.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_gpu/Models
|
||||
configparameters: FeedForward.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_gpu/Models/cntkSpeechFF.dnn
|
||||
configparameters: FeedForward.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_gpu
|
||||
configparameters: FeedForward.cntk:parallelTrain=true
|
||||
configparameters: FeedForward.cntk:precision=float
|
||||
configparameters: FeedForward.cntk:RootDir=..
|
||||
configparameters: FeedForward.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_gpu
|
||||
configparameters: FeedForward.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_gpu
|
||||
configparameters: FeedForward.cntk:speechTrain=[
|
||||
action = "train"
|
||||
SimpleNetworkBuilder = [
|
||||
|
|
@ -258,24 +274,24 @@ configparameters: FeedForward.cntk:speechTrain=[
|
|||
labelType = "category"
|
||||
]
|
||||
]
|
||||
] [SGD=[maxEpochs=1]] [SGD=[epochSize=2048]]
|
||||
] [SGD=[maxEpochs=1]] [SGD=[epochSize=2048]] [reader=[useMersenneTwisterRand=true]]
|
||||
|
||||
configparameters: FeedForward.cntk:timestamping=true
|
||||
configparameters: FeedForward.cntk:traceLevel=1
|
||||
05/03/2016 13:22:25: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/03/2016 13:22:25: Commands: speechTrain
|
||||
05/03/2016 13:22:25: Precision = "float"
|
||||
05/03/2016 13:22:25: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_gpu/Models/cntkSpeechFF.dnn
|
||||
05/03/2016 13:22:25: CNTKCommandTrainInfo: speechTrain : 1
|
||||
05/03/2016 13:22:25: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 1
|
||||
08/16/2016 03:20:19: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:20:19: Commands: speechTrain
|
||||
08/16/2016 03:20:19: Precision = "float"
|
||||
08/16/2016 03:20:19: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_gpu/Models/cntkSpeechFF.dnn
|
||||
08/16/2016 03:20:19: CNTKCommandTrainInfo: speechTrain : 1
|
||||
08/16/2016 03:20:19: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 1
|
||||
|
||||
05/03/2016 13:22:25: ##############################################################################
|
||||
05/03/2016 13:22:25: # #
|
||||
05/03/2016 13:22:25: # Action "train" #
|
||||
05/03/2016 13:22:25: # #
|
||||
05/03/2016 13:22:25: ##############################################################################
|
||||
08/16/2016 03:20:19: ##############################################################################
|
||||
08/16/2016 03:20:19: # #
|
||||
08/16/2016 03:20:19: # Action "train" #
|
||||
08/16/2016 03:20:19: # #
|
||||
08/16/2016 03:20:19: ##############################################################################
|
||||
|
||||
05/03/2016 13:22:25: CNTKCommandTrainBegin: speechTrain
|
||||
08/16/2016 03:20:19: CNTKCommandTrainBegin: speechTrain
|
||||
SimpleNetworkBuilder Using GPU 0
|
||||
reading script file C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data/glob_0000.scp ... 948 entries
|
||||
total 132 state names in state list C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data/state.list
|
||||
|
|
@ -284,8 +300,20 @@ htkmlfreader: reading MLF file C:\jenkins\workspace\CNTK-Test-Windows-W1\Example
|
|||
label set 0: 129 classes
|
||||
minibatchutterancesource: 948 utterances grouped into 3 chunks, av. chunk size: 316.0 utterances, 84244.7 frames
|
||||
|
||||
05/03/2016 13:22:25: Creating virgin network.
|
||||
08/16/2016 03:20:19: Creating virgin network.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[512 x 363] <- 0.000000.
|
||||
Node 'W0' (LearnableParameter operation): Initializing Parameter[512 x 363] <- uniform(seed=1, range=0.050000*1.000000, onCPU=false).
|
||||
Microsoft::MSR::CNTK::GPUMatrix<ElemType>::SetUniformRandomValue (GPU): creating curand object with seed 1, sizeof(ElemType)==4
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[512 x 1] <- 0.000000.
|
||||
Node 'B0' (LearnableParameter operation): Initializing Parameter[512 x 1] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[512 x 512] <- 0.000000.
|
||||
Node 'W1' (LearnableParameter operation): Initializing Parameter[512 x 512] <- uniform(seed=2, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[512 x 1] <- 0.000000.
|
||||
Node 'B1' (LearnableParameter operation): Initializing Parameter[512 x 1] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[132 x 512] <- 0.000000.
|
||||
Node 'W2' (LearnableParameter operation): Initializing Parameter[132 x 512] <- uniform(seed=3, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[132 x 1] <- 0.000000.
|
||||
Node 'B2' (LearnableParameter operation): Initializing Parameter[132 x 1] <- 0.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -337,70 +365,70 @@ Validating network, final pass.
|
|||
|
||||
Post-processing network complete.
|
||||
|
||||
05/03/2016 13:22:26: Created model with 25 nodes on GPU 0.
|
||||
08/16/2016 03:20:20: Created model with 25 nodes on GPU 0.
|
||||
|
||||
05/03/2016 13:22:26: Training criterion node(s):
|
||||
05/03/2016 13:22:26: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
|
||||
08/16/2016 03:20:20: Training criterion node(s):
|
||||
08/16/2016 03:20:20: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
|
||||
|
||||
05/03/2016 13:22:26: Evaluation criterion node(s):
|
||||
|
||||
05/03/2016 13:22:26: EvalErrorPrediction = ErrorPrediction
|
||||
08/16/2016 03:20:20: Evaluation criterion node(s):
|
||||
08/16/2016 03:20:20: EvalErrorPrediction = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 40 matrices, 19 are shared as 8, and 21 are not shared.
|
||||
|
||||
0000000000000000: {[EvalErrorPrediction Gradient[1]] [InvStdOfFeatures Gradient[363]] [LogOfPrior Gradient[132]] [MVNormalizedFeatures Gradient[363 x *]] [MeanOfFeatures Gradient[363]] [PosteriorProb Gradient[132 x 1 x *]] [PosteriorProb Value[132 x 1 x *]] [Prior Gradient[132]] [ScaledLogLikelihood Gradient[132 x 1 x *]] [features Gradient[363 x *]] [labels Gradient[132 x *]] }
|
||||
00000087D360C610: {[features Value[363 x *]] }
|
||||
00000087EB4FEEF0: {[W0 Value[512 x 363]] }
|
||||
00000087EB4FF530: {[B1 Value[512 x 1]] }
|
||||
00000087EB4FF850: {[W1 Value[512 x 512]] }
|
||||
00000087EB4FFC10: {[W2 Value[132 x 512]] }
|
||||
00000087EB500070: {[B2 Value[132 x 1]] }
|
||||
00000087EB5001B0: {[MeanOfFeatures Value[363]] }
|
||||
00000087EB500250: {[InvStdOfFeatures Value[363]] }
|
||||
00000087EB5004D0: {[B0 Value[512 x 1]] }
|
||||
00000087EDA2B150: {[labels Value[132 x *]] }
|
||||
00000087EDA2B330: {[B1 Gradient[512 x 1]] [H2 Gradient[512 x 1 x *]] [HLast Gradient[132 x 1 x *]] }
|
||||
00000087EDA2B3D0: {[Prior Value[132]] }
|
||||
00000087EDA2B6F0: {[HLast Value[132 x 1 x *]] [W2 Gradient[132 x 512]] }
|
||||
00000087EDA2B8D0: {[W0 Gradient[512 x 363]] [W0*features+B0 Value[512 x 1 x *]] }
|
||||
00000087EDA2BB50: {[CrossEntropyWithSoftmax Value[1]] }
|
||||
00000087EDA2BC90: {[W0*features+B0 Gradient[512 x 1 x *]] [W1*H1 Value[512 x 1 x *]] }
|
||||
00000087EDA2C0F0: {[EvalErrorPrediction Value[1]] }
|
||||
00000087EDA2C190: {[W0*features Value[512 x *]] }
|
||||
00000087EDA2C2D0: {[H1 Value[512 x 1 x *]] [W0*features Gradient[512 x *]] }
|
||||
00000087EDA2C370: {[W2*H1 Gradient[132 x 1 x *]] }
|
||||
00000087EDA2C410: {[B2 Gradient[132 x 1]] }
|
||||
00000087EDA2C730: {[ScaledLogLikelihood Value[132 x 1 x *]] }
|
||||
00000087EDA2C7D0: {[LogOfPrior Value[132]] }
|
||||
00000087EDA2CAF0: {[MVNormalizedFeatures Value[363 x *]] }
|
||||
00000087EDA2CB90: {[H2 Value[512 x 1 x *]] [W1*H1 Gradient[512 x 1 x *]] }
|
||||
00000087EDA2CCD0: {[B0 Gradient[512 x 1]] [H1 Gradient[512 x 1 x *]] [W1*H1+B1 Gradient[512 x 1 x *]] [W2*H1 Value[132 x 1 x *]] }
|
||||
00000087EDA2CEB0: {[CrossEntropyWithSoftmax Gradient[1]] }
|
||||
00000087EDA2CFF0: {[W1 Gradient[512 x 512]] [W1*H1+B1 Value[512 x 1 x *]] }
|
||||
{ W0*features+B0 : [512 x 1 x *] (gradient)
|
||||
W1*H1 : [512 x 1 x *] }
|
||||
{ H2 : [512 x 1 x *]
|
||||
W1*H1 : [512 x 1 x *] (gradient) }
|
||||
{ HLast : [132 x 1 x *]
|
||||
W2 : [132 x 512] (gradient) }
|
||||
{ W0 : [512 x 363] (gradient)
|
||||
W0*features+B0 : [512 x 1 x *] }
|
||||
{ B0 : [512 x 1] (gradient)
|
||||
H1 : [512 x 1 x *] (gradient)
|
||||
W1*H1+B1 : [512 x 1 x *] (gradient)
|
||||
W2*H1 : [132 x 1 x *] }
|
||||
{ H1 : [512 x 1 x *]
|
||||
W0*features : [512 x *] (gradient) }
|
||||
{ W1 : [512 x 512] (gradient)
|
||||
W1*H1+B1 : [512 x 1 x *] }
|
||||
{ B1 : [512 x 1] (gradient)
|
||||
H2 : [512 x 1 x *] (gradient)
|
||||
HLast : [132 x 1 x *] (gradient) }
|
||||
|
||||
|
||||
05/03/2016 13:22:26: Precomputing --> 3 PreCompute nodes found.
|
||||
08/16/2016 03:20:20: Training 516740 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:
|
||||
|
||||
05/03/2016 13:22:26: MeanOfFeatures = Mean()
|
||||
05/03/2016 13:22:26: InvStdOfFeatures = InvStdDev()
|
||||
05/03/2016 13:22:26: Prior = Mean()
|
||||
08/16/2016 03:20:20: Node 'B0' (LearnableParameter operation) : [512 x 1]
|
||||
08/16/2016 03:20:20: Node 'B1' (LearnableParameter operation) : [512 x 1]
|
||||
08/16/2016 03:20:20: Node 'B2' (LearnableParameter operation) : [132 x 1]
|
||||
08/16/2016 03:20:20: Node 'W0' (LearnableParameter operation) : [512 x 363]
|
||||
08/16/2016 03:20:20: Node 'W1' (LearnableParameter operation) : [512 x 512]
|
||||
08/16/2016 03:20:20: Node 'W2' (LearnableParameter operation) : [132 x 512]
|
||||
|
||||
|
||||
08/16/2016 03:20:20: Precomputing --> 3 PreCompute nodes found.
|
||||
|
||||
08/16/2016 03:20:20: MeanOfFeatures = Mean()
|
||||
08/16/2016 03:20:20: InvStdOfFeatures = InvStdDev()
|
||||
08/16/2016 03:20:20: Prior = Mean()
|
||||
minibatchiterator: epoch 0: frames [0..252734] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses
|
||||
requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms
|
||||
|
||||
05/03/2016 13:22:27: Precomputing --> Completed.
|
||||
08/16/2016 03:20:21: Precomputing --> Completed.
|
||||
|
||||
|
||||
05/03/2016 13:22:27: Starting Epoch 1: learning rate per sample = 0.003906 effective momentum = 0.900000 momentum as time constant = 2429.8 samples
|
||||
08/16/2016 03:20:21: Starting Epoch 1: learning rate per sample = 0.003906 effective momentum = 0.900000 momentum as time constant = 2429.8 samples
|
||||
minibatchiterator: epoch 0: frames [0..2048] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses
|
||||
|
||||
05/03/2016 13:22:27: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1), distributed reading is ENABLED.
|
||||
05/03/2016 13:22:27: Finished Epoch[ 1 of 1]: [Training] CrossEntropyWithSoftmax = 4.42832291 * 2048; EvalErrorPrediction = 0.91357422 * 2048; totalSamplesSeen = 2048; learningRatePerSample = 0.00390625; epochTime=0.052947s
|
||||
05/03/2016 13:22:27: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503132211.330996\Examples\Speech\AN4_FeedForward@release_gpu/Models/cntkSpeechFF.dnn'
|
||||
05/03/2016 13:22:27: CNTKCommandTrainEnd: speechTrain
|
||||
08/16/2016 03:20:21: Starting minibatch loop, DataParallelSGD training (MyRank = 0, NumNodes = 1, NumGradientBits = 1), distributed reading is ENABLED.
|
||||
08/16/2016 03:20:21: Finished Epoch[ 1 of 1]: [Training] CrossEntropyWithSoftmax = 4.41144794 * 2048; EvalErrorPrediction = 0.92773438 * 2048; totalSamplesSeen = 2048; learningRatePerSample = 0.00390625; epochTime=0.05551s
|
||||
08/16/2016 03:20:21: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_FeedForward@release_gpu/Models/cntkSpeechFF.dnn'
|
||||
08/16/2016 03:20:21: CNTKCommandTrainEnd: speechTrain
|
||||
|
||||
05/03/2016 13:22:27: Action "train" complete.
|
||||
08/16/2016 03:20:21: Action "train" complete.
|
||||
|
||||
05/03/2016 13:22:27: __COMPLETED__
|
||||
08/16/2016 03:20:21: __COMPLETED__
|
||||
~MPIWrapper
|
||||
|
|
@ -5,5 +5,5 @@
|
|||
ConfigDir=$TEST_DIR/../../../../../../Examples/Speech/AN4/Config
|
||||
|
||||
# cntkrun <CNTK config file name> <additional CNTK args>
|
||||
cntkrun FeedForward.cntk "speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=2048]]" || exit $?
|
||||
cntkrun FeedForward.cntk "speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=2048]] speechTrain=[reader=[useMersenneTwisterRand=true]]" || exit $?
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,682 @@
|
|||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 264172964 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/1bitsgd/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/LSTM/../../../../../../Examples/Speech/AN4/Config/LSTM-NDL.cntk currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_cpu DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/LSTM/../../../../../../Examples/Speech/AN4/Config OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_cpu DeviceId=-1 timestamping=true speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=64]] speechTrain=[reader=[useMersenneTwisterRand=true]] parallelTrain=false
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: Aug 16 2016 09:41:57
|
||||
Last modified date: Mon Aug 15 23:39:17 2016
|
||||
Build type: release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: yes
|
||||
Math lib: mkl
|
||||
CUDA_PATH: /usr/local/cuda-7.5
|
||||
CUB_PATH: /usr/local/cub-1.4.1
|
||||
CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by philly on 643085f7f8c2
|
||||
Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
08/16/2016 10:01:47: -------------------------------------------------------------------
|
||||
08/16/2016 10:01:47: Build info:
|
||||
|
||||
08/16/2016 10:01:47: Built time: Aug 16 2016 09:41:57
|
||||
08/16/2016 10:01:47: Last modified date: Mon Aug 15 23:39:17 2016
|
||||
08/16/2016 10:01:47: Build type: release
|
||||
08/16/2016 10:01:47: Build target: GPU
|
||||
08/16/2016 10:01:47: With 1bit-SGD: yes
|
||||
08/16/2016 10:01:47: Math lib: mkl
|
||||
08/16/2016 10:01:47: CUDA_PATH: /usr/local/cuda-7.5
|
||||
08/16/2016 10:01:47: CUB_PATH: /usr/local/cub-1.4.1
|
||||
08/16/2016 10:01:47: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
08/16/2016 10:01:47: Build Branch: HEAD
|
||||
08/16/2016 10:01:47: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 10:01:47: Built by philly on 643085f7f8c2
|
||||
08/16/2016 10:01:47: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
08/16/2016 10:01:47: -------------------------------------------------------------------
|
||||
08/16/2016 10:01:47: -------------------------------------------------------------------
|
||||
08/16/2016 10:01:47: GPU info:
|
||||
|
||||
08/16/2016 10:01:47: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:01:47: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:01:47: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:01:47: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:01:47: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 10:01:47: Running on localhost at 2016/08/16 10:01:47
|
||||
08/16/2016 10:01:47: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/1bitsgd/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/LSTM/../../../../../../Examples/Speech/AN4/Config/LSTM-NDL.cntk currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_cpu DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/LSTM/../../../../../../Examples/Speech/AN4/Config OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_cpu DeviceId=-1 timestamping=true speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=64]] speechTrain=[reader=[useMersenneTwisterRand=true]] parallelTrain=false
|
||||
|
||||
|
||||
|
||||
08/16/2016 10:01:47: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:01:47: RootDir = ".."
|
||||
ConfigDir = "$RootDir$/Config"
|
||||
DataDir = "$RootDir$/Data"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
ModelDir = "$OutputDir$/Models"
|
||||
deviceId = -1
|
||||
command = speechTrain
|
||||
precision = "float"
|
||||
traceLevel = 1
|
||||
modelPath = "$ModelDir$/cntkSpeechLSTM.dnn"
|
||||
parallelTrain = true
|
||||
frameMode = false
|
||||
truncated = true
|
||||
speechTrain = [
|
||||
action = "train"
|
||||
nbrUttsIneachRecurrentIter = 16
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "$ConfigDir$/lstmp-3layer-opt.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 0
|
||||
minibatchSize = 16
|
||||
learningRatesPerMB = 0.5
|
||||
numMBsToShowResult = 10
|
||||
momentumPerMB = 0:0.9
|
||||
maxEpochs = 4
|
||||
keepCheckPointFiles = true
|
||||
]
|
||||
reader = [
|
||||
readerType = "HTKMLFReader"
|
||||
readMethod = "blockRandomize"
|
||||
miniBatchMode = "partial"
|
||||
randomize = "auto"
|
||||
verbosity = 0
|
||||
features = [
|
||||
dim = 363
|
||||
type = "real"
|
||||
scpFile = "$DataDir$/glob_0000.scp"
|
||||
]
|
||||
labels = [
|
||||
mlfFile = "$DataDir$/glob_0000.mlf"
|
||||
labelMappingFile = "$DataDir$/state.list"
|
||||
labelDim = 132
|
||||
labelType = "category"
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_cpu
|
||||
DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/LSTM/../../../../../../Examples/Speech/AN4/Config
|
||||
OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_cpu
|
||||
DeviceId=-1
|
||||
timestamping=true
|
||||
speechTrain=[SGD=[maxEpochs=1]]
|
||||
speechTrain=[SGD=[epochSize=64]]
|
||||
speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
parallelTrain=false
|
||||
|
||||
08/16/2016 10:01:47: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
08/16/2016 10:01:47: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:01:47: RootDir = ".."
|
||||
ConfigDir = "../Config"
|
||||
DataDir = "../Data"
|
||||
OutputDir = "../Output"
|
||||
ModelDir = "/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_cpu/Models"
|
||||
deviceId = -1
|
||||
command = speechTrain
|
||||
precision = "float"
|
||||
traceLevel = 1
|
||||
modelPath = "/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_cpu/Models/cntkSpeechLSTM.dnn"
|
||||
parallelTrain = true
|
||||
frameMode = false
|
||||
truncated = true
|
||||
speechTrain = [
|
||||
action = "train"
|
||||
nbrUttsIneachRecurrentIter = 16
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/LSTM/../../../../../../Examples/Speech/AN4/Config/lstmp-3layer-opt.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 0
|
||||
minibatchSize = 16
|
||||
learningRatesPerMB = 0.5
|
||||
numMBsToShowResult = 10
|
||||
momentumPerMB = 0:0.9
|
||||
maxEpochs = 4
|
||||
keepCheckPointFiles = true
|
||||
]
|
||||
reader = [
|
||||
readerType = "HTKMLFReader"
|
||||
readMethod = "blockRandomize"
|
||||
miniBatchMode = "partial"
|
||||
randomize = "auto"
|
||||
verbosity = 0
|
||||
features = [
|
||||
dim = 363
|
||||
type = "real"
|
||||
scpFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.scp"
|
||||
]
|
||||
labels = [
|
||||
mlfFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.mlf"
|
||||
labelMappingFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/state.list"
|
||||
labelDim = 132
|
||||
labelType = "category"
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_cpu
|
||||
DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/LSTM/../../../../../../Examples/Speech/AN4/Config
|
||||
OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_cpu
|
||||
DeviceId=-1
|
||||
timestamping=true
|
||||
speechTrain=[SGD=[maxEpochs=1]]
|
||||
speechTrain=[SGD=[epochSize=64]]
|
||||
speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
parallelTrain=false
|
||||
|
||||
08/16/2016 10:01:47: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
08/16/2016 10:01:47: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: LSTM-NDL.cntk:command=speechTrain
|
||||
configparameters: LSTM-NDL.cntk:ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/LSTM/../../../../../../Examples/Speech/AN4/Config
|
||||
configparameters: LSTM-NDL.cntk:currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
configparameters: LSTM-NDL.cntk:DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
configparameters: LSTM-NDL.cntk:deviceId=-1
|
||||
configparameters: LSTM-NDL.cntk:frameMode=false
|
||||
configparameters: LSTM-NDL.cntk:ModelDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_cpu/Models
|
||||
configparameters: LSTM-NDL.cntk:modelPath=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_cpu/Models/cntkSpeechLSTM.dnn
|
||||
configparameters: LSTM-NDL.cntk:OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_cpu
|
||||
configparameters: LSTM-NDL.cntk:parallelTrain=false
|
||||
configparameters: LSTM-NDL.cntk:precision=float
|
||||
configparameters: LSTM-NDL.cntk:RootDir=..
|
||||
configparameters: LSTM-NDL.cntk:RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_cpu
|
||||
configparameters: LSTM-NDL.cntk:speechTrain=[
|
||||
action = "train"
|
||||
nbrUttsIneachRecurrentIter = 16
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/LSTM/../../../../../../Examples/Speech/AN4/Config/lstmp-3layer-opt.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 0
|
||||
minibatchSize = 16
|
||||
learningRatesPerMB = 0.5
|
||||
numMBsToShowResult = 10
|
||||
momentumPerMB = 0:0.9
|
||||
maxEpochs = 4
|
||||
keepCheckPointFiles = true
|
||||
]
|
||||
reader = [
|
||||
readerType = "HTKMLFReader"
|
||||
readMethod = "blockRandomize"
|
||||
miniBatchMode = "partial"
|
||||
randomize = "auto"
|
||||
verbosity = 0
|
||||
features = [
|
||||
dim = 363
|
||||
type = "real"
|
||||
scpFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.scp"
|
||||
]
|
||||
labels = [
|
||||
mlfFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.mlf"
|
||||
labelMappingFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/state.list"
|
||||
labelDim = 132
|
||||
labelType = "category"
|
||||
]
|
||||
]
|
||||
] [SGD=[maxEpochs=1]] [SGD=[epochSize=64]] [reader=[useMersenneTwisterRand=true]]
|
||||
|
||||
configparameters: LSTM-NDL.cntk:timestamping=true
|
||||
configparameters: LSTM-NDL.cntk:traceLevel=1
|
||||
configparameters: LSTM-NDL.cntk:truncated=true
|
||||
08/16/2016 10:01:47: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:01:47: Commands: speechTrain
|
||||
08/16/2016 10:01:47: Precision = "float"
|
||||
08/16/2016 10:01:47: CNTKModelPath: /tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_cpu/Models/cntkSpeechLSTM.dnn
|
||||
08/16/2016 10:01:47: CNTKCommandTrainInfo: speechTrain : 1
|
||||
08/16/2016 10:01:47: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 1
|
||||
|
||||
08/16/2016 10:01:47: ##############################################################################
|
||||
08/16/2016 10:01:47: # #
|
||||
08/16/2016 10:01:47: # Action "train" #
|
||||
08/16/2016 10:01:47: # #
|
||||
08/16/2016 10:01:47: ##############################################################################
|
||||
|
||||
08/16/2016 10:01:47: CNTKCommandTrainBegin: speechTrain
|
||||
NDLBuilder Using CPU
|
||||
reading script file /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.scp ... 948 entries
|
||||
total 132 state names in state list /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/state.list
|
||||
htkmlfreader: reading MLF file /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.mlf ... total 948 entries
|
||||
...............................................................................................feature set 0: 252734 frames in 948 out of 948 utterances
|
||||
label set 0: 129 classes
|
||||
minibatchutterancesource: 948 utterances grouped into 3 chunks, av. chunk size: 316.0 utterances, 84244.7 frames
|
||||
useParallelTrain option is not enabled. ParallelTrain config will be ignored.
|
||||
08/16/2016 10:01:48: Creating virgin network.
|
||||
Node 'LSTMoutput1.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput1.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput1.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput1.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput1.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- 0.000000.
|
||||
Node 'LSTMoutput2.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput2.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput2.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput2.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput2.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- 0.000000.
|
||||
Node 'LSTMoutput3.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput3.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput3.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput3.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput3.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- 0.000000.
|
||||
Node 'b' (LearnableParameter operation): Initializing Parameter[132 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput1.wx' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput1.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput1.Wh' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput1.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=3, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput1.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=4, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput1.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=5, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput1.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- uniform(seed=6, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput2.wx' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput2.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput2.Wh' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput2.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=9, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput2.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=10, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput2.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=11, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput2.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- uniform(seed=12, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput3.wx' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput3.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput3.Wh' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput3.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=15, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput3.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=16, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput3.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=17, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput3.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- uniform(seed=18, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'W' (LearnableParameter operation): Initializating Parameter[132 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'b' (LearnableParameter operation): Initializing Parameter[132 x 1] <- 0.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
6 roots:
|
||||
ce = CrossEntropyWithSoftmax()
|
||||
err = ErrorPrediction()
|
||||
featNorm.xMean = Mean()
|
||||
featNorm.xStdDev = InvStdDev()
|
||||
logPrior.prior = Mean()
|
||||
scaledLogLikelihood = Minus()
|
||||
|
||||
Loop[0] --> Loop_LSTMoutput1.output -> 24 nodes
|
||||
|
||||
LSTMoutput1.dh LSTMoutput1.whh LSTMoutput1.wxxpbpwhh
|
||||
LSTMoutput1.G4 LSTMoutput1.G3 LSTMoutput1.dc
|
||||
LSTMoutput1.Wcfdc LSTMoutput1.unnamed165 LSTMoutput1.ft
|
||||
LSTMoutput1.bft LSTMoutput1.G1 LSTMoutput1.Wcidc
|
||||
LSTMoutput1.unnamed163 LSTMoutput1.it LSTMoutput1.G2
|
||||
LSTMoutput1.unnamed164 LSTMoutput1.bit LSTMoutput1.ct
|
||||
LSTMoutput1.Wcoct LSTMoutput1.unnamed166 LSTMoutput1.ot
|
||||
LSTMoutput1.unnamed167 LSTMoutput1.mt LSTMoutput1.output
|
||||
|
||||
Loop[1] --> Loop_LSTMoutput2.output -> 24 nodes
|
||||
|
||||
LSTMoutput2.dh LSTMoutput2.whh LSTMoutput2.wxxpbpwhh
|
||||
LSTMoutput2.G4 LSTMoutput2.G3 LSTMoutput2.dc
|
||||
LSTMoutput2.Wcfdc LSTMoutput2.unnamed175 LSTMoutput2.ft
|
||||
LSTMoutput2.bft LSTMoutput2.G1 LSTMoutput2.Wcidc
|
||||
LSTMoutput2.unnamed173 LSTMoutput2.it LSTMoutput2.G2
|
||||
LSTMoutput2.unnamed174 LSTMoutput2.bit LSTMoutput2.ct
|
||||
LSTMoutput2.Wcoct LSTMoutput2.unnamed176 LSTMoutput2.ot
|
||||
LSTMoutput2.unnamed177 LSTMoutput2.mt LSTMoutput2.output
|
||||
|
||||
Loop[2] --> Loop_LSTMoutput3.output -> 24 nodes
|
||||
|
||||
LSTMoutput3.dh LSTMoutput3.whh LSTMoutput3.wxxpbpwhh
|
||||
LSTMoutput3.G4 LSTMoutput3.G3 LSTMoutput3.dc
|
||||
LSTMoutput3.Wcfdc LSTMoutput3.unnamed185 LSTMoutput3.ft
|
||||
LSTMoutput3.bft LSTMoutput3.G1 LSTMoutput3.Wcidc
|
||||
LSTMoutput3.unnamed183 LSTMoutput3.it LSTMoutput3.G2
|
||||
LSTMoutput3.unnamed184 LSTMoutput3.bit LSTMoutput3.ct
|
||||
LSTMoutput3.Wcoct LSTMoutput3.unnamed186 LSTMoutput3.ot
|
||||
LSTMoutput3.unnamed187 LSTMoutput3.mt LSTMoutput3.output
|
||||
|
||||
Validating network. 113 nodes to process in pass 1.
|
||||
|
||||
Validating --> labels = InputValue() : -> [132 x *]
|
||||
Validating --> W = LearnableParameter() : -> [132 x 0]
|
||||
Validating --> LSTMoutput3.Wmr = LearnableParameter() : -> [512 x 1024]
|
||||
Validating --> LSTMoutput3.wx = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> LSTMoutput2.Wmr = LearnableParameter() : -> [512 x 1024]
|
||||
Validating --> LSTMoutput2.wx = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> LSTMoutput1.Wmr = LearnableParameter() : -> [512 x 1024]
|
||||
Validating --> LSTMoutput1.wx = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> features = InputValue() : -> [363 x *]
|
||||
Validating --> featNorm.xMean = Mean (features) : [363 x *] -> [363]
|
||||
Validating --> featNorm.xStdDev = InvStdDev (features) : [363 x *] -> [363]
|
||||
Validating --> featNorm.xNorm = PerDimMeanVarNormalization (features, featNorm.xMean, featNorm.xStdDev) : [363 x *], [363], [363] -> [363 x *]
|
||||
Node 'LSTMoutput1.wx' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 363].
|
||||
Node 'LSTMoutput1.wx' (LearnableParameter operation): Initializing Parameter[4096 x 363] <- uniform(seed=1, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput1.wxx = Times (LSTMoutput1.wx, featNorm.xNorm) : [4096 x 363], [363 x *] -> [4096 x *]
|
||||
Validating --> LSTMoutput1.b = LearnableParameter() : -> [4096 x 1]
|
||||
Validating --> LSTMoutput1.wxxpb = Plus (LSTMoutput1.wxx, LSTMoutput1.b) : [4096 x *], [4096 x 1] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wh = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> LSTMoutput1.Wco = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput1.Wcf = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput1.Wci = LearnableParameter() : -> [1024]
|
||||
Node 'LSTMoutput1.Wh' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 512].
|
||||
Node 'LSTMoutput1.Wh' (LearnableParameter operation): Initializing Parameter[4096 x 512] <- uniform(seed=2, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput1.whh = Times (LSTMoutput1.Wh, LSTMoutput1.dh) : [4096 x 512], [512] -> [4096]
|
||||
Validating --> LSTMoutput1.wxxpbpwhh = Plus (LSTMoutput1.wxxpb, LSTMoutput1.whh) : [4096 x 1 x *], [4096] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput1.G4 = Slice (LSTMoutput1.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.G3 = Slice (LSTMoutput1.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wcfdc = DiagTimes (LSTMoutput1.Wcf, LSTMoutput1.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput1.unnamed165 = Plus (LSTMoutput1.G3, LSTMoutput1.Wcfdc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.ft = Sigmoid (LSTMoutput1.unnamed165) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.bft = ElementTimes (LSTMoutput1.ft, LSTMoutput1.dc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.G1 = Slice (LSTMoutput1.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wcidc = DiagTimes (LSTMoutput1.Wci, LSTMoutput1.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput1.unnamed163 = Plus (LSTMoutput1.G1, LSTMoutput1.Wcidc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.it = Sigmoid (LSTMoutput1.unnamed163) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.G2 = Slice (LSTMoutput1.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.unnamed164 = Tanh (LSTMoutput1.G2) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.bit = ElementTimes (LSTMoutput1.it, LSTMoutput1.unnamed164) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.ct = Plus (LSTMoutput1.bft, LSTMoutput1.bit) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wcoct = DiagTimes (LSTMoutput1.Wco, LSTMoutput1.ct) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.unnamed166 = Plus (LSTMoutput1.G4, LSTMoutput1.Wcoct) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.ot = Sigmoid (LSTMoutput1.unnamed166) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.unnamed167 = Tanh (LSTMoutput1.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.mt = ElementTimes (LSTMoutput1.ot, LSTMoutput1.unnamed167) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.output = Times (LSTMoutput1.Wmr, LSTMoutput1.mt) : [512 x 1024], [1024 x 1 x *] -> [512 x 1 x *]
|
||||
Node 'LSTMoutput2.wx' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 512 x 1].
|
||||
Node 'LSTMoutput2.wx' (LearnableParameter operation): Initializing Parameter[4096 x 512 x 1] <- uniform(seed=7, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput2.wxx = Times (LSTMoutput2.wx, LSTMoutput1.output) : [4096 x 512 x 1], [512 x 1 x *] -> [4096 x *]
|
||||
Validating --> LSTMoutput2.b = LearnableParameter() : -> [4096 x 1]
|
||||
Validating --> LSTMoutput2.wxxpb = Plus (LSTMoutput2.wxx, LSTMoutput2.b) : [4096 x *], [4096 x 1] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wh = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> LSTMoutput2.Wco = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput2.Wcf = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput2.Wci = LearnableParameter() : -> [1024]
|
||||
Node 'LSTMoutput2.Wh' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 512].
|
||||
Node 'LSTMoutput2.Wh' (LearnableParameter operation): Initializing Parameter[4096 x 512] <- uniform(seed=8, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput2.whh = Times (LSTMoutput2.Wh, LSTMoutput2.dh) : [4096 x 512], [512] -> [4096]
|
||||
Validating --> LSTMoutput2.wxxpbpwhh = Plus (LSTMoutput2.wxxpb, LSTMoutput2.whh) : [4096 x 1 x *], [4096] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput2.G4 = Slice (LSTMoutput2.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.G3 = Slice (LSTMoutput2.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wcfdc = DiagTimes (LSTMoutput2.Wcf, LSTMoutput2.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput2.unnamed175 = Plus (LSTMoutput2.G3, LSTMoutput2.Wcfdc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.ft = Sigmoid (LSTMoutput2.unnamed175) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.bft = ElementTimes (LSTMoutput2.ft, LSTMoutput2.dc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.G1 = Slice (LSTMoutput2.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wcidc = DiagTimes (LSTMoutput2.Wci, LSTMoutput2.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput2.unnamed173 = Plus (LSTMoutput2.G1, LSTMoutput2.Wcidc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.it = Sigmoid (LSTMoutput2.unnamed173) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.G2 = Slice (LSTMoutput2.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.unnamed174 = Tanh (LSTMoutput2.G2) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.bit = ElementTimes (LSTMoutput2.it, LSTMoutput2.unnamed174) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.ct = Plus (LSTMoutput2.bft, LSTMoutput2.bit) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wcoct = DiagTimes (LSTMoutput2.Wco, LSTMoutput2.ct) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.unnamed176 = Plus (LSTMoutput2.G4, LSTMoutput2.Wcoct) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.ot = Sigmoid (LSTMoutput2.unnamed176) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.unnamed177 = Tanh (LSTMoutput2.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.mt = ElementTimes (LSTMoutput2.ot, LSTMoutput2.unnamed177) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.output = Times (LSTMoutput2.Wmr, LSTMoutput2.mt) : [512 x 1024], [1024 x 1 x *] -> [512 x 1 x *]
|
||||
Node 'LSTMoutput3.wx' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 512 x 1].
|
||||
Node 'LSTMoutput3.wx' (LearnableParameter operation): Initializing Parameter[4096 x 512 x 1] <- uniform(seed=13, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput3.wxx = Times (LSTMoutput3.wx, LSTMoutput2.output) : [4096 x 512 x 1], [512 x 1 x *] -> [4096 x *]
|
||||
Validating --> LSTMoutput3.b = LearnableParameter() : -> [4096 x 1]
|
||||
Validating --> LSTMoutput3.wxxpb = Plus (LSTMoutput3.wxx, LSTMoutput3.b) : [4096 x *], [4096 x 1] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wh = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> LSTMoutput3.Wco = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput3.Wcf = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput3.Wci = LearnableParameter() : -> [1024]
|
||||
Node 'LSTMoutput3.Wh' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 512].
|
||||
Node 'LSTMoutput3.Wh' (LearnableParameter operation): Initializing Parameter[4096 x 512] <- uniform(seed=14, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput3.whh = Times (LSTMoutput3.Wh, LSTMoutput3.dh) : [4096 x 512], [512] -> [4096]
|
||||
Validating --> LSTMoutput3.wxxpbpwhh = Plus (LSTMoutput3.wxxpb, LSTMoutput3.whh) : [4096 x 1 x *], [4096] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput3.G4 = Slice (LSTMoutput3.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.G3 = Slice (LSTMoutput3.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wcfdc = DiagTimes (LSTMoutput3.Wcf, LSTMoutput3.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput3.unnamed185 = Plus (LSTMoutput3.G3, LSTMoutput3.Wcfdc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.ft = Sigmoid (LSTMoutput3.unnamed185) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.bft = ElementTimes (LSTMoutput3.ft, LSTMoutput3.dc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.G1 = Slice (LSTMoutput3.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wcidc = DiagTimes (LSTMoutput3.Wci, LSTMoutput3.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput3.unnamed183 = Plus (LSTMoutput3.G1, LSTMoutput3.Wcidc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.it = Sigmoid (LSTMoutput3.unnamed183) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.G2 = Slice (LSTMoutput3.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.unnamed184 = Tanh (LSTMoutput3.G2) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.bit = ElementTimes (LSTMoutput3.it, LSTMoutput3.unnamed184) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.ct = Plus (LSTMoutput3.bft, LSTMoutput3.bit) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wcoct = DiagTimes (LSTMoutput3.Wco, LSTMoutput3.ct) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.unnamed186 = Plus (LSTMoutput3.G4, LSTMoutput3.Wcoct) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.ot = Sigmoid (LSTMoutput3.unnamed186) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.unnamed187 = Tanh (LSTMoutput3.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.mt = ElementTimes (LSTMoutput3.ot, LSTMoutput3.unnamed187) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.output = Times (LSTMoutput3.Wmr, LSTMoutput3.mt) : [512 x 1024], [1024 x 1 x *] -> [512 x 1 x *]
|
||||
Node 'W' (LearnableParameter operation) operation: Tensor shape was inferred as [132 x 512 x 1].
|
||||
Node 'W' (LearnableParameter operation): Initializing Parameter[132 x 512 x 1] <- uniform(seed=19, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> unnamed193 = Times (W, LSTMoutput3.output) : [132 x 512 x 1], [512 x 1 x *] -> [132 x *]
|
||||
Validating --> b = LearnableParameter() : -> [132 x 1]
|
||||
Validating --> LSTMoutputW = Plus (unnamed193, b) : [132 x *], [132 x 1] -> [132 x 1 x *]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, LSTMoutputW) : [132 x *], [132 x 1 x *] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, LSTMoutputW) : [132 x *], [132 x 1 x *] -> [1]
|
||||
Validating --> logPrior.prior = Mean (labels) : [132 x *] -> [132]
|
||||
Validating --> logPrior.logPrior = Log (logPrior.prior) : [132] -> [132]
|
||||
Validating --> scaledLogLikelihood = Minus (LSTMoutputW, logPrior.logPrior) : [132 x 1 x *], [132] -> [132 x 1 x *]
|
||||
|
||||
Validating network. 88 nodes to process in pass 2.
|
||||
|
||||
Validating --> LSTMoutput1.dh = PastValue (LSTMoutput1.output) : [512 x 1 x *] -> [512 x 1 x *]
|
||||
Validating --> LSTMoutput1.whh = Times (LSTMoutput1.Wh, LSTMoutput1.dh) : [4096 x 512], [512 x 1 x *] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput1.dc = PastValue (LSTMoutput1.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wcfdc = DiagTimes (LSTMoutput1.Wcf, LSTMoutput1.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wcidc = DiagTimes (LSTMoutput1.Wci, LSTMoutput1.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.dh = PastValue (LSTMoutput2.output) : [512 x 1 x *] -> [512 x 1 x *]
|
||||
Validating --> LSTMoutput2.whh = Times (LSTMoutput2.Wh, LSTMoutput2.dh) : [4096 x 512], [512 x 1 x *] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput2.dc = PastValue (LSTMoutput2.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wcfdc = DiagTimes (LSTMoutput2.Wcf, LSTMoutput2.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wcidc = DiagTimes (LSTMoutput2.Wci, LSTMoutput2.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.dh = PastValue (LSTMoutput3.output) : [512 x 1 x *] -> [512 x 1 x *]
|
||||
Validating --> LSTMoutput3.whh = Times (LSTMoutput3.Wh, LSTMoutput3.dh) : [4096 x 512], [512 x 1 x *] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput3.dc = PastValue (LSTMoutput3.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wcfdc = DiagTimes (LSTMoutput3.Wcf, LSTMoutput3.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wcidc = DiagTimes (LSTMoutput3.Wci, LSTMoutput3.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
|
||||
Validating network. 15 nodes to process in pass 3.
|
||||
|
||||
|
||||
Validating network, final pass.
|
||||
|
||||
|
||||
|
||||
29 out of 113 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
08/16/2016 10:01:48: Created model with 113 nodes on CPU.
|
||||
|
||||
08/16/2016 10:01:48: Training criterion node(s):
|
||||
08/16/2016 10:01:48: ce = CrossEntropyWithSoftmax
|
||||
|
||||
08/16/2016 10:01:48: Evaluation criterion node(s):
|
||||
08/16/2016 10:01:48: err = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing: Out of 217 matrices, 125 are shared as 56, and 92 are not shared.
|
||||
|
||||
{ LSTMoutput1.dh : [512 x 1 x *]
|
||||
LSTMoutput1.wxx : [4096 x *] (gradient) }
|
||||
{ LSTMoutput2.Wco : [1024] (gradient)
|
||||
LSTMoutput3.dc : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.Wmr : [512 x 1024] (gradient)
|
||||
LSTMoutput2.wxx : [4096 x *] }
|
||||
{ LSTMoutput2.wx : [4096 x 512 x 1] (gradient)
|
||||
LSTMoutput2.wxxpb : [4096 x 1 x *] }
|
||||
{ LSTMoutput1.ot : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.whh : [4096 x 1 x *] }
|
||||
{ LSTMoutput1.ct : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.wxxpbpwhh : [4096 x 1 x *] }
|
||||
{ LSTMoutput1.G4 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.G4 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.unnamed164 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.Wcfdc : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.wxxpbpwhh : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput2.unnamed175 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.G1 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.ft : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.Wci : [1024] (gradient)
|
||||
LSTMoutput2.G1 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.G3 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.Wcidc : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.Wcf : [1024] (gradient)
|
||||
LSTMoutput2.it : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.whh : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput2.G2 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.b : [4096 x 1] (gradient)
|
||||
LSTMoutput1.dh : [512 x 1 x *] (gradient)
|
||||
LSTMoutput2.unnamed174 : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.Wmr : [512 x 1024] (gradient)
|
||||
LSTMoutput3.wxx : [4096 x *] }
|
||||
{ LSTMoutput3.wx : [4096 x 512 x 1] (gradient)
|
||||
LSTMoutput3.wxxpb : [4096 x 1 x *] }
|
||||
{ LSTMoutput2.ot : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.whh : [4096 x 1 x *] }
|
||||
{ LSTMoutput2.ct : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.wxxpbpwhh : [4096 x 1 x *] }
|
||||
{ LSTMoutput1.Wcoct : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.G4 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.G4 : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.unnamed174 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.Wcfdc : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.unnamed166 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.wxxpbpwhh : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput3.unnamed185 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.dc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.G1 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.ft : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.unnamed165 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.bft : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.Wci : [1024] (gradient)
|
||||
LSTMoutput3.G1 : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.G3 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.Wcidc : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.it : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.unnamed183 : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.Wcf : [1024] (gradient)
|
||||
LSTMoutput3.it : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.unnamed167 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.whh : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput3.G2 : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.b : [4096 x 1] (gradient)
|
||||
LSTMoutput2.dh : [512 x 1 x *] (gradient)
|
||||
LSTMoutput3.unnamed184 : [1024 x 1 x *] }
|
||||
{ LSTMoutput3.Wmr : [512 x 1024] (gradient)
|
||||
unnamed193 : [132 x *] }
|
||||
{ LSTMoutputW : [132 x 1 x *]
|
||||
W : [132 x 512 x 1] (gradient) }
|
||||
{ LSTMoutput1.mt : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.dh : [512 x 1 x *]
|
||||
LSTMoutput2.wxx : [4096 x *] (gradient) }
|
||||
{ LSTMoutput1.wx : [4096 x 363] (gradient)
|
||||
LSTMoutput1.wxxpb : [4096 x 1 x *] }
|
||||
{ LSTMoutput2.mt : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.dh : [512 x 1 x *]
|
||||
LSTMoutput3.wxx : [4096 x *] (gradient) }
|
||||
{ LSTMoutput3.output : [512 x 1 x *] (gradient)
|
||||
LSTMoutputW : [132 x 1 x *] (gradient) }
|
||||
{ LSTMoutput3.mt : [1024 x 1 x *] (gradient)
|
||||
unnamed193 : [132 x *] (gradient) }
|
||||
{ LSTMoutput2.Wcoct : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.G4 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.ft : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.bft : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.output : [512 x 1 x *] (gradient)
|
||||
LSTMoutput2.wxxpb : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput3.it : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.Wh : [4096 x 512] (gradient)
|
||||
LSTMoutput3.G2 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.unnamed176 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.wxxpbpwhh : [4096 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.bit : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.unnamed183 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.bft : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.dc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.G1 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.G2 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.Wcfdc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.Wcidc : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.unnamed163 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.unnamed175 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.Wcidc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.ft : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.bft : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.dc : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.Wcfdc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.Wcidc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.ft : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.unnamed173 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.unnamed185 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.Wh : [4096 x 512] (gradient)
|
||||
LSTMoutput2.G2 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.Wcfdc : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.wxxpb : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput2.it : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.output : [512 x 1 x *] (gradient)
|
||||
LSTMoutput3.wxxpb : [4096 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.unnamed177 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.whh : [4096 x 1 x *] (gradient) }
|
||||
{ LSTMoutput3.b : [4096 x 1] (gradient)
|
||||
LSTMoutput3.dh : [512 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.Wco : [1024] (gradient)
|
||||
LSTMoutput2.dc : [1024 x 1 x *] }
|
||||
|
||||
|
||||
08/16/2016 10:01:48: Training 13634692 parameters in 23 out of 23 parameter tensors and 104 nodes with gradient:
|
||||
|
||||
08/16/2016 10:01:48: Node 'LSTMoutput1.Wcf' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 10:01:48: Node 'LSTMoutput1.Wci' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 10:01:48: Node 'LSTMoutput1.Wco' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 10:01:48: Node 'LSTMoutput1.Wh' (LearnableParameter operation) : [4096 x 512]
|
||||
08/16/2016 10:01:48: Node 'LSTMoutput1.Wmr' (LearnableParameter operation) : [512 x 1024]
|
||||
08/16/2016 10:01:48: Node 'LSTMoutput1.b' (LearnableParameter operation) : [4096 x 1]
|
||||
08/16/2016 10:01:48: Node 'LSTMoutput1.wx' (LearnableParameter operation) : [4096 x 363]
|
||||
08/16/2016 10:01:48: Node 'LSTMoutput2.Wcf' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 10:01:48: Node 'LSTMoutput2.Wci' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 10:01:48: Node 'LSTMoutput2.Wco' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 10:01:48: Node 'LSTMoutput2.Wh' (LearnableParameter operation) : [4096 x 512]
|
||||
08/16/2016 10:01:48: Node 'LSTMoutput2.Wmr' (LearnableParameter operation) : [512 x 1024]
|
||||
08/16/2016 10:01:48: Node 'LSTMoutput2.b' (LearnableParameter operation) : [4096 x 1]
|
||||
08/16/2016 10:01:48: Node 'LSTMoutput2.wx' (LearnableParameter operation) : [4096 x 512 x 1]
|
||||
08/16/2016 10:01:48: Node 'LSTMoutput3.Wcf' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 10:01:48: Node 'LSTMoutput3.Wci' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 10:01:48: Node 'LSTMoutput3.Wco' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 10:01:48: Node 'LSTMoutput3.Wh' (LearnableParameter operation) : [4096 x 512]
|
||||
08/16/2016 10:01:48: Node 'LSTMoutput3.Wmr' (LearnableParameter operation) : [512 x 1024]
|
||||
08/16/2016 10:01:48: Node 'LSTMoutput3.b' (LearnableParameter operation) : [4096 x 1]
|
||||
08/16/2016 10:01:48: Node 'LSTMoutput3.wx' (LearnableParameter operation) : [4096 x 512 x 1]
|
||||
08/16/2016 10:01:48: Node 'W' (LearnableParameter operation) : [132 x 512 x 1]
|
||||
08/16/2016 10:01:48: Node 'b' (LearnableParameter operation) : [132 x 1]
|
||||
|
||||
|
||||
08/16/2016 10:01:48: Precomputing --> 3 PreCompute nodes found.
|
||||
|
||||
08/16/2016 10:01:48: featNorm.xMean = Mean()
|
||||
08/16/2016 10:01:48: featNorm.xStdDev = InvStdDev()
|
||||
08/16/2016 10:01:48: logPrior.prior = Mean()
|
||||
minibatchiterator: epoch 0: frames [0..252734] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses
|
||||
requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms
|
||||
|
||||
08/16/2016 10:01:49: Precomputing --> Completed.
|
||||
|
||||
|
||||
08/16/2016 10:01:50: Starting Epoch 1: learning rate per sample = 0.001953 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
minibatchiterator: epoch 0: frames [0..64] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses
|
||||
|
||||
08/16/2016 10:01:50: Starting minibatch loop.
|
||||
08/16/2016 10:01:53: Epoch[ 1 of 1]-Minibatch[ 1- 10, 250.00%]: ce = 4.87313957 * 160; err = 0.90625000 * 160; time = 3.3910s; samplesPerSecond = 47.2
|
||||
08/16/2016 10:01:56: Epoch[ 1 of 1]-Minibatch[ 11- 20, 500.00%]: ce = 4.84521751 * 160; err = 0.69375000 * 160; time = 2.9626s; samplesPerSecond = 54.0
|
||||
08/16/2016 10:01:58: Finished Epoch[ 1 of 1]: [Training] ce = 4.85644356 * 418; err = 0.80382775 * 418; totalSamplesSeen = 418; learningRatePerSample = 0.001953125; epochTime=8.39953s
|
||||
08/16/2016 10:01:59: SGD: Saving checkpoint model '/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_cpu/Models/cntkSpeechLSTM.dnn'
|
||||
08/16/2016 10:01:59: CNTKCommandTrainEnd: speechTrain
|
||||
|
||||
08/16/2016 10:01:59: Action "train" complete.
|
||||
|
||||
08/16/2016 10:01:59: __COMPLETED__
|
||||
|
|
@ -1 +0,0 @@
|
|||
__COMPLETED__
|
||||
|
|
@ -1 +0,0 @@
|
|||
__COMPLETED__
|
||||
|
|
@ -0,0 +1,683 @@
|
|||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 264172964 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/1bitsgd/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/LSTM/../../../../../../Examples/Speech/AN4/Config/LSTM-NDL.cntk currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_gpu DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/LSTM/../../../../../../Examples/Speech/AN4/Config OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_gpu DeviceId=0 timestamping=true speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=64]] speechTrain=[reader=[useMersenneTwisterRand=true]] parallelTrain=false
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: Aug 16 2016 09:41:57
|
||||
Last modified date: Mon Aug 15 23:39:17 2016
|
||||
Build type: release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: yes
|
||||
Math lib: mkl
|
||||
CUDA_PATH: /usr/local/cuda-7.5
|
||||
CUB_PATH: /usr/local/cub-1.4.1
|
||||
CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by philly on 643085f7f8c2
|
||||
Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
08/16/2016 10:02:00: -------------------------------------------------------------------
|
||||
08/16/2016 10:02:00: Build info:
|
||||
|
||||
08/16/2016 10:02:00: Built time: Aug 16 2016 09:41:57
|
||||
08/16/2016 10:02:00: Last modified date: Mon Aug 15 23:39:17 2016
|
||||
08/16/2016 10:02:00: Build type: release
|
||||
08/16/2016 10:02:00: Build target: GPU
|
||||
08/16/2016 10:02:00: With 1bit-SGD: yes
|
||||
08/16/2016 10:02:00: Math lib: mkl
|
||||
08/16/2016 10:02:00: CUDA_PATH: /usr/local/cuda-7.5
|
||||
08/16/2016 10:02:00: CUB_PATH: /usr/local/cub-1.4.1
|
||||
08/16/2016 10:02:00: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
08/16/2016 10:02:00: Build Branch: HEAD
|
||||
08/16/2016 10:02:00: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 10:02:00: Built by philly on 643085f7f8c2
|
||||
08/16/2016 10:02:00: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
08/16/2016 10:02:00: -------------------------------------------------------------------
|
||||
08/16/2016 10:02:01: -------------------------------------------------------------------
|
||||
08/16/2016 10:02:01: GPU info:
|
||||
|
||||
08/16/2016 10:02:01: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:02:01: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:02:01: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:02:01: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 10:02:01: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 10:02:01: Running on localhost at 2016/08/16 10:02:01
|
||||
08/16/2016 10:02:01: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/1bitsgd/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/LSTM/../../../../../../Examples/Speech/AN4/Config/LSTM-NDL.cntk currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_gpu DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/LSTM/../../../../../../Examples/Speech/AN4/Config OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_gpu DeviceId=0 timestamping=true speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=64]] speechTrain=[reader=[useMersenneTwisterRand=true]] parallelTrain=false
|
||||
|
||||
|
||||
|
||||
08/16/2016 10:02:01: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:02:01: RootDir = ".."
|
||||
ConfigDir = "$RootDir$/Config"
|
||||
DataDir = "$RootDir$/Data"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
ModelDir = "$OutputDir$/Models"
|
||||
deviceId = -1
|
||||
command = speechTrain
|
||||
precision = "float"
|
||||
traceLevel = 1
|
||||
modelPath = "$ModelDir$/cntkSpeechLSTM.dnn"
|
||||
parallelTrain = true
|
||||
frameMode = false
|
||||
truncated = true
|
||||
speechTrain = [
|
||||
action = "train"
|
||||
nbrUttsIneachRecurrentIter = 16
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "$ConfigDir$/lstmp-3layer-opt.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 0
|
||||
minibatchSize = 16
|
||||
learningRatesPerMB = 0.5
|
||||
numMBsToShowResult = 10
|
||||
momentumPerMB = 0:0.9
|
||||
maxEpochs = 4
|
||||
keepCheckPointFiles = true
|
||||
]
|
||||
reader = [
|
||||
readerType = "HTKMLFReader"
|
||||
readMethod = "blockRandomize"
|
||||
miniBatchMode = "partial"
|
||||
randomize = "auto"
|
||||
verbosity = 0
|
||||
features = [
|
||||
dim = 363
|
||||
type = "real"
|
||||
scpFile = "$DataDir$/glob_0000.scp"
|
||||
]
|
||||
labels = [
|
||||
mlfFile = "$DataDir$/glob_0000.mlf"
|
||||
labelMappingFile = "$DataDir$/state.list"
|
||||
labelDim = 132
|
||||
labelType = "category"
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_gpu
|
||||
DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/LSTM/../../../../../../Examples/Speech/AN4/Config
|
||||
OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
speechTrain=[SGD=[maxEpochs=1]]
|
||||
speechTrain=[SGD=[epochSize=64]]
|
||||
speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
parallelTrain=false
|
||||
|
||||
08/16/2016 10:02:01: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
08/16/2016 10:02:01: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 10:02:01: RootDir = ".."
|
||||
ConfigDir = "../Config"
|
||||
DataDir = "../Data"
|
||||
OutputDir = "../Output"
|
||||
ModelDir = "/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_gpu/Models"
|
||||
deviceId = -1
|
||||
command = speechTrain
|
||||
precision = "float"
|
||||
traceLevel = 1
|
||||
modelPath = "/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_gpu/Models/cntkSpeechLSTM.dnn"
|
||||
parallelTrain = true
|
||||
frameMode = false
|
||||
truncated = true
|
||||
speechTrain = [
|
||||
action = "train"
|
||||
nbrUttsIneachRecurrentIter = 16
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/LSTM/../../../../../../Examples/Speech/AN4/Config/lstmp-3layer-opt.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 0
|
||||
minibatchSize = 16
|
||||
learningRatesPerMB = 0.5
|
||||
numMBsToShowResult = 10
|
||||
momentumPerMB = 0:0.9
|
||||
maxEpochs = 4
|
||||
keepCheckPointFiles = true
|
||||
]
|
||||
reader = [
|
||||
readerType = "HTKMLFReader"
|
||||
readMethod = "blockRandomize"
|
||||
miniBatchMode = "partial"
|
||||
randomize = "auto"
|
||||
verbosity = 0
|
||||
features = [
|
||||
dim = 363
|
||||
type = "real"
|
||||
scpFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.scp"
|
||||
]
|
||||
labels = [
|
||||
mlfFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.mlf"
|
||||
labelMappingFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/state.list"
|
||||
labelDim = 132
|
||||
labelType = "category"
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_gpu
|
||||
DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/LSTM/../../../../../../Examples/Speech/AN4/Config
|
||||
OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
speechTrain=[SGD=[maxEpochs=1]]
|
||||
speechTrain=[SGD=[epochSize=64]]
|
||||
speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
parallelTrain=false
|
||||
|
||||
08/16/2016 10:02:01: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
08/16/2016 10:02:01: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: LSTM-NDL.cntk:command=speechTrain
|
||||
configparameters: LSTM-NDL.cntk:ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/LSTM/../../../../../../Examples/Speech/AN4/Config
|
||||
configparameters: LSTM-NDL.cntk:currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
configparameters: LSTM-NDL.cntk:DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data
|
||||
configparameters: LSTM-NDL.cntk:deviceId=0
|
||||
configparameters: LSTM-NDL.cntk:frameMode=false
|
||||
configparameters: LSTM-NDL.cntk:ModelDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_gpu/Models
|
||||
configparameters: LSTM-NDL.cntk:modelPath=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_gpu/Models/cntkSpeechLSTM.dnn
|
||||
configparameters: LSTM-NDL.cntk:OutputDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_gpu
|
||||
configparameters: LSTM-NDL.cntk:parallelTrain=false
|
||||
configparameters: LSTM-NDL.cntk:precision=float
|
||||
configparameters: LSTM-NDL.cntk:RootDir=..
|
||||
configparameters: LSTM-NDL.cntk:RunDir=/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_gpu
|
||||
configparameters: LSTM-NDL.cntk:speechTrain=[
|
||||
action = "train"
|
||||
nbrUttsIneachRecurrentIter = 16
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Speech/AN4/LSTM/../../../../../../Examples/Speech/AN4/Config/lstmp-3layer-opt.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 0
|
||||
minibatchSize = 16
|
||||
learningRatesPerMB = 0.5
|
||||
numMBsToShowResult = 10
|
||||
momentumPerMB = 0:0.9
|
||||
maxEpochs = 4
|
||||
keepCheckPointFiles = true
|
||||
]
|
||||
reader = [
|
||||
readerType = "HTKMLFReader"
|
||||
readMethod = "blockRandomize"
|
||||
miniBatchMode = "partial"
|
||||
randomize = "auto"
|
||||
verbosity = 0
|
||||
features = [
|
||||
dim = 363
|
||||
type = "real"
|
||||
scpFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.scp"
|
||||
]
|
||||
labels = [
|
||||
mlfFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.mlf"
|
||||
labelMappingFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/state.list"
|
||||
labelDim = 132
|
||||
labelType = "category"
|
||||
]
|
||||
]
|
||||
] [SGD=[maxEpochs=1]] [SGD=[epochSize=64]] [reader=[useMersenneTwisterRand=true]]
|
||||
|
||||
configparameters: LSTM-NDL.cntk:timestamping=true
|
||||
configparameters: LSTM-NDL.cntk:traceLevel=1
|
||||
configparameters: LSTM-NDL.cntk:truncated=true
|
||||
08/16/2016 10:02:01: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 10:02:01: Commands: speechTrain
|
||||
08/16/2016 10:02:01: Precision = "float"
|
||||
08/16/2016 10:02:01: CNTKModelPath: /tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_gpu/Models/cntkSpeechLSTM.dnn
|
||||
08/16/2016 10:02:01: CNTKCommandTrainInfo: speechTrain : 1
|
||||
08/16/2016 10:02:01: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 1
|
||||
|
||||
08/16/2016 10:02:01: ##############################################################################
|
||||
08/16/2016 10:02:01: # #
|
||||
08/16/2016 10:02:01: # Action "train" #
|
||||
08/16/2016 10:02:01: # #
|
||||
08/16/2016 10:02:01: ##############################################################################
|
||||
|
||||
08/16/2016 10:02:01: CNTKCommandTrainBegin: speechTrain
|
||||
NDLBuilder Using GPU 0
|
||||
reading script file /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.scp ... 948 entries
|
||||
total 132 state names in state list /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/state.list
|
||||
htkmlfreader: reading MLF file /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Speech/AN4/Data/glob_0000.mlf ... total 948 entries
|
||||
...............................................................................................feature set 0: 252734 frames in 948 out of 948 utterances
|
||||
label set 0: 129 classes
|
||||
minibatchutterancesource: 948 utterances grouped into 3 chunks, av. chunk size: 316.0 utterances, 84244.7 frames
|
||||
useParallelTrain option is not enabled. ParallelTrain config will be ignored.
|
||||
08/16/2016 10:02:01: Creating virgin network.
|
||||
Node 'LSTMoutput1.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput1.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput1.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput1.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput1.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- 0.000000.
|
||||
Node 'LSTMoutput2.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput2.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput2.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput2.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput2.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- 0.000000.
|
||||
Node 'LSTMoutput3.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput3.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput3.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput3.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput3.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- 0.000000.
|
||||
Node 'b' (LearnableParameter operation): Initializing Parameter[132 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput1.wx' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput1.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput1.Wh' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput1.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=3, range=0.050000*1.000000, onCPU=false).
|
||||
SetUniformRandomValue (GPU): creating curand object with seed 3, sizeof(ElemType)==4
|
||||
Node 'LSTMoutput1.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=4, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput1.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=5, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput1.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- uniform(seed=6, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput2.wx' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput2.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput2.Wh' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput2.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=9, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput2.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=10, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput2.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=11, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput2.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- uniform(seed=12, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput3.wx' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput3.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput3.Wh' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput3.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=15, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput3.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=16, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput3.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=17, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput3.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- uniform(seed=18, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'W' (LearnableParameter operation): Initializating Parameter[132 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'b' (LearnableParameter operation): Initializing Parameter[132 x 1] <- 0.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
6 roots:
|
||||
ce = CrossEntropyWithSoftmax()
|
||||
err = ErrorPrediction()
|
||||
featNorm.xMean = Mean()
|
||||
featNorm.xStdDev = InvStdDev()
|
||||
logPrior.prior = Mean()
|
||||
scaledLogLikelihood = Minus()
|
||||
|
||||
Loop[0] --> Loop_LSTMoutput1.output -> 24 nodes
|
||||
|
||||
LSTMoutput1.dh LSTMoutput1.whh LSTMoutput1.wxxpbpwhh
|
||||
LSTMoutput1.G4 LSTMoutput1.G3 LSTMoutput1.dc
|
||||
LSTMoutput1.Wcfdc LSTMoutput1.unnamed165 LSTMoutput1.ft
|
||||
LSTMoutput1.bft LSTMoutput1.G1 LSTMoutput1.Wcidc
|
||||
LSTMoutput1.unnamed163 LSTMoutput1.it LSTMoutput1.G2
|
||||
LSTMoutput1.unnamed164 LSTMoutput1.bit LSTMoutput1.ct
|
||||
LSTMoutput1.Wcoct LSTMoutput1.unnamed166 LSTMoutput1.ot
|
||||
LSTMoutput1.unnamed167 LSTMoutput1.mt LSTMoutput1.output
|
||||
|
||||
Loop[1] --> Loop_LSTMoutput2.output -> 24 nodes
|
||||
|
||||
LSTMoutput2.dh LSTMoutput2.whh LSTMoutput2.wxxpbpwhh
|
||||
LSTMoutput2.G4 LSTMoutput2.G3 LSTMoutput2.dc
|
||||
LSTMoutput2.Wcfdc LSTMoutput2.unnamed175 LSTMoutput2.ft
|
||||
LSTMoutput2.bft LSTMoutput2.G1 LSTMoutput2.Wcidc
|
||||
LSTMoutput2.unnamed173 LSTMoutput2.it LSTMoutput2.G2
|
||||
LSTMoutput2.unnamed174 LSTMoutput2.bit LSTMoutput2.ct
|
||||
LSTMoutput2.Wcoct LSTMoutput2.unnamed176 LSTMoutput2.ot
|
||||
LSTMoutput2.unnamed177 LSTMoutput2.mt LSTMoutput2.output
|
||||
|
||||
Loop[2] --> Loop_LSTMoutput3.output -> 24 nodes
|
||||
|
||||
LSTMoutput3.dh LSTMoutput3.whh LSTMoutput3.wxxpbpwhh
|
||||
LSTMoutput3.G4 LSTMoutput3.G3 LSTMoutput3.dc
|
||||
LSTMoutput3.Wcfdc LSTMoutput3.unnamed185 LSTMoutput3.ft
|
||||
LSTMoutput3.bft LSTMoutput3.G1 LSTMoutput3.Wcidc
|
||||
LSTMoutput3.unnamed183 LSTMoutput3.it LSTMoutput3.G2
|
||||
LSTMoutput3.unnamed184 LSTMoutput3.bit LSTMoutput3.ct
|
||||
LSTMoutput3.Wcoct LSTMoutput3.unnamed186 LSTMoutput3.ot
|
||||
LSTMoutput3.unnamed187 LSTMoutput3.mt LSTMoutput3.output
|
||||
|
||||
Validating network. 113 nodes to process in pass 1.
|
||||
|
||||
Validating --> labels = InputValue() : -> [132 x *]
|
||||
Validating --> W = LearnableParameter() : -> [132 x 0]
|
||||
Validating --> LSTMoutput3.Wmr = LearnableParameter() : -> [512 x 1024]
|
||||
Validating --> LSTMoutput3.wx = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> LSTMoutput2.Wmr = LearnableParameter() : -> [512 x 1024]
|
||||
Validating --> LSTMoutput2.wx = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> LSTMoutput1.Wmr = LearnableParameter() : -> [512 x 1024]
|
||||
Validating --> LSTMoutput1.wx = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> features = InputValue() : -> [363 x *]
|
||||
Validating --> featNorm.xMean = Mean (features) : [363 x *] -> [363]
|
||||
Validating --> featNorm.xStdDev = InvStdDev (features) : [363 x *] -> [363]
|
||||
Validating --> featNorm.xNorm = PerDimMeanVarNormalization (features, featNorm.xMean, featNorm.xStdDev) : [363 x *], [363], [363] -> [363 x *]
|
||||
Node 'LSTMoutput1.wx' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 363].
|
||||
Node 'LSTMoutput1.wx' (LearnableParameter operation): Initializing Parameter[4096 x 363] <- uniform(seed=1, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput1.wxx = Times (LSTMoutput1.wx, featNorm.xNorm) : [4096 x 363], [363 x *] -> [4096 x *]
|
||||
Validating --> LSTMoutput1.b = LearnableParameter() : -> [4096 x 1]
|
||||
Validating --> LSTMoutput1.wxxpb = Plus (LSTMoutput1.wxx, LSTMoutput1.b) : [4096 x *], [4096 x 1] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wh = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> LSTMoutput1.Wco = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput1.Wcf = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput1.Wci = LearnableParameter() : -> [1024]
|
||||
Node 'LSTMoutput1.Wh' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 512].
|
||||
Node 'LSTMoutput1.Wh' (LearnableParameter operation): Initializing Parameter[4096 x 512] <- uniform(seed=2, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput1.whh = Times (LSTMoutput1.Wh, LSTMoutput1.dh) : [4096 x 512], [512] -> [4096]
|
||||
Validating --> LSTMoutput1.wxxpbpwhh = Plus (LSTMoutput1.wxxpb, LSTMoutput1.whh) : [4096 x 1 x *], [4096] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput1.G4 = Slice (LSTMoutput1.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.G3 = Slice (LSTMoutput1.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wcfdc = DiagTimes (LSTMoutput1.Wcf, LSTMoutput1.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput1.unnamed165 = Plus (LSTMoutput1.G3, LSTMoutput1.Wcfdc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.ft = Sigmoid (LSTMoutput1.unnamed165) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.bft = ElementTimes (LSTMoutput1.ft, LSTMoutput1.dc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.G1 = Slice (LSTMoutput1.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wcidc = DiagTimes (LSTMoutput1.Wci, LSTMoutput1.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput1.unnamed163 = Plus (LSTMoutput1.G1, LSTMoutput1.Wcidc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.it = Sigmoid (LSTMoutput1.unnamed163) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.G2 = Slice (LSTMoutput1.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.unnamed164 = Tanh (LSTMoutput1.G2) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.bit = ElementTimes (LSTMoutput1.it, LSTMoutput1.unnamed164) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.ct = Plus (LSTMoutput1.bft, LSTMoutput1.bit) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wcoct = DiagTimes (LSTMoutput1.Wco, LSTMoutput1.ct) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.unnamed166 = Plus (LSTMoutput1.G4, LSTMoutput1.Wcoct) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.ot = Sigmoid (LSTMoutput1.unnamed166) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.unnamed167 = Tanh (LSTMoutput1.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.mt = ElementTimes (LSTMoutput1.ot, LSTMoutput1.unnamed167) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.output = Times (LSTMoutput1.Wmr, LSTMoutput1.mt) : [512 x 1024], [1024 x 1 x *] -> [512 x 1 x *]
|
||||
Node 'LSTMoutput2.wx' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 512 x 1].
|
||||
Node 'LSTMoutput2.wx' (LearnableParameter operation): Initializing Parameter[4096 x 512 x 1] <- uniform(seed=7, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput2.wxx = Times (LSTMoutput2.wx, LSTMoutput1.output) : [4096 x 512 x 1], [512 x 1 x *] -> [4096 x *]
|
||||
Validating --> LSTMoutput2.b = LearnableParameter() : -> [4096 x 1]
|
||||
Validating --> LSTMoutput2.wxxpb = Plus (LSTMoutput2.wxx, LSTMoutput2.b) : [4096 x *], [4096 x 1] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wh = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> LSTMoutput2.Wco = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput2.Wcf = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput2.Wci = LearnableParameter() : -> [1024]
|
||||
Node 'LSTMoutput2.Wh' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 512].
|
||||
Node 'LSTMoutput2.Wh' (LearnableParameter operation): Initializing Parameter[4096 x 512] <- uniform(seed=8, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput2.whh = Times (LSTMoutput2.Wh, LSTMoutput2.dh) : [4096 x 512], [512] -> [4096]
|
||||
Validating --> LSTMoutput2.wxxpbpwhh = Plus (LSTMoutput2.wxxpb, LSTMoutput2.whh) : [4096 x 1 x *], [4096] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput2.G4 = Slice (LSTMoutput2.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.G3 = Slice (LSTMoutput2.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wcfdc = DiagTimes (LSTMoutput2.Wcf, LSTMoutput2.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput2.unnamed175 = Plus (LSTMoutput2.G3, LSTMoutput2.Wcfdc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.ft = Sigmoid (LSTMoutput2.unnamed175) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.bft = ElementTimes (LSTMoutput2.ft, LSTMoutput2.dc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.G1 = Slice (LSTMoutput2.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wcidc = DiagTimes (LSTMoutput2.Wci, LSTMoutput2.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput2.unnamed173 = Plus (LSTMoutput2.G1, LSTMoutput2.Wcidc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.it = Sigmoid (LSTMoutput2.unnamed173) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.G2 = Slice (LSTMoutput2.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.unnamed174 = Tanh (LSTMoutput2.G2) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.bit = ElementTimes (LSTMoutput2.it, LSTMoutput2.unnamed174) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.ct = Plus (LSTMoutput2.bft, LSTMoutput2.bit) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wcoct = DiagTimes (LSTMoutput2.Wco, LSTMoutput2.ct) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.unnamed176 = Plus (LSTMoutput2.G4, LSTMoutput2.Wcoct) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.ot = Sigmoid (LSTMoutput2.unnamed176) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.unnamed177 = Tanh (LSTMoutput2.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.mt = ElementTimes (LSTMoutput2.ot, LSTMoutput2.unnamed177) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.output = Times (LSTMoutput2.Wmr, LSTMoutput2.mt) : [512 x 1024], [1024 x 1 x *] -> [512 x 1 x *]
|
||||
Node 'LSTMoutput3.wx' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 512 x 1].
|
||||
Node 'LSTMoutput3.wx' (LearnableParameter operation): Initializing Parameter[4096 x 512 x 1] <- uniform(seed=13, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput3.wxx = Times (LSTMoutput3.wx, LSTMoutput2.output) : [4096 x 512 x 1], [512 x 1 x *] -> [4096 x *]
|
||||
Validating --> LSTMoutput3.b = LearnableParameter() : -> [4096 x 1]
|
||||
Validating --> LSTMoutput3.wxxpb = Plus (LSTMoutput3.wxx, LSTMoutput3.b) : [4096 x *], [4096 x 1] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wh = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> LSTMoutput3.Wco = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput3.Wcf = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput3.Wci = LearnableParameter() : -> [1024]
|
||||
Node 'LSTMoutput3.Wh' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 512].
|
||||
Node 'LSTMoutput3.Wh' (LearnableParameter operation): Initializing Parameter[4096 x 512] <- uniform(seed=14, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput3.whh = Times (LSTMoutput3.Wh, LSTMoutput3.dh) : [4096 x 512], [512] -> [4096]
|
||||
Validating --> LSTMoutput3.wxxpbpwhh = Plus (LSTMoutput3.wxxpb, LSTMoutput3.whh) : [4096 x 1 x *], [4096] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput3.G4 = Slice (LSTMoutput3.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.G3 = Slice (LSTMoutput3.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wcfdc = DiagTimes (LSTMoutput3.Wcf, LSTMoutput3.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput3.unnamed185 = Plus (LSTMoutput3.G3, LSTMoutput3.Wcfdc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.ft = Sigmoid (LSTMoutput3.unnamed185) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.bft = ElementTimes (LSTMoutput3.ft, LSTMoutput3.dc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.G1 = Slice (LSTMoutput3.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wcidc = DiagTimes (LSTMoutput3.Wci, LSTMoutput3.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput3.unnamed183 = Plus (LSTMoutput3.G1, LSTMoutput3.Wcidc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.it = Sigmoid (LSTMoutput3.unnamed183) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.G2 = Slice (LSTMoutput3.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.unnamed184 = Tanh (LSTMoutput3.G2) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.bit = ElementTimes (LSTMoutput3.it, LSTMoutput3.unnamed184) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.ct = Plus (LSTMoutput3.bft, LSTMoutput3.bit) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wcoct = DiagTimes (LSTMoutput3.Wco, LSTMoutput3.ct) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.unnamed186 = Plus (LSTMoutput3.G4, LSTMoutput3.Wcoct) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.ot = Sigmoid (LSTMoutput3.unnamed186) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.unnamed187 = Tanh (LSTMoutput3.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.mt = ElementTimes (LSTMoutput3.ot, LSTMoutput3.unnamed187) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.output = Times (LSTMoutput3.Wmr, LSTMoutput3.mt) : [512 x 1024], [1024 x 1 x *] -> [512 x 1 x *]
|
||||
Node 'W' (LearnableParameter operation) operation: Tensor shape was inferred as [132 x 512 x 1].
|
||||
Node 'W' (LearnableParameter operation): Initializing Parameter[132 x 512 x 1] <- uniform(seed=19, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> unnamed193 = Times (W, LSTMoutput3.output) : [132 x 512 x 1], [512 x 1 x *] -> [132 x *]
|
||||
Validating --> b = LearnableParameter() : -> [132 x 1]
|
||||
Validating --> LSTMoutputW = Plus (unnamed193, b) : [132 x *], [132 x 1] -> [132 x 1 x *]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, LSTMoutputW) : [132 x *], [132 x 1 x *] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, LSTMoutputW) : [132 x *], [132 x 1 x *] -> [1]
|
||||
Validating --> logPrior.prior = Mean (labels) : [132 x *] -> [132]
|
||||
Validating --> logPrior.logPrior = Log (logPrior.prior) : [132] -> [132]
|
||||
Validating --> scaledLogLikelihood = Minus (LSTMoutputW, logPrior.logPrior) : [132 x 1 x *], [132] -> [132 x 1 x *]
|
||||
|
||||
Validating network. 88 nodes to process in pass 2.
|
||||
|
||||
Validating --> LSTMoutput1.dh = PastValue (LSTMoutput1.output) : [512 x 1 x *] -> [512 x 1 x *]
|
||||
Validating --> LSTMoutput1.whh = Times (LSTMoutput1.Wh, LSTMoutput1.dh) : [4096 x 512], [512 x 1 x *] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput1.dc = PastValue (LSTMoutput1.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wcfdc = DiagTimes (LSTMoutput1.Wcf, LSTMoutput1.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wcidc = DiagTimes (LSTMoutput1.Wci, LSTMoutput1.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.dh = PastValue (LSTMoutput2.output) : [512 x 1 x *] -> [512 x 1 x *]
|
||||
Validating --> LSTMoutput2.whh = Times (LSTMoutput2.Wh, LSTMoutput2.dh) : [4096 x 512], [512 x 1 x *] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput2.dc = PastValue (LSTMoutput2.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wcfdc = DiagTimes (LSTMoutput2.Wcf, LSTMoutput2.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wcidc = DiagTimes (LSTMoutput2.Wci, LSTMoutput2.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.dh = PastValue (LSTMoutput3.output) : [512 x 1 x *] -> [512 x 1 x *]
|
||||
Validating --> LSTMoutput3.whh = Times (LSTMoutput3.Wh, LSTMoutput3.dh) : [4096 x 512], [512 x 1 x *] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput3.dc = PastValue (LSTMoutput3.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wcfdc = DiagTimes (LSTMoutput3.Wcf, LSTMoutput3.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wcidc = DiagTimes (LSTMoutput3.Wci, LSTMoutput3.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
|
||||
Validating network. 15 nodes to process in pass 3.
|
||||
|
||||
|
||||
Validating network, final pass.
|
||||
|
||||
|
||||
|
||||
29 out of 113 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
08/16/2016 10:02:01: Created model with 113 nodes on GPU 0.
|
||||
|
||||
08/16/2016 10:02:01: Training criterion node(s):
|
||||
08/16/2016 10:02:01: ce = CrossEntropyWithSoftmax
|
||||
|
||||
08/16/2016 10:02:01: Evaluation criterion node(s):
|
||||
08/16/2016 10:02:01: err = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing: Out of 217 matrices, 125 are shared as 56, and 92 are not shared.
|
||||
|
||||
{ LSTMoutput2.mt : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.dh : [512 x 1 x *]
|
||||
LSTMoutput3.wxx : [4096 x *] (gradient) }
|
||||
{ LSTMoutput2.Wco : [1024] (gradient)
|
||||
LSTMoutput3.dc : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.wx : [4096 x 363] (gradient)
|
||||
LSTMoutput1.wxxpb : [4096 x 1 x *] }
|
||||
{ LSTMoutput1.Wmr : [512 x 1024] (gradient)
|
||||
LSTMoutput2.wxx : [4096 x *] }
|
||||
{ LSTMoutput2.wx : [4096 x 512 x 1] (gradient)
|
||||
LSTMoutput2.wxxpb : [4096 x 1 x *] }
|
||||
{ LSTMoutput1.ot : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.whh : [4096 x 1 x *] }
|
||||
{ LSTMoutput1.ct : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.wxxpbpwhh : [4096 x 1 x *] }
|
||||
{ LSTMoutput1.G4 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.G4 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.unnamed164 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.Wcfdc : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.wxxpbpwhh : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput2.unnamed175 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.G1 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.ft : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.Wci : [1024] (gradient)
|
||||
LSTMoutput2.G1 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.G3 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.Wcidc : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.Wcf : [1024] (gradient)
|
||||
LSTMoutput2.it : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.whh : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput2.G2 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.b : [4096 x 1] (gradient)
|
||||
LSTMoutput1.dh : [512 x 1 x *] (gradient)
|
||||
LSTMoutput2.unnamed174 : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.Wmr : [512 x 1024] (gradient)
|
||||
LSTMoutput3.wxx : [4096 x *] }
|
||||
{ LSTMoutput3.wx : [4096 x 512 x 1] (gradient)
|
||||
LSTMoutput3.wxxpb : [4096 x 1 x *] }
|
||||
{ LSTMoutput2.ot : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.whh : [4096 x 1 x *] }
|
||||
{ LSTMoutput2.ct : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.wxxpbpwhh : [4096 x 1 x *] }
|
||||
{ LSTMoutput1.Wcoct : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.G4 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.G4 : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.unnamed174 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.Wcfdc : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.unnamed166 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.wxxpbpwhh : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput3.unnamed185 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.dc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.G1 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.ft : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.unnamed165 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.bft : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.Wci : [1024] (gradient)
|
||||
LSTMoutput3.G1 : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.G3 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.Wcidc : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.it : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.unnamed183 : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.Wcf : [1024] (gradient)
|
||||
LSTMoutput3.it : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.unnamed167 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.whh : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput3.G2 : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.b : [4096 x 1] (gradient)
|
||||
LSTMoutput2.dh : [512 x 1 x *] (gradient)
|
||||
LSTMoutput3.unnamed184 : [1024 x 1 x *] }
|
||||
{ LSTMoutput3.Wmr : [512 x 1024] (gradient)
|
||||
unnamed193 : [132 x *] }
|
||||
{ LSTMoutputW : [132 x 1 x *]
|
||||
W : [132 x 512 x 1] (gradient) }
|
||||
{ LSTMoutput3.output : [512 x 1 x *] (gradient)
|
||||
LSTMoutputW : [132 x 1 x *] (gradient) }
|
||||
{ LSTMoutput3.mt : [1024 x 1 x *] (gradient)
|
||||
unnamed193 : [132 x *] (gradient) }
|
||||
{ LSTMoutput2.Wcoct : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.G4 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.ft : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.bft : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.output : [512 x 1 x *] (gradient)
|
||||
LSTMoutput2.wxxpb : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput3.it : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.Wh : [4096 x 512] (gradient)
|
||||
LSTMoutput3.G2 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.unnamed176 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.wxxpbpwhh : [4096 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.bit : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.unnamed183 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.bft : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.dc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.G1 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.G2 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.Wcfdc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.Wcidc : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.unnamed163 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.unnamed175 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.Wcidc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.ft : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.bft : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.dc : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.Wcfdc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.Wcidc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.ft : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.unnamed173 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.unnamed185 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.Wh : [4096 x 512] (gradient)
|
||||
LSTMoutput2.G2 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.Wcfdc : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.wxxpb : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput2.it : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.output : [512 x 1 x *] (gradient)
|
||||
LSTMoutput3.wxxpb : [4096 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.unnamed177 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.whh : [4096 x 1 x *] (gradient) }
|
||||
{ LSTMoutput3.b : [4096 x 1] (gradient)
|
||||
LSTMoutput3.dh : [512 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.dh : [512 x 1 x *]
|
||||
LSTMoutput1.wxx : [4096 x *] (gradient) }
|
||||
{ LSTMoutput1.mt : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.dh : [512 x 1 x *]
|
||||
LSTMoutput2.wxx : [4096 x *] (gradient) }
|
||||
{ LSTMoutput1.Wco : [1024] (gradient)
|
||||
LSTMoutput2.dc : [1024 x 1 x *] }
|
||||
|
||||
|
||||
08/16/2016 10:02:01: Training 13634692 parameters in 23 out of 23 parameter tensors and 104 nodes with gradient:
|
||||
|
||||
08/16/2016 10:02:01: Node 'LSTMoutput1.Wcf' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 10:02:01: Node 'LSTMoutput1.Wci' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 10:02:01: Node 'LSTMoutput1.Wco' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 10:02:01: Node 'LSTMoutput1.Wh' (LearnableParameter operation) : [4096 x 512]
|
||||
08/16/2016 10:02:01: Node 'LSTMoutput1.Wmr' (LearnableParameter operation) : [512 x 1024]
|
||||
08/16/2016 10:02:01: Node 'LSTMoutput1.b' (LearnableParameter operation) : [4096 x 1]
|
||||
08/16/2016 10:02:01: Node 'LSTMoutput1.wx' (LearnableParameter operation) : [4096 x 363]
|
||||
08/16/2016 10:02:01: Node 'LSTMoutput2.Wcf' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 10:02:01: Node 'LSTMoutput2.Wci' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 10:02:01: Node 'LSTMoutput2.Wco' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 10:02:01: Node 'LSTMoutput2.Wh' (LearnableParameter operation) : [4096 x 512]
|
||||
08/16/2016 10:02:01: Node 'LSTMoutput2.Wmr' (LearnableParameter operation) : [512 x 1024]
|
||||
08/16/2016 10:02:01: Node 'LSTMoutput2.b' (LearnableParameter operation) : [4096 x 1]
|
||||
08/16/2016 10:02:01: Node 'LSTMoutput2.wx' (LearnableParameter operation) : [4096 x 512 x 1]
|
||||
08/16/2016 10:02:01: Node 'LSTMoutput3.Wcf' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 10:02:01: Node 'LSTMoutput3.Wci' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 10:02:01: Node 'LSTMoutput3.Wco' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 10:02:01: Node 'LSTMoutput3.Wh' (LearnableParameter operation) : [4096 x 512]
|
||||
08/16/2016 10:02:01: Node 'LSTMoutput3.Wmr' (LearnableParameter operation) : [512 x 1024]
|
||||
08/16/2016 10:02:01: Node 'LSTMoutput3.b' (LearnableParameter operation) : [4096 x 1]
|
||||
08/16/2016 10:02:01: Node 'LSTMoutput3.wx' (LearnableParameter operation) : [4096 x 512 x 1]
|
||||
08/16/2016 10:02:01: Node 'W' (LearnableParameter operation) : [132 x 512 x 1]
|
||||
08/16/2016 10:02:01: Node 'b' (LearnableParameter operation) : [132 x 1]
|
||||
|
||||
|
||||
08/16/2016 10:02:01: Precomputing --> 3 PreCompute nodes found.
|
||||
|
||||
08/16/2016 10:02:01: featNorm.xMean = Mean()
|
||||
08/16/2016 10:02:01: featNorm.xStdDev = InvStdDev()
|
||||
08/16/2016 10:02:01: logPrior.prior = Mean()
|
||||
minibatchiterator: epoch 0: frames [0..252734] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses
|
||||
requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms
|
||||
|
||||
08/16/2016 10:02:02: Precomputing --> Completed.
|
||||
|
||||
|
||||
08/16/2016 10:02:02: Starting Epoch 1: learning rate per sample = 0.001953 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
minibatchiterator: epoch 0: frames [0..64] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses
|
||||
|
||||
08/16/2016 10:02:03: Starting minibatch loop.
|
||||
08/16/2016 10:02:03: Epoch[ 1 of 1]-Minibatch[ 1- 10, 250.00%]: ce = 4.87453079 * 160; err = 0.90625000 * 160; time = 0.5069s; samplesPerSecond = 315.6
|
||||
08/16/2016 10:02:03: Epoch[ 1 of 1]-Minibatch[ 11- 20, 500.00%]: ce = 4.84628143 * 160; err = 0.69375000 * 160; time = 0.4852s; samplesPerSecond = 329.8
|
||||
08/16/2016 10:02:04: Finished Epoch[ 1 of 1]: [Training] ce = 4.85708837 * 418; err = 0.80382775 * 418; totalSamplesSeen = 418; learningRatePerSample = 0.001953125; epochTime=1.33633s
|
||||
08/16/2016 10:02:04: SGD: Saving checkpoint model '/tmp/cntk-test-20160816100054.995555/Examples/Speech/AN4_LSTM@release_gpu/Models/cntkSpeechLSTM.dnn'
|
||||
08/16/2016 10:02:05: CNTKCommandTrainEnd: speechTrain
|
||||
|
||||
08/16/2016 10:02:05: Action "train" complete.
|
||||
|
||||
08/16/2016 10:02:05: __COMPLETED__
|
||||
|
|
@ -1 +0,0 @@
|
|||
__COMPLETED__
|
||||
|
|
@ -1 +0,0 @@
|
|||
__COMPLETED__
|
||||
|
|
@ -0,0 +1,681 @@
|
|||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 268381192 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config/LSTM-NDL.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_cpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_cpu DeviceId=-1 timestamping=true speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=64]] speechTrain=[reader=[useMersenneTwisterRand=true]] parallelTrain=false
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: Aug 16 2016 03:09:16
|
||||
Last modified date: Fri Aug 12 05:28:23 2016
|
||||
Build type: Release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: yes
|
||||
Math lib: mkl
|
||||
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
CUB_PATH: c:\src\cub-1.4.1
|
||||
CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by svcphil on Philly-Pool1
|
||||
Build Path: c:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
08/16/2016 03:20:22: -------------------------------------------------------------------
|
||||
08/16/2016 03:20:22: Build info:
|
||||
|
||||
08/16/2016 03:20:22: Built time: Aug 16 2016 03:09:16
|
||||
08/16/2016 03:20:22: Last modified date: Fri Aug 12 05:28:23 2016
|
||||
08/16/2016 03:20:22: Build type: Release
|
||||
08/16/2016 03:20:22: Build target: GPU
|
||||
08/16/2016 03:20:22: With 1bit-SGD: yes
|
||||
08/16/2016 03:20:22: Math lib: mkl
|
||||
08/16/2016 03:20:22: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
08/16/2016 03:20:22: CUB_PATH: c:\src\cub-1.4.1
|
||||
08/16/2016 03:20:22: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
08/16/2016 03:20:22: Build Branch: HEAD
|
||||
08/16/2016 03:20:22: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 03:20:22: Built by svcphil on Philly-Pool1
|
||||
08/16/2016 03:20:22: Build Path: c:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
08/16/2016 03:20:22: -------------------------------------------------------------------
|
||||
08/16/2016 03:20:23: -------------------------------------------------------------------
|
||||
08/16/2016 03:20:23: GPU info:
|
||||
|
||||
08/16/2016 03:20:23: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:20:23: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:20:23: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:20:23: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 03:20:23: Running on DPHAIM-25 at 2016/08/16 03:20:23
|
||||
08/16/2016 03:20:23: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config/LSTM-NDL.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_cpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_cpu DeviceId=-1 timestamping=true speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=64]] speechTrain=[reader=[useMersenneTwisterRand=true]] parallelTrain=false
|
||||
|
||||
|
||||
|
||||
08/16/2016 03:20:23: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:20:23: RootDir = ".."
|
||||
ConfigDir = "$RootDir$/Config"
|
||||
DataDir = "$RootDir$/Data"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
ModelDir = "$OutputDir$/Models"
|
||||
deviceId = -1
|
||||
command = speechTrain
|
||||
precision = "float"
|
||||
traceLevel = 1
|
||||
modelPath = "$ModelDir$/cntkSpeechLSTM.dnn"
|
||||
parallelTrain = true
|
||||
frameMode = false
|
||||
truncated = true
|
||||
speechTrain = [
|
||||
action = "train"
|
||||
nbrUttsIneachRecurrentIter = 16
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "$ConfigDir$/lstmp-3layer-opt.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 0
|
||||
minibatchSize = 16
|
||||
learningRatesPerMB = 0.5
|
||||
numMBsToShowResult = 10
|
||||
momentumPerMB = 0:0.9
|
||||
maxEpochs = 4
|
||||
keepCheckPointFiles = true
|
||||
]
|
||||
reader = [
|
||||
readerType = "HTKMLFReader"
|
||||
readMethod = "blockRandomize"
|
||||
miniBatchMode = "partial"
|
||||
randomize = "auto"
|
||||
verbosity = 0
|
||||
features = [
|
||||
dim = 363
|
||||
type = "real"
|
||||
scpFile = "$DataDir$/glob_0000.scp"
|
||||
]
|
||||
labels = [
|
||||
mlfFile = "$DataDir$/glob_0000.mlf"
|
||||
labelMappingFile = "$DataDir$/state.list"
|
||||
labelDim = 132
|
||||
labelType = "category"
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_cpu
|
||||
DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_cpu
|
||||
DeviceId=-1
|
||||
timestamping=true
|
||||
speechTrain=[SGD=[maxEpochs=1]]
|
||||
speechTrain=[SGD=[epochSize=64]]
|
||||
speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
parallelTrain=false
|
||||
|
||||
08/16/2016 03:20:23: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
08/16/2016 03:20:23: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:20:23: RootDir = ".."
|
||||
ConfigDir = "../Config"
|
||||
DataDir = "../Data"
|
||||
OutputDir = "../Output"
|
||||
ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_cpu/Models"
|
||||
deviceId = -1
|
||||
command = speechTrain
|
||||
precision = "float"
|
||||
traceLevel = 1
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_cpu/Models/cntkSpeechLSTM.dnn"
|
||||
parallelTrain = true
|
||||
frameMode = false
|
||||
truncated = true
|
||||
speechTrain = [
|
||||
action = "train"
|
||||
nbrUttsIneachRecurrentIter = 16
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config/lstmp-3layer-opt.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 0
|
||||
minibatchSize = 16
|
||||
learningRatesPerMB = 0.5
|
||||
numMBsToShowResult = 10
|
||||
momentumPerMB = 0:0.9
|
||||
maxEpochs = 4
|
||||
keepCheckPointFiles = true
|
||||
]
|
||||
reader = [
|
||||
readerType = "HTKMLFReader"
|
||||
readMethod = "blockRandomize"
|
||||
miniBatchMode = "partial"
|
||||
randomize = "auto"
|
||||
verbosity = 0
|
||||
features = [
|
||||
dim = 363
|
||||
type = "real"
|
||||
scpFile = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data/glob_0000.scp"
|
||||
]
|
||||
labels = [
|
||||
mlfFile = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data/glob_0000.mlf"
|
||||
labelMappingFile = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data/state.list"
|
||||
labelDim = 132
|
||||
labelType = "category"
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_cpu
|
||||
DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_cpu
|
||||
DeviceId=-1
|
||||
timestamping=true
|
||||
speechTrain=[SGD=[maxEpochs=1]]
|
||||
speechTrain=[SGD=[epochSize=64]]
|
||||
speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
parallelTrain=false
|
||||
|
||||
08/16/2016 03:20:23: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
08/16/2016 03:20:23: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: LSTM-NDL.cntk:command=speechTrain
|
||||
configparameters: LSTM-NDL.cntk:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config
|
||||
configparameters: LSTM-NDL.cntk:currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
configparameters: LSTM-NDL.cntk:DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
configparameters: LSTM-NDL.cntk:deviceId=-1
|
||||
configparameters: LSTM-NDL.cntk:frameMode=false
|
||||
configparameters: LSTM-NDL.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_cpu/Models
|
||||
configparameters: LSTM-NDL.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_cpu/Models/cntkSpeechLSTM.dnn
|
||||
configparameters: LSTM-NDL.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_cpu
|
||||
configparameters: LSTM-NDL.cntk:parallelTrain=false
|
||||
configparameters: LSTM-NDL.cntk:precision=float
|
||||
configparameters: LSTM-NDL.cntk:RootDir=..
|
||||
configparameters: LSTM-NDL.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_cpu
|
||||
configparameters: LSTM-NDL.cntk:speechTrain=[
|
||||
action = "train"
|
||||
nbrUttsIneachRecurrentIter = 16
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config/lstmp-3layer-opt.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 0
|
||||
minibatchSize = 16
|
||||
learningRatesPerMB = 0.5
|
||||
numMBsToShowResult = 10
|
||||
momentumPerMB = 0:0.9
|
||||
maxEpochs = 4
|
||||
keepCheckPointFiles = true
|
||||
]
|
||||
reader = [
|
||||
readerType = "HTKMLFReader"
|
||||
readMethod = "blockRandomize"
|
||||
miniBatchMode = "partial"
|
||||
randomize = "auto"
|
||||
verbosity = 0
|
||||
features = [
|
||||
dim = 363
|
||||
type = "real"
|
||||
scpFile = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data/glob_0000.scp"
|
||||
]
|
||||
labels = [
|
||||
mlfFile = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data/glob_0000.mlf"
|
||||
labelMappingFile = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data/state.list"
|
||||
labelDim = 132
|
||||
labelType = "category"
|
||||
]
|
||||
]
|
||||
] [SGD=[maxEpochs=1]] [SGD=[epochSize=64]] [reader=[useMersenneTwisterRand=true]]
|
||||
|
||||
configparameters: LSTM-NDL.cntk:timestamping=true
|
||||
configparameters: LSTM-NDL.cntk:traceLevel=1
|
||||
configparameters: LSTM-NDL.cntk:truncated=true
|
||||
08/16/2016 03:20:23: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:20:23: Commands: speechTrain
|
||||
08/16/2016 03:20:23: Precision = "float"
|
||||
08/16/2016 03:20:23: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_cpu/Models/cntkSpeechLSTM.dnn
|
||||
08/16/2016 03:20:23: CNTKCommandTrainInfo: speechTrain : 1
|
||||
08/16/2016 03:20:23: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 1
|
||||
|
||||
08/16/2016 03:20:23: ##############################################################################
|
||||
08/16/2016 03:20:23: # #
|
||||
08/16/2016 03:20:23: # Action "train" #
|
||||
08/16/2016 03:20:23: # #
|
||||
08/16/2016 03:20:23: ##############################################################################
|
||||
|
||||
08/16/2016 03:20:23: CNTKCommandTrainBegin: speechTrain
|
||||
NDLBuilder Using CPU
|
||||
reading script file C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data/glob_0000.scp ... 948 entries
|
||||
total 132 state names in state list C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data/state.list
|
||||
htkmlfreader: reading MLF file C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data/glob_0000.mlf ... total 948 entries
|
||||
...............................................................................................feature set 0: 252734 frames in 948 out of 948 utterances
|
||||
label set 0: 129 classes
|
||||
minibatchutterancesource: 948 utterances grouped into 3 chunks, av. chunk size: 316.0 utterances, 84244.7 frames
|
||||
useParallelTrain option is not enabled. ParallelTrain config will be ignored.
|
||||
08/16/2016 03:20:24: Creating virgin network.
|
||||
Node 'LSTMoutput1.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput1.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput1.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput1.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput1.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- 0.000000.
|
||||
Node 'LSTMoutput2.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput2.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput2.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput2.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput2.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- 0.000000.
|
||||
Node 'LSTMoutput3.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput3.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput3.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput3.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput3.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- 0.000000.
|
||||
Node 'b' (LearnableParameter operation): Initializing Parameter[132 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput1.wx' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput1.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput1.Wh' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput1.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=3, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput1.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=4, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput1.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=5, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput1.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- uniform(seed=6, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput2.wx' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput2.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput2.Wh' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput2.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=9, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput2.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=10, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput2.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=11, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput2.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- uniform(seed=12, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput3.wx' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput3.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput3.Wh' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput3.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=15, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput3.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=16, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput3.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=17, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput3.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- uniform(seed=18, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'W' (LearnableParameter operation): Initializating Parameter[132 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'b' (LearnableParameter operation): Initializing Parameter[132 x 1] <- 0.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
6 roots:
|
||||
ce = CrossEntropyWithSoftmax()
|
||||
err = ErrorPrediction()
|
||||
featNorm.xMean = Mean()
|
||||
featNorm.xStdDev = InvStdDev()
|
||||
logPrior.prior = Mean()
|
||||
scaledLogLikelihood = Minus()
|
||||
|
||||
Loop[0] --> Loop_LSTMoutput1.output -> 24 nodes
|
||||
|
||||
LSTMoutput1.dh LSTMoutput1.whh LSTMoutput1.wxxpbpwhh
|
||||
LSTMoutput1.G4 LSTMoutput1.G3 LSTMoutput1.dc
|
||||
LSTMoutput1.Wcfdc LSTMoutput1.unnamed165 LSTMoutput1.ft
|
||||
LSTMoutput1.bft LSTMoutput1.G1 LSTMoutput1.Wcidc
|
||||
LSTMoutput1.unnamed163 LSTMoutput1.it LSTMoutput1.G2
|
||||
LSTMoutput1.unnamed164 LSTMoutput1.bit LSTMoutput1.ct
|
||||
LSTMoutput1.Wcoct LSTMoutput1.unnamed166 LSTMoutput1.ot
|
||||
LSTMoutput1.unnamed167 LSTMoutput1.mt LSTMoutput1.output
|
||||
|
||||
Loop[1] --> Loop_LSTMoutput2.output -> 24 nodes
|
||||
|
||||
LSTMoutput2.dh LSTMoutput2.whh LSTMoutput2.wxxpbpwhh
|
||||
LSTMoutput2.G4 LSTMoutput2.G3 LSTMoutput2.dc
|
||||
LSTMoutput2.Wcfdc LSTMoutput2.unnamed175 LSTMoutput2.ft
|
||||
LSTMoutput2.bft LSTMoutput2.G1 LSTMoutput2.Wcidc
|
||||
LSTMoutput2.unnamed173 LSTMoutput2.it LSTMoutput2.G2
|
||||
LSTMoutput2.unnamed174 LSTMoutput2.bit LSTMoutput2.ct
|
||||
LSTMoutput2.Wcoct LSTMoutput2.unnamed176 LSTMoutput2.ot
|
||||
LSTMoutput2.unnamed177 LSTMoutput2.mt LSTMoutput2.output
|
||||
|
||||
Loop[2] --> Loop_LSTMoutput3.output -> 24 nodes
|
||||
|
||||
LSTMoutput3.dh LSTMoutput3.whh LSTMoutput3.wxxpbpwhh
|
||||
LSTMoutput3.G4 LSTMoutput3.G3 LSTMoutput3.dc
|
||||
LSTMoutput3.Wcfdc LSTMoutput3.unnamed185 LSTMoutput3.ft
|
||||
LSTMoutput3.bft LSTMoutput3.G1 LSTMoutput3.Wcidc
|
||||
LSTMoutput3.unnamed183 LSTMoutput3.it LSTMoutput3.G2
|
||||
LSTMoutput3.unnamed184 LSTMoutput3.bit LSTMoutput3.ct
|
||||
LSTMoutput3.Wcoct LSTMoutput3.unnamed186 LSTMoutput3.ot
|
||||
LSTMoutput3.unnamed187 LSTMoutput3.mt LSTMoutput3.output
|
||||
|
||||
Validating network. 113 nodes to process in pass 1.
|
||||
|
||||
Validating --> labels = InputValue() : -> [132 x *]
|
||||
Validating --> W = LearnableParameter() : -> [132 x 0]
|
||||
Validating --> LSTMoutput3.Wmr = LearnableParameter() : -> [512 x 1024]
|
||||
Validating --> LSTMoutput3.wx = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> LSTMoutput2.Wmr = LearnableParameter() : -> [512 x 1024]
|
||||
Validating --> LSTMoutput2.wx = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> LSTMoutput1.Wmr = LearnableParameter() : -> [512 x 1024]
|
||||
Validating --> LSTMoutput1.wx = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> features = InputValue() : -> [363 x *]
|
||||
Validating --> featNorm.xMean = Mean (features) : [363 x *] -> [363]
|
||||
Validating --> featNorm.xStdDev = InvStdDev (features) : [363 x *] -> [363]
|
||||
Validating --> featNorm.xNorm = PerDimMeanVarNormalization (features, featNorm.xMean, featNorm.xStdDev) : [363 x *], [363], [363] -> [363 x *]
|
||||
Node 'LSTMoutput1.wx' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 363].
|
||||
Node 'LSTMoutput1.wx' (LearnableParameter operation): Initializing Parameter[4096 x 363] <- uniform(seed=1, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput1.wxx = Times (LSTMoutput1.wx, featNorm.xNorm) : [4096 x 363], [363 x *] -> [4096 x *]
|
||||
Validating --> LSTMoutput1.b = LearnableParameter() : -> [4096 x 1]
|
||||
Validating --> LSTMoutput1.wxxpb = Plus (LSTMoutput1.wxx, LSTMoutput1.b) : [4096 x *], [4096 x 1] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wh = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> LSTMoutput1.Wco = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput1.Wcf = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput1.Wci = LearnableParameter() : -> [1024]
|
||||
Node 'LSTMoutput1.Wh' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 512].
|
||||
Node 'LSTMoutput1.Wh' (LearnableParameter operation): Initializing Parameter[4096 x 512] <- uniform(seed=2, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput1.whh = Times (LSTMoutput1.Wh, LSTMoutput1.dh) : [4096 x 512], [512] -> [4096]
|
||||
Validating --> LSTMoutput1.wxxpbpwhh = Plus (LSTMoutput1.wxxpb, LSTMoutput1.whh) : [4096 x 1 x *], [4096] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput1.G4 = Slice (LSTMoutput1.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.G3 = Slice (LSTMoutput1.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wcfdc = DiagTimes (LSTMoutput1.Wcf, LSTMoutput1.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput1.unnamed165 = Plus (LSTMoutput1.G3, LSTMoutput1.Wcfdc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.ft = Sigmoid (LSTMoutput1.unnamed165) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.bft = ElementTimes (LSTMoutput1.ft, LSTMoutput1.dc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.G1 = Slice (LSTMoutput1.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wcidc = DiagTimes (LSTMoutput1.Wci, LSTMoutput1.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput1.unnamed163 = Plus (LSTMoutput1.G1, LSTMoutput1.Wcidc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.it = Sigmoid (LSTMoutput1.unnamed163) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.G2 = Slice (LSTMoutput1.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.unnamed164 = Tanh (LSTMoutput1.G2) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.bit = ElementTimes (LSTMoutput1.it, LSTMoutput1.unnamed164) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.ct = Plus (LSTMoutput1.bft, LSTMoutput1.bit) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wcoct = DiagTimes (LSTMoutput1.Wco, LSTMoutput1.ct) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.unnamed166 = Plus (LSTMoutput1.G4, LSTMoutput1.Wcoct) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.ot = Sigmoid (LSTMoutput1.unnamed166) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.unnamed167 = Tanh (LSTMoutput1.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.mt = ElementTimes (LSTMoutput1.ot, LSTMoutput1.unnamed167) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.output = Times (LSTMoutput1.Wmr, LSTMoutput1.mt) : [512 x 1024], [1024 x 1 x *] -> [512 x 1 x *]
|
||||
Node 'LSTMoutput2.wx' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 512 x 1].
|
||||
Node 'LSTMoutput2.wx' (LearnableParameter operation): Initializing Parameter[4096 x 512 x 1] <- uniform(seed=7, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput2.wxx = Times (LSTMoutput2.wx, LSTMoutput1.output) : [4096 x 512 x 1], [512 x 1 x *] -> [4096 x *]
|
||||
Validating --> LSTMoutput2.b = LearnableParameter() : -> [4096 x 1]
|
||||
Validating --> LSTMoutput2.wxxpb = Plus (LSTMoutput2.wxx, LSTMoutput2.b) : [4096 x *], [4096 x 1] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wh = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> LSTMoutput2.Wco = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput2.Wcf = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput2.Wci = LearnableParameter() : -> [1024]
|
||||
Node 'LSTMoutput2.Wh' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 512].
|
||||
Node 'LSTMoutput2.Wh' (LearnableParameter operation): Initializing Parameter[4096 x 512] <- uniform(seed=8, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput2.whh = Times (LSTMoutput2.Wh, LSTMoutput2.dh) : [4096 x 512], [512] -> [4096]
|
||||
Validating --> LSTMoutput2.wxxpbpwhh = Plus (LSTMoutput2.wxxpb, LSTMoutput2.whh) : [4096 x 1 x *], [4096] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput2.G4 = Slice (LSTMoutput2.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.G3 = Slice (LSTMoutput2.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wcfdc = DiagTimes (LSTMoutput2.Wcf, LSTMoutput2.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput2.unnamed175 = Plus (LSTMoutput2.G3, LSTMoutput2.Wcfdc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.ft = Sigmoid (LSTMoutput2.unnamed175) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.bft = ElementTimes (LSTMoutput2.ft, LSTMoutput2.dc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.G1 = Slice (LSTMoutput2.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wcidc = DiagTimes (LSTMoutput2.Wci, LSTMoutput2.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput2.unnamed173 = Plus (LSTMoutput2.G1, LSTMoutput2.Wcidc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.it = Sigmoid (LSTMoutput2.unnamed173) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.G2 = Slice (LSTMoutput2.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.unnamed174 = Tanh (LSTMoutput2.G2) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.bit = ElementTimes (LSTMoutput2.it, LSTMoutput2.unnamed174) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.ct = Plus (LSTMoutput2.bft, LSTMoutput2.bit) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wcoct = DiagTimes (LSTMoutput2.Wco, LSTMoutput2.ct) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.unnamed176 = Plus (LSTMoutput2.G4, LSTMoutput2.Wcoct) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.ot = Sigmoid (LSTMoutput2.unnamed176) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.unnamed177 = Tanh (LSTMoutput2.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.mt = ElementTimes (LSTMoutput2.ot, LSTMoutput2.unnamed177) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.output = Times (LSTMoutput2.Wmr, LSTMoutput2.mt) : [512 x 1024], [1024 x 1 x *] -> [512 x 1 x *]
|
||||
Node 'LSTMoutput3.wx' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 512 x 1].
|
||||
Node 'LSTMoutput3.wx' (LearnableParameter operation): Initializing Parameter[4096 x 512 x 1] <- uniform(seed=13, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput3.wxx = Times (LSTMoutput3.wx, LSTMoutput2.output) : [4096 x 512 x 1], [512 x 1 x *] -> [4096 x *]
|
||||
Validating --> LSTMoutput3.b = LearnableParameter() : -> [4096 x 1]
|
||||
Validating --> LSTMoutput3.wxxpb = Plus (LSTMoutput3.wxx, LSTMoutput3.b) : [4096 x *], [4096 x 1] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wh = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> LSTMoutput3.Wco = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput3.Wcf = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput3.Wci = LearnableParameter() : -> [1024]
|
||||
Node 'LSTMoutput3.Wh' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 512].
|
||||
Node 'LSTMoutput3.Wh' (LearnableParameter operation): Initializing Parameter[4096 x 512] <- uniform(seed=14, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput3.whh = Times (LSTMoutput3.Wh, LSTMoutput3.dh) : [4096 x 512], [512] -> [4096]
|
||||
Validating --> LSTMoutput3.wxxpbpwhh = Plus (LSTMoutput3.wxxpb, LSTMoutput3.whh) : [4096 x 1 x *], [4096] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput3.G4 = Slice (LSTMoutput3.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.G3 = Slice (LSTMoutput3.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wcfdc = DiagTimes (LSTMoutput3.Wcf, LSTMoutput3.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput3.unnamed185 = Plus (LSTMoutput3.G3, LSTMoutput3.Wcfdc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.ft = Sigmoid (LSTMoutput3.unnamed185) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.bft = ElementTimes (LSTMoutput3.ft, LSTMoutput3.dc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.G1 = Slice (LSTMoutput3.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wcidc = DiagTimes (LSTMoutput3.Wci, LSTMoutput3.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput3.unnamed183 = Plus (LSTMoutput3.G1, LSTMoutput3.Wcidc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.it = Sigmoid (LSTMoutput3.unnamed183) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.G2 = Slice (LSTMoutput3.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.unnamed184 = Tanh (LSTMoutput3.G2) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.bit = ElementTimes (LSTMoutput3.it, LSTMoutput3.unnamed184) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.ct = Plus (LSTMoutput3.bft, LSTMoutput3.bit) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wcoct = DiagTimes (LSTMoutput3.Wco, LSTMoutput3.ct) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.unnamed186 = Plus (LSTMoutput3.G4, LSTMoutput3.Wcoct) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.ot = Sigmoid (LSTMoutput3.unnamed186) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.unnamed187 = Tanh (LSTMoutput3.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.mt = ElementTimes (LSTMoutput3.ot, LSTMoutput3.unnamed187) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.output = Times (LSTMoutput3.Wmr, LSTMoutput3.mt) : [512 x 1024], [1024 x 1 x *] -> [512 x 1 x *]
|
||||
Node 'W' (LearnableParameter operation) operation: Tensor shape was inferred as [132 x 512 x 1].
|
||||
Node 'W' (LearnableParameter operation): Initializing Parameter[132 x 512 x 1] <- uniform(seed=19, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> unnamed193 = Times (W, LSTMoutput3.output) : [132 x 512 x 1], [512 x 1 x *] -> [132 x *]
|
||||
Validating --> b = LearnableParameter() : -> [132 x 1]
|
||||
Validating --> LSTMoutputW = Plus (unnamed193, b) : [132 x *], [132 x 1] -> [132 x 1 x *]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, LSTMoutputW) : [132 x *], [132 x 1 x *] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, LSTMoutputW) : [132 x *], [132 x 1 x *] -> [1]
|
||||
Validating --> logPrior.prior = Mean (labels) : [132 x *] -> [132]
|
||||
Validating --> logPrior.logPrior = Log (logPrior.prior) : [132] -> [132]
|
||||
Validating --> scaledLogLikelihood = Minus (LSTMoutputW, logPrior.logPrior) : [132 x 1 x *], [132] -> [132 x 1 x *]
|
||||
|
||||
Validating network. 88 nodes to process in pass 2.
|
||||
|
||||
Validating --> LSTMoutput1.dh = PastValue (LSTMoutput1.output) : [512 x 1 x *] -> [512 x 1 x *]
|
||||
Validating --> LSTMoutput1.whh = Times (LSTMoutput1.Wh, LSTMoutput1.dh) : [4096 x 512], [512 x 1 x *] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput1.dc = PastValue (LSTMoutput1.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wcfdc = DiagTimes (LSTMoutput1.Wcf, LSTMoutput1.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wcidc = DiagTimes (LSTMoutput1.Wci, LSTMoutput1.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.dh = PastValue (LSTMoutput2.output) : [512 x 1 x *] -> [512 x 1 x *]
|
||||
Validating --> LSTMoutput2.whh = Times (LSTMoutput2.Wh, LSTMoutput2.dh) : [4096 x 512], [512 x 1 x *] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput2.dc = PastValue (LSTMoutput2.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wcfdc = DiagTimes (LSTMoutput2.Wcf, LSTMoutput2.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wcidc = DiagTimes (LSTMoutput2.Wci, LSTMoutput2.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.dh = PastValue (LSTMoutput3.output) : [512 x 1 x *] -> [512 x 1 x *]
|
||||
Validating --> LSTMoutput3.whh = Times (LSTMoutput3.Wh, LSTMoutput3.dh) : [4096 x 512], [512 x 1 x *] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput3.dc = PastValue (LSTMoutput3.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wcfdc = DiagTimes (LSTMoutput3.Wcf, LSTMoutput3.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wcidc = DiagTimes (LSTMoutput3.Wci, LSTMoutput3.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
|
||||
Validating network. 15 nodes to process in pass 3.
|
||||
|
||||
|
||||
Validating network, final pass.
|
||||
|
||||
|
||||
|
||||
29 out of 113 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
08/16/2016 03:20:24: Created model with 113 nodes on CPU.
|
||||
|
||||
08/16/2016 03:20:24: Training criterion node(s):
|
||||
08/16/2016 03:20:24: ce = CrossEntropyWithSoftmax
|
||||
|
||||
08/16/2016 03:20:24: Evaluation criterion node(s):
|
||||
08/16/2016 03:20:24: err = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing: Out of 217 matrices, 125 are shared as 56, and 92 are not shared.
|
||||
|
||||
{ LSTMoutput1.dh : [512 x 1 x *]
|
||||
LSTMoutput1.wxx : [4096 x *] (gradient) }
|
||||
{ LSTMoutput2.mt : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.dh : [512 x 1 x *]
|
||||
LSTMoutput3.wxx : [4096 x *] (gradient) }
|
||||
{ LSTMoutput2.Wco : [1024] (gradient)
|
||||
LSTMoutput3.dc : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.mt : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.dh : [512 x 1 x *]
|
||||
LSTMoutput2.wxx : [4096 x *] (gradient) }
|
||||
{ LSTMoutput1.Wco : [1024] (gradient)
|
||||
LSTMoutput2.dc : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.G3 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.Wcidc : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.unnamed164 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.Wcfdc : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.Wci : [1024] (gradient)
|
||||
LSTMoutput2.G1 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.wxxpbpwhh : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput2.unnamed175 : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.Wcf : [1024] (gradient)
|
||||
LSTMoutput3.it : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.ct : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.wxxpbpwhh : [4096 x 1 x *] }
|
||||
{ LSTMoutput3.wx : [4096 x 512 x 1] (gradient)
|
||||
LSTMoutput3.wxxpb : [4096 x 1 x *] }
|
||||
{ LSTMoutput1.Wmr : [512 x 1024] (gradient)
|
||||
LSTMoutput2.wxx : [4096 x *] }
|
||||
{ LSTMoutput1.Wcoct : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.G4 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.G4 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.Wcf : [1024] (gradient)
|
||||
LSTMoutput2.it : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.unnamed174 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.Wcfdc : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.G1 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.ft : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.dc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.G1 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.ft : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.unnamed165 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.bft : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.G3 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.Wcidc : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.ot : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.whh : [4096 x 1 x *] }
|
||||
{ LSTMoutput2.ot : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.whh : [4096 x 1 x *] }
|
||||
{ LSTMoutput2.ct : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.wxxpbpwhh : [4096 x 1 x *] }
|
||||
{ LSTMoutput1.whh : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput2.G2 : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.wx : [4096 x 512 x 1] (gradient)
|
||||
LSTMoutput2.wxxpb : [4096 x 1 x *] }
|
||||
{ LSTMoutput1.b : [4096 x 1] (gradient)
|
||||
LSTMoutput1.dh : [512 x 1 x *] (gradient)
|
||||
LSTMoutput2.unnamed174 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.unnamed166 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.wxxpbpwhh : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput3.unnamed185 : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.Wci : [1024] (gradient)
|
||||
LSTMoutput3.G1 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.it : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.unnamed183 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.unnamed167 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.whh : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput3.G2 : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.Wmr : [512 x 1024] (gradient)
|
||||
LSTMoutput3.wxx : [4096 x *] }
|
||||
{ LSTMoutput2.b : [4096 x 1] (gradient)
|
||||
LSTMoutput2.dh : [512 x 1 x *] (gradient)
|
||||
LSTMoutput3.unnamed184 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.G4 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.G4 : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.unnamed176 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.wxxpbpwhh : [4096 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.bit : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.unnamed183 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.Wh : [4096 x 512] (gradient)
|
||||
LSTMoutput2.G2 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.Wcfdc : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.wxxpb : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput2.it : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.unnamed177 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.whh : [4096 x 1 x *] (gradient) }
|
||||
{ LSTMoutput3.output : [512 x 1 x *] (gradient)
|
||||
LSTMoutputW : [132 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.bft : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.dc : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.Wh : [4096 x 512] (gradient)
|
||||
LSTMoutput3.G2 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.bft : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.dc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.G1 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.unnamed163 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.unnamed175 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.unnamed173 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.unnamed185 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.output : [512 x 1 x *] (gradient)
|
||||
LSTMoutput3.wxxpb : [4096 x 1 x *] (gradient) }
|
||||
{ LSTMoutput3.b : [4096 x 1] (gradient)
|
||||
LSTMoutput3.dh : [512 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.Wcoct : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.G4 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.Wcidc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.ft : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.G2 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.Wcfdc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.Wcidc : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput3.Wmr : [512 x 1024] (gradient)
|
||||
unnamed193 : [132 x *] }
|
||||
{ LSTMoutput1.output : [512 x 1 x *] (gradient)
|
||||
LSTMoutput2.wxxpb : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput3.it : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput3.mt : [1024 x 1 x *] (gradient)
|
||||
unnamed193 : [132 x *] (gradient) }
|
||||
{ LSTMoutput1.Wcfdc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.Wcidc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.ft : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutputW : [132 x 1 x *]
|
||||
W : [132 x 512 x 1] (gradient) }
|
||||
{ LSTMoutput1.ft : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.bft : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.wx : [4096 x 363] (gradient)
|
||||
LSTMoutput1.wxxpb : [4096 x 1 x *] }
|
||||
|
||||
|
||||
08/16/2016 03:20:24: Training 13634692 parameters in 23 out of 23 parameter tensors and 104 nodes with gradient:
|
||||
|
||||
08/16/2016 03:20:24: Node 'LSTMoutput1.Wcf' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 03:20:24: Node 'LSTMoutput1.Wci' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 03:20:24: Node 'LSTMoutput1.Wco' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 03:20:24: Node 'LSTMoutput1.Wh' (LearnableParameter operation) : [4096 x 512]
|
||||
08/16/2016 03:20:24: Node 'LSTMoutput1.Wmr' (LearnableParameter operation) : [512 x 1024]
|
||||
08/16/2016 03:20:24: Node 'LSTMoutput1.b' (LearnableParameter operation) : [4096 x 1]
|
||||
08/16/2016 03:20:24: Node 'LSTMoutput1.wx' (LearnableParameter operation) : [4096 x 363]
|
||||
08/16/2016 03:20:24: Node 'LSTMoutput2.Wcf' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 03:20:24: Node 'LSTMoutput2.Wci' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 03:20:24: Node 'LSTMoutput2.Wco' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 03:20:24: Node 'LSTMoutput2.Wh' (LearnableParameter operation) : [4096 x 512]
|
||||
08/16/2016 03:20:24: Node 'LSTMoutput2.Wmr' (LearnableParameter operation) : [512 x 1024]
|
||||
08/16/2016 03:20:24: Node 'LSTMoutput2.b' (LearnableParameter operation) : [4096 x 1]
|
||||
08/16/2016 03:20:24: Node 'LSTMoutput2.wx' (LearnableParameter operation) : [4096 x 512 x 1]
|
||||
08/16/2016 03:20:24: Node 'LSTMoutput3.Wcf' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 03:20:24: Node 'LSTMoutput3.Wci' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 03:20:24: Node 'LSTMoutput3.Wco' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 03:20:24: Node 'LSTMoutput3.Wh' (LearnableParameter operation) : [4096 x 512]
|
||||
08/16/2016 03:20:24: Node 'LSTMoutput3.Wmr' (LearnableParameter operation) : [512 x 1024]
|
||||
08/16/2016 03:20:24: Node 'LSTMoutput3.b' (LearnableParameter operation) : [4096 x 1]
|
||||
08/16/2016 03:20:24: Node 'LSTMoutput3.wx' (LearnableParameter operation) : [4096 x 512 x 1]
|
||||
08/16/2016 03:20:24: Node 'W' (LearnableParameter operation) : [132 x 512 x 1]
|
||||
08/16/2016 03:20:24: Node 'b' (LearnableParameter operation) : [132 x 1]
|
||||
|
||||
|
||||
08/16/2016 03:20:24: Precomputing --> 3 PreCompute nodes found.
|
||||
|
||||
08/16/2016 03:20:24: featNorm.xMean = Mean()
|
||||
08/16/2016 03:20:24: featNorm.xStdDev = InvStdDev()
|
||||
08/16/2016 03:20:24: logPrior.prior = Mean()
|
||||
minibatchiterator: epoch 0: frames [0..252734] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses
|
||||
requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms
|
||||
|
||||
08/16/2016 03:20:27: Precomputing --> Completed.
|
||||
|
||||
|
||||
08/16/2016 03:20:28: Starting Epoch 1: learning rate per sample = 0.001953 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
minibatchiterator: epoch 0: frames [0..64] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses
|
||||
|
||||
08/16/2016 03:20:28: Starting minibatch loop.
|
||||
08/16/2016 03:20:31: Epoch[ 1 of 1]-Minibatch[ 1- 10, 250.00%]: ce = 4.87950134 * 160; err = 0.90625000 * 160; time = 3.6415s; samplesPerSecond = 43.9
|
||||
08/16/2016 03:20:35: Epoch[ 1 of 1]-Minibatch[ 11- 20, 500.00%]: ce = 4.84555817 * 160; err = 0.69375000 * 160; time = 3.6742s; samplesPerSecond = 43.5
|
||||
08/16/2016 03:20:38: Finished Epoch[ 1 of 1]: [Training] ce = 4.85900003 * 418; err = 0.80382775 * 418; totalSamplesSeen = 418; learningRatePerSample = 0.001953125; epochTime=9.76851s
|
||||
08/16/2016 03:20:38: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_cpu/Models/cntkSpeechLSTM.dnn'
|
||||
08/16/2016 03:20:39: CNTKCommandTrainEnd: speechTrain
|
||||
|
||||
08/16/2016 03:20:39: Action "train" complete.
|
||||
|
||||
08/16/2016 03:20:39: __COMPLETED__
|
||||
|
|
@ -0,0 +1,682 @@
|
|||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 268381192 kB
|
||||
-------------------------------------------------------------------
|
||||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config/LSTM-NDL.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_gpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_gpu DeviceId=0 timestamping=true speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=64]] speechTrain=[reader=[useMersenneTwisterRand=true]] parallelTrain=false
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: Aug 16 2016 03:09:16
|
||||
Last modified date: Fri Aug 12 05:28:23 2016
|
||||
Build type: Release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: yes
|
||||
Math lib: mkl
|
||||
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
CUB_PATH: c:\src\cub-1.4.1
|
||||
CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by svcphil on Philly-Pool1
|
||||
Build Path: c:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
08/16/2016 03:20:41: -------------------------------------------------------------------
|
||||
08/16/2016 03:20:41: Build info:
|
||||
|
||||
08/16/2016 03:20:41: Built time: Aug 16 2016 03:09:16
|
||||
08/16/2016 03:20:41: Last modified date: Fri Aug 12 05:28:23 2016
|
||||
08/16/2016 03:20:41: Build type: Release
|
||||
08/16/2016 03:20:41: Build target: GPU
|
||||
08/16/2016 03:20:41: With 1bit-SGD: yes
|
||||
08/16/2016 03:20:41: Math lib: mkl
|
||||
08/16/2016 03:20:41: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
08/16/2016 03:20:41: CUB_PATH: c:\src\cub-1.4.1
|
||||
08/16/2016 03:20:41: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
08/16/2016 03:20:41: Build Branch: HEAD
|
||||
08/16/2016 03:20:41: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 03:20:41: Built by svcphil on Philly-Pool1
|
||||
08/16/2016 03:20:41: Build Path: c:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
08/16/2016 03:20:41: -------------------------------------------------------------------
|
||||
08/16/2016 03:20:43: -------------------------------------------------------------------
|
||||
08/16/2016 03:20:43: GPU info:
|
||||
|
||||
08/16/2016 03:20:43: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:20:43: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:20:43: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
|
||||
08/16/2016 03:20:43: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 03:20:43: Running on DPHAIM-25 at 2016/08/16 03:20:43
|
||||
08/16/2016 03:20:43: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config/LSTM-NDL.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_gpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_gpu DeviceId=0 timestamping=true speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=64]] speechTrain=[reader=[useMersenneTwisterRand=true]] parallelTrain=false
|
||||
|
||||
|
||||
|
||||
08/16/2016 03:20:43: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:20:43: RootDir = ".."
|
||||
ConfigDir = "$RootDir$/Config"
|
||||
DataDir = "$RootDir$/Data"
|
||||
OutputDir = "$RootDir$/Output"
|
||||
ModelDir = "$OutputDir$/Models"
|
||||
deviceId = -1
|
||||
command = speechTrain
|
||||
precision = "float"
|
||||
traceLevel = 1
|
||||
modelPath = "$ModelDir$/cntkSpeechLSTM.dnn"
|
||||
parallelTrain = true
|
||||
frameMode = false
|
||||
truncated = true
|
||||
speechTrain = [
|
||||
action = "train"
|
||||
nbrUttsIneachRecurrentIter = 16
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "$ConfigDir$/lstmp-3layer-opt.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 0
|
||||
minibatchSize = 16
|
||||
learningRatesPerMB = 0.5
|
||||
numMBsToShowResult = 10
|
||||
momentumPerMB = 0:0.9
|
||||
maxEpochs = 4
|
||||
keepCheckPointFiles = true
|
||||
]
|
||||
reader = [
|
||||
readerType = "HTKMLFReader"
|
||||
readMethod = "blockRandomize"
|
||||
miniBatchMode = "partial"
|
||||
randomize = "auto"
|
||||
verbosity = 0
|
||||
features = [
|
||||
dim = 363
|
||||
type = "real"
|
||||
scpFile = "$DataDir$/glob_0000.scp"
|
||||
]
|
||||
labels = [
|
||||
mlfFile = "$DataDir$/glob_0000.mlf"
|
||||
labelMappingFile = "$DataDir$/state.list"
|
||||
labelDim = 132
|
||||
labelType = "category"
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_gpu
|
||||
DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
speechTrain=[SGD=[maxEpochs=1]]
|
||||
speechTrain=[SGD=[epochSize=64]]
|
||||
speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
parallelTrain=false
|
||||
|
||||
08/16/2016 03:20:43: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
08/16/2016 03:20:43: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:20:43: RootDir = ".."
|
||||
ConfigDir = "../Config"
|
||||
DataDir = "../Data"
|
||||
OutputDir = "../Output"
|
||||
ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_gpu/Models"
|
||||
deviceId = -1
|
||||
command = speechTrain
|
||||
precision = "float"
|
||||
traceLevel = 1
|
||||
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_gpu/Models/cntkSpeechLSTM.dnn"
|
||||
parallelTrain = true
|
||||
frameMode = false
|
||||
truncated = true
|
||||
speechTrain = [
|
||||
action = "train"
|
||||
nbrUttsIneachRecurrentIter = 16
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config/lstmp-3layer-opt.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 0
|
||||
minibatchSize = 16
|
||||
learningRatesPerMB = 0.5
|
||||
numMBsToShowResult = 10
|
||||
momentumPerMB = 0:0.9
|
||||
maxEpochs = 4
|
||||
keepCheckPointFiles = true
|
||||
]
|
||||
reader = [
|
||||
readerType = "HTKMLFReader"
|
||||
readMethod = "blockRandomize"
|
||||
miniBatchMode = "partial"
|
||||
randomize = "auto"
|
||||
verbosity = 0
|
||||
features = [
|
||||
dim = 363
|
||||
type = "real"
|
||||
scpFile = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data/glob_0000.scp"
|
||||
]
|
||||
labels = [
|
||||
mlfFile = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data/glob_0000.mlf"
|
||||
labelMappingFile = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data/state.list"
|
||||
labelDim = 132
|
||||
labelType = "category"
|
||||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_gpu
|
||||
DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
speechTrain=[SGD=[maxEpochs=1]]
|
||||
speechTrain=[SGD=[epochSize=64]]
|
||||
speechTrain=[reader=[useMersenneTwisterRand=true]]
|
||||
parallelTrain=false
|
||||
|
||||
08/16/2016 03:20:43: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
08/16/2016 03:20:43: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: LSTM-NDL.cntk:command=speechTrain
|
||||
configparameters: LSTM-NDL.cntk:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config
|
||||
configparameters: LSTM-NDL.cntk:currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
configparameters: LSTM-NDL.cntk:DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data
|
||||
configparameters: LSTM-NDL.cntk:deviceId=0
|
||||
configparameters: LSTM-NDL.cntk:frameMode=false
|
||||
configparameters: LSTM-NDL.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_gpu/Models
|
||||
configparameters: LSTM-NDL.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_gpu/Models/cntkSpeechLSTM.dnn
|
||||
configparameters: LSTM-NDL.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_gpu
|
||||
configparameters: LSTM-NDL.cntk:parallelTrain=false
|
||||
configparameters: LSTM-NDL.cntk:precision=float
|
||||
configparameters: LSTM-NDL.cntk:RootDir=..
|
||||
configparameters: LSTM-NDL.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_gpu
|
||||
configparameters: LSTM-NDL.cntk:speechTrain=[
|
||||
action = "train"
|
||||
nbrUttsIneachRecurrentIter = 16
|
||||
NDLNetworkBuilder = [
|
||||
networkDescription = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Config/lstmp-3layer-opt.ndl"
|
||||
]
|
||||
SGD = [
|
||||
epochSize = 0
|
||||
minibatchSize = 16
|
||||
learningRatesPerMB = 0.5
|
||||
numMBsToShowResult = 10
|
||||
momentumPerMB = 0:0.9
|
||||
maxEpochs = 4
|
||||
keepCheckPointFiles = true
|
||||
]
|
||||
reader = [
|
||||
readerType = "HTKMLFReader"
|
||||
readMethod = "blockRandomize"
|
||||
miniBatchMode = "partial"
|
||||
randomize = "auto"
|
||||
verbosity = 0
|
||||
features = [
|
||||
dim = 363
|
||||
type = "real"
|
||||
scpFile = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data/glob_0000.scp"
|
||||
]
|
||||
labels = [
|
||||
mlfFile = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data/glob_0000.mlf"
|
||||
labelMappingFile = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data/state.list"
|
||||
labelDim = 132
|
||||
labelType = "category"
|
||||
]
|
||||
]
|
||||
] [SGD=[maxEpochs=1]] [SGD=[epochSize=64]] [reader=[useMersenneTwisterRand=true]]
|
||||
|
||||
configparameters: LSTM-NDL.cntk:timestamping=true
|
||||
configparameters: LSTM-NDL.cntk:traceLevel=1
|
||||
configparameters: LSTM-NDL.cntk:truncated=true
|
||||
08/16/2016 03:20:43: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:20:43: Commands: speechTrain
|
||||
08/16/2016 03:20:43: Precision = "float"
|
||||
08/16/2016 03:20:43: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_gpu/Models/cntkSpeechLSTM.dnn
|
||||
08/16/2016 03:20:43: CNTKCommandTrainInfo: speechTrain : 1
|
||||
08/16/2016 03:20:43: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 1
|
||||
|
||||
08/16/2016 03:20:43: ##############################################################################
|
||||
08/16/2016 03:20:43: # #
|
||||
08/16/2016 03:20:43: # Action "train" #
|
||||
08/16/2016 03:20:43: # #
|
||||
08/16/2016 03:20:43: ##############################################################################
|
||||
|
||||
08/16/2016 03:20:43: CNTKCommandTrainBegin: speechTrain
|
||||
NDLBuilder Using GPU 0
|
||||
reading script file C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data/glob_0000.scp ... 948 entries
|
||||
total 132 state names in state list C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data/state.list
|
||||
htkmlfreader: reading MLF file C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Speech\AN4\Data/glob_0000.mlf ... total 948 entries
|
||||
...............................................................................................feature set 0: 252734 frames in 948 out of 948 utterances
|
||||
label set 0: 129 classes
|
||||
minibatchutterancesource: 948 utterances grouped into 3 chunks, av. chunk size: 316.0 utterances, 84244.7 frames
|
||||
useParallelTrain option is not enabled. ParallelTrain config will be ignored.
|
||||
08/16/2016 03:20:43: Creating virgin network.
|
||||
Node 'LSTMoutput1.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput1.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput1.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput1.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput1.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- 0.000000.
|
||||
Node 'LSTMoutput2.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput2.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput2.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput2.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput2.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- 0.000000.
|
||||
Node 'LSTMoutput3.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput3.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput3.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput3.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- 0.000000.
|
||||
Node 'LSTMoutput3.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- 0.000000.
|
||||
Node 'b' (LearnableParameter operation): Initializing Parameter[132 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput1.wx' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput1.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput1.Wh' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput1.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=3, range=0.050000*1.000000, onCPU=false).
|
||||
Microsoft::MSR::CNTK::GPUMatrix<ElemType>::SetUniformRandomValue (GPU): creating curand object with seed 3, sizeof(ElemType)==4
|
||||
Node 'LSTMoutput1.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=4, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput1.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=5, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput1.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- uniform(seed=6, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput2.wx' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput2.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput2.Wh' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput2.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=9, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput2.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=10, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput2.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=11, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput2.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- uniform(seed=12, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput3.wx' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput3.b' (LearnableParameter operation): Initializing Parameter[4096 x 1] <- 0.000000.
|
||||
Node 'LSTMoutput3.Wh' (LearnableParameter operation): Initializating Parameter[4096 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'LSTMoutput3.Wci' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=15, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput3.Wcf' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=16, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput3.Wco' (LearnableParameter operation): Initializing Parameter[1024] <- uniform(seed=17, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'LSTMoutput3.Wmr' (LearnableParameter operation): Initializing Parameter[512 x 1024] <- uniform(seed=18, range=0.050000*1.000000, onCPU=false).
|
||||
Node 'W' (LearnableParameter operation): Initializating Parameter[132 x 0] as uniform later when dimensions are fully known.
|
||||
Node 'b' (LearnableParameter operation): Initializing Parameter[132 x 1] <- 0.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
6 roots:
|
||||
ce = CrossEntropyWithSoftmax()
|
||||
err = ErrorPrediction()
|
||||
featNorm.xMean = Mean()
|
||||
featNorm.xStdDev = InvStdDev()
|
||||
logPrior.prior = Mean()
|
||||
scaledLogLikelihood = Minus()
|
||||
|
||||
Loop[0] --> Loop_LSTMoutput1.output -> 24 nodes
|
||||
|
||||
LSTMoutput1.dh LSTMoutput1.whh LSTMoutput1.wxxpbpwhh
|
||||
LSTMoutput1.G4 LSTMoutput1.G3 LSTMoutput1.dc
|
||||
LSTMoutput1.Wcfdc LSTMoutput1.unnamed165 LSTMoutput1.ft
|
||||
LSTMoutput1.bft LSTMoutput1.G1 LSTMoutput1.Wcidc
|
||||
LSTMoutput1.unnamed163 LSTMoutput1.it LSTMoutput1.G2
|
||||
LSTMoutput1.unnamed164 LSTMoutput1.bit LSTMoutput1.ct
|
||||
LSTMoutput1.Wcoct LSTMoutput1.unnamed166 LSTMoutput1.ot
|
||||
LSTMoutput1.unnamed167 LSTMoutput1.mt LSTMoutput1.output
|
||||
|
||||
Loop[1] --> Loop_LSTMoutput2.output -> 24 nodes
|
||||
|
||||
LSTMoutput2.dh LSTMoutput2.whh LSTMoutput2.wxxpbpwhh
|
||||
LSTMoutput2.G4 LSTMoutput2.G3 LSTMoutput2.dc
|
||||
LSTMoutput2.Wcfdc LSTMoutput2.unnamed175 LSTMoutput2.ft
|
||||
LSTMoutput2.bft LSTMoutput2.G1 LSTMoutput2.Wcidc
|
||||
LSTMoutput2.unnamed173 LSTMoutput2.it LSTMoutput2.G2
|
||||
LSTMoutput2.unnamed174 LSTMoutput2.bit LSTMoutput2.ct
|
||||
LSTMoutput2.Wcoct LSTMoutput2.unnamed176 LSTMoutput2.ot
|
||||
LSTMoutput2.unnamed177 LSTMoutput2.mt LSTMoutput2.output
|
||||
|
||||
Loop[2] --> Loop_LSTMoutput3.output -> 24 nodes
|
||||
|
||||
LSTMoutput3.dh LSTMoutput3.whh LSTMoutput3.wxxpbpwhh
|
||||
LSTMoutput3.G4 LSTMoutput3.G3 LSTMoutput3.dc
|
||||
LSTMoutput3.Wcfdc LSTMoutput3.unnamed185 LSTMoutput3.ft
|
||||
LSTMoutput3.bft LSTMoutput3.G1 LSTMoutput3.Wcidc
|
||||
LSTMoutput3.unnamed183 LSTMoutput3.it LSTMoutput3.G2
|
||||
LSTMoutput3.unnamed184 LSTMoutput3.bit LSTMoutput3.ct
|
||||
LSTMoutput3.Wcoct LSTMoutput3.unnamed186 LSTMoutput3.ot
|
||||
LSTMoutput3.unnamed187 LSTMoutput3.mt LSTMoutput3.output
|
||||
|
||||
Validating network. 113 nodes to process in pass 1.
|
||||
|
||||
Validating --> labels = InputValue() : -> [132 x *]
|
||||
Validating --> W = LearnableParameter() : -> [132 x 0]
|
||||
Validating --> LSTMoutput3.Wmr = LearnableParameter() : -> [512 x 1024]
|
||||
Validating --> LSTMoutput3.wx = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> LSTMoutput2.Wmr = LearnableParameter() : -> [512 x 1024]
|
||||
Validating --> LSTMoutput2.wx = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> LSTMoutput1.Wmr = LearnableParameter() : -> [512 x 1024]
|
||||
Validating --> LSTMoutput1.wx = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> features = InputValue() : -> [363 x *]
|
||||
Validating --> featNorm.xMean = Mean (features) : [363 x *] -> [363]
|
||||
Validating --> featNorm.xStdDev = InvStdDev (features) : [363 x *] -> [363]
|
||||
Validating --> featNorm.xNorm = PerDimMeanVarNormalization (features, featNorm.xMean, featNorm.xStdDev) : [363 x *], [363], [363] -> [363 x *]
|
||||
Node 'LSTMoutput1.wx' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 363].
|
||||
Node 'LSTMoutput1.wx' (LearnableParameter operation): Initializing Parameter[4096 x 363] <- uniform(seed=1, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput1.wxx = Times (LSTMoutput1.wx, featNorm.xNorm) : [4096 x 363], [363 x *] -> [4096 x *]
|
||||
Validating --> LSTMoutput1.b = LearnableParameter() : -> [4096 x 1]
|
||||
Validating --> LSTMoutput1.wxxpb = Plus (LSTMoutput1.wxx, LSTMoutput1.b) : [4096 x *], [4096 x 1] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wh = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> LSTMoutput1.Wco = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput1.Wcf = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput1.Wci = LearnableParameter() : -> [1024]
|
||||
Node 'LSTMoutput1.Wh' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 512].
|
||||
Node 'LSTMoutput1.Wh' (LearnableParameter operation): Initializing Parameter[4096 x 512] <- uniform(seed=2, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput1.whh = Times (LSTMoutput1.Wh, LSTMoutput1.dh) : [4096 x 512], [512] -> [4096]
|
||||
Validating --> LSTMoutput1.wxxpbpwhh = Plus (LSTMoutput1.wxxpb, LSTMoutput1.whh) : [4096 x 1 x *], [4096] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput1.G4 = Slice (LSTMoutput1.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.G3 = Slice (LSTMoutput1.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wcfdc = DiagTimes (LSTMoutput1.Wcf, LSTMoutput1.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput1.unnamed165 = Plus (LSTMoutput1.G3, LSTMoutput1.Wcfdc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.ft = Sigmoid (LSTMoutput1.unnamed165) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.bft = ElementTimes (LSTMoutput1.ft, LSTMoutput1.dc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.G1 = Slice (LSTMoutput1.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wcidc = DiagTimes (LSTMoutput1.Wci, LSTMoutput1.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput1.unnamed163 = Plus (LSTMoutput1.G1, LSTMoutput1.Wcidc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.it = Sigmoid (LSTMoutput1.unnamed163) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.G2 = Slice (LSTMoutput1.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.unnamed164 = Tanh (LSTMoutput1.G2) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.bit = ElementTimes (LSTMoutput1.it, LSTMoutput1.unnamed164) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.ct = Plus (LSTMoutput1.bft, LSTMoutput1.bit) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wcoct = DiagTimes (LSTMoutput1.Wco, LSTMoutput1.ct) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.unnamed166 = Plus (LSTMoutput1.G4, LSTMoutput1.Wcoct) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.ot = Sigmoid (LSTMoutput1.unnamed166) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.unnamed167 = Tanh (LSTMoutput1.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.mt = ElementTimes (LSTMoutput1.ot, LSTMoutput1.unnamed167) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.output = Times (LSTMoutput1.Wmr, LSTMoutput1.mt) : [512 x 1024], [1024 x 1 x *] -> [512 x 1 x *]
|
||||
Node 'LSTMoutput2.wx' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 512 x 1].
|
||||
Node 'LSTMoutput2.wx' (LearnableParameter operation): Initializing Parameter[4096 x 512 x 1] <- uniform(seed=7, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput2.wxx = Times (LSTMoutput2.wx, LSTMoutput1.output) : [4096 x 512 x 1], [512 x 1 x *] -> [4096 x *]
|
||||
Validating --> LSTMoutput2.b = LearnableParameter() : -> [4096 x 1]
|
||||
Validating --> LSTMoutput2.wxxpb = Plus (LSTMoutput2.wxx, LSTMoutput2.b) : [4096 x *], [4096 x 1] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wh = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> LSTMoutput2.Wco = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput2.Wcf = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput2.Wci = LearnableParameter() : -> [1024]
|
||||
Node 'LSTMoutput2.Wh' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 512].
|
||||
Node 'LSTMoutput2.Wh' (LearnableParameter operation): Initializing Parameter[4096 x 512] <- uniform(seed=8, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput2.whh = Times (LSTMoutput2.Wh, LSTMoutput2.dh) : [4096 x 512], [512] -> [4096]
|
||||
Validating --> LSTMoutput2.wxxpbpwhh = Plus (LSTMoutput2.wxxpb, LSTMoutput2.whh) : [4096 x 1 x *], [4096] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput2.G4 = Slice (LSTMoutput2.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.G3 = Slice (LSTMoutput2.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wcfdc = DiagTimes (LSTMoutput2.Wcf, LSTMoutput2.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput2.unnamed175 = Plus (LSTMoutput2.G3, LSTMoutput2.Wcfdc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.ft = Sigmoid (LSTMoutput2.unnamed175) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.bft = ElementTimes (LSTMoutput2.ft, LSTMoutput2.dc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.G1 = Slice (LSTMoutput2.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wcidc = DiagTimes (LSTMoutput2.Wci, LSTMoutput2.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput2.unnamed173 = Plus (LSTMoutput2.G1, LSTMoutput2.Wcidc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.it = Sigmoid (LSTMoutput2.unnamed173) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.G2 = Slice (LSTMoutput2.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.unnamed174 = Tanh (LSTMoutput2.G2) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.bit = ElementTimes (LSTMoutput2.it, LSTMoutput2.unnamed174) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.ct = Plus (LSTMoutput2.bft, LSTMoutput2.bit) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wcoct = DiagTimes (LSTMoutput2.Wco, LSTMoutput2.ct) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.unnamed176 = Plus (LSTMoutput2.G4, LSTMoutput2.Wcoct) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.ot = Sigmoid (LSTMoutput2.unnamed176) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.unnamed177 = Tanh (LSTMoutput2.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.mt = ElementTimes (LSTMoutput2.ot, LSTMoutput2.unnamed177) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.output = Times (LSTMoutput2.Wmr, LSTMoutput2.mt) : [512 x 1024], [1024 x 1 x *] -> [512 x 1 x *]
|
||||
Node 'LSTMoutput3.wx' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 512 x 1].
|
||||
Node 'LSTMoutput3.wx' (LearnableParameter operation): Initializing Parameter[4096 x 512 x 1] <- uniform(seed=13, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput3.wxx = Times (LSTMoutput3.wx, LSTMoutput2.output) : [4096 x 512 x 1], [512 x 1 x *] -> [4096 x *]
|
||||
Validating --> LSTMoutput3.b = LearnableParameter() : -> [4096 x 1]
|
||||
Validating --> LSTMoutput3.wxxpb = Plus (LSTMoutput3.wxx, LSTMoutput3.b) : [4096 x *], [4096 x 1] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wh = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> LSTMoutput3.Wco = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput3.Wcf = LearnableParameter() : -> [1024]
|
||||
Validating --> LSTMoutput3.Wci = LearnableParameter() : -> [1024]
|
||||
Node 'LSTMoutput3.Wh' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 512].
|
||||
Node 'LSTMoutput3.Wh' (LearnableParameter operation): Initializing Parameter[4096 x 512] <- uniform(seed=14, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> LSTMoutput3.whh = Times (LSTMoutput3.Wh, LSTMoutput3.dh) : [4096 x 512], [512] -> [4096]
|
||||
Validating --> LSTMoutput3.wxxpbpwhh = Plus (LSTMoutput3.wxxpb, LSTMoutput3.whh) : [4096 x 1 x *], [4096] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput3.G4 = Slice (LSTMoutput3.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.G3 = Slice (LSTMoutput3.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wcfdc = DiagTimes (LSTMoutput3.Wcf, LSTMoutput3.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput3.unnamed185 = Plus (LSTMoutput3.G3, LSTMoutput3.Wcfdc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.ft = Sigmoid (LSTMoutput3.unnamed185) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.bft = ElementTimes (LSTMoutput3.ft, LSTMoutput3.dc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.G1 = Slice (LSTMoutput3.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wcidc = DiagTimes (LSTMoutput3.Wci, LSTMoutput3.dc) : [1024], [1024] -> [1024]
|
||||
Validating --> LSTMoutput3.unnamed183 = Plus (LSTMoutput3.G1, LSTMoutput3.Wcidc) : [1024 x 1 x *], [1024] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.it = Sigmoid (LSTMoutput3.unnamed183) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.G2 = Slice (LSTMoutput3.wxxpbpwhh) : [4096 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.unnamed184 = Tanh (LSTMoutput3.G2) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.bit = ElementTimes (LSTMoutput3.it, LSTMoutput3.unnamed184) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.ct = Plus (LSTMoutput3.bft, LSTMoutput3.bit) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wcoct = DiagTimes (LSTMoutput3.Wco, LSTMoutput3.ct) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.unnamed186 = Plus (LSTMoutput3.G4, LSTMoutput3.Wcoct) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.ot = Sigmoid (LSTMoutput3.unnamed186) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.unnamed187 = Tanh (LSTMoutput3.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.mt = ElementTimes (LSTMoutput3.ot, LSTMoutput3.unnamed187) : [1024 x 1 x *], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.output = Times (LSTMoutput3.Wmr, LSTMoutput3.mt) : [512 x 1024], [1024 x 1 x *] -> [512 x 1 x *]
|
||||
Node 'W' (LearnableParameter operation) operation: Tensor shape was inferred as [132 x 512 x 1].
|
||||
Node 'W' (LearnableParameter operation): Initializing Parameter[132 x 512 x 1] <- uniform(seed=19, range=0.050000*1.000000, onCPU=false).
|
||||
Validating --> unnamed193 = Times (W, LSTMoutput3.output) : [132 x 512 x 1], [512 x 1 x *] -> [132 x *]
|
||||
Validating --> b = LearnableParameter() : -> [132 x 1]
|
||||
Validating --> LSTMoutputW = Plus (unnamed193, b) : [132 x *], [132 x 1] -> [132 x 1 x *]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, LSTMoutputW) : [132 x *], [132 x 1 x *] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, LSTMoutputW) : [132 x *], [132 x 1 x *] -> [1]
|
||||
Validating --> logPrior.prior = Mean (labels) : [132 x *] -> [132]
|
||||
Validating --> logPrior.logPrior = Log (logPrior.prior) : [132] -> [132]
|
||||
Validating --> scaledLogLikelihood = Minus (LSTMoutputW, logPrior.logPrior) : [132 x 1 x *], [132] -> [132 x 1 x *]
|
||||
|
||||
Validating network. 88 nodes to process in pass 2.
|
||||
|
||||
Validating --> LSTMoutput1.dh = PastValue (LSTMoutput1.output) : [512 x 1 x *] -> [512 x 1 x *]
|
||||
Validating --> LSTMoutput1.whh = Times (LSTMoutput1.Wh, LSTMoutput1.dh) : [4096 x 512], [512 x 1 x *] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput1.dc = PastValue (LSTMoutput1.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wcfdc = DiagTimes (LSTMoutput1.Wcf, LSTMoutput1.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput1.Wcidc = DiagTimes (LSTMoutput1.Wci, LSTMoutput1.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.dh = PastValue (LSTMoutput2.output) : [512 x 1 x *] -> [512 x 1 x *]
|
||||
Validating --> LSTMoutput2.whh = Times (LSTMoutput2.Wh, LSTMoutput2.dh) : [4096 x 512], [512 x 1 x *] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput2.dc = PastValue (LSTMoutput2.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wcfdc = DiagTimes (LSTMoutput2.Wcf, LSTMoutput2.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput2.Wcidc = DiagTimes (LSTMoutput2.Wci, LSTMoutput2.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.dh = PastValue (LSTMoutput3.output) : [512 x 1 x *] -> [512 x 1 x *]
|
||||
Validating --> LSTMoutput3.whh = Times (LSTMoutput3.Wh, LSTMoutput3.dh) : [4096 x 512], [512 x 1 x *] -> [4096 x 1 x *]
|
||||
Validating --> LSTMoutput3.dc = PastValue (LSTMoutput3.ct) : [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wcfdc = DiagTimes (LSTMoutput3.Wcf, LSTMoutput3.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
Validating --> LSTMoutput3.Wcidc = DiagTimes (LSTMoutput3.Wci, LSTMoutput3.dc) : [1024], [1024 x 1 x *] -> [1024 x 1 x *]
|
||||
|
||||
Validating network. 15 nodes to process in pass 3.
|
||||
|
||||
|
||||
Validating network, final pass.
|
||||
|
||||
|
||||
|
||||
29 out of 113 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
08/16/2016 03:20:44: Created model with 113 nodes on GPU 0.
|
||||
|
||||
08/16/2016 03:20:44: Training criterion node(s):
|
||||
08/16/2016 03:20:44: ce = CrossEntropyWithSoftmax
|
||||
|
||||
08/16/2016 03:20:44: Evaluation criterion node(s):
|
||||
08/16/2016 03:20:44: err = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing: Out of 217 matrices, 125 are shared as 56, and 92 are not shared.
|
||||
|
||||
{ LSTMoutput2.mt : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.dh : [512 x 1 x *]
|
||||
LSTMoutput3.wxx : [4096 x *] (gradient) }
|
||||
{ LSTMoutput2.Wco : [1024] (gradient)
|
||||
LSTMoutput3.dc : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.dh : [512 x 1 x *]
|
||||
LSTMoutput1.wxx : [4096 x *] (gradient) }
|
||||
{ LSTMoutput1.mt : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.dh : [512 x 1 x *]
|
||||
LSTMoutput2.wxx : [4096 x *] (gradient) }
|
||||
{ LSTMoutput1.Wco : [1024] (gradient)
|
||||
LSTMoutput2.dc : [1024 x 1 x *] }
|
||||
{ LSTMoutput3.b : [4096 x 1] (gradient)
|
||||
LSTMoutput3.dh : [512 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.bft : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.dc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.G1 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.G2 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.Wcfdc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.Wcidc : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.unnamed163 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.unnamed175 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.Wh : [4096 x 512] (gradient)
|
||||
LSTMoutput2.G2 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.Wcfdc : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.bft : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.dc : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.unnamed173 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.unnamed185 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.unnamed177 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.whh : [4096 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.Wcidc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.ft : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.Wcfdc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.Wcidc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.ft : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.wxxpb : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput2.it : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.output : [512 x 1 x *] (gradient)
|
||||
LSTMoutput3.wxxpb : [4096 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.wx : [4096 x 512 x 1] (gradient)
|
||||
LSTMoutput2.wxxpb : [4096 x 1 x *] }
|
||||
{ LSTMoutput1.ct : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.wxxpbpwhh : [4096 x 1 x *] }
|
||||
{ LSTMoutput1.unnamed164 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.Wcfdc : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.G1 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.ft : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.Wci : [1024] (gradient)
|
||||
LSTMoutput2.G1 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.Wcf : [1024] (gradient)
|
||||
LSTMoutput2.it : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.ot : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.whh : [4096 x 1 x *] }
|
||||
{ LSTMoutput1.G4 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.G4 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.Wmr : [512 x 1024] (gradient)
|
||||
LSTMoutput2.wxx : [4096 x *] }
|
||||
{ LSTMoutput1.G3 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.Wcidc : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.whh : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput2.G2 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.b : [4096 x 1] (gradient)
|
||||
LSTMoutput1.dh : [512 x 1 x *] (gradient)
|
||||
LSTMoutput2.unnamed174 : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.Wmr : [512 x 1024] (gradient)
|
||||
LSTMoutput3.wxx : [4096 x *] }
|
||||
{ LSTMoutput1.wxxpbpwhh : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput2.unnamed175 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.wx : [4096 x 363] (gradient)
|
||||
LSTMoutput1.wxxpb : [4096 x 1 x *] }
|
||||
{ LSTMoutput2.unnamed174 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.Wcfdc : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.G3 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.Wcidc : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.Wcoct : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.G4 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput2.b : [4096 x 1] (gradient)
|
||||
LSTMoutput2.dh : [512 x 1 x *] (gradient)
|
||||
LSTMoutput3.unnamed184 : [1024 x 1 x *] }
|
||||
{ LSTMoutput3.output : [512 x 1 x *] (gradient)
|
||||
LSTMoutputW : [132 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.ft : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.bft : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.output : [512 x 1 x *] (gradient)
|
||||
LSTMoutput2.wxxpb : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput3.it : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.unnamed167 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.whh : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput3.G2 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.unnamed166 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.wxxpbpwhh : [4096 x 1 x *] (gradient)
|
||||
LSTMoutput3.unnamed185 : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.unnamed176 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.wxxpbpwhh : [4096 x 1 x *] (gradient) }
|
||||
{ LSTMoutput3.wx : [4096 x 512 x 1] (gradient)
|
||||
LSTMoutput3.wxxpb : [4096 x 1 x *] }
|
||||
{ LSTMoutput2.ct : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.wxxpbpwhh : [4096 x 1 x *] }
|
||||
{ LSTMoutput2.ot : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.whh : [4096 x 1 x *] }
|
||||
{ LSTMoutput3.mt : [1024 x 1 x *] (gradient)
|
||||
unnamed193 : [132 x *] (gradient) }
|
||||
{ LSTMoutput2.Wh : [4096 x 512] (gradient)
|
||||
LSTMoutput3.G2 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput1.bit : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.unnamed183 : [1024 x 1 x *] (gradient) }
|
||||
{ LSTMoutput3.Wmr : [512 x 1024] (gradient)
|
||||
unnamed193 : [132 x *] }
|
||||
{ LSTMoutput1.unnamed165 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.bft : [1024 x 1 x *] }
|
||||
{ LSTMoutputW : [132 x 1 x *]
|
||||
W : [132 x 512 x 1] (gradient) }
|
||||
{ LSTMoutput2.Wci : [1024] (gradient)
|
||||
LSTMoutput3.G1 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.dc : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.G1 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.ft : [1024 x 1 x *] }
|
||||
{ LSTMoutput2.Wcf : [1024] (gradient)
|
||||
LSTMoutput3.it : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.it : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.unnamed183 : [1024 x 1 x *] }
|
||||
{ LSTMoutput1.Wcoct : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput2.G4 : [1024 x 1 x *] (gradient)
|
||||
LSTMoutput3.G4 : [1024 x 1 x *] }
|
||||
|
||||
|
||||
08/16/2016 03:20:44: Training 13634692 parameters in 23 out of 23 parameter tensors and 104 nodes with gradient:
|
||||
|
||||
08/16/2016 03:20:44: Node 'LSTMoutput1.Wcf' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 03:20:44: Node 'LSTMoutput1.Wci' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 03:20:44: Node 'LSTMoutput1.Wco' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 03:20:44: Node 'LSTMoutput1.Wh' (LearnableParameter operation) : [4096 x 512]
|
||||
08/16/2016 03:20:44: Node 'LSTMoutput1.Wmr' (LearnableParameter operation) : [512 x 1024]
|
||||
08/16/2016 03:20:44: Node 'LSTMoutput1.b' (LearnableParameter operation) : [4096 x 1]
|
||||
08/16/2016 03:20:44: Node 'LSTMoutput1.wx' (LearnableParameter operation) : [4096 x 363]
|
||||
08/16/2016 03:20:44: Node 'LSTMoutput2.Wcf' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 03:20:44: Node 'LSTMoutput2.Wci' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 03:20:44: Node 'LSTMoutput2.Wco' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 03:20:44: Node 'LSTMoutput2.Wh' (LearnableParameter operation) : [4096 x 512]
|
||||
08/16/2016 03:20:44: Node 'LSTMoutput2.Wmr' (LearnableParameter operation) : [512 x 1024]
|
||||
08/16/2016 03:20:44: Node 'LSTMoutput2.b' (LearnableParameter operation) : [4096 x 1]
|
||||
08/16/2016 03:20:44: Node 'LSTMoutput2.wx' (LearnableParameter operation) : [4096 x 512 x 1]
|
||||
08/16/2016 03:20:44: Node 'LSTMoutput3.Wcf' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 03:20:44: Node 'LSTMoutput3.Wci' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 03:20:44: Node 'LSTMoutput3.Wco' (LearnableParameter operation) : [1024]
|
||||
08/16/2016 03:20:44: Node 'LSTMoutput3.Wh' (LearnableParameter operation) : [4096 x 512]
|
||||
08/16/2016 03:20:44: Node 'LSTMoutput3.Wmr' (LearnableParameter operation) : [512 x 1024]
|
||||
08/16/2016 03:20:44: Node 'LSTMoutput3.b' (LearnableParameter operation) : [4096 x 1]
|
||||
08/16/2016 03:20:44: Node 'LSTMoutput3.wx' (LearnableParameter operation) : [4096 x 512 x 1]
|
||||
08/16/2016 03:20:44: Node 'W' (LearnableParameter operation) : [132 x 512 x 1]
|
||||
08/16/2016 03:20:44: Node 'b' (LearnableParameter operation) : [132 x 1]
|
||||
|
||||
|
||||
08/16/2016 03:20:44: Precomputing --> 3 PreCompute nodes found.
|
||||
|
||||
08/16/2016 03:20:44: featNorm.xMean = Mean()
|
||||
08/16/2016 03:20:44: featNorm.xStdDev = InvStdDev()
|
||||
08/16/2016 03:20:44: logPrior.prior = Mean()
|
||||
minibatchiterator: epoch 0: frames [0..252734] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses
|
||||
requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms
|
||||
|
||||
08/16/2016 03:20:45: Precomputing --> Completed.
|
||||
|
||||
|
||||
08/16/2016 03:20:46: Starting Epoch 1: learning rate per sample = 0.001953 effective momentum = 0.000000 momentum as time constant = 0.0 samples
|
||||
minibatchiterator: epoch 0: frames [0..64] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses
|
||||
|
||||
08/16/2016 03:20:46: Starting minibatch loop.
|
||||
08/16/2016 03:20:47: Epoch[ 1 of 1]-Minibatch[ 1- 10, 250.00%]: ce = 4.87453079 * 160; err = 0.90625000 * 160; time = 1.1338s; samplesPerSecond = 141.1
|
||||
08/16/2016 03:20:48: Epoch[ 1 of 1]-Minibatch[ 11- 20, 500.00%]: ce = 4.84628143 * 160; err = 0.69375000 * 160; time = 1.0409s; samplesPerSecond = 153.7
|
||||
08/16/2016 03:20:49: Finished Epoch[ 1 of 1]: [Training] ce = 4.85708837 * 418; err = 0.80382775 * 418; totalSamplesSeen = 418; learningRatePerSample = 0.001953125; epochTime=2.90303s
|
||||
08/16/2016 03:20:50: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816031849.416502\Examples\Speech\AN4_LSTM@release_gpu/Models/cntkSpeechLSTM.dnn'
|
||||
08/16/2016 03:20:51: CNTKCommandTrainEnd: speechTrain
|
||||
|
||||
08/16/2016 03:20:51: Action "train" complete.
|
||||
|
||||
08/16/2016 03:20:51: __COMPLETED__
|
||||
|
|
@ -5,5 +5,5 @@
|
|||
ConfigDir=$TEST_DIR/../../../../../../Examples/Speech/AN4/Config
|
||||
|
||||
# cntkrun <CNTK config file name> <additional CNTK args>
|
||||
cntkrun LSTM-NDL.cntk "speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=64]] parallelTrain=false" || exit $?
|
||||
cntkrun LSTM-NDL.cntk "speechTrain=[SGD=[maxEpochs=1]] speechTrain=[SGD=[epochSize=64]] speechTrain=[reader=[useMersenneTwisterRand=true]] parallelTrain=false" || exit $?
|
||||
|
||||
|
|
|
|||
Разница между файлами не показана из-за своего большого размера
Загрузить разницу
|
|
@ -6,4 +6,4 @@
|
|||
. $TEST_DIR/../run-timit-test-common
|
||||
|
||||
# cntkrun <CNTK config file name> <additional CNTK arg>
|
||||
cntkrun TIMIT_AdaptLearnRate.cntk "$CntkArguments" || exit $?
|
||||
cntkrun TIMIT_AdaptLearnRate.cntk "$CntkArguments TIMIT_TrainAdaptLR=[reader=[useMersenneTwisterRand=true]] TIMIT_TrainAdaptLR=[cvReader=[useMersenneTwisterRand=true]]" || exit $?
|
||||
|
|
|
|||
Разница между файлами не показана из-за своего большого размера
Загрузить разницу
|
|
@ -6,7 +6,7 @@
|
|||
. $TEST_DIR/../run-timit-test-common
|
||||
|
||||
# Train:
|
||||
cntkrun TIMIT_TrainSimpleNetwork.cntk "$CntkArguments" || exit $?
|
||||
cntkrun TIMIT_TrainSimpleNetwork.cntk "$CntkArguments TIMIT_TrainSimple=[reader=[useMersenneTwisterRand=true]]" || exit $?
|
||||
|
||||
# Validate:
|
||||
cntkrun TIMIT_CrossValidateSimpleNetwork.cntk "$CntkArguments" || exit $?
|
||||
cntkrun TIMIT_CrossValidateSimpleNetwork.cntk "$CntkArguments" || exit $?
|
||||
|
|
|
|||
Разница между файлами не показана из-за своего большого размера
Загрузить разницу
|
|
@ -6,7 +6,7 @@
|
|||
. $TEST_DIR/../run-timit-test-common
|
||||
|
||||
# Train:
|
||||
cntkrun TIMIT_TrainSimpleNetwork.cntk "$CntkArguments" || exit $?
|
||||
cntkrun TIMIT_TrainSimpleNetwork.cntk "$CntkArguments TIMIT_TrainSimple=[reader=[useMersenneTwisterRand=true]]" || exit $?
|
||||
|
||||
# Validate:
|
||||
cntkrun TIMIT_EvalSimpleNetwork.cntk "$CntkArguments" || exit $?
|
||||
cntkrun TIMIT_EvalSimpleNetwork.cntk "$CntkArguments" || exit $?
|
||||
|
|
|
|||
Разница между файлами не показана из-за своего большого размера
Загрузить разницу
|
|
@ -5,4 +5,4 @@
|
|||
# specific TIMIT variables
|
||||
. $TEST_DIR/../run-timit-test-common
|
||||
|
||||
cntkrun TIMIT_TrainAutoEncoder.cntk "$CntkArguments" || exit $?
|
||||
cntkrun TIMIT_TrainAutoEncoder.cntk "$CntkArguments TIMIT_TrainAutoEncoder=[reader=[useMersenneTwisterRand=true]]" || exit $?
|
||||
|
|
|
|||
Разница между файлами не показана из-за своего большого размера
Загрузить разницу
|
|
@ -6,4 +6,4 @@
|
|||
. $TEST_DIR/../run-timit-test-common
|
||||
|
||||
# Running only 3 epochs (~1000s gpu release), because full run takes a lot of time.
|
||||
cntkrun TIMIT_TrainLSTM.cntk "$CntkArguments TIMIT_TrainLSTM=[SGD=[maxEpochs=3]]" || exit $?
|
||||
cntkrun TIMIT_TrainLSTM.cntk "$CntkArguments TIMIT_TrainLSTM=[SGD=[maxEpochs=3]] TIMIT_TrainLSTM=[reader=[useMersenneTwisterRand=true]] TIMIT_TrainLSTM=[cvReader=[useMersenneTwisterRand=true]]" || exit $?
|
||||
|
|
|
|||
Разница между файлами не показана из-за своего большого размера
Загрузить разницу
|
|
@ -5,4 +5,4 @@
|
|||
# specific TIMIT variables
|
||||
. $TEST_DIR/../run-timit-test-common
|
||||
|
||||
cntkrun TIMIT_TrainMultiInput.cntk "$CntkArguments" || exit $?
|
||||
cntkrun TIMIT_TrainMultiInput.cntk "$CntkArguments TIMIT_TrainMultiInput=[reader=[useMersenneTwisterRand=true]]" || exit $?
|
||||
|
|
|
|||
Разница между файлами не показана из-за своего большого размера
Загрузить разницу
|
|
@ -5,4 +5,4 @@
|
|||
# specific TIMIT variables
|
||||
. $TEST_DIR/../run-timit-test-common
|
||||
|
||||
cntkrun TIMIT_TrainMultiTask.cntk "$CntkArguments" || exit $?
|
||||
cntkrun TIMIT_TrainMultiTask.cntk "$CntkArguments TIMIT_TrainMultiTask=[reader=[useMersenneTwisterRand=true]]" || exit $?
|
||||
|
|
|
|||
Разница между файлами не показана из-за своего большого размера
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|
|
@ -5,4 +5,4 @@
|
|||
# specific TIMIT variables
|
||||
. $TEST_DIR/../run-timit-test-common
|
||||
|
||||
cntkrun TIMIT_TrainNDLNetwork.cntk "$CntkArguments" || exit $?
|
||||
cntkrun TIMIT_TrainNDLNetwork.cntk "$CntkArguments TIMIT_TrainNDL=[reader=[useMersenneTwisterRand=true]]" || exit $?
|
||||
|
|
|
|||
Разница между файлами не показана из-за своего большого размера
Загрузить разницу
Разница между файлами не показана из-за своего большого размера
Загрузить разницу
|
|
@ -5,4 +5,4 @@
|
|||
# specific TIMIT variables
|
||||
. $TEST_DIR/../run-timit-test-common
|
||||
|
||||
cntkrun TIMIT_TrainSimpleNetwork.cntk "$CntkArguments" || exit $?
|
||||
cntkrun TIMIT_TrainSimpleNetwork.cntk "$CntkArguments TIMIT_TrainSimple=[reader=[useMersenneTwisterRand=true]]" || exit $?
|
||||
|
|
|
|||
Разница между файлами не показана из-за своего большого размера
Загрузить разницу
|
|
@ -5,4 +5,4 @@
|
|||
# specific TIMIT variables
|
||||
. $TEST_DIR/../run-timit-test-common
|
||||
|
||||
cntkrun TIMIT_TrainWithPreTrain.cntk "$CntkArguments" || exit $?
|
||||
cntkrun TIMIT_TrainWithPreTrain.cntk "$CntkArguments reader=[useMersenneTwisterRand=true]" || exit $?
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@
|
|||
. $TEST_DIR/../run-timit-test-common
|
||||
|
||||
# Train:
|
||||
cntkrun TIMIT_TrainAutoEncoder.cntk "$CntkArguments" || exit $?
|
||||
cntkrun TIMIT_TrainAutoEncoder.cntk "$CntkArguments TIMIT_TrainAutoEncoder=[reader=[useMersenneTwisterRand=true]]" || exit $?
|
||||
|
||||
# Copy the test data to the test run directory, so that we do not damage anything
|
||||
DataDir=$TEST_RUN_DIR/TestData
|
||||
|
|
@ -14,7 +14,7 @@ mkdir $DataDir
|
|||
cp -R $DataSourceDir/* $DataDir || exit $?
|
||||
|
||||
# Write:
|
||||
cntkrun TIMIT_WriteBottleneck.cntk "$CntkArguments"
|
||||
cntkrun TIMIT_WriteBottleneck.cntk "$CntkArguments TIMIT_WriteBottleneck=[reader=[useMersenneTwisterRand=true]]"
|
||||
ExitCode=$?
|
||||
|
||||
if [ $ExitCode == 0 ]; then
|
||||
|
|
|
|||
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|
|
@ -1,192 +1,192 @@
|
|||
008199bc8de688c21c4337d79e5a2d54 *./test/dr6/fmgd0/test-dr6-fmgd0-sx214.mfc
|
||||
a09454946bbae1fcd76a8e9e86e0dbce *./test/dr6/fmgd0/test-dr6-fmgd0-si1564.mfc
|
||||
cda241d0dda0b2f48829af9e90ade6bd *./test/dr6/fmgd0/test-dr6-fmgd0-sx124.mfc
|
||||
f3fa8f7569d4a61e8c9d57a2627e599a *./test/dr6/fmgd0/test-dr6-fmgd0-si934.mfc
|
||||
8b9c8813a495e66e82a99e809b3e3e48 *./test/dr6/fmgd0/test-dr6-fmgd0-sx34.mfc
|
||||
6e0a154ee5a70463299d028b509376d3 *./test/dr6/fmgd0/test-dr6-fmgd0-si2194.mfc
|
||||
40c03da4f49736bb90d280620078e4ad *./test/dr6/fmgd0/test-dr6-fmgd0-sx304.mfc
|
||||
5235543f7be1d0842e30951c2dd94646 *./test/dr6/fmgd0/test-dr6-fmgd0-sx394.mfc
|
||||
36b39c1f8242fc5fb3a4abd93269f0fb *./test/dr6/mjdh0/test-dr6-mjdh0-sx94.mfc
|
||||
60924645f6f9fc7b1f628ae0b0afeb43 *./test/dr6/mjdh0/test-dr6-mjdh0-si1984.mfc
|
||||
fab29be1ddc1a639e2fc32c27ef5f7fd *./test/dr6/mjdh0/test-dr6-mjdh0-si724.mfc
|
||||
9bf33afe6dd857990fdf9bd2fef7445c *./test/dr6/mjdh0/test-dr6-mjdh0-si1354.mfc
|
||||
58978d83757b6c7a368a4741b52df6e9 *./test/dr6/mjdh0/test-dr6-mjdh0-sx184.mfc
|
||||
911ce221f83e75801ad7ab66248119ac *./test/dr6/mjdh0/test-dr6-mjdh0-sx274.mfc
|
||||
c030e2273100bb0a7aa8de910ab6d79e *./test/dr6/mjdh0/test-dr6-mjdh0-sx4.mfc
|
||||
54e71a5b8ea778f7e5fc8485ef899b58 *./test/dr6/mjdh0/test-dr6-mjdh0-sx364.mfc
|
||||
8968c27639613df928e02c1eadbabd7c *./test/dr6/mcmj0/test-dr6-mcmj0-si464.mfc
|
||||
a02ebfce2f26fd27942944ce72d53545 *./test/dr6/mcmj0/test-dr6-mcmj0-si602.mfc
|
||||
1593dae1d16b132aa45bafd135118d12 *./test/dr6/mcmj0/test-dr6-mcmj0-sx284.mfc
|
||||
f682879d6969fb77f7fecf47122a6086 *./test/dr6/mcmj0/test-dr6-mcmj0-sx374.mfc
|
||||
029a35db879f51bdcfc3cf36696e679f *./test/dr6/mcmj0/test-dr6-mcmj0-sx194.mfc
|
||||
4430f105960f827ddcd65e679db92a0c *./test/dr6/mcmj0/test-dr6-mcmj0-sx14.mfc
|
||||
e20f3bd86b74ac114adf450fa7979760 *./test/dr6/mcmj0/test-dr6-mcmj0-sx104.mfc
|
||||
9e4f776518b246354c3e896946c1b087 *./test/dr6/mcmj0/test-dr6-mcmj0-si1094.mfc
|
||||
0dbf102b2fb6e49e0bc3a2168c39a7a4 *./test/dr4/mlll0/test-dr4-mlll0-si733.mfc
|
||||
1da967c0ca60ddc2ade51656adebefd4 *./test/dr4/mlll0/test-dr4-mlll0-si1993.mfc
|
||||
e62d79867fff8a0dbbfccac83f7b9b94 *./test/dr4/mlll0/test-dr4-mlll0-sx283.mfc
|
||||
e514efb114be7b4887d7d8e3a05db32f *./test/dr4/mlll0/test-dr4-mlll0-sx103.mfc
|
||||
ca3463b42d4f6abac46fe402828eb5cb *./test/dr4/mlll0/test-dr4-mlll0-si1363.mfc
|
||||
9e083cf50bd386ee092b5edb9d37792f *./test/dr4/mlll0/test-dr4-mlll0-sx193.mfc
|
||||
683b4108b96f11e9acb07b3c400d13b6 *./test/dr4/mlll0/test-dr4-mlll0-sx13.mfc
|
||||
54f241efad72d43f6c212559d880b6d4 *./test/dr4/mlll0/test-dr4-mlll0-sx373.mfc
|
||||
cefca983fb158b599db45d458e73d7e2 *./test/dr4/fjlm0/test-dr4-fjlm0-si1043.mfc
|
||||
4d64b945b3cdca3ed09c6519f7a61587 *./test/dr4/fjlm0/test-dr4-fjlm0-sx323.mfc
|
||||
c2ffb046aa6471fcee7c991c51ad7f32 *./test/dr4/fjlm0/test-dr4-fjlm0-sx413.mfc
|
||||
1f71221a5ae059ed429e0a44f0672583 *./test/dr4/fjlm0/test-dr4-fjlm0-si1673.mfc
|
||||
1bcb4776ad609b3350f7fa09c47f5601 *./test/dr4/fjlm0/test-dr4-fjlm0-sx53.mfc
|
||||
cca9dad22152de5f882d4fb592fb0bb6 *./test/dr4/fjlm0/test-dr4-fjlm0-sx143.mfc
|
||||
3076b36964132ca361c327545c21fb7a *./test/dr4/fjlm0/test-dr4-fjlm0-si2303.mfc
|
||||
6db1b910ef258ced9b7ad8b91890f88d *./test/dr4/fjlm0/test-dr4-fjlm0-sx233.mfc
|
||||
447c990a84018e217af9a1b26dc6c7bf *./test/dr4/mtls0/test-dr4-mtls0-si740.mfc
|
||||
d7e72ac62bb6fe2dad3d811f2115fe6f *./test/dr4/mtls0/test-dr4-mtls0-si1370.mfc
|
||||
1e2876b76919b231093330ef9dd8b112 *./test/dr4/mtls0/test-dr4-mtls0-sx380.mfc
|
||||
ddac5699320d12800ec22727e5df202e *./test/dr4/mtls0/test-dr4-mtls0-sx200.mfc
|
||||
81fd1df548bdb101903a7b126b7cc001 *./test/dr4/mtls0/test-dr4-mtls0-sx110.mfc
|
||||
70a84b62d6eabaecbc155b03d7f2b1c7 *./test/dr4/mtls0/test-dr4-mtls0-si2000.mfc
|
||||
f0c78988063079215548f2471aa01430 *./test/dr4/mtls0/test-dr4-mtls0-sx20.mfc
|
||||
9e91c73d9c2520d64fa01c654fb759e9 *./test/dr4/mtls0/test-dr4-mtls0-sx290.mfc
|
||||
8a834dfcc67cb8b89267b93ebb15a130 *./test/dr2/mwew0/test-dr2-mwew0-sx281.mfc
|
||||
8c81455edfa6f966b4734e53f23dc8b6 *./test/dr2/mwew0/test-dr2-mwew0-si1361.mfc
|
||||
88880d1543e5fed8210ddb5b47b2c9b4 *./test/dr2/mwew0/test-dr2-mwew0-si731.mfc
|
||||
6a2d4faa44d909dcacaa0697b8b5ca75 *./test/dr2/mwew0/test-dr2-mwew0-si1991.mfc
|
||||
a8c92f05369916093b6c4003b3c218e5 *./test/dr2/mwew0/test-dr2-mwew0-sx371.mfc
|
||||
b2ba0fc21dd4b8ff3da2daaed7405b85 *./test/dr2/mwew0/test-dr2-mwew0-sx101.mfc
|
||||
7f7679dc5fcb62f293f5a0505dc41120 *./test/dr2/mwew0/test-dr2-mwew0-sx11.mfc
|
||||
f7c0807de7e957e54fabdfa4dd515b68 *./test/dr2/mwew0/test-dr2-mwew0-sx191.mfc
|
||||
3c0e06bc402b045db13f0a00cc88e60d *./test/dr2/fpas0/test-dr2-fpas0-si1272.mfc
|
||||
44b81b626bb5fe4f3cbbb1aeab6d7123 *./test/dr2/fpas0/test-dr2-fpas0-sx404.mfc
|
||||
974528370db8c1f4d45f66f3508104b5 *./test/dr2/fpas0/test-dr2-fpas0-sx224.mfc
|
||||
9e20ebf987c3ca2760c9bc2b53a44669 *./test/dr2/fpas0/test-dr2-fpas0-si944.mfc
|
||||
a286e3e0d89c1aa3b9b03ee22431cd82 *./test/dr2/fpas0/test-dr2-fpas0-si2204.mfc
|
||||
8e35b333120bd9ed8d974fad1a5b2cf7 *./test/dr2/fpas0/test-dr2-fpas0-sx134.mfc
|
||||
bbbfd97699365ddb38cf5cae52737c7a *./test/dr2/fpas0/test-dr2-fpas0-sx314.mfc
|
||||
7f546e31fcd8d18101f319a2d3897706 *./test/dr2/fpas0/test-dr2-fpas0-sx44.mfc
|
||||
647d5dd9f817bc32349f55a82256e169 *./test/dr2/mtas1/test-dr2-mtas1-sx298.mfc
|
||||
cb3e118936cfca96f6334b2a54f490b2 *./test/dr2/mtas1/test-dr2-mtas1-sx388.mfc
|
||||
7e5478a076b52944c3c1e99d71b1d6b8 *./test/dr2/mtas1/test-dr2-mtas1-si1473.mfc
|
||||
7ca01ad520a5d512e2ba344e62cba222 *./test/dr2/mtas1/test-dr2-mtas1-sx208.mfc
|
||||
140323d4f4ca92bb4b4e8078900ea543 *./test/dr2/mtas1/test-dr2-mtas1-si838.mfc
|
||||
27b3a518a68f2660b005caaecc22c0e1 *./test/dr2/mtas1/test-dr2-mtas1-sx28.mfc
|
||||
5a999b2a663309b0aa465bdaf7268ca3 *./test/dr2/mtas1/test-dr2-mtas1-si2098.mfc
|
||||
062fbc0e54961731c91333d501e58a9f *./test/dr2/mtas1/test-dr2-mtas1-sx118.mfc
|
||||
924815fb114abaecfd6cef1023a746f2 *./test/dr5/fnlp0/test-dr5-fnlp0-sx318.mfc
|
||||
aeebe1234b63bc31e114adf7502c380e *./test/dr5/fnlp0/test-dr5-fnlp0-sx48.mfc
|
||||
62e5b81fe11a9f1dcc1500eba6dece50 *./test/dr5/fnlp0/test-dr5-fnlp0-sx138.mfc
|
||||
861409908ee0430362e3dbf3d851ba7d *./test/dr5/fnlp0/test-dr5-fnlp0-si1308.mfc
|
||||
fb62d81b35b54aa39c08374414d575ff *./test/dr5/fnlp0/test-dr5-fnlp0-si678.mfc
|
||||
6f4f98b45da068942f12eb216c3293b7 *./test/dr5/fnlp0/test-dr5-fnlp0-si1938.mfc
|
||||
4e1176f25969dcf9d16d741f87d7f3c6 *./test/dr5/fnlp0/test-dr5-fnlp0-sx408.mfc
|
||||
af64ba27116e9ca2c94110e6ea1d6193 *./test/dr5/fnlp0/test-dr5-fnlp0-sx228.mfc
|
||||
b333633b2d9f468a36eed992d4732de8 *./test/dr5/mklt0/test-dr5-mklt0-si1843.mfc
|
||||
0d067eb40753307483bb1c2e949e883d *./test/dr5/mklt0/test-dr5-mklt0-sx313.mfc
|
||||
8d5aa3d8c6cb0958effc6b0d7b8bb08d *./test/dr5/mklt0/test-dr5-mklt0-sx403.mfc
|
||||
6cfeac9e9883702e42dad3005cf20d13 *./test/dr5/mklt0/test-dr5-mklt0-sx43.mfc
|
||||
c2557774237aba661b654b6679fc9f24 *./test/dr5/mklt0/test-dr5-mklt0-sx223.mfc
|
||||
16a378457fd8927ddbac3c8691b7de3c *./test/dr5/mklt0/test-dr5-mklt0-sx133.mfc
|
||||
417ad64b6e67b8bff0d38d7c3d8872c0 *./test/dr5/mklt0/test-dr5-mklt0-si1213.mfc
|
||||
e093fb5e78bb772e324b3a93b608b6d6 *./test/dr5/mklt0/test-dr5-mklt0-si583.mfc
|
||||
81d4c2ca418f57e8d2dd3fca1843e4aa *./test/dr5/mbpm0/test-dr5-mbpm0-sx47.mfc
|
||||
30999743f1c335782b8a99cc59e40ab8 *./test/dr5/mbpm0/test-dr5-mbpm0-sx407.mfc
|
||||
73891bfeb2c918223d5ab98747a08bfb *./test/dr5/mbpm0/test-dr5-mbpm0-sx137.mfc
|
||||
21f85c04df7a63fffb902191f52d2d36 *./test/dr5/mbpm0/test-dr5-mbpm0-si1584.mfc
|
||||
66121d0f1761ca1b9b610c3c249c532e *./test/dr5/mbpm0/test-dr5-mbpm0-si947.mfc
|
||||
9b8eab44215bfe06c4feec05bf425b26 *./test/dr5/mbpm0/test-dr5-mbpm0-si1577.mfc
|
||||
c59b8f9646c111251ce0c50dd384c2eb *./test/dr5/mbpm0/test-dr5-mbpm0-sx317.mfc
|
||||
929b2b9ce14114856689847fb99cdb57 *./test/dr5/mbpm0/test-dr5-mbpm0-sx227.mfc
|
||||
5ece7f6cc073c433415d419a107771f3 *./test/dr1/mwbt0/test-dr1-mwbt0-si1553.mfc
|
||||
12269b7e28a9377e848b1f8d71c9107a *./test/dr1/mwbt0/test-dr1-mwbt0-sx293.mfc
|
||||
ca6e2cd158e9bdea4b3cb96588fee9e7 *./test/dr1/mwbt0/test-dr1-mwbt0-sx203.mfc
|
||||
6e01e230e76cc78c6e6d0c76aaa1b5e7 *./test/dr1/mwbt0/test-dr1-mwbt0-sx383.mfc
|
||||
a44309d0711466684c2a45dc89f1fb18 *./test/dr1/mwbt0/test-dr1-mwbt0-sx113.mfc
|
||||
b93673dbdfc9aa77b4c2a45863ad5a59 *./test/dr1/mwbt0/test-dr1-mwbt0-si923.mfc
|
||||
c272c146ab82cebb96fe7bf0cbc0641d *./test/dr1/mwbt0/test-dr1-mwbt0-sx23.mfc
|
||||
e2e1dc6714afaf661c6d2133f217a32c *./test/dr1/mwbt0/test-dr1-mwbt0-si2183.mfc
|
||||
b6161b5f3eddf14e46737099079eb2f5 *./test/dr1/felc0/test-dr1-felc0-sx216.mfc
|
||||
ce8fa844066519dab32a880ae159df1e *./test/dr1/felc0/test-dr1-felc0-si1386.mfc
|
||||
fd0272cc2ca58ee0b39c099a4bfef3b4 *./test/dr1/felc0/test-dr1-felc0-sx36.mfc
|
||||
8d4454b9cc1357f9c72a8d63914e9763 *./test/dr1/felc0/test-dr1-felc0-sx306.mfc
|
||||
0eaf25920a21bc4c97ea0aad1eb1e0f0 *./test/dr1/felc0/test-dr1-felc0-sx126.mfc
|
||||
2fe03ccbef1345402e7dd1394d7c3a52 *./test/dr1/felc0/test-dr1-felc0-sx396.mfc
|
||||
5c3d0e5e3561bb9baca88f7f93b830bd *./test/dr1/felc0/test-dr1-felc0-si2016.mfc
|
||||
9e6b407f1a569f07c3018e607d97b029 *./test/dr1/felc0/test-dr1-felc0-si756.mfc
|
||||
bdc3575486a50bedf2a0c1ec5b2c61ab *./test/dr1/mdab0/test-dr1-mdab0-sx319.mfc
|
||||
3a484f664b67f52a5fad0773ca155c17 *./test/dr1/mdab0/test-dr1-mdab0-sx409.mfc
|
||||
9336d98d64c586878d962014ceb0b9c1 *./test/dr1/mdab0/test-dr1-mdab0-sx49.mfc
|
||||
394267eb026cb93f14aff4c1b80b431d *./test/dr1/mdab0/test-dr1-mdab0-si1039.mfc
|
||||
1a83225df3baa6ed925d37d228ac8892 *./test/dr1/mdab0/test-dr1-mdab0-sx139.mfc
|
||||
390d7cdf8e040ef585202bea92a090ac *./test/dr1/mdab0/test-dr1-mdab0-sx229.mfc
|
||||
eacb7f7afdaa3c5f5ce81893c2875660 *./test/dr1/mdab0/test-dr1-mdab0-si2299.mfc
|
||||
9d5e358659b84c1466ef8e08d5076490 *./test/dr1/mdab0/test-dr1-mdab0-si1669.mfc
|
||||
a718377d99525714aed4397a2b527275 *./test/dr3/mjmp0/test-dr3-mjmp0-si1535.mfc
|
||||
1495b8fc45a4957d8e6fea3faa1f2736 *./test/dr3/mjmp0/test-dr3-mjmp0-sx185.mfc
|
||||
d5fdf9e2f75e49b5c8f36657216e64aa *./test/dr3/mjmp0/test-dr3-mjmp0-si905.mfc
|
||||
5bd3679ae219b07c2f5c3d1ea2e52b5e *./test/dr3/mjmp0/test-dr3-mjmp0-si1791.mfc
|
||||
2ed17e351f8f7771858596bc64d89bba *./test/dr3/mjmp0/test-dr3-mjmp0-sx275.mfc
|
||||
cbc1ef7fa4c1c953438599a886c06a4c *./test/dr3/mjmp0/test-dr3-mjmp0-sx365.mfc
|
||||
e4e0a69315f35747c7b4d4502a92c0d5 *./test/dr3/mjmp0/test-dr3-mjmp0-sx5.mfc
|
||||
925b9fffe853911d7ff96599ddeacac5 *./test/dr3/mjmp0/test-dr3-mjmp0-sx95.mfc
|
||||
ad8d100175ac9c972f9e4ab8a0bce6ba *./test/dr3/fpkt0/test-dr3-fpkt0-sx188.mfc
|
||||
1e0b979f3a74868b770cd5dd5338c797 *./test/dr3/fpkt0/test-dr3-fpkt0-sx8.mfc
|
||||
027129dc52160f3e8ae72b85e6aaefd8 *./test/dr3/fpkt0/test-dr3-fpkt0-si2168.mfc
|
||||
9def895c1d836cfc60377c41562cb417 *./test/dr3/fpkt0/test-dr3-fpkt0-sx368.mfc
|
||||
55ac12d3d594c816c0247e8628ebee60 *./test/dr3/fpkt0/test-dr3-fpkt0-sx278.mfc
|
||||
cd65be1bf1ea4b7d64b6f42fc12f40e5 *./test/dr3/fpkt0/test-dr3-fpkt0-sx98.mfc
|
||||
b479ff6fed48fd9c912ce4754279d8ce *./test/dr3/fpkt0/test-dr3-fpkt0-si908.mfc
|
||||
1996f043ef709354a67e18080ccb008c *./test/dr3/fpkt0/test-dr3-fpkt0-si1538.mfc
|
||||
9fbaa1a0f138c61e08a62701ea6b3ef1 *./test/dr3/mlnt0/test-dr3-mlnt0-si1574.mfc
|
||||
b27ec963d087eb7e4111a5b46724f4cb *./test/dr3/mlnt0/test-dr3-mlnt0-sx372.mfc
|
||||
0de11a9047d3b751724a64eea1011613 *./test/dr3/mlnt0/test-dr3-mlnt0-si1902.mfc
|
||||
d928d9c74b78100df740dfa6b42966a1 *./test/dr3/mlnt0/test-dr3-mlnt0-sx12.mfc
|
||||
23951f69bcac074e948e5b51522c2645 *./test/dr3/mlnt0/test-dr3-mlnt0-sx102.mfc
|
||||
c8ef0e56da8cfeabcdcf845af50d8a6c *./test/dr3/mlnt0/test-dr3-mlnt0-sx192.mfc
|
||||
8811978c6da973f50fb0fd6ae2972b8a *./test/dr3/mlnt0/test-dr3-mlnt0-sx282.mfc
|
||||
8385218511bc8e26c55a2b69f8ec3609 *./test/dr3/mlnt0/test-dr3-mlnt0-si642.mfc
|
||||
688d620c7903a881162481a27a8b7c09 *./test/dr7/mgrt0/test-dr7-mgrt0-si820.mfc
|
||||
51dd3429fa9d1b8cd6cf6518c7de8de9 *./test/dr7/mgrt0/test-dr7-mgrt0-sx280.mfc
|
||||
bae55ae5080af76372ab0a6929e801b9 *./test/dr7/mgrt0/test-dr7-mgrt0-sx370.mfc
|
||||
83f68583cb2f1b19d74a4c7046c6d62f *./test/dr7/mgrt0/test-dr7-mgrt0-si1450.mfc
|
||||
3564132d9b76a6def1566394539629e1 *./test/dr7/mgrt0/test-dr7-mgrt0-sx190.mfc
|
||||
d161cbe022fd4c806a323da921ce4d56 *./test/dr7/mgrt0/test-dr7-mgrt0-sx10.mfc
|
||||
9a9ef7e4ab66da0c7ae498a3906c8a52 *./test/dr7/mgrt0/test-dr7-mgrt0-sx100.mfc
|
||||
3c5967eff1e9fa115885651d79a5733b *./test/dr7/mgrt0/test-dr7-mgrt0-si2080.mfc
|
||||
9a9e9470d8400ce27063e6d28ff1f15b *./test/dr7/mnjm0/test-dr7-mnjm0-sx320.mfc
|
||||
9e7be5e7a1716f3a34b6971d726f20cb *./test/dr7/mnjm0/test-dr7-mnjm0-sx230.mfc
|
||||
7a3de9626a8b8399d5b6370333e2e35d *./test/dr7/mnjm0/test-dr7-mnjm0-si1580.mfc
|
||||
55ab6bc3f71b80b3b3c2fa8150167fa0 *./test/dr7/mnjm0/test-dr7-mnjm0-sx50.mfc
|
||||
6d46e1cc0fd50fa477e671b140944b88 *./test/dr7/mnjm0/test-dr7-mnjm0-si2210.mfc
|
||||
b423d4be8c113a5af7abe6ad97ed6da5 *./test/dr7/mnjm0/test-dr7-mnjm0-si950.mfc
|
||||
c9bbbd2ca7ff68f01c2b9063c4d398c5 *./test/dr7/mnjm0/test-dr7-mnjm0-sx410.mfc
|
||||
a69b03327312770c0e1702e39697be4a *./test/dr7/mnjm0/test-dr7-mnjm0-sx140.mfc
|
||||
24923249a28636ae9a9b28b9bf965a94 *./test/dr7/fdhc0/test-dr7-fdhc0-si929.mfc
|
||||
1a190740b7ca783d9df1906513f158ae *./test/dr7/fdhc0/test-dr7-fdhc0-si1559.mfc
|
||||
f35c276dbb3368fb5fab5ba53946dd28 *./test/dr7/fdhc0/test-dr7-fdhc0-sx389.mfc
|
||||
cf41463caea22607454d26938683748d *./test/dr7/fdhc0/test-dr7-fdhc0-sx209.mfc
|
||||
fe02e1e36bb8e4be1f08c9d28f239f5d *./test/dr7/fdhc0/test-dr7-fdhc0-sx29.mfc
|
||||
a246a323faa7e3f462c34399af4cf4bc *./test/dr7/fdhc0/test-dr7-fdhc0-sx119.mfc
|
||||
690e2b399763139a22328eeee4c2b311 *./test/dr7/fdhc0/test-dr7-fdhc0-sx299.mfc
|
||||
35ca97af9bdaf49842a8120987f151cb *./test/dr7/fdhc0/test-dr7-fdhc0-si2189.mfc
|
||||
1bdc0ae811834afcf489fa21539223dd *./test/dr8/mpam0/test-dr8-mpam0-si1961.mfc
|
||||
c245c8cd5a45e01d100e224650fa9b4f *./test/dr8/mpam0/test-dr8-mpam0-sx109.mfc
|
||||
0d7e36c02f2f27d760879c9b80145c0a *./test/dr8/mpam0/test-dr8-mpam0-si1189.mfc
|
||||
a748c297bbd31ad8debc9e38ec233910 *./test/dr8/mpam0/test-dr8-mpam0-sx199.mfc
|
||||
9af2232dbd7e48d68bf8d0898ee35ffd *./test/dr8/mpam0/test-dr8-mpam0-sx289.mfc
|
||||
859b63746ac8e00425381d108c82e790 *./test/dr8/mpam0/test-dr8-mpam0-sx19.mfc
|
||||
6a3516280d03082cb0945b821f21f1d0 *./test/dr8/mpam0/test-dr8-mpam0-sx379.mfc
|
||||
41fab34edd37aefef76aafc81d277d4d *./test/dr8/mpam0/test-dr8-mpam0-si1819.mfc
|
||||
433782ab0866db919468a01ddde5da5e *./test/dr8/fmld0/test-dr8-fmld0-sx295.mfc
|
||||
d90f96b83a6540e8738d4dd82544ab7a *./test/dr8/fmld0/test-dr8-fmld0-si2185.mfc
|
||||
28a78f8fcbc102acc82b92b78be18ade *./test/dr8/fmld0/test-dr8-fmld0-sx25.mfc
|
||||
250cf0e5a6a497465dd3b06db67d3936 *./test/dr8/fmld0/test-dr8-fmld0-sx115.mfc
|
||||
99ef136e00ab2cfbc311256f1a760ab3 *./test/dr8/fmld0/test-dr8-fmld0-sx205.mfc
|
||||
54a5c553a5cf4a95f97d0be07f5df387 *./test/dr8/fmld0/test-dr8-fmld0-si925.mfc
|
||||
bfdb255edff890f5842d555d02caf893 *./test/dr8/fmld0/test-dr8-fmld0-si822.mfc
|
||||
f00a1c191bda4912504ba78f676b1899 *./test/dr8/fmld0/test-dr8-fmld0-sx385.mfc
|
||||
2c28c6da8d15b9686fc2ec2492958eed *./test/dr8/mjln0/test-dr8-mjln0-sx189.mfc
|
||||
90bcd4128830cd1a2c2bd8b333ffc54b *./test/dr8/mjln0/test-dr8-mjln0-sx99.mfc
|
||||
f85234706f1847a21ed74913b404a9be *./test/dr8/mjln0/test-dr8-mjln0-sx9.mfc
|
||||
39cead4f5650a08c4ca6479449a610e9 *./test/dr8/mjln0/test-dr8-mjln0-si819.mfc
|
||||
c9484df53aa6ba750ae94a4e13d6f21d *./test/dr8/mjln0/test-dr8-mjln0-si1449.mfc
|
||||
92c3cf4ce7e1c281deb5904dc639dc2f *./test/dr8/mjln0/test-dr8-mjln0-si2079.mfc
|
||||
a79393fe16771f0b1be83b6da534a07a *./test/dr8/mjln0/test-dr8-mjln0-sx279.mfc
|
||||
0804a410e2d123c5488d711af5dac71a *./test/dr8/mjln0/test-dr8-mjln0-sx369.mfc
|
||||
678b0d5bfef75745f7c439f05faaf4a0 *./test/dr1/mdab0/test-dr1-mdab0-sx139.mfc
|
||||
ea0123c014719f549973805106a112f7 *./test/dr1/mdab0/test-dr1-mdab0-si1669.mfc
|
||||
aafa0a2a28620dd0c31b15a9681033af *./test/dr1/mdab0/test-dr1-mdab0-si1039.mfc
|
||||
8b43df02b27fa746e063b843652f51c8 *./test/dr1/mdab0/test-dr1-mdab0-si2299.mfc
|
||||
90202493a294d8ab763080674c6092a2 *./test/dr1/mdab0/test-dr1-mdab0-sx49.mfc
|
||||
3c10d45a0c3b63a44f2381494b615159 *./test/dr1/mdab0/test-dr1-mdab0-sx229.mfc
|
||||
d8ebb351ed8a8a82cb3efbccfc492747 *./test/dr1/mdab0/test-dr1-mdab0-sx319.mfc
|
||||
4b9936f5caa2eebf5a5730527693fdf8 *./test/dr1/mdab0/test-dr1-mdab0-sx409.mfc
|
||||
fba6cda32b16545fd63f554702bcbfd1 *./test/dr1/felc0/test-dr1-felc0-sx396.mfc
|
||||
cc1623ee2a25d9d2a3a887ae330c1426 *./test/dr1/felc0/test-dr1-felc0-si1386.mfc
|
||||
ed8d4e3e62bd0317a830654ca4ef56b1 *./test/dr1/felc0/test-dr1-felc0-si2016.mfc
|
||||
0666d4e677fecd9c23ae14fe4a2c543a *./test/dr1/felc0/test-dr1-felc0-sx126.mfc
|
||||
ecc214f709f62a1513bf34a11bfd16e5 *./test/dr1/felc0/test-dr1-felc0-sx306.mfc
|
||||
74ed8d4727853c9e90aed218975d06f4 *./test/dr1/felc0/test-dr1-felc0-si756.mfc
|
||||
8254c8db07cf7c01f553405e65f9b11b *./test/dr1/felc0/test-dr1-felc0-sx216.mfc
|
||||
da82160bdfaa059567fe5e4b1f1e9dfc *./test/dr1/felc0/test-dr1-felc0-sx36.mfc
|
||||
8b7c6a53a366ef13693c7452dfc9cf2a *./test/dr1/mwbt0/test-dr1-mwbt0-si1553.mfc
|
||||
00ef06aaa5825377f9bbf8fc4d71923e *./test/dr1/mwbt0/test-dr1-mwbt0-sx23.mfc
|
||||
1ea782f8d9ebdd476c0d786827cae34b *./test/dr1/mwbt0/test-dr1-mwbt0-si2183.mfc
|
||||
a118d83159b806ce50bac3ca72549e92 *./test/dr1/mwbt0/test-dr1-mwbt0-si923.mfc
|
||||
569b88833a4da01e15724e4c6e95bfed *./test/dr1/mwbt0/test-dr1-mwbt0-sx383.mfc
|
||||
58b1f7b399193bd80f8b6653cdb03652 *./test/dr1/mwbt0/test-dr1-mwbt0-sx203.mfc
|
||||
407fb638a4790dcba65b1368e8e08d50 *./test/dr1/mwbt0/test-dr1-mwbt0-sx293.mfc
|
||||
b6f88607cb6611b5ed027c88faaa6c93 *./test/dr1/mwbt0/test-dr1-mwbt0-sx113.mfc
|
||||
1005106e85c2b2388c22e4492cb21b8d *./test/dr3/mjmp0/test-dr3-mjmp0-sx185.mfc
|
||||
5b26cd0004f300b8e4e2585d91a792ff *./test/dr3/mjmp0/test-dr3-mjmp0-sx5.mfc
|
||||
ceca906f0b4164d5f682c499c22ef0a4 *./test/dr3/mjmp0/test-dr3-mjmp0-si905.mfc
|
||||
e9758e6fcbfcb72af6d69a8b1eb4fa7c *./test/dr3/mjmp0/test-dr3-mjmp0-sx275.mfc
|
||||
eefa58e59c066d2de34fe5dde9b7014c *./test/dr3/mjmp0/test-dr3-mjmp0-si1791.mfc
|
||||
dd805963a48ef1e2cdcfbc4f977cb894 *./test/dr3/mjmp0/test-dr3-mjmp0-sx95.mfc
|
||||
59b6dcf1126eb07a5e3abfab42920cc9 *./test/dr3/mjmp0/test-dr3-mjmp0-si1535.mfc
|
||||
a9443f9e91986be82614e8a4d60c8156 *./test/dr3/mjmp0/test-dr3-mjmp0-sx365.mfc
|
||||
b233732febae3faf80ab7cadb19ff0e5 *./test/dr3/mlnt0/test-dr3-mlnt0-si1902.mfc
|
||||
0cf8e4f83e9f5ab525113703094fee41 *./test/dr3/mlnt0/test-dr3-mlnt0-si642.mfc
|
||||
1cf3e62c06cd5a9365cd0adfce6d06c3 *./test/dr3/mlnt0/test-dr3-mlnt0-sx102.mfc
|
||||
c7d697fb5434186c4aada37904fe9f2a *./test/dr3/mlnt0/test-dr3-mlnt0-sx12.mfc
|
||||
d37d590b506dd8e934649f93dcb54fd3 *./test/dr3/mlnt0/test-dr3-mlnt0-sx372.mfc
|
||||
d9bb0340ee7e58269b2967dd382ad4a2 *./test/dr3/mlnt0/test-dr3-mlnt0-si1574.mfc
|
||||
a12487372fed2de2875fb1a63d6269f2 *./test/dr3/mlnt0/test-dr3-mlnt0-sx192.mfc
|
||||
66217b84955e2b5b711f2ddac4b2e8e3 *./test/dr3/mlnt0/test-dr3-mlnt0-sx282.mfc
|
||||
cca8754cc10895f2f56a83c436331e4e *./test/dr3/fpkt0/test-dr3-fpkt0-sx188.mfc
|
||||
c2bc75061d6fe1f1517b957584905565 *./test/dr3/fpkt0/test-dr3-fpkt0-sx368.mfc
|
||||
707101da163bba7b755ac8d23ff86fa5 *./test/dr3/fpkt0/test-dr3-fpkt0-si2168.mfc
|
||||
0116c675501b69bd9ca160ee2d404760 *./test/dr3/fpkt0/test-dr3-fpkt0-sx8.mfc
|
||||
f04a79cc985a98de9e5d1cfe6a4c86ff *./test/dr3/fpkt0/test-dr3-fpkt0-si908.mfc
|
||||
28da5617e9c1af623bc2e0c1d99589cf *./test/dr3/fpkt0/test-dr3-fpkt0-sx278.mfc
|
||||
d49430a4d82095b727347b5e2fcaf6c2 *./test/dr3/fpkt0/test-dr3-fpkt0-sx98.mfc
|
||||
098e99a50edb4ce2a5e096357c89fff2 *./test/dr3/fpkt0/test-dr3-fpkt0-si1538.mfc
|
||||
6e590997178c69e1355ef7bf5dffa005 *./test/dr2/fpas0/test-dr2-fpas0-si2204.mfc
|
||||
30aac0adba7f379d57f29274f52864fe *./test/dr2/fpas0/test-dr2-fpas0-sx314.mfc
|
||||
bb894f6cb131cb500e0103ad8a3e789b *./test/dr2/fpas0/test-dr2-fpas0-sx404.mfc
|
||||
3b9ee5caa3ce9f78fc2ee71343f7aa41 *./test/dr2/fpas0/test-dr2-fpas0-sx224.mfc
|
||||
abf5b628f6b64a71449ff077c2f1f582 *./test/dr2/fpas0/test-dr2-fpas0-sx44.mfc
|
||||
3459423b2ceff40f165603e1a0fa9427 *./test/dr2/fpas0/test-dr2-fpas0-si944.mfc
|
||||
5a7b59b100d35b6e2e0cc950b15545c6 *./test/dr2/fpas0/test-dr2-fpas0-sx134.mfc
|
||||
29a845b56c4cd6170d9b523d1de55e83 *./test/dr2/fpas0/test-dr2-fpas0-si1272.mfc
|
||||
5c77cd5c112ad926f6c049c5d3437745 *./test/dr2/mwew0/test-dr2-mwew0-sx101.mfc
|
||||
a4568e958324983d512c0ffd68b353e4 *./test/dr2/mwew0/test-dr2-mwew0-si731.mfc
|
||||
c85526115f00236269ebfa9b8b3e74dc *./test/dr2/mwew0/test-dr2-mwew0-si1361.mfc
|
||||
5aa7ed996a30f1701445b3e1f9cebb21 *./test/dr2/mwew0/test-dr2-mwew0-sx11.mfc
|
||||
a901b23a44dba48bac3f63cb05472466 *./test/dr2/mwew0/test-dr2-mwew0-si1991.mfc
|
||||
2d001415dcc0c13e095ed54e4a04d0ba *./test/dr2/mwew0/test-dr2-mwew0-sx371.mfc
|
||||
b08674e36117624be782b67204031949 *./test/dr2/mwew0/test-dr2-mwew0-sx191.mfc
|
||||
ea04d838c64a5c4b87b082d7c0948b68 *./test/dr2/mwew0/test-dr2-mwew0-sx281.mfc
|
||||
2af6b995fdd1fc773c05bdec839cd2e7 *./test/dr2/mtas1/test-dr2-mtas1-sx388.mfc
|
||||
4d1ebb56bfb84c36434c3280e5ace83a *./test/dr2/mtas1/test-dr2-mtas1-si1473.mfc
|
||||
18d2d884cd5194af7423baac9b637849 *./test/dr2/mtas1/test-dr2-mtas1-sx298.mfc
|
||||
fbd0b0bbe662675df11375cef1b5a253 *./test/dr2/mtas1/test-dr2-mtas1-sx118.mfc
|
||||
e8bacc35d90947d9b19df6e8ee659c54 *./test/dr2/mtas1/test-dr2-mtas1-si2098.mfc
|
||||
f6b17ff53923e862a40050259808c988 *./test/dr2/mtas1/test-dr2-mtas1-sx208.mfc
|
||||
941b0d59d2f285063acaaf5f66d91a72 *./test/dr2/mtas1/test-dr2-mtas1-si838.mfc
|
||||
faa5a46d1f0b6d502c963c05366c559a *./test/dr2/mtas1/test-dr2-mtas1-sx28.mfc
|
||||
0f5c9efb8e860cdd7268c89e1544f45f *./test/dr5/mbpm0/test-dr5-mbpm0-sx227.mfc
|
||||
f9a9b409aa50166dbe9fb6d979739365 *./test/dr5/mbpm0/test-dr5-mbpm0-si1584.mfc
|
||||
6b45a90af2c5a1fa04bff2d7a41b2666 *./test/dr5/mbpm0/test-dr5-mbpm0-sx317.mfc
|
||||
54fe99455f29499a4a0e809b6e2b8f0e *./test/dr5/mbpm0/test-dr5-mbpm0-si1577.mfc
|
||||
362d6e5df4e68cea1812f663ec10de17 *./test/dr5/mbpm0/test-dr5-mbpm0-si947.mfc
|
||||
f9b007a573fb3ce814908e173592ae4c *./test/dr5/mbpm0/test-dr5-mbpm0-sx47.mfc
|
||||
b26cb72f2296d7d0cf5a67bda521fcdd *./test/dr5/mbpm0/test-dr5-mbpm0-sx407.mfc
|
||||
5ce8b919635b9dd2b3f31c374718c393 *./test/dr5/mbpm0/test-dr5-mbpm0-sx137.mfc
|
||||
89b5e922d36c28800dc642162029fe03 *./test/dr5/fnlp0/test-dr5-fnlp0-si678.mfc
|
||||
37a38419a563509b2ba0e7e99d92c4fd *./test/dr5/fnlp0/test-dr5-fnlp0-sx138.mfc
|
||||
ceb6f926da80c1da241e9b9077a16cdf *./test/dr5/fnlp0/test-dr5-fnlp0-si1308.mfc
|
||||
0ceddeaf66f600b150392267f49459df *./test/dr5/fnlp0/test-dr5-fnlp0-sx48.mfc
|
||||
80efcadb9b79bd6ef1547f00809f671f *./test/dr5/fnlp0/test-dr5-fnlp0-sx228.mfc
|
||||
11734af4a80b58672c53573635fb812e *./test/dr5/fnlp0/test-dr5-fnlp0-sx408.mfc
|
||||
f96279e68334eef016140cd19253a075 *./test/dr5/fnlp0/test-dr5-fnlp0-si1938.mfc
|
||||
1d3f8491f88cb7c4297f745fd429ba78 *./test/dr5/fnlp0/test-dr5-fnlp0-sx318.mfc
|
||||
b6cf55a805a9e208007758ad1efd1e73 *./test/dr5/mklt0/test-dr5-mklt0-sx403.mfc
|
||||
fc501de222b8846757f4632b91b882d3 *./test/dr5/mklt0/test-dr5-mklt0-si583.mfc
|
||||
f4a0c50719907638d2609d724890df24 *./test/dr5/mklt0/test-dr5-mklt0-si1843.mfc
|
||||
57ca8e85220e0b0803616d49f586e98b *./test/dr5/mklt0/test-dr5-mklt0-si1213.mfc
|
||||
fe40ec7361d62eb7be427bd866eafe7a *./test/dr5/mklt0/test-dr5-mklt0-sx133.mfc
|
||||
95a8cf6a201bdbbacb7154f4bf668468 *./test/dr5/mklt0/test-dr5-mklt0-sx223.mfc
|
||||
c62f471ce260e6ae53b39ca54e45a226 *./test/dr5/mklt0/test-dr5-mklt0-sx43.mfc
|
||||
c1e4314c2616e7c62ee8bb27eb37d867 *./test/dr5/mklt0/test-dr5-mklt0-sx313.mfc
|
||||
dd09a94e5ac40160b8f6b93ed05efbb6 *./test/dr4/mlll0/test-dr4-mlll0-si1993.mfc
|
||||
a1898b9530522f92cc54b50009aa6abf *./test/dr4/mlll0/test-dr4-mlll0-sx283.mfc
|
||||
4d984d3657b044c7f04e570bbc347332 *./test/dr4/mlll0/test-dr4-mlll0-sx13.mfc
|
||||
051c1f4787ed5dfd64a6e93a0cc61299 *./test/dr4/mlll0/test-dr4-mlll0-sx193.mfc
|
||||
e9611120533f80fd10e2fb7525f58310 *./test/dr4/mlll0/test-dr4-mlll0-si733.mfc
|
||||
ae01c129272acc71e80a972e2369c83e *./test/dr4/mlll0/test-dr4-mlll0-sx373.mfc
|
||||
a00f1c2cd4ec73854cce7c32ebaae7a3 *./test/dr4/mlll0/test-dr4-mlll0-si1363.mfc
|
||||
3b77ee8722997d61c6e4559c8fb0b849 *./test/dr4/mlll0/test-dr4-mlll0-sx103.mfc
|
||||
9a051584282a6c790cb2b75950172d19 *./test/dr4/mtls0/test-dr4-mtls0-si2000.mfc
|
||||
a190bbc890607e4e15a0a5cce72ddb8a *./test/dr4/mtls0/test-dr4-mtls0-sx200.mfc
|
||||
54ccd8d5f9c07ef5835b0930fc942a3f *./test/dr4/mtls0/test-dr4-mtls0-sx20.mfc
|
||||
c995d1f28d106d640895c0a88b62c40a *./test/dr4/mtls0/test-dr4-mtls0-si740.mfc
|
||||
4fe5e1abedfb9cdbae8ac69ec5594767 *./test/dr4/mtls0/test-dr4-mtls0-si1370.mfc
|
||||
efd094f86ee4441a026262c5a8a0d915 *./test/dr4/mtls0/test-dr4-mtls0-sx290.mfc
|
||||
4d4b02d83a5d30c3ebac8445a236fcab *./test/dr4/mtls0/test-dr4-mtls0-sx110.mfc
|
||||
fcbd9a041fcfb321d19ee5e375858ef1 *./test/dr4/mtls0/test-dr4-mtls0-sx380.mfc
|
||||
a9dec416a5819578b0e534a4bbc6ff32 *./test/dr4/fjlm0/test-dr4-fjlm0-sx413.mfc
|
||||
645041575265a4f670829f654d1d06b9 *./test/dr4/fjlm0/test-dr4-fjlm0-sx323.mfc
|
||||
8921dd5222a3476f68a0976ead09dd0b *./test/dr4/fjlm0/test-dr4-fjlm0-sx53.mfc
|
||||
a743ae1b57186399604cb81c23a930f9 *./test/dr4/fjlm0/test-dr4-fjlm0-si1673.mfc
|
||||
9062a2b55567e69b55391ae2b713471d *./test/dr4/fjlm0/test-dr4-fjlm0-si1043.mfc
|
||||
786294b83085b1c1776f6263916515d5 *./test/dr4/fjlm0/test-dr4-fjlm0-sx143.mfc
|
||||
78e08bd88ffdc6db5cad742fad4fe955 *./test/dr4/fjlm0/test-dr4-fjlm0-sx233.mfc
|
||||
54e30d82c82b3f57e7e25aa90773382d *./test/dr4/fjlm0/test-dr4-fjlm0-si2303.mfc
|
||||
026e9214c154952ed951f2f8efaaaf14 *./test/dr6/mjdh0/test-dr6-mjdh0-si1984.mfc
|
||||
af98d75b7ef11ca9350c16c87654763f *./test/dr6/mjdh0/test-dr6-mjdh0-sx4.mfc
|
||||
5042282d0ec4bc9b3465640464dbe075 *./test/dr6/mjdh0/test-dr6-mjdh0-sx274.mfc
|
||||
a0c8f8eecd7f9d74e65fe6205f4097b0 *./test/dr6/mjdh0/test-dr6-mjdh0-sx364.mfc
|
||||
3219f97dc48ea247c53e38f3793508f3 *./test/dr6/mjdh0/test-dr6-mjdh0-sx94.mfc
|
||||
b446b7ebbccac01acbefeab34c52f3e6 *./test/dr6/mjdh0/test-dr6-mjdh0-sx184.mfc
|
||||
f9c5d3f8ff5ffff3fb7026e6387b3b25 *./test/dr6/mjdh0/test-dr6-mjdh0-si724.mfc
|
||||
c482a3078d5897bf7911c020e7836c16 *./test/dr6/mjdh0/test-dr6-mjdh0-si1354.mfc
|
||||
e0d2b68dc88fe4fe8e88f6bfda1e09b3 *./test/dr6/mcmj0/test-dr6-mcmj0-si1094.mfc
|
||||
0bcd3048c3e1f91743bf2ea95b78d7c1 *./test/dr6/mcmj0/test-dr6-mcmj0-sx14.mfc
|
||||
0eccd9c8227d2c15a409ece069179433 *./test/dr6/mcmj0/test-dr6-mcmj0-sx104.mfc
|
||||
4fcb58274af4ebc91131a50ef877bc6e *./test/dr6/mcmj0/test-dr6-mcmj0-sx194.mfc
|
||||
074325a2c92f24f2209e2f58d801ae5c *./test/dr6/mcmj0/test-dr6-mcmj0-si464.mfc
|
||||
93d0fb09071e068d66edaee943e40eab *./test/dr6/mcmj0/test-dr6-mcmj0-sx284.mfc
|
||||
b03a93f5077e55df009be82ddcc982db *./test/dr6/mcmj0/test-dr6-mcmj0-sx374.mfc
|
||||
15fcdc0399b1b67b566d187e5bc6d857 *./test/dr6/mcmj0/test-dr6-mcmj0-si602.mfc
|
||||
706968ed5242f106fc6b40c4ce78e767 *./test/dr6/fmgd0/test-dr6-fmgd0-sx394.mfc
|
||||
8b4ed154d663bcbb5f53e14ebe825f4f *./test/dr6/fmgd0/test-dr6-fmgd0-sx304.mfc
|
||||
24bd7427b39f85aee397b528189d455a *./test/dr6/fmgd0/test-dr6-fmgd0-si934.mfc
|
||||
ca5c01c02b00f52831b73d5dd2590075 *./test/dr6/fmgd0/test-dr6-fmgd0-si2194.mfc
|
||||
d38d8a848e864cb4b4994e4614fe96ab *./test/dr6/fmgd0/test-dr6-fmgd0-si1564.mfc
|
||||
2f2e60c98a3f67ee82b83a546da5764c *./test/dr6/fmgd0/test-dr6-fmgd0-sx34.mfc
|
||||
6335ff1e1a1bab89dd6ab720a389272b *./test/dr6/fmgd0/test-dr6-fmgd0-sx124.mfc
|
||||
c4276cbc65e125736e5d4321e2d580f7 *./test/dr6/fmgd0/test-dr6-fmgd0-sx214.mfc
|
||||
614436c0c5ba820fff1a5999e4ee8e29 *./test/dr8/mpam0/test-dr8-mpam0-sx289.mfc
|
||||
01d8a917183706b93c8781f3d39cc491 *./test/dr8/mpam0/test-dr8-mpam0-si1961.mfc
|
||||
3b0f7b29fc52e1c4cd745063c69fc064 *./test/dr8/mpam0/test-dr8-mpam0-sx199.mfc
|
||||
01f7338f621efa81ed046c0184b139cb *./test/dr8/mpam0/test-dr8-mpam0-si1189.mfc
|
||||
45d266f03b7fcd1b2fb3e8ba59493b25 *./test/dr8/mpam0/test-dr8-mpam0-si1819.mfc
|
||||
553ff05cdd58d2d7a99de856395dc660 *./test/dr8/mpam0/test-dr8-mpam0-sx19.mfc
|
||||
0c71f3d072bac3c7a64fe59dac71e446 *./test/dr8/mpam0/test-dr8-mpam0-sx379.mfc
|
||||
dd161c0fbd18fdb855e49214bcd75ab2 *./test/dr8/mpam0/test-dr8-mpam0-sx109.mfc
|
||||
cfa379368e1ab8e853cc884cbb8d9e8f *./test/dr8/fmld0/test-dr8-fmld0-sx385.mfc
|
||||
787db98d59d17cbfc3ad3db798aed4ef *./test/dr8/fmld0/test-dr8-fmld0-sx295.mfc
|
||||
985f4c97a0cbc30f33bcabf5a1cf902b *./test/dr8/fmld0/test-dr8-fmld0-sx25.mfc
|
||||
2e1b1d1cdfcef99630cb2a1e17a49625 *./test/dr8/fmld0/test-dr8-fmld0-si2185.mfc
|
||||
5dcdbd992556ea9bd3d832954676462d *./test/dr8/fmld0/test-dr8-fmld0-si822.mfc
|
||||
488c2f2db324dd809daf2ac824d15bdf *./test/dr8/fmld0/test-dr8-fmld0-sx205.mfc
|
||||
2ae8bb07c1044be3a8abc274f4b04da3 *./test/dr8/fmld0/test-dr8-fmld0-sx115.mfc
|
||||
33f48f91698c87fbaeefb52630e217a8 *./test/dr8/fmld0/test-dr8-fmld0-si925.mfc
|
||||
a4e9ca6cb29af32406b8a162177df6f7 *./test/dr8/mjln0/test-dr8-mjln0-si2079.mfc
|
||||
d0540ae9bfa8fcb5da1b17e0ba05969a *./test/dr8/mjln0/test-dr8-mjln0-sx279.mfc
|
||||
9938e811371f5175500ac3bf6e0991e9 *./test/dr8/mjln0/test-dr8-mjln0-si1449.mfc
|
||||
a8015747d2ddc473bb8f9503ea2741c5 *./test/dr8/mjln0/test-dr8-mjln0-si819.mfc
|
||||
e400a7bd6535f707676211d23abb793f *./test/dr8/mjln0/test-dr8-mjln0-sx189.mfc
|
||||
39ba509d439f101e0a712e3b27195074 *./test/dr8/mjln0/test-dr8-mjln0-sx369.mfc
|
||||
5d6064d659f35a759281e202e641229d *./test/dr8/mjln0/test-dr8-mjln0-sx99.mfc
|
||||
f3077d30442706cfaf253066ee51a2bd *./test/dr8/mjln0/test-dr8-mjln0-sx9.mfc
|
||||
5a03cbc5aa89a92d8954d9453526218e *./test/dr7/mgrt0/test-dr7-mgrt0-sx370.mfc
|
||||
50746ef37efea2af0cfb91fa9d47845a *./test/dr7/mgrt0/test-dr7-mgrt0-si2080.mfc
|
||||
be89128a8e86dea11d092d39394ec93b *./test/dr7/mgrt0/test-dr7-mgrt0-sx100.mfc
|
||||
d10cc81d6d4855275ee5ea0e065bc1ac *./test/dr7/mgrt0/test-dr7-mgrt0-si1450.mfc
|
||||
6b587b59df1a6bcc360aa02ab3ef331b *./test/dr7/mgrt0/test-dr7-mgrt0-sx280.mfc
|
||||
cf65506fa7be322a8ca66d50baca667e *./test/dr7/mgrt0/test-dr7-mgrt0-sx190.mfc
|
||||
a97ac7406e1907bb417b13f710c74896 *./test/dr7/mgrt0/test-dr7-mgrt0-sx10.mfc
|
||||
b4c20328a08d4c1b907cfb16dca97c81 *./test/dr7/mgrt0/test-dr7-mgrt0-si820.mfc
|
||||
c30c6d42be1740015723db86764b8e13 *./test/dr7/fdhc0/test-dr7-fdhc0-sx299.mfc
|
||||
5cffb4da01648a45dc65e16d5323c59e *./test/dr7/fdhc0/test-dr7-fdhc0-si1559.mfc
|
||||
cb98f997d35420ffb69af59d7bedc5f3 *./test/dr7/fdhc0/test-dr7-fdhc0-sx29.mfc
|
||||
e053d15fc8ef62097b976c4f61cc734c *./test/dr7/fdhc0/test-dr7-fdhc0-sx119.mfc
|
||||
145008fe49136aa1225b72afd681afc4 *./test/dr7/fdhc0/test-dr7-fdhc0-sx209.mfc
|
||||
40d4b1600d737b48bc0a55b7b823f7d6 *./test/dr7/fdhc0/test-dr7-fdhc0-si929.mfc
|
||||
29bb39cb6b685f32b3f6220a4dbcfc08 *./test/dr7/fdhc0/test-dr7-fdhc0-sx389.mfc
|
||||
9fb776d41d58f610e4c1c214332a94b2 *./test/dr7/fdhc0/test-dr7-fdhc0-si2189.mfc
|
||||
f4557145e2a19ef5dcf99cde82837464 *./test/dr7/mnjm0/test-dr7-mnjm0-sx140.mfc
|
||||
1a7a44d0da74136fefc5e7987987de01 *./test/dr7/mnjm0/test-dr7-mnjm0-sx410.mfc
|
||||
33f7bc0bb628ee2214e251b7cc02fb81 *./test/dr7/mnjm0/test-dr7-mnjm0-sx320.mfc
|
||||
90b468144ccaa3158a72cde2ab61528d *./test/dr7/mnjm0/test-dr7-mnjm0-si950.mfc
|
||||
69d141b68eb5c5ed1509a9bc71b47f81 *./test/dr7/mnjm0/test-dr7-mnjm0-si1580.mfc
|
||||
e92af753349772f61aff1772aad18095 *./test/dr7/mnjm0/test-dr7-mnjm0-si2210.mfc
|
||||
af9a43ffbdcefa16a92cd61931b37da9 *./test/dr7/mnjm0/test-dr7-mnjm0-sx230.mfc
|
||||
439bf2bddf24c36a0ce1ad8262a8565f *./test/dr7/mnjm0/test-dr7-mnjm0-sx50.mfc
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@
|
|||
. $TEST_DIR/../run-timit-test-common
|
||||
|
||||
# Train:
|
||||
cntkrun TIMIT_TrainSimpleNetwork.cntk "$CntkArguments" || exit $?
|
||||
cntkrun TIMIT_TrainSimpleNetwork.cntk "$CntkArguments TIMIT_TrainSimple=[reader=[useMersenneTwisterRand=true]]" || exit $?
|
||||
|
||||
# Copy the test data to the test run directory, so that we do not damage anything
|
||||
DataDir=$TEST_RUN_DIR/TestData
|
||||
|
|
@ -14,7 +14,7 @@ mkdir $DataDir
|
|||
cp -R $DataSourceDir/* $DataDir || exit $?
|
||||
|
||||
# Write:
|
||||
cntkrun TIMIT_WriteScaledLogLike.cntk "$CntkArguments"
|
||||
cntkrun TIMIT_WriteScaledLogLike.cntk "$CntkArguments TIMIT_WriteScaledLogLike=[reader=[useMersenneTwisterRand=true]]"
|
||||
ExitCode=$?
|
||||
|
||||
if [ $ExitCode == 0 ]; then
|
||||
|
|
|
|||
Разница между файлами не показана из-за своего большого размера
Загрузить разницу
|
|
@ -1,49 +1,62 @@
|
|||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 264172964 kB
|
||||
-------------------------------------------------------------------
|
||||
Copying test data to local directory
|
||||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/AlexNet.cntk currentDirectory=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/TestData RunDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu DataDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet OutputDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu DeviceId=0 timestamping=true
|
||||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/AlexNetCommon.cntk currentDirectory=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/TestData RunDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu DataDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet OutputDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu DeviceId=0 timestamping=true configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/AlexNet.cntk
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 3 2016 17:56:15
|
||||
Last modified date: Tue May 3 11:36:22 2016
|
||||
Built time: Aug 16 2016 09:41:56
|
||||
Last modified date: Fri Aug 12 07:32:43 2016
|
||||
Build type: release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: no
|
||||
Math lib: acml
|
||||
Math lib: mkl
|
||||
CUDA_PATH: /usr/local/cuda-7.5
|
||||
CUB_PATH: /usr/local/cub-1.4.1
|
||||
CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 571b092d60e131fd529081a5ed52af2dc815dc82
|
||||
Built by philly on 18750d26eb32
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by philly on f67b30a647de
|
||||
Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to /tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/TestData
|
||||
05/03/2016 18:06:53: -------------------------------------------------------------------
|
||||
05/03/2016 18:06:53: Build info:
|
||||
Changed current directory to /tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/TestData
|
||||
08/16/2016 09:55:24: -------------------------------------------------------------------
|
||||
08/16/2016 09:55:24: Build info:
|
||||
|
||||
05/03/2016 18:06:53: Built time: May 3 2016 17:56:15
|
||||
05/03/2016 18:06:53: Last modified date: Tue May 3 11:36:22 2016
|
||||
05/03/2016 18:06:53: Build type: release
|
||||
05/03/2016 18:06:53: Build target: GPU
|
||||
05/03/2016 18:06:53: With 1bit-SGD: no
|
||||
05/03/2016 18:06:53: Math lib: acml
|
||||
05/03/2016 18:06:53: CUDA_PATH: /usr/local/cuda-7.5
|
||||
05/03/2016 18:06:53: CUB_PATH: /usr/local/cub-1.4.1
|
||||
05/03/2016 18:06:53: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
05/03/2016 18:06:53: Build Branch: HEAD
|
||||
05/03/2016 18:06:53: Build SHA1: 571b092d60e131fd529081a5ed52af2dc815dc82
|
||||
05/03/2016 18:06:53: Built by philly on 18750d26eb32
|
||||
05/03/2016 18:06:53: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
05/03/2016 18:06:53: -------------------------------------------------------------------
|
||||
08/16/2016 09:55:24: Built time: Aug 16 2016 09:41:56
|
||||
08/16/2016 09:55:24: Last modified date: Fri Aug 12 07:32:43 2016
|
||||
08/16/2016 09:55:24: Build type: release
|
||||
08/16/2016 09:55:24: Build target: GPU
|
||||
08/16/2016 09:55:24: With 1bit-SGD: no
|
||||
08/16/2016 09:55:24: Math lib: mkl
|
||||
08/16/2016 09:55:24: CUDA_PATH: /usr/local/cuda-7.5
|
||||
08/16/2016 09:55:24: CUB_PATH: /usr/local/cub-1.4.1
|
||||
08/16/2016 09:55:24: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
08/16/2016 09:55:24: Build Branch: HEAD
|
||||
08/16/2016 09:55:24: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 09:55:24: Built by philly on f67b30a647de
|
||||
08/16/2016 09:55:24: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
08/16/2016 09:55:24: -------------------------------------------------------------------
|
||||
08/16/2016 09:55:25: -------------------------------------------------------------------
|
||||
08/16/2016 09:55:25: GPU info:
|
||||
|
||||
05/03/2016 18:06:53: Running on localhost at 2016/05/03 18:06:53
|
||||
05/03/2016 18:06:53: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/AlexNet.cntk currentDirectory=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/TestData RunDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu DataDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet OutputDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu DeviceId=0 timestamping=true
|
||||
08/16/2016 09:55:25: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 09:55:25: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 09:55:25: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 09:55:25: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 09:55:25: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 09:55:25: Running on localhost at 2016/08/16 09:55:25
|
||||
08/16/2016 09:55:25: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/AlexNetCommon.cntk currentDirectory=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/TestData RunDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu DataDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet OutputDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu DeviceId=0 timestamping=true configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/AlexNet.cntk
|
||||
|
||||
|
||||
|
||||
05/03/2016 18:06:53: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 18:06:53: ModelDir = "$RunDir$/models"
|
||||
08/16/2016 09:55:25: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 09:55:25: ModelDir = "$RunDir$/models"
|
||||
ndlMacros=$ConfigDir$/Macros.ndl
|
||||
precision=float
|
||||
deviceId=Auto
|
||||
|
|
@ -76,6 +89,29 @@ Train=[
|
|||
]
|
||||
numMBsToShowResult=100
|
||||
]
|
||||
]
|
||||
AddTop5Eval=[
|
||||
action=edit
|
||||
CurModel=$ModelDir$/AlexNet
|
||||
NewModel=$ModelDir$/AlexNet.Top5
|
||||
editPath=$ConfigDir$/add_top5_layer.mel
|
||||
]
|
||||
Test=[
|
||||
action=test
|
||||
modelPath=$ModelDir$/AlexNet.Top5
|
||||
minibatchSize=16
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=$ConfigDir$/AlexNet.ndl
|
||||
]
|
||||
]
|
||||
currentDirectory=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet
|
||||
OutputDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Train=[
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=$ConfigDir$/train_map.txt
|
||||
|
|
@ -95,19 +131,7 @@ Train=[
|
|||
]
|
||||
]
|
||||
]
|
||||
AddTop5Eval=[
|
||||
action=edit
|
||||
CurModel=$ModelDir$/AlexNet
|
||||
NewModel=$ModelDir$/AlexNet.Top5
|
||||
editPath=$ConfigDir$/add_top5_layer.mel
|
||||
]
|
||||
Test=[
|
||||
action=test
|
||||
modelPath=$ModelDir$/AlexNet.Top5
|
||||
minibatchSize=16
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=$ConfigDir$/AlexNet.ndl
|
||||
]
|
||||
Test=[
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=$ConfigDir$/val_map.txt
|
||||
|
|
@ -124,18 +148,11 @@ Test=[
|
|||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet
|
||||
OutputDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
|
||||
05/03/2016 18:06:53: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 09:55:25: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 18:06:53: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 18:06:53: ModelDir = "/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models"
|
||||
08/16/2016 09:55:25: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 09:55:25: ModelDir = "/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models"
|
||||
ndlMacros=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/Macros.ndl
|
||||
precision=float
|
||||
deviceId=Auto
|
||||
|
|
@ -145,7 +162,7 @@ traceLevel=1
|
|||
numMBsToShowResult=100
|
||||
Train=[
|
||||
action=train
|
||||
modelPath=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet
|
||||
modelPath=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/AlexNet.ndl
|
||||
]
|
||||
|
|
@ -168,6 +185,29 @@ Train=[
|
|||
]
|
||||
numMBsToShowResult=100
|
||||
]
|
||||
]
|
||||
AddTop5Eval=[
|
||||
action=edit
|
||||
CurModel=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet
|
||||
NewModel=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
editPath=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/add_top5_layer.mel
|
||||
]
|
||||
Test=[
|
||||
action=test
|
||||
modelPath=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
minibatchSize=16
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/AlexNet.ndl
|
||||
]
|
||||
]
|
||||
currentDirectory=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet
|
||||
OutputDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Train=[
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/train_map.txt
|
||||
|
|
@ -187,19 +227,7 @@ Train=[
|
|||
]
|
||||
]
|
||||
]
|
||||
AddTop5Eval=[
|
||||
action=edit
|
||||
CurModel=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet
|
||||
NewModel=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
editPath=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/add_top5_layer.mel
|
||||
]
|
||||
Test=[
|
||||
action=test
|
||||
modelPath=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
minibatchSize=16
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/AlexNet.ndl
|
||||
]
|
||||
Test=[
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/val_map.txt
|
||||
|
|
@ -216,43 +244,37 @@ Test=[
|
|||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet
|
||||
OutputDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
|
||||
05/03/2016 18:06:53: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 09:55:25: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 18:06:53: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 09:55:25: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: AlexNet.cntk:AddTop5Eval=[
|
||||
action=edit
|
||||
CurModel=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet
|
||||
NewModel=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
CurModel=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet
|
||||
NewModel=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
editPath=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/add_top5_layer.mel
|
||||
]
|
||||
|
||||
configparameters: AlexNet.cntk:command=Train:AddTop5Eval:Test
|
||||
configparameters: AlexNet.cntk:ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet
|
||||
configparameters: AlexNet.cntk:currentDirectory=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/TestData
|
||||
configparameters: AlexNet.cntk:DataDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/TestData
|
||||
configparameters: AlexNet.cntk:currentDirectory=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/TestData
|
||||
configparameters: AlexNet.cntk:DataDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/TestData
|
||||
configparameters: AlexNet.cntk:deviceId=0
|
||||
configparameters: AlexNet.cntk:ModelDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models
|
||||
configparameters: AlexNet.cntk:ModelDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models
|
||||
configparameters: AlexNet.cntk:ndlMacros=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/Macros.ndl
|
||||
configparameters: AlexNet.cntk:numMBsToShowResult=100
|
||||
configparameters: AlexNet.cntk:OutputDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu
|
||||
configparameters: AlexNet.cntk:OutputDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu
|
||||
configparameters: AlexNet.cntk:parallelTrain=false
|
||||
configparameters: AlexNet.cntk:precision=float
|
||||
configparameters: AlexNet.cntk:RunDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu
|
||||
configparameters: AlexNet.cntk:RunDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu
|
||||
configparameters: AlexNet.cntk:Test=[
|
||||
action=test
|
||||
modelPath=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
modelPath=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
minibatchSize=16
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/AlexNet.ndl
|
||||
]
|
||||
] [
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/val_map.txt
|
||||
|
|
@ -274,7 +296,7 @@ configparameters: AlexNet.cntk:timestamping=true
|
|||
configparameters: AlexNet.cntk:traceLevel=1
|
||||
configparameters: AlexNet.cntk:Train=[
|
||||
action=train
|
||||
modelPath=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet
|
||||
modelPath=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/AlexNet.ndl
|
||||
]
|
||||
|
|
@ -297,6 +319,7 @@ configparameters: AlexNet.cntk:Train=[
|
|||
]
|
||||
numMBsToShowResult=100
|
||||
]
|
||||
] [
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/train_map.txt
|
||||
|
|
@ -317,24 +340,54 @@ configparameters: AlexNet.cntk:Train=[
|
|||
]
|
||||
]
|
||||
|
||||
05/03/2016 18:06:53: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/03/2016 18:06:53: Commands: Train AddTop5Eval Test
|
||||
05/03/2016 18:06:53: Precision = "float"
|
||||
05/03/2016 18:06:53: CNTKModelPath: /tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet
|
||||
05/03/2016 18:06:53: CNTKCommandTrainInfo: Train : 3
|
||||
05/03/2016 18:06:53: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
08/16/2016 09:55:25: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 09:55:25: Commands: Train AddTop5Eval Test
|
||||
08/16/2016 09:55:25: Precision = "float"
|
||||
08/16/2016 09:55:25: CNTKModelPath: /tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet
|
||||
08/16/2016 09:55:25: CNTKCommandTrainInfo: Train : 3
|
||||
08/16/2016 09:55:25: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
|
||||
05/03/2016 18:06:53: ##############################################################################
|
||||
05/03/2016 18:06:53: # #
|
||||
05/03/2016 18:06:53: # Action "train" #
|
||||
05/03/2016 18:06:53: # #
|
||||
05/03/2016 18:06:53: ##############################################################################
|
||||
08/16/2016 09:55:25: ##############################################################################
|
||||
08/16/2016 09:55:25: # #
|
||||
08/16/2016 09:55:25: # Action "train" #
|
||||
08/16/2016 09:55:25: # #
|
||||
08/16/2016 09:55:25: ##############################################################################
|
||||
|
||||
05/03/2016 18:06:53: CNTKCommandTrainBegin: Train
|
||||
08/16/2016 09:55:25: CNTKCommandTrainBegin: Train
|
||||
NDLBuilder Using GPU 0
|
||||
|
||||
05/03/2016 18:06:53: Creating virgin network.
|
||||
useParallelTrain option is not enabled. ParallelTrain config will be ignored.
|
||||
08/16/2016 09:55:25: Creating virgin network.
|
||||
Node 'conv1.W' (LearnableParameter operation): Initializing Parameter[64 x 363] <- 0.000000.
|
||||
Node 'conv1.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv2.W' (LearnableParameter operation): Initializing Parameter[192 x 1600] <- 0.000000.
|
||||
Node 'conv2.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 192] <- 0.000000.
|
||||
Node 'conv3.W' (LearnableParameter operation): Initializing Parameter[384 x 1728] <- 0.000000.
|
||||
Node 'conv3.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 384] <- 0.000000.
|
||||
Node 'conv4.W' (LearnableParameter operation): Initializing Parameter[256 x 3456] <- 0.000000.
|
||||
Node 'conv4.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 0.000000.
|
||||
Node 'conv5.W' (LearnableParameter operation): Initializing Parameter[256 x 2304] <- 0.000000.
|
||||
Node 'conv5.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[4096 x 6 x 6 x 256] <- 0.000000.
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[4096] <- 0.000000.
|
||||
Node 'h2.b' (LearnableParameter operation): Initializing Parameter[4096] <- 0.000000.
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[1000] <- 0.000000.
|
||||
Node 'conv1.W' (LearnableParameter operation): Initializing Parameter[64 x 363] <- gaussian(seed=1, range=0.010497*0.950000, onCPU=false).
|
||||
SetGaussianRandomValue (GPU): creating curand object with seed 1, sizeof(ElemType)==4
|
||||
Node 'conv1.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv2.W' (LearnableParameter operation): Initializing Parameter[192 x 1600] <- gaussian(seed=2, range=0.005000*2.000000, onCPU=false).
|
||||
Node 'conv2.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 192] <- 1.000000.
|
||||
Node 'conv3.W' (LearnableParameter operation): Initializing Parameter[384 x 1728] <- gaussian(seed=3, range=0.004811*2.070000, onCPU=false).
|
||||
Node 'conv3.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 384] <- 0.000000.
|
||||
Node 'conv4.W' (LearnableParameter operation): Initializing Parameter[256 x 3456] <- gaussian(seed=4, range=0.003402*2.900000, onCPU=false).
|
||||
Node 'conv4.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 1.000000.
|
||||
Node 'conv5.W' (LearnableParameter operation): Initializing Parameter[256 x 2304] <- gaussian(seed=5, range=0.004167*2.400000, onCPU=false).
|
||||
Node 'conv5.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 1.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[4096 x 6 x 6 x 256] <- gaussian(seed=6, range=0.002083*6.400000, onCPU=false).
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[4096] <- 1.000000.
|
||||
Node 'h2.W' (LearnableParameter operation): Initializating Parameter[4096 x 0] as gaussian later when dimensions are fully known.
|
||||
Node 'h2.b' (LearnableParameter operation): Initializing Parameter[4096] <- 1.000000.
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializating Parameter[1000 x 0] as gaussian later when dimensions are fully known.
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[1000] <- 1.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -345,8 +398,8 @@ Post-processing network...
|
|||
|
||||
Validating network. 48 nodes to process in pass 1.
|
||||
|
||||
Validating --> OutputNodes.W = LearnableParameter() : -> [1000 x 4096]
|
||||
Validating --> h2.W = LearnableParameter() : -> [4096 x 4096]
|
||||
Validating --> OutputNodes.W = LearnableParameter() : -> [1000 x 0]
|
||||
Validating --> h2.W = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> h1.W = LearnableParameter() : -> [4096 x 6 x 6 x 256]
|
||||
Validating --> conv5.W = LearnableParameter() : -> [256 x 2304]
|
||||
Validating --> conv4.W = LearnableParameter() : -> [256 x 3456]
|
||||
|
|
@ -382,11 +435,15 @@ Validating --> h1.b = LearnableParameter() : -> [4096]
|
|||
Validating --> h1.z = Plus (h1.t, h1.b) : [4096 x *], [4096] -> [4096 x *]
|
||||
Validating --> h1.y = RectifiedLinear (h1.z) : [4096 x *] -> [4096 x *]
|
||||
Validating --> h1_d = Dropout (h1.y) : [4096 x *] -> [4096 x *]
|
||||
Node 'h2.W' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 4096].
|
||||
Node 'h2.W' (LearnableParameter operation): Initializing Parameter[4096 x 4096] <- gaussian(seed=7, range=0.003125*3.200000, onCPU=false).
|
||||
Validating --> h2.t = Times (h2.W, h1_d) : [4096 x 4096], [4096 x *] -> [4096 x *]
|
||||
Validating --> h2.b = LearnableParameter() : -> [4096]
|
||||
Validating --> h2.z = Plus (h2.t, h2.b) : [4096 x *], [4096] -> [4096 x *]
|
||||
Validating --> h2.y = RectifiedLinear (h2.z) : [4096 x *] -> [4096 x *]
|
||||
Validating --> h2_d = Dropout (h2.y) : [4096 x *] -> [4096 x *]
|
||||
Node 'OutputNodes.W' (LearnableParameter operation) operation: Tensor shape was inferred as [1000 x 4096].
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializing Parameter[1000 x 4096] <- gaussian(seed=8, range=0.003125*3.200000, onCPU=false).
|
||||
Validating --> OutputNodes.t = Times (OutputNodes.W, h2_d) : [1000 x 4096], [4096 x *] -> [1000 x *]
|
||||
Validating --> OutputNodes.b = LearnableParameter() : -> [1000]
|
||||
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [1000 x *], [1000] -> [1000 x *]
|
||||
|
|
@ -400,134 +457,157 @@ Validating network. 30 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c: using cuDNN convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c: using cuDNN convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv4.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv5.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool3: using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
|
||||
18 out of 48 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
05/03/2016 18:06:53: Created model with 48 nodes on GPU 0.
|
||||
08/16/2016 09:55:25: Created model with 48 nodes on GPU 0.
|
||||
|
||||
05/03/2016 18:06:53: Training criterion node(s):
|
||||
05/03/2016 18:06:53: ce = CrossEntropyWithSoftmax
|
||||
08/16/2016 09:55:25: Training criterion node(s):
|
||||
08/16/2016 09:55:25: ce = CrossEntropyWithSoftmax
|
||||
|
||||
05/03/2016 18:06:53: Evaluation criterion node(s):
|
||||
|
||||
05/03/2016 18:06:53: err = ErrorPrediction
|
||||
08/16/2016 09:55:25: Evaluation criterion node(s):
|
||||
08/16/2016 09:55:25: err = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 93 matrices, 61 are shared as 27, and 32 are not shared.
|
||||
|
||||
(nil): {[err Gradient[1]] [features Gradient[224 x 224 x 3 x *]] [labels Gradient[1000 x *]] }
|
||||
0x1eb05c8: {[features Value[224 x 224 x 3 x *]] }
|
||||
0x27d0c58: {[conv1.W Value[64 x 363]] }
|
||||
0x27d1a38: {[conv1.b Value[1 x 1 x 64]] }
|
||||
0x27d32a8: {[conv2.W Value[192 x 1600]] }
|
||||
0x27d49b8: {[conv2.b Value[1 x 1 x 192]] }
|
||||
0x27d5c88: {[conv3.W Value[384 x 1728]] }
|
||||
0x27d7378: {[conv3.b Value[1 x 1 x 384]] }
|
||||
0x27d8698: {[conv4.W Value[256 x 3456]] }
|
||||
0x27d9798: {[OutputNodes.b Value[1000]] }
|
||||
0x27d9b88: {[conv4.b Value[1 x 1 x 256]] }
|
||||
0x27dadf8: {[conv5.W Value[256 x 2304]] }
|
||||
0x27dbff8: {[conv5.b Value[1 x 1 x 256]] }
|
||||
0x27dd778: {[h1.W Value[4096 x 6 x 6 x 256]] }
|
||||
0x27de688: {[h1.b Value[4096]] }
|
||||
0x2c0cab8: {[labels Value[1000 x *]] }
|
||||
0x2ea6e78: {[h2.W Value[4096 x 4096]] }
|
||||
0x2ea7c18: {[h2.b Value[4096]] }
|
||||
0x2ea8838: {[OutputNodes.W Value[1000 x 4096]] }
|
||||
0x7f47b2c352e8: {[conv1.c Gradient[56 x 56 x 64 x *]] [conv1.y Value[56 x 56 x 64 x *]] }
|
||||
0x7f47b2c35448: {[conv1.W Gradient[64 x 363]] [conv1.z Value[56 x 56 x 64 x *]] }
|
||||
0x7f47b2c35648: {[conv1.z Gradient[56 x 56 x 64 x *]] [pool1 Value[27 x 27 x 64 x *]] }
|
||||
0x7f47b2c35948: {[conv1.c Value[56 x 56 x 64 x *]] }
|
||||
0x7f47b2e95948: {[conv1.b Gradient[1 x 1 x 64]] [conv1.y Gradient[56 x 56 x 64 x *]] }
|
||||
0x7f47b2e95b08: {[conv2.W Gradient[192 x 1600]] [conv2.z Value[27 x 27 x 192 x *]] }
|
||||
0x7f47b2e95cc8: {[conv2.c Gradient[27 x 27 x 192 x *]] [conv2.y Value[27 x 27 x 192 x *]] }
|
||||
0x7f47b2e95e88: {[conv2.z Gradient[27 x 27 x 192 x *]] [pool1 Gradient[27 x 27 x 64 x *]] [pool2 Value[13 x 13 x 192 x *]] }
|
||||
0x7f47b2e96048: {[conv3.c Value[13 x 13 x 384 x *]] }
|
||||
0x7f47b2e96208: {[conv2.b Gradient[1 x 1 x 192]] [conv2.y Gradient[27 x 27 x 192 x *]] }
|
||||
0x7f47b2e963c8: {[conv3.W Gradient[384 x 1728]] [conv3.z Value[13 x 13 x 384 x *]] }
|
||||
0x7f47b2e96588: {[conv3.c Gradient[13 x 13 x 384 x *]] [conv3.y Value[13 x 13 x 384 x *]] }
|
||||
0x7f47b2e96748: {[conv4.c Value[13 x 13 x 256 x *]] }
|
||||
0x7f47b2e96908: {[conv3.z Gradient[13 x 13 x 384 x *]] [pool2 Gradient[13 x 13 x 192 x *]] }
|
||||
0x7f47b2e96ac8: {[conv4.W Gradient[256 x 3456]] [conv4.z Value[13 x 13 x 256 x *]] }
|
||||
0x7f47b2e96c88: {[conv4.c Gradient[13 x 13 x 256 x *]] [conv4.y Value[13 x 13 x 256 x *]] }
|
||||
0x7f47b2e96e48: {[conv5.c Value[13 x 13 x 256 x *]] }
|
||||
0x7f47b2e97008: {[conv3.b Gradient[1 x 1 x 384]] [conv3.y Gradient[13 x 13 x 384 x *]] [conv4.z Gradient[13 x 13 x 256 x *]] }
|
||||
0x7f47b2e971c8: {[conv5.W Gradient[256 x 2304]] [conv5.z Value[13 x 13 x 256 x *]] }
|
||||
0x7f47b2e97388: {[conv5.c Gradient[13 x 13 x 256 x *]] [conv5.y Value[13 x 13 x 256 x *]] }
|
||||
0x7f47b2e97548: {[conv4.b Gradient[1 x 1 x 256]] [conv4.y Gradient[13 x 13 x 256 x *]] [conv5.z Gradient[13 x 13 x 256 x *]] [pool3 Value[6 x 6 x 256 x *]] }
|
||||
0x7f47b2e97708: {[conv5.b Gradient[1 x 1 x 256]] [conv5.y Gradient[13 x 13 x 256 x *]] [h1.t Value[4096 x *]] }
|
||||
0x7f47b2e978c8: {[h1.W Gradient[4096 x 6 x 6 x 256]] [h1.z Value[4096 x *]] }
|
||||
0x7f47b2e97a88: {[h1.t Gradient[4096 x *]] [h1.y Value[4096 x *]] }
|
||||
0x7f47b2e97c48: {[h1_d Value[4096 x *]] }
|
||||
0x7f47b2e97e08: {[h1.z Gradient[4096 x *]] [pool3 Gradient[6 x 6 x 256 x *]] }
|
||||
0x7f47b2e97fc8: {[h1.b Gradient[4096]] [h1.y Gradient[4096 x *]] [h2.t Value[4096 x *]] }
|
||||
0x7f47b2e98188: {[h2.W Gradient[4096 x 4096]] [h2.z Value[4096 x *]] }
|
||||
0x7f47b2e98348: {[h2.t Gradient[4096 x *]] [h2.y Value[4096 x *]] }
|
||||
0x7f47b2e98508: {[h2_d Value[4096 x *]] }
|
||||
0x7f47b2e986c8: {[h1_d Gradient[4096 x *]] [h2.z Gradient[4096 x *]] }
|
||||
0x7f47b2e98888: {[OutputNodes.t Value[1000 x *]] [h2.b Gradient[4096]] [h2.y Gradient[4096 x *]] }
|
||||
0x7f47b2e99428: {[ce Gradient[1]] }
|
||||
0x7f47b2e995e8: {[OutputNodes.W Gradient[1000 x 4096]] [OutputNodes.z Gradient[1000 x *]] }
|
||||
0x7f47b2e997a8: {[OutputNodes.t Gradient[1000 x *]] }
|
||||
0x7f47b2e99968: {[OutputNodes.b Gradient[1000]] }
|
||||
0x7f47b2e99b28: {[h2_d Gradient[4096 x *]] }
|
||||
0x7f47b2e9aa08: {[OutputNodes.z Value[1000 x *]] }
|
||||
0x7f47b2e9abc8: {[ce Value[1]] }
|
||||
0x7f47b2e9b2f8: {[conv2.c Value[27 x 27 x 192 x *]] }
|
||||
0x7f47b2ef4ce8: {[err Value[1]] }
|
||||
|
||||
05/03/2016 18:06:53: No PreCompute nodes found, skipping PreCompute step.
|
||||
|
||||
05/03/2016 18:06:55: Starting Epoch 1: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
|
||||
05/03/2016 18:06:55: Starting minibatch loop.
|
||||
05/03/2016 18:07:02: Epoch[ 1 of 3]-Minibatch[ 1- 100]: ce = 7.41642395 * 1600; err = 1.00000000 * 1600; time = 7.0425s; samplesPerSecond = 227.2
|
||||
05/03/2016 18:07:08: Finished Epoch[ 1 of 3]: [Training] ce = 7.22737918 * 2999; err = 0.99966656 * 2999; totalSamplesSeen = 2999; learningRatePerSample = 0.00062499999; epochTime=12.9259s
|
||||
05/03/2016 18:07:10: SGD: Saving checkpoint model '/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet.1'
|
||||
|
||||
05/03/2016 18:07:13: Starting Epoch 2: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
|
||||
05/03/2016 18:07:13: Starting minibatch loop.
|
||||
05/03/2016 18:07:19: Epoch[ 2 of 3]-Minibatch[ 1- 100, 100.00%]: ce = 6.90983215 * 1600; err = 1.00000000 * 1600; time = 6.2320s; samplesPerSecond = 256.7
|
||||
05/03/2016 18:07:25: Finished Epoch[ 2 of 3]: [Training] ce = 6.91963923 * 2999; err = 0.99866622 * 2999; totalSamplesSeen = 5998; learningRatePerSample = 0.00062499999; epochTime=12.2905s
|
||||
05/03/2016 18:07:27: SGD: Saving checkpoint model '/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet.2'
|
||||
|
||||
05/03/2016 18:07:29: Starting Epoch 3: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
|
||||
05/03/2016 18:07:29: Starting minibatch loop.
|
||||
05/03/2016 18:07:36: Epoch[ 3 of 3]-Minibatch[ 1- 100, 100.00%]: ce = 6.87519836 * 1600; err = 0.99937500 * 1600; time = 6.4714s; samplesPerSecond = 247.2
|
||||
05/03/2016 18:07:42: Finished Epoch[ 3 of 3]: [Training] ce = 6.88608052 * 2999; err = 0.99833278 * 2999; totalSamplesSeen = 8997; learningRatePerSample = 0.00062499999; epochTime=12.1425s
|
||||
05/03/2016 18:07:44: SGD: Saving checkpoint model '/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet'
|
||||
05/03/2016 18:07:46: CNTKCommandTrainEnd: Train
|
||||
|
||||
05/03/2016 18:07:46: Action "train" complete.
|
||||
{ conv2.b : [1 x 1 x 192] (gradient)
|
||||
conv2.y : [27 x 27 x 192 x *] (gradient) }
|
||||
{ conv3.W : [384 x 1728] (gradient)
|
||||
conv3.z : [13 x 13 x 384 x *] }
|
||||
{ conv3.c : [13 x 13 x 384 x *] (gradient)
|
||||
conv3.y : [13 x 13 x 384 x *] }
|
||||
{ conv3.z : [13 x 13 x 384 x *] (gradient)
|
||||
pool2 : [13 x 13 x 192 x *] (gradient) }
|
||||
{ conv4.W : [256 x 3456] (gradient)
|
||||
conv4.z : [13 x 13 x 256 x *] }
|
||||
{ conv4.c : [13 x 13 x 256 x *] (gradient)
|
||||
conv4.y : [13 x 13 x 256 x *] }
|
||||
{ conv3.b : [1 x 1 x 384] (gradient)
|
||||
conv3.y : [13 x 13 x 384 x *] (gradient)
|
||||
conv4.z : [13 x 13 x 256 x *] (gradient) }
|
||||
{ conv5.W : [256 x 2304] (gradient)
|
||||
conv5.z : [13 x 13 x 256 x *] }
|
||||
{ conv5.c : [13 x 13 x 256 x *] (gradient)
|
||||
conv5.y : [13 x 13 x 256 x *] }
|
||||
{ conv4.b : [1 x 1 x 256] (gradient)
|
||||
conv4.y : [13 x 13 x 256 x *] (gradient)
|
||||
conv5.z : [13 x 13 x 256 x *] (gradient)
|
||||
pool3 : [6 x 6 x 256 x *] }
|
||||
{ conv5.b : [1 x 1 x 256] (gradient)
|
||||
conv5.y : [13 x 13 x 256 x *] (gradient)
|
||||
h1.t : [4096 x *] }
|
||||
{ h1.W : [4096 x 6 x 6 x 256] (gradient)
|
||||
h1.z : [4096 x *] }
|
||||
{ h1.t : [4096 x *] (gradient)
|
||||
h1.y : [4096 x *] }
|
||||
{ h1.z : [4096 x *] (gradient)
|
||||
pool3 : [6 x 6 x 256 x *] (gradient) }
|
||||
{ h1.b : [4096] (gradient)
|
||||
h1.y : [4096 x *] (gradient)
|
||||
h2.t : [4096 x *] }
|
||||
{ h2.W : [4096 x 4096] (gradient)
|
||||
h2.z : [4096 x *] }
|
||||
{ h2.t : [4096 x *] (gradient)
|
||||
h2.y : [4096 x *] }
|
||||
{ h1_d : [4096 x *] (gradient)
|
||||
h2.z : [4096 x *] (gradient) }
|
||||
{ OutputNodes.t : [1000 x *]
|
||||
h2.b : [4096] (gradient)
|
||||
h2.y : [4096 x *] (gradient) }
|
||||
{ OutputNodes.W : [1000 x 4096] (gradient)
|
||||
OutputNodes.z : [1000 x *] (gradient) }
|
||||
{ conv1.z : [56 x 56 x 64 x *] (gradient)
|
||||
pool1 : [27 x 27 x 64 x *] }
|
||||
{ conv1.c : [56 x 56 x 64 x *] (gradient)
|
||||
conv1.y : [56 x 56 x 64 x *] }
|
||||
{ conv1.W : [64 x 363] (gradient)
|
||||
conv1.z : [56 x 56 x 64 x *] }
|
||||
{ conv2.c : [27 x 27 x 192 x *] (gradient)
|
||||
conv2.y : [27 x 27 x 192 x *] }
|
||||
{ conv2.z : [27 x 27 x 192 x *] (gradient)
|
||||
pool1 : [27 x 27 x 64 x *] (gradient)
|
||||
pool2 : [13 x 13 x 192 x *] }
|
||||
{ conv1.b : [1 x 1 x 64] (gradient)
|
||||
conv1.y : [56 x 56 x 64 x *] (gradient) }
|
||||
{ conv2.W : [192 x 1600] (gradient)
|
||||
conv2.z : [27 x 27 x 192 x *] }
|
||||
|
||||
|
||||
05/03/2016 18:07:46: ##############################################################################
|
||||
05/03/2016 18:07:46: # #
|
||||
05/03/2016 18:07:46: # Action "edit" #
|
||||
05/03/2016 18:07:46: # #
|
||||
05/03/2016 18:07:46: ##############################################################################
|
||||
08/16/2016 09:55:25: Training 61100840 parameters in 16 out of 16 parameter tensors and 45 nodes with gradient:
|
||||
|
||||
08/16/2016 09:55:25: Node 'OutputNodes.W' (LearnableParameter operation) : [1000 x 4096]
|
||||
08/16/2016 09:55:25: Node 'OutputNodes.b' (LearnableParameter operation) : [1000]
|
||||
08/16/2016 09:55:25: Node 'conv1.W' (LearnableParameter operation) : [64 x 363]
|
||||
08/16/2016 09:55:25: Node 'conv1.b' (LearnableParameter operation) : [1 x 1 x 64]
|
||||
08/16/2016 09:55:25: Node 'conv2.W' (LearnableParameter operation) : [192 x 1600]
|
||||
08/16/2016 09:55:25: Node 'conv2.b' (LearnableParameter operation) : [1 x 1 x 192]
|
||||
08/16/2016 09:55:25: Node 'conv3.W' (LearnableParameter operation) : [384 x 1728]
|
||||
08/16/2016 09:55:25: Node 'conv3.b' (LearnableParameter operation) : [1 x 1 x 384]
|
||||
08/16/2016 09:55:25: Node 'conv4.W' (LearnableParameter operation) : [256 x 3456]
|
||||
08/16/2016 09:55:25: Node 'conv4.b' (LearnableParameter operation) : [1 x 1 x 256]
|
||||
08/16/2016 09:55:25: Node 'conv5.W' (LearnableParameter operation) : [256 x 2304]
|
||||
08/16/2016 09:55:25: Node 'conv5.b' (LearnableParameter operation) : [1 x 1 x 256]
|
||||
08/16/2016 09:55:25: Node 'h1.W' (LearnableParameter operation) : [4096 x 6 x 6 x 256]
|
||||
08/16/2016 09:55:25: Node 'h1.b' (LearnableParameter operation) : [4096]
|
||||
08/16/2016 09:55:25: Node 'h2.W' (LearnableParameter operation) : [4096 x 4096]
|
||||
08/16/2016 09:55:25: Node 'h2.b' (LearnableParameter operation) : [4096]
|
||||
|
||||
08/16/2016 09:55:25: No PreCompute nodes found, or all already computed. Skipping pre-computation step.
|
||||
|
||||
08/16/2016 09:55:27: Starting Epoch 1: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..2999] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
08/16/2016 09:55:27: Starting minibatch loop.
|
||||
08/16/2016 09:55:36: Epoch[ 1 of 3]-Minibatch[ 1- 100]: ce = 7.41094299 * 1600; err = 0.99937500 * 1600; time = 8.3724s; samplesPerSecond = 191.1
|
||||
08/16/2016 09:55:42: Finished Epoch[ 1 of 3]: [Training] ce = 7.23292074 * 2999; err = 0.99899967 * 2999; totalSamplesSeen = 2999; learningRatePerSample = 0.00062499999; epochTime=14.0535s
|
||||
08/16/2016 09:55:44: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet.1'
|
||||
|
||||
08/16/2016 09:55:46: Starting Epoch 2: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [2999..5998] (first sequence at sample 2999), data subset 0 of 1
|
||||
|
||||
08/16/2016 09:55:46: Starting minibatch loop.
|
||||
08/16/2016 09:55:53: Epoch[ 2 of 3]-Minibatch[ 1- 100, 100.00%]: ce = 6.91068848 * 1600; err = 0.99875000 * 1600; time = 7.2054s; samplesPerSecond = 222.1
|
||||
08/16/2016 09:56:00: Finished Epoch[ 2 of 3]: [Training] ce = 6.91553955 * 2999; err = 0.99933311 * 2999; totalSamplesSeen = 5998; learningRatePerSample = 0.00062499999; epochTime=13.8615s
|
||||
08/16/2016 09:56:03: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet.2'
|
||||
|
||||
08/16/2016 09:56:05: Starting Epoch 3: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [5998..8997] (first sequence at sample 5998), data subset 0 of 1
|
||||
|
||||
08/16/2016 09:56:05: Starting minibatch loop.
|
||||
08/16/2016 09:56:12: Epoch[ 3 of 3]-Minibatch[ 1- 100, 100.00%]: ce = 6.88422668 * 1600; err = 0.99687500 * 1600; time = 7.1340s; samplesPerSecond = 224.3
|
||||
08/16/2016 09:56:19: Finished Epoch[ 3 of 3]: [Training] ce = 6.88836513 * 2999; err = 0.99766589 * 2999; totalSamplesSeen = 8997; learningRatePerSample = 0.00062499999; epochTime=13.7378s
|
||||
08/16/2016 09:56:21: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet'
|
||||
08/16/2016 09:56:25: CNTKCommandTrainEnd: Train
|
||||
|
||||
08/16/2016 09:56:25: Action "train" complete.
|
||||
|
||||
|
||||
08/16/2016 09:56:25: ##############################################################################
|
||||
08/16/2016 09:56:25: # #
|
||||
08/16/2016 09:56:25: # Action "edit" #
|
||||
08/16/2016 09:56:25: # #
|
||||
08/16/2016 09:56:25: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
|
@ -594,27 +674,29 @@ Validating network. 30 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c: using GEMM convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using GEMM convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c: using GEMM convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using GEMM convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3.c: using GEMM convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv4.c: using GEMM convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv5.c: using GEMM convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool3: using GEMM convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
|
||||
18 out of 48 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
Node 'unnamed143' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.000000.
|
||||
Node 'unnamed143' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 5.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -674,8 +756,8 @@ Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [1000 x *1]
|
|||
Validating --> labels = InputValue() : -> [1000 x *1]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [1000 x *1], [1000 x *1] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, OutputNodes.z) : [1000 x *1], [1000 x *1] -> [1]
|
||||
Validating --> unnamed137 = LearnableParameter() : -> [1 x 1]
|
||||
Validating --> errTop5 = ErrorPrediction (labels, OutputNodes.z, unnamed137) : [1000 x *1], [1000 x *1], [1 x 1] -> [1]
|
||||
Validating --> unnamed143 = LearnableParameter() : -> [1 x 1]
|
||||
Validating --> errTop5 = ErrorPrediction (labels, OutputNodes.z, unnamed143) : [1000 x *1], [1000 x *1], [1 x 1] -> [1]
|
||||
|
||||
Validating network. 31 nodes to process in pass 2.
|
||||
|
||||
|
|
@ -689,28 +771,58 @@ Validating network, final pass.
|
|||
Post-processing network complete.
|
||||
|
||||
|
||||
05/03/2016 18:07:51: Action "edit" complete.
|
||||
08/16/2016 09:56:31: Action "edit" complete.
|
||||
|
||||
|
||||
05/03/2016 18:07:51: ##############################################################################
|
||||
05/03/2016 18:07:51: # #
|
||||
05/03/2016 18:07:51: # Action "test" #
|
||||
05/03/2016 18:07:51: # #
|
||||
05/03/2016 18:07:51: ##############################################################################
|
||||
08/16/2016 09:56:31: ##############################################################################
|
||||
08/16/2016 09:56:31: # #
|
||||
08/16/2016 09:56:31: # Action "test" #
|
||||
08/16/2016 09:56:31: # #
|
||||
08/16/2016 09:56:31: ##############################################################################
|
||||
|
||||
NDLBuilder Using GPU 0
|
||||
Node 'conv1.W' (LearnableParameter operation): Initializing Parameter[64 x 363] <- 0.000000.
|
||||
Node 'conv1.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv2.W' (LearnableParameter operation): Initializing Parameter[192 x 1600] <- 0.000000.
|
||||
Node 'conv2.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 192] <- 0.000000.
|
||||
Node 'conv3.W' (LearnableParameter operation): Initializing Parameter[384 x 1728] <- 0.000000.
|
||||
Node 'conv3.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 384] <- 0.000000.
|
||||
Node 'conv4.W' (LearnableParameter operation): Initializing Parameter[256 x 3456] <- 0.000000.
|
||||
Node 'conv4.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 0.000000.
|
||||
Node 'conv5.W' (LearnableParameter operation): Initializing Parameter[256 x 2304] <- 0.000000.
|
||||
Node 'conv5.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[4096 x 6 x 6 x 256] <- 0.000000.
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[4096] <- 0.000000.
|
||||
Node 'h2.b' (LearnableParameter operation): Initializing Parameter[4096] <- 0.000000.
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[1000] <- 0.000000.
|
||||
Node 'conv1.W' (LearnableParameter operation): Initializing Parameter[64 x 363] <- gaussian(seed=9, range=0.010497*0.950000, onCPU=false).
|
||||
Node 'conv1.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv2.W' (LearnableParameter operation): Initializing Parameter[192 x 1600] <- gaussian(seed=10, range=0.005000*2.000000, onCPU=false).
|
||||
Node 'conv2.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 192] <- 1.000000.
|
||||
Node 'conv3.W' (LearnableParameter operation): Initializing Parameter[384 x 1728] <- gaussian(seed=11, range=0.004811*2.070000, onCPU=false).
|
||||
Node 'conv3.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 384] <- 0.000000.
|
||||
Node 'conv4.W' (LearnableParameter operation): Initializing Parameter[256 x 3456] <- gaussian(seed=12, range=0.003402*2.900000, onCPU=false).
|
||||
Node 'conv4.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 1.000000.
|
||||
Node 'conv5.W' (LearnableParameter operation): Initializing Parameter[256 x 2304] <- gaussian(seed=13, range=0.004167*2.400000, onCPU=false).
|
||||
Node 'conv5.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 1.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[4096 x 6 x 6 x 256] <- gaussian(seed=14, range=0.002083*6.400000, onCPU=false).
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[4096] <- 1.000000.
|
||||
Node 'h2.W' (LearnableParameter operation): Initializating Parameter[4096 x 0] as gaussian later when dimensions are fully known.
|
||||
Node 'h2.b' (LearnableParameter operation): Initializing Parameter[4096] <- 1.000000.
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializating Parameter[1000 x 0] as gaussian later when dimensions are fully known.
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[1000] <- 1.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
4 roots:
|
||||
3 roots:
|
||||
OutputNodes.z = Plus()
|
||||
ce = CrossEntropyWithSoftmax()
|
||||
err = ErrorPrediction()
|
||||
errTop5 = ErrorPrediction()
|
||||
|
||||
Validating network. 50 nodes to process in pass 1.
|
||||
Validating network. 48 nodes to process in pass 1.
|
||||
|
||||
Validating --> OutputNodes.W = LearnableParameter() : -> [1000 x 4096]
|
||||
Validating --> h2.W = LearnableParameter() : -> [4096 x 4096]
|
||||
Validating --> OutputNodes.W = LearnableParameter() : -> [1000 x 0]
|
||||
Validating --> h2.W = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> h1.W = LearnableParameter() : -> [4096 x 6 x 6 x 256]
|
||||
Validating --> conv5.W = LearnableParameter() : -> [256 x 2304]
|
||||
Validating --> conv4.W = LearnableParameter() : -> [256 x 3456]
|
||||
|
|
@ -746,44 +858,46 @@ Validating --> h1.b = LearnableParameter() : -> [4096]
|
|||
Validating --> h1.z = Plus (h1.t, h1.b) : [4096 x *2], [4096] -> [4096 x *2]
|
||||
Validating --> h1.y = RectifiedLinear (h1.z) : [4096 x *2] -> [4096 x *2]
|
||||
Validating --> h1_d = Dropout (h1.y) : [4096 x *2] -> [4096 x *2]
|
||||
Node 'h2.W' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 4096].
|
||||
Node 'h2.W' (LearnableParameter operation): Initializing Parameter[4096 x 4096] <- gaussian(seed=15, range=0.003125*3.200000, onCPU=false).
|
||||
Validating --> h2.t = Times (h2.W, h1_d) : [4096 x 4096], [4096 x *2] -> [4096 x *2]
|
||||
Validating --> h2.b = LearnableParameter() : -> [4096]
|
||||
Validating --> h2.z = Plus (h2.t, h2.b) : [4096 x *2], [4096] -> [4096 x *2]
|
||||
Validating --> h2.y = RectifiedLinear (h2.z) : [4096 x *2] -> [4096 x *2]
|
||||
Validating --> h2_d = Dropout (h2.y) : [4096 x *2] -> [4096 x *2]
|
||||
Node 'OutputNodes.W' (LearnableParameter operation) operation: Tensor shape was inferred as [1000 x 4096].
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializing Parameter[1000 x 4096] <- gaussian(seed=16, range=0.003125*3.200000, onCPU=false).
|
||||
Validating --> OutputNodes.t = Times (OutputNodes.W, h2_d) : [1000 x 4096], [4096 x *2] -> [1000 x *2]
|
||||
Validating --> OutputNodes.b = LearnableParameter() : -> [1000]
|
||||
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [1000 x *2], [1000] -> [1000 x *2]
|
||||
Validating --> labels = InputValue() : -> [1000 x *2]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [1000 x *2], [1000 x *2] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, OutputNodes.z) : [1000 x *2], [1000 x *2] -> [1]
|
||||
Validating --> unnamed137 = LearnableParameter() : -> [1 x 1]
|
||||
Validating --> errTop5 = ErrorPrediction (labels, OutputNodes.z, unnamed137) : [1000 x *2], [1000 x *2], [1 x 1] -> [1]
|
||||
|
||||
Validating network. 31 nodes to process in pass 2.
|
||||
Validating network. 30 nodes to process in pass 2.
|
||||
|
||||
|
||||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c: using cuDNN convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c: using cuDNN convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv4.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv5.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool3: using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
|
||||
20 out of 50 nodes do not share the minibatch layout with the input data.
|
||||
18 out of 48 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
|
|
@ -792,62 +906,12 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 48 matrices, 0 are shared as 0, and 48 are not shared.
|
||||
|
||||
(nil): {[OutputNodes.W Gradient[1000 x 4096]] [OutputNodes.b Gradient[1000]] [OutputNodes.t Gradient[1000 x *2]] [OutputNodes.z Gradient[1000 x *2]] [ce Gradient[1]] [conv1.W Gradient[64 x 363]] [conv1.b Gradient[1 x 1 x 64]] [conv1.c Gradient[56 x 56 x 64 x *2]] [conv1.y Gradient[56 x 56 x 64 x *2]] [conv1.z Gradient[56 x 56 x 64 x *2]] [conv2.W Gradient[192 x 1600]] [conv2.b Gradient[1 x 1 x 192]] [conv2.c Gradient[27 x 27 x 192 x *2]] [conv2.y Gradient[27 x 27 x 192 x *2]] [conv2.z Gradient[27 x 27 x 192 x *2]] [conv3.W Gradient[384 x 1728]] [conv3.b Gradient[1 x 1 x 384]] [conv3.c Gradient[13 x 13 x 384 x *2]] [conv3.y Gradient[13 x 13 x 384 x *2]] [conv3.z Gradient[13 x 13 x 384 x *2]] [conv4.W Gradient[256 x 3456]] [conv4.b Gradient[1 x 1 x 256]] [conv4.c Gradient[13 x 13 x 256 x *2]] [conv4.y Gradient[13 x 13 x 256 x *2]] [conv4.z Gradient[13 x 13 x 256 x *2]] [conv5.W Gradient[256 x 2304]] [conv5.b Gradient[1 x 1 x 256]] [conv5.c Gradient[13 x 13 x 256 x *2]] [conv5.y Gradient[13 x 13 x 256 x *2]] [conv5.z Gradient[13 x 13 x 256 x *2]] [err Gradient[1]] [errTop5 Gradient[1]] [features Gradient[224 x 224 x 3 x *2]] [h1.W Gradient[4096 x 6 x 6 x 256]] [h1.b Gradient[4096]] [h1.t Gradient[4096 x *2]] [h1.y Gradient[4096 x *2]] [h1.z Gradient[4096 x *2]] [h1_d Gradient[4096 x *2]] [h2.W Gradient[4096 x 4096]] [h2.b Gradient[4096]] [h2.t Gradient[4096 x *2]] [h2.y Gradient[4096 x *2]] [h2.z Gradient[4096 x *2]] [h2_d Gradient[4096 x *2]] [labels Gradient[1000 x *2]] [pool1 Gradient[27 x 27 x 64 x *2]] [pool2 Gradient[13 x 13 x 192 x *2]] [pool3 Gradient[6 x 6 x 256 x *2]] [unnamed137 Gradient[1 x 1]] }
|
||||
0x7f479db02088: {[conv1.b Value[1 x 1 x 64]] }
|
||||
0x7f479db2c418: {[conv1.W Value[64 x 363]] }
|
||||
0x7f479db2d7a8: {[conv2.W Value[192 x 1600]] }
|
||||
0x7f479db2dae8: {[conv2.b Value[1 x 1 x 192]] }
|
||||
0x7f479db2fdd8: {[conv3.W Value[384 x 1728]] }
|
||||
0x7f479db30118: {[conv3.b Value[1 x 1 x 384]] }
|
||||
0x7f479db30908: {[conv4.b Value[1 x 1 x 256]] }
|
||||
0x7f479db33f08: {[conv4.W Value[256 x 3456]] }
|
||||
0x7f479db35358: {[conv5.b Value[1 x 1 x 256]] }
|
||||
0x7f479db36608: {[conv5.W Value[256 x 2304]] }
|
||||
0x7f479db37d68: {[features Value[224 x 224 x 3 x *2]] }
|
||||
0x7f479db38858: {[h1.W Value[4096 x 6 x 6 x 256]] }
|
||||
0x7f479db38b98: {[h1.b Value[4096]] }
|
||||
0x7f479db3aa98: {[h2.b Value[4096]] }
|
||||
0x7f479db3b5d8: {[h2.W Value[4096 x 4096]] }
|
||||
0x7f479db3ca98: {[labels Value[1000 x *2]] }
|
||||
0x7f479db3de18: {[OutputNodes.b Value[1000]] }
|
||||
0x7f479db3e628: {[OutputNodes.W Value[1000 x 4096]] }
|
||||
0x7f479db40748: {[unnamed137 Value[1 x 1]] }
|
||||
0x7f479db413e8: {[errTop5 Value[1]] }
|
||||
0x7f479db42138: {[ce Value[1]] }
|
||||
0x7f479db48378: {[err Value[1]] }
|
||||
0x7f479db53e18: {[pool3 Value[6 x 6 x 256 x *2]] }
|
||||
0x7f479db53fd8: {[h1.t Value[4096 x *2]] }
|
||||
0x7f479db54198: {[h1.z Value[4096 x *2]] }
|
||||
0x7f479db54358: {[h1.y Value[4096 x *2]] }
|
||||
0x7f479db54518: {[h1_d Value[4096 x *2]] }
|
||||
0x7f479db54898: {[h2.t Value[4096 x *2]] }
|
||||
0x7f479db54a58: {[h2.z Value[4096 x *2]] }
|
||||
0x7f479db54c18: {[h2.y Value[4096 x *2]] }
|
||||
0x7f479db54dd8: {[h2_d Value[4096 x *2]] }
|
||||
0x7f479db55158: {[OutputNodes.t Value[1000 x *2]] }
|
||||
0x7f479db55318: {[OutputNodes.z Value[1000 x *2]] }
|
||||
0x7f47a644f258: {[conv1.z Value[56 x 56 x 64 x *2]] }
|
||||
0x7f47a644f558: {[conv1.c Value[56 x 56 x 64 x *2]] }
|
||||
0x7f47a6450068: {[conv1.y Value[56 x 56 x 64 x *2]] }
|
||||
0x7f47a64506b8: {[pool1 Value[27 x 27 x 64 x *2]] }
|
||||
0x7f47a6450878: {[conv2.c Value[27 x 27 x 192 x *2]] }
|
||||
0x7f47a6450bf8: {[conv2.z Value[27 x 27 x 192 x *2]] }
|
||||
0x7f47a6450db8: {[conv2.y Value[27 x 27 x 192 x *2]] }
|
||||
0x7f47a6450f78: {[pool2 Value[13 x 13 x 192 x *2]] }
|
||||
0x7f47a6451138: {[conv3.c Value[13 x 13 x 384 x *2]] }
|
||||
0x7f47a64514b8: {[conv3.z Value[13 x 13 x 384 x *2]] }
|
||||
0x7f47a6451678: {[conv3.y Value[13 x 13 x 384 x *2]] }
|
||||
0x7f47a6451838: {[conv4.c Value[13 x 13 x 256 x *2]] }
|
||||
0x7f47a6451bb8: {[conv4.z Value[13 x 13 x 256 x *2]] }
|
||||
0x7f47a6451d78: {[conv4.y Value[13 x 13 x 256 x *2]] }
|
||||
0x7f47a6451f38: {[conv5.c Value[13 x 13 x 256 x *2]] }
|
||||
0x7f47a64522b8: {[conv5.z Value[13 x 13 x 256 x *2]] }
|
||||
0x7f47a6452478: {[conv5.y Value[13 x 13 x 256 x *2]] }
|
||||
|
||||
05/03/2016 18:07:55: Final Results: Minibatch[1-32]: err = 0.99800000 * 500; errTop5 = 0.99400000 * 500; ce = 6.96324823 * 500; perplexity = 1057.06156985
|
||||
08/16/2016 09:56:33: Minibatch[1-32]: err = 0.99800000 * 500; ce = 7.32804733 * 500
|
||||
08/16/2016 09:56:33: Final Results: Minibatch[1-32]: err = 0.99800000 * 500; ce = 7.32804733 * 500; perplexity = 1522.40611516
|
||||
|
||||
05/03/2016 18:07:55: Action "test" complete.
|
||||
08/16/2016 09:56:33: Action "test" complete.
|
||||
|
||||
05/03/2016 18:07:55: __COMPLETED__
|
||||
08/16/2016 09:56:33: __COMPLETED__
|
||||
Разница между файлами не показана из-за своего большого размера
Загрузить разницу
|
|
@ -1,47 +1,59 @@
|
|||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU W3530 @ 2.80GHz
|
||||
Hardware threads: 4
|
||||
Total Memory: 12580404 kB
|
||||
-------------------------------------------------------------------
|
||||
Copying test data to local directory
|
||||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/AlexNet.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu DeviceId=0 timestamping=true
|
||||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/AlexNetCommon.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu DeviceId=0 timestamping=true configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/AlexNet.cntk
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 3 2016 13:23:06
|
||||
Last modified date: Mon Apr 18 00:00:12 2016
|
||||
Built time: Aug 16 2016 02:54:53
|
||||
Last modified date: Fri Aug 12 05:31:21 2016
|
||||
Build type: Release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: no
|
||||
Math lib: mkl
|
||||
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
CUB_PATH: C:\src\cub-1.4.1
|
||||
CUB_PATH: c:\src\cub-1.4.1
|
||||
CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
Build Branch: HEAD
|
||||
Build SHA1: af96f7cce6c3c78a4f1e9315e061291c79360e12
|
||||
Built by svcphil on LIANA-09-w
|
||||
Build Path: c:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by svcphil on Philly-Pool3
|
||||
Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu\TestData
|
||||
05/03/2016 14:11:01: -------------------------------------------------------------------
|
||||
05/03/2016 14:11:01: Build info:
|
||||
Changed current directory to C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu\TestData
|
||||
08/16/2016 03:03:44: -------------------------------------------------------------------
|
||||
08/16/2016 03:03:44: Build info:
|
||||
|
||||
05/03/2016 14:11:01: Built time: May 3 2016 13:23:06
|
||||
05/03/2016 14:11:01: Last modified date: Mon Apr 18 00:00:12 2016
|
||||
05/03/2016 14:11:01: Build type: Release
|
||||
05/03/2016 14:11:01: Build target: GPU
|
||||
05/03/2016 14:11:01: With 1bit-SGD: no
|
||||
05/03/2016 14:11:01: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
05/03/2016 14:11:01: CUB_PATH: C:\src\cub-1.4.1
|
||||
05/03/2016 14:11:01: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
05/03/2016 14:11:01: Build Branch: HEAD
|
||||
05/03/2016 14:11:01: Build SHA1: af96f7cce6c3c78a4f1e9315e061291c79360e12
|
||||
05/03/2016 14:11:01: Built by svcphil on LIANA-09-w
|
||||
05/03/2016 14:11:01: Build Path: c:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
05/03/2016 14:11:01: -------------------------------------------------------------------
|
||||
08/16/2016 03:03:44: Built time: Aug 16 2016 02:54:53
|
||||
08/16/2016 03:03:44: Last modified date: Fri Aug 12 05:31:21 2016
|
||||
08/16/2016 03:03:44: Build type: Release
|
||||
08/16/2016 03:03:44: Build target: GPU
|
||||
08/16/2016 03:03:44: With 1bit-SGD: no
|
||||
08/16/2016 03:03:44: Math lib: mkl
|
||||
08/16/2016 03:03:44: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
08/16/2016 03:03:44: CUB_PATH: c:\src\cub-1.4.1
|
||||
08/16/2016 03:03:44: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
08/16/2016 03:03:44: Build Branch: HEAD
|
||||
08/16/2016 03:03:44: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 03:03:44: Built by svcphil on Philly-Pool3
|
||||
08/16/2016 03:03:44: Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
08/16/2016 03:03:44: -------------------------------------------------------------------
|
||||
08/16/2016 03:03:45: -------------------------------------------------------------------
|
||||
08/16/2016 03:03:45: GPU info:
|
||||
|
||||
05/03/2016 14:11:01: Running on DPHAIM-25 at 2016/05/03 14:11:01
|
||||
05/03/2016 14:11:01: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/AlexNet.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu DeviceId=0 timestamping=true
|
||||
08/16/2016 03:03:45: Device[0]: cores = 2496; computeCapability = 5.2; type = "Quadro M4000"; memory = 8090 MB
|
||||
08/16/2016 03:03:45: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 03:03:45: Running on cntk-muc00 at 2016/08/16 03:03:45
|
||||
08/16/2016 03:03:45: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/AlexNetCommon.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu DeviceId=0 timestamping=true configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/AlexNet.cntk
|
||||
|
||||
|
||||
|
||||
05/03/2016 14:11:01: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 14:11:01: ModelDir = "$RunDir$/models"
|
||||
08/16/2016 03:03:45: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:03:45: ModelDir = "$RunDir$/models"
|
||||
ndlMacros=$ConfigDir$/Macros.ndl
|
||||
precision=float
|
||||
deviceId=Auto
|
||||
|
|
@ -74,6 +86,29 @@ Train=[
|
|||
]
|
||||
numMBsToShowResult=100
|
||||
]
|
||||
]
|
||||
AddTop5Eval=[
|
||||
action=edit
|
||||
CurModel=$ModelDir$/AlexNet
|
||||
NewModel=$ModelDir$/AlexNet.Top5
|
||||
editPath=$ConfigDir$/add_top5_layer.mel
|
||||
]
|
||||
Test=[
|
||||
action=test
|
||||
modelPath=$ModelDir$/AlexNet.Top5
|
||||
minibatchSize=16
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=$ConfigDir$/AlexNet.ndl
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu\TestData
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Train=[
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=$ConfigDir$/train_map.txt
|
||||
|
|
@ -93,19 +128,7 @@ Train=[
|
|||
]
|
||||
]
|
||||
]
|
||||
AddTop5Eval=[
|
||||
action=edit
|
||||
CurModel=$ModelDir$/AlexNet
|
||||
NewModel=$ModelDir$/AlexNet.Top5
|
||||
editPath=$ConfigDir$/add_top5_layer.mel
|
||||
]
|
||||
Test=[
|
||||
action=test
|
||||
modelPath=$ModelDir$/AlexNet.Top5
|
||||
minibatchSize=16
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=$ConfigDir$/AlexNet.ndl
|
||||
]
|
||||
Test=[
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=$ConfigDir$/val_map.txt
|
||||
|
|
@ -122,18 +145,11 @@ Test=[
|
|||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu\TestData
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
|
||||
05/03/2016 14:11:01: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:03:45: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 14:11:01: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 14:11:01: ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models"
|
||||
08/16/2016 03:03:45: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:03:45: ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models"
|
||||
ndlMacros=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/Macros.ndl
|
||||
precision=float
|
||||
deviceId=Auto
|
||||
|
|
@ -143,7 +159,7 @@ traceLevel=1
|
|||
numMBsToShowResult=100
|
||||
Train=[
|
||||
action=train
|
||||
modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet
|
||||
modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/AlexNet.ndl
|
||||
]
|
||||
|
|
@ -166,6 +182,29 @@ Train=[
|
|||
]
|
||||
numMBsToShowResult=100
|
||||
]
|
||||
]
|
||||
AddTop5Eval=[
|
||||
action=edit
|
||||
CurModel=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet
|
||||
NewModel=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
editPath=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/add_top5_layer.mel
|
||||
]
|
||||
Test=[
|
||||
action=test
|
||||
modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
minibatchSize=16
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/AlexNet.ndl
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu\TestData
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Train=[
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/train_map.txt
|
||||
|
|
@ -185,19 +224,7 @@ Train=[
|
|||
]
|
||||
]
|
||||
]
|
||||
AddTop5Eval=[
|
||||
action=edit
|
||||
CurModel=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet
|
||||
NewModel=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
editPath=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/add_top5_layer.mel
|
||||
]
|
||||
Test=[
|
||||
action=test
|
||||
modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
minibatchSize=16
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/AlexNet.ndl
|
||||
]
|
||||
Test=[
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/val_map.txt
|
||||
|
|
@ -214,43 +241,37 @@ Test=[
|
|||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu\TestData
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
|
||||
05/03/2016 14:11:01: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:03:45: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 14:11:01: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:03:45: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: AlexNet.cntk:AddTop5Eval=[
|
||||
action=edit
|
||||
CurModel=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet
|
||||
NewModel=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
CurModel=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet
|
||||
NewModel=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
editPath=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/add_top5_layer.mel
|
||||
]
|
||||
|
||||
configparameters: AlexNet.cntk:command=Train:AddTop5Eval:Test
|
||||
configparameters: AlexNet.cntk:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet
|
||||
configparameters: AlexNet.cntk:currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu\TestData
|
||||
configparameters: AlexNet.cntk:DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu\TestData
|
||||
configparameters: AlexNet.cntk:currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu\TestData
|
||||
configparameters: AlexNet.cntk:DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu\TestData
|
||||
configparameters: AlexNet.cntk:deviceId=0
|
||||
configparameters: AlexNet.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models
|
||||
configparameters: AlexNet.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models
|
||||
configparameters: AlexNet.cntk:ndlMacros=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/Macros.ndl
|
||||
configparameters: AlexNet.cntk:numMBsToShowResult=100
|
||||
configparameters: AlexNet.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu
|
||||
configparameters: AlexNet.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu
|
||||
configparameters: AlexNet.cntk:parallelTrain=false
|
||||
configparameters: AlexNet.cntk:precision=float
|
||||
configparameters: AlexNet.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu
|
||||
configparameters: AlexNet.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu
|
||||
configparameters: AlexNet.cntk:Test=[
|
||||
action=test
|
||||
modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
minibatchSize=16
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/AlexNet.ndl
|
||||
]
|
||||
] [
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/val_map.txt
|
||||
|
|
@ -272,7 +293,7 @@ configparameters: AlexNet.cntk:timestamping=true
|
|||
configparameters: AlexNet.cntk:traceLevel=1
|
||||
configparameters: AlexNet.cntk:Train=[
|
||||
action=train
|
||||
modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet
|
||||
modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/AlexNet.ndl
|
||||
]
|
||||
|
|
@ -295,6 +316,7 @@ configparameters: AlexNet.cntk:Train=[
|
|||
]
|
||||
numMBsToShowResult=100
|
||||
]
|
||||
] [
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/train_map.txt
|
||||
|
|
@ -315,24 +337,54 @@ configparameters: AlexNet.cntk:Train=[
|
|||
]
|
||||
]
|
||||
|
||||
05/03/2016 14:11:01: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/03/2016 14:11:01: Commands: Train AddTop5Eval Test
|
||||
05/03/2016 14:11:01: Precision = "float"
|
||||
05/03/2016 14:11:01: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet
|
||||
05/03/2016 14:11:01: CNTKCommandTrainInfo: Train : 3
|
||||
05/03/2016 14:11:01: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
08/16/2016 03:03:45: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:03:45: Commands: Train AddTop5Eval Test
|
||||
08/16/2016 03:03:45: Precision = "float"
|
||||
08/16/2016 03:03:45: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet
|
||||
08/16/2016 03:03:45: CNTKCommandTrainInfo: Train : 3
|
||||
08/16/2016 03:03:45: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
|
||||
05/03/2016 14:11:01: ##############################################################################
|
||||
05/03/2016 14:11:01: # #
|
||||
05/03/2016 14:11:01: # Action "train" #
|
||||
05/03/2016 14:11:01: # #
|
||||
05/03/2016 14:11:01: ##############################################################################
|
||||
08/16/2016 03:03:45: ##############################################################################
|
||||
08/16/2016 03:03:45: # #
|
||||
08/16/2016 03:03:45: # Action "train" #
|
||||
08/16/2016 03:03:45: # #
|
||||
08/16/2016 03:03:45: ##############################################################################
|
||||
|
||||
05/03/2016 14:11:01: CNTKCommandTrainBegin: Train
|
||||
08/16/2016 03:03:45: CNTKCommandTrainBegin: Train
|
||||
NDLBuilder Using GPU 0
|
||||
|
||||
05/03/2016 14:11:01: Creating virgin network.
|
||||
useParallelTrain option is not enabled. ParallelTrain config will be ignored.
|
||||
08/16/2016 03:03:45: Creating virgin network.
|
||||
Node 'conv1.W' (LearnableParameter operation): Initializing Parameter[64 x 363] <- 0.000000.
|
||||
Node 'conv1.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv2.W' (LearnableParameter operation): Initializing Parameter[192 x 1600] <- 0.000000.
|
||||
Node 'conv2.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 192] <- 0.000000.
|
||||
Node 'conv3.W' (LearnableParameter operation): Initializing Parameter[384 x 1728] <- 0.000000.
|
||||
Node 'conv3.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 384] <- 0.000000.
|
||||
Node 'conv4.W' (LearnableParameter operation): Initializing Parameter[256 x 3456] <- 0.000000.
|
||||
Node 'conv4.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 0.000000.
|
||||
Node 'conv5.W' (LearnableParameter operation): Initializing Parameter[256 x 2304] <- 0.000000.
|
||||
Node 'conv5.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[4096 x 6 x 6 x 256] <- 0.000000.
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[4096] <- 0.000000.
|
||||
Node 'h2.b' (LearnableParameter operation): Initializing Parameter[4096] <- 0.000000.
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[1000] <- 0.000000.
|
||||
Node 'conv1.W' (LearnableParameter operation): Initializing Parameter[64 x 363] <- gaussian(seed=1, range=0.010497*0.950000, onCPU=false).
|
||||
Microsoft::MSR::CNTK::GPUMatrix<ElemType>::SetGaussianRandomValue (GPU): creating curand object with seed 1, sizeof(ElemType)==4
|
||||
Node 'conv1.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv2.W' (LearnableParameter operation): Initializing Parameter[192 x 1600] <- gaussian(seed=2, range=0.005000*2.000000, onCPU=false).
|
||||
Node 'conv2.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 192] <- 1.000000.
|
||||
Node 'conv3.W' (LearnableParameter operation): Initializing Parameter[384 x 1728] <- gaussian(seed=3, range=0.004811*2.070000, onCPU=false).
|
||||
Node 'conv3.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 384] <- 0.000000.
|
||||
Node 'conv4.W' (LearnableParameter operation): Initializing Parameter[256 x 3456] <- gaussian(seed=4, range=0.003402*2.900000, onCPU=false).
|
||||
Node 'conv4.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 1.000000.
|
||||
Node 'conv5.W' (LearnableParameter operation): Initializing Parameter[256 x 2304] <- gaussian(seed=5, range=0.004167*2.400000, onCPU=false).
|
||||
Node 'conv5.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 1.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[4096 x 6 x 6 x 256] <- gaussian(seed=6, range=0.002083*6.400000, onCPU=false).
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[4096] <- 1.000000.
|
||||
Node 'h2.W' (LearnableParameter operation): Initializating Parameter[4096 x 0] as gaussian later when dimensions are fully known.
|
||||
Node 'h2.b' (LearnableParameter operation): Initializing Parameter[4096] <- 1.000000.
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializating Parameter[1000 x 0] as gaussian later when dimensions are fully known.
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[1000] <- 1.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -343,8 +395,8 @@ Post-processing network...
|
|||
|
||||
Validating network. 48 nodes to process in pass 1.
|
||||
|
||||
Validating --> OutputNodes.W = LearnableParameter() : -> [1000 x 4096]
|
||||
Validating --> h2.W = LearnableParameter() : -> [4096 x 4096]
|
||||
Validating --> OutputNodes.W = LearnableParameter() : -> [1000 x 0]
|
||||
Validating --> h2.W = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> h1.W = LearnableParameter() : -> [4096 x 6 x 6 x 256]
|
||||
Validating --> conv5.W = LearnableParameter() : -> [256 x 2304]
|
||||
Validating --> conv4.W = LearnableParameter() : -> [256 x 3456]
|
||||
|
|
@ -380,11 +432,15 @@ Validating --> h1.b = LearnableParameter() : -> [4096]
|
|||
Validating --> h1.z = Plus (h1.t, h1.b) : [4096 x *], [4096] -> [4096 x *]
|
||||
Validating --> h1.y = RectifiedLinear (h1.z) : [4096 x *] -> [4096 x *]
|
||||
Validating --> h1_d = Dropout (h1.y) : [4096 x *] -> [4096 x *]
|
||||
Node 'h2.W' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 4096].
|
||||
Node 'h2.W' (LearnableParameter operation): Initializing Parameter[4096 x 4096] <- gaussian(seed=7, range=0.003125*3.200000, onCPU=false).
|
||||
Validating --> h2.t = Times (h2.W, h1_d) : [4096 x 4096], [4096 x *] -> [4096 x *]
|
||||
Validating --> h2.b = LearnableParameter() : -> [4096]
|
||||
Validating --> h2.z = Plus (h2.t, h2.b) : [4096 x *], [4096] -> [4096 x *]
|
||||
Validating --> h2.y = RectifiedLinear (h2.z) : [4096 x *] -> [4096 x *]
|
||||
Validating --> h2_d = Dropout (h2.y) : [4096 x *] -> [4096 x *]
|
||||
Node 'OutputNodes.W' (LearnableParameter operation) operation: Tensor shape was inferred as [1000 x 4096].
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializing Parameter[1000 x 4096] <- gaussian(seed=8, range=0.003125*3.200000, onCPU=false).
|
||||
Validating --> OutputNodes.t = Times (OutputNodes.W, h2_d) : [1000 x 4096], [4096 x *] -> [1000 x *]
|
||||
Validating --> OutputNodes.b = LearnableParameter() : -> [1000]
|
||||
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [1000 x *], [1000] -> [1000 x *]
|
||||
|
|
@ -398,134 +454,157 @@ Validating network. 30 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c: using cuDNN convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c: using cuDNN convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv4.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv5.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool3: using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
|
||||
18 out of 48 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
05/03/2016 14:11:02: Created model with 48 nodes on GPU 0.
|
||||
08/16/2016 03:03:45: Created model with 48 nodes on GPU 0.
|
||||
|
||||
05/03/2016 14:11:02: Training criterion node(s):
|
||||
05/03/2016 14:11:02: ce = CrossEntropyWithSoftmax
|
||||
08/16/2016 03:03:45: Training criterion node(s):
|
||||
08/16/2016 03:03:45: ce = CrossEntropyWithSoftmax
|
||||
|
||||
05/03/2016 14:11:02: Evaluation criterion node(s):
|
||||
|
||||
05/03/2016 14:11:02: err = ErrorPrediction
|
||||
08/16/2016 03:03:45: Evaluation criterion node(s):
|
||||
08/16/2016 03:03:45: err = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 93 matrices, 61 are shared as 27, and 32 are not shared.
|
||||
|
||||
0000000000000000: {[err Gradient[1]] [features Gradient[224 x 224 x 3 x *]] [labels Gradient[1000 x *]] }
|
||||
000000E290039200: {[conv2.W Value[192 x 1600]] }
|
||||
000000E290039340: {[conv1.W Value[64 x 363]] }
|
||||
000000E290039480: {[conv1.b Value[1 x 1 x 64]] }
|
||||
000000E290039520: {[conv2.b Value[1 x 1 x 192]] }
|
||||
000000E29003A060: {[features Value[224 x 224 x 3 x *]] }
|
||||
000000E29003A240: {[labels Value[1000 x *]] }
|
||||
000000E2A80AE1D0: {[OutputNodes.b Value[1000]] }
|
||||
000000E2A80AE270: {[conv3.W Value[384 x 1728]] }
|
||||
000000E2A80AE310: {[h1.W Value[4096 x 6 x 6 x 256]] }
|
||||
000000E2A80AE950: {[conv5.b Value[1 x 1 x 256]] }
|
||||
000000E2A80AEC70: {[h1.b Value[4096]] }
|
||||
000000E2A80AF350: {[h2.W Value[4096 x 4096]] }
|
||||
000000E2A80AF530: {[conv3.b Value[1 x 1 x 384]] }
|
||||
000000E2A80AF710: {[conv4.b Value[1 x 1 x 256]] }
|
||||
000000E2A80AFA30: {[h2.b Value[4096]] }
|
||||
000000E2A80AFDF0: {[conv5.W Value[256 x 2304]] }
|
||||
000000E2A80AFE90: {[conv4.W Value[256 x 3456]] }
|
||||
000000E2A80AFF30: {[OutputNodes.W Value[1000 x 4096]] }
|
||||
000000E2AE0BA220: {[conv4.c Value[13 x 13 x 256 x *]] }
|
||||
000000E2AE0BA2C0: {[h2.W Gradient[4096 x 4096]] [h2.z Value[4096 x *]] }
|
||||
000000E2AE0BA360: {[conv5.c Gradient[13 x 13 x 256 x *]] [conv5.y Value[13 x 13 x 256 x *]] }
|
||||
000000E2AE0BA400: {[OutputNodes.t Value[1000 x *]] [h2.b Gradient[4096]] [h2.y Gradient[4096 x *]] }
|
||||
000000E2AE0BA720: {[err Value[1]] }
|
||||
000000E2AE0BA7C0: {[conv3.b Gradient[1 x 1 x 384]] [conv3.y Gradient[13 x 13 x 384 x *]] [conv4.z Gradient[13 x 13 x 256 x *]] }
|
||||
000000E2AE0BA860: {[conv1.c Gradient[56 x 56 x 64 x *]] [conv1.y Value[56 x 56 x 64 x *]] }
|
||||
000000E2AE0BA900: {[conv1.b Gradient[1 x 1 x 64]] [conv1.y Gradient[56 x 56 x 64 x *]] }
|
||||
000000E2AE0BA9A0: {[conv1.z Gradient[56 x 56 x 64 x *]] [pool1 Value[27 x 27 x 64 x *]] }
|
||||
000000E2AE0BAA40: {[conv3.z Gradient[13 x 13 x 384 x *]] [pool2 Gradient[13 x 13 x 192 x *]] }
|
||||
000000E2AE0BAAE0: {[conv5.W Gradient[256 x 2304]] [conv5.z Value[13 x 13 x 256 x *]] }
|
||||
000000E2AE0BAB80: {[h1_d Value[4096 x *]] }
|
||||
000000E2AE0BACC0: {[conv3.c Gradient[13 x 13 x 384 x *]] [conv3.y Value[13 x 13 x 384 x *]] }
|
||||
000000E2AE0BAE00: {[conv3.c Value[13 x 13 x 384 x *]] }
|
||||
000000E2AE0BAEA0: {[conv4.W Gradient[256 x 3456]] [conv4.z Value[13 x 13 x 256 x *]] }
|
||||
000000E2AE0BAFE0: {[h2_d Value[4096 x *]] }
|
||||
000000E2AE0BB080: {[conv4.c Gradient[13 x 13 x 256 x *]] [conv4.y Value[13 x 13 x 256 x *]] }
|
||||
000000E2AE0BB120: {[h1.W Gradient[4096 x 6 x 6 x 256]] [h1.z Value[4096 x *]] }
|
||||
000000E2AE0BB1C0: {[ce Gradient[1]] }
|
||||
000000E2AE0BB260: {[OutputNodes.b Gradient[1000]] }
|
||||
000000E2AE0BB3A0: {[conv2.W Gradient[192 x 1600]] [conv2.z Value[27 x 27 x 192 x *]] }
|
||||
000000E2AE0BB4E0: {[conv1.W Gradient[64 x 363]] [conv1.z Value[56 x 56 x 64 x *]] }
|
||||
000000E2AE0BB800: {[conv2.b Gradient[1 x 1 x 192]] [conv2.y Gradient[27 x 27 x 192 x *]] }
|
||||
000000E2AE0BB940: {[h1.z Gradient[4096 x *]] [pool3 Gradient[6 x 6 x 256 x *]] }
|
||||
000000E2AE0BB9E0: {[h1.b Gradient[4096]] [h1.y Gradient[4096 x *]] [h2.t Value[4096 x *]] }
|
||||
000000E2AE0BBB20: {[OutputNodes.t Gradient[1000 x *]] }
|
||||
000000E2AE0BBBC0: {[conv4.b Gradient[1 x 1 x 256]] [conv4.y Gradient[13 x 13 x 256 x *]] [conv5.z Gradient[13 x 13 x 256 x *]] [pool3 Value[6 x 6 x 256 x *]] }
|
||||
000000E2AE0BBD00: {[ce Value[1]] }
|
||||
000000E2AE0BBDA0: {[conv2.c Value[27 x 27 x 192 x *]] }
|
||||
000000E2AE0BBE40: {[conv1.c Value[56 x 56 x 64 x *]] }
|
||||
000000E2AE0BBF80: {[conv2.c Gradient[27 x 27 x 192 x *]] [conv2.y Value[27 x 27 x 192 x *]] }
|
||||
000000E2AE0BC020: {[h2.t Gradient[4096 x *]] [h2.y Value[4096 x *]] }
|
||||
000000E2AE0BC160: {[conv5.c Value[13 x 13 x 256 x *]] }
|
||||
000000E2AE0BC200: {[conv2.z Gradient[27 x 27 x 192 x *]] [pool1 Gradient[27 x 27 x 64 x *]] [pool2 Value[13 x 13 x 192 x *]] }
|
||||
000000E2AE0BC2A0: {[OutputNodes.z Value[1000 x *]] }
|
||||
000000E2AE0BC340: {[h1_d Gradient[4096 x *]] [h2.z Gradient[4096 x *]] }
|
||||
000000E2AE0BC480: {[OutputNodes.W Gradient[1000 x 4096]] [OutputNodes.z Gradient[1000 x *]] }
|
||||
000000E2AE0BC520: {[h2_d Gradient[4096 x *]] }
|
||||
000000E2AE0BC840: {[conv3.W Gradient[384 x 1728]] [conv3.z Value[13 x 13 x 384 x *]] }
|
||||
000000E2AE0BC8E0: {[conv5.b Gradient[1 x 1 x 256]] [conv5.y Gradient[13 x 13 x 256 x *]] [h1.t Value[4096 x *]] }
|
||||
000000E2AE0BC980: {[h1.t Gradient[4096 x *]] [h1.y Value[4096 x *]] }
|
||||
|
||||
05/03/2016 14:11:02: No PreCompute nodes found, skipping PreCompute step.
|
||||
|
||||
05/03/2016 14:11:05: Starting Epoch 1: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
|
||||
05/03/2016 14:11:05: Starting minibatch loop.
|
||||
05/03/2016 14:11:14: Epoch[ 1 of 3]-Minibatch[ 1- 100]: ce = 7.43287354 * 1600; err = 0.99937500 * 1600; time = 8.8275s; samplesPerSecond = 181.3
|
||||
05/03/2016 14:11:20: Finished Epoch[ 1 of 3]: [Training] ce = 7.24222462 * 2999; err = 0.99933311 * 2999; totalSamplesSeen = 2999; learningRatePerSample = 0.00062499999; epochTime=14.8733s
|
||||
05/03/2016 14:11:24: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet.1'
|
||||
|
||||
05/03/2016 14:11:27: Starting Epoch 2: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
|
||||
05/03/2016 14:11:27: Starting minibatch loop.
|
||||
05/03/2016 14:11:34: Epoch[ 2 of 3]-Minibatch[ 1- 100, 100.00%]: ce = 6.90465576 * 1600; err = 0.99937500 * 1600; time = 6.9523s; samplesPerSecond = 230.1
|
||||
05/03/2016 14:11:40: Finished Epoch[ 2 of 3]: [Training] ce = 6.91868774 * 2999; err = 0.99899967 * 2999; totalSamplesSeen = 5998; learningRatePerSample = 0.00062499999; epochTime=12.9929s
|
||||
05/03/2016 14:11:43: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet.2'
|
||||
|
||||
05/03/2016 14:11:46: Starting Epoch 3: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
|
||||
05/03/2016 14:11:46: Starting minibatch loop.
|
||||
05/03/2016 14:11:53: Epoch[ 3 of 3]-Minibatch[ 1- 100, 100.00%]: ce = 6.87353699 * 1600; err = 0.99750000 * 1600; time = 7.0845s; samplesPerSecond = 225.8
|
||||
05/03/2016 14:11:59: Finished Epoch[ 3 of 3]: [Training] ce = 6.88654161 * 2999; err = 0.99799933 * 2999; totalSamplesSeen = 8997; learningRatePerSample = 0.00062499999; epochTime=13.0423s
|
||||
05/03/2016 14:12:03: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet'
|
||||
05/03/2016 14:12:06: CNTKCommandTrainEnd: Train
|
||||
|
||||
05/03/2016 14:12:06: Action "train" complete.
|
||||
{ conv1.W : [64 x 363] (gradient)
|
||||
conv1.z : [56 x 56 x 64 x *] }
|
||||
{ conv1.c : [56 x 56 x 64 x *] (gradient)
|
||||
conv1.y : [56 x 56 x 64 x *] }
|
||||
{ conv2.c : [27 x 27 x 192 x *] (gradient)
|
||||
conv2.y : [27 x 27 x 192 x *] }
|
||||
{ conv2.z : [27 x 27 x 192 x *] (gradient)
|
||||
pool1 : [27 x 27 x 64 x *] (gradient)
|
||||
pool2 : [13 x 13 x 192 x *] }
|
||||
{ conv3.W : [384 x 1728] (gradient)
|
||||
conv3.z : [13 x 13 x 384 x *] }
|
||||
{ conv1.z : [56 x 56 x 64 x *] (gradient)
|
||||
pool1 : [27 x 27 x 64 x *] }
|
||||
{ conv3.c : [13 x 13 x 384 x *] (gradient)
|
||||
conv3.y : [13 x 13 x 384 x *] }
|
||||
{ conv2.b : [1 x 1 x 192] (gradient)
|
||||
conv2.y : [27 x 27 x 192 x *] (gradient) }
|
||||
{ conv1.b : [1 x 1 x 64] (gradient)
|
||||
conv1.y : [56 x 56 x 64 x *] (gradient) }
|
||||
{ conv2.W : [192 x 1600] (gradient)
|
||||
conv2.z : [27 x 27 x 192 x *] }
|
||||
{ conv5.b : [1 x 1 x 256] (gradient)
|
||||
conv5.y : [13 x 13 x 256 x *] (gradient)
|
||||
h1.t : [4096 x *] }
|
||||
{ h1_d : [4096 x *] (gradient)
|
||||
h2.z : [4096 x *] (gradient) }
|
||||
{ h1.W : [4096 x 6 x 6 x 256] (gradient)
|
||||
h1.z : [4096 x *] }
|
||||
{ h1.z : [4096 x *] (gradient)
|
||||
pool3 : [6 x 6 x 256 x *] (gradient) }
|
||||
{ OutputNodes.t : [1000 x *]
|
||||
h2.b : [4096] (gradient)
|
||||
h2.y : [4096 x *] (gradient) }
|
||||
{ conv4.b : [1 x 1 x 256] (gradient)
|
||||
conv4.y : [13 x 13 x 256 x *] (gradient)
|
||||
conv5.z : [13 x 13 x 256 x *] (gradient)
|
||||
pool3 : [6 x 6 x 256 x *] }
|
||||
{ conv5.c : [13 x 13 x 256 x *] (gradient)
|
||||
conv5.y : [13 x 13 x 256 x *] }
|
||||
{ OutputNodes.W : [1000 x 4096] (gradient)
|
||||
OutputNodes.z : [1000 x *] (gradient) }
|
||||
{ conv3.b : [1 x 1 x 384] (gradient)
|
||||
conv3.y : [13 x 13 x 384 x *] (gradient)
|
||||
conv4.z : [13 x 13 x 256 x *] (gradient) }
|
||||
{ h1.t : [4096 x *] (gradient)
|
||||
h1.y : [4096 x *] }
|
||||
{ conv4.c : [13 x 13 x 256 x *] (gradient)
|
||||
conv4.y : [13 x 13 x 256 x *] }
|
||||
{ h2.W : [4096 x 4096] (gradient)
|
||||
h2.z : [4096 x *] }
|
||||
{ h2.t : [4096 x *] (gradient)
|
||||
h2.y : [4096 x *] }
|
||||
{ h1.b : [4096] (gradient)
|
||||
h1.y : [4096 x *] (gradient)
|
||||
h2.t : [4096 x *] }
|
||||
{ conv5.W : [256 x 2304] (gradient)
|
||||
conv5.z : [13 x 13 x 256 x *] }
|
||||
{ conv3.z : [13 x 13 x 384 x *] (gradient)
|
||||
pool2 : [13 x 13 x 192 x *] (gradient) }
|
||||
{ conv4.W : [256 x 3456] (gradient)
|
||||
conv4.z : [13 x 13 x 256 x *] }
|
||||
|
||||
|
||||
05/03/2016 14:12:06: ##############################################################################
|
||||
05/03/2016 14:12:06: # #
|
||||
05/03/2016 14:12:06: # Action "edit" #
|
||||
05/03/2016 14:12:06: # #
|
||||
05/03/2016 14:12:06: ##############################################################################
|
||||
08/16/2016 03:03:45: Training 61100840 parameters in 16 out of 16 parameter tensors and 45 nodes with gradient:
|
||||
|
||||
08/16/2016 03:03:45: Node 'OutputNodes.W' (LearnableParameter operation) : [1000 x 4096]
|
||||
08/16/2016 03:03:45: Node 'OutputNodes.b' (LearnableParameter operation) : [1000]
|
||||
08/16/2016 03:03:45: Node 'conv1.W' (LearnableParameter operation) : [64 x 363]
|
||||
08/16/2016 03:03:45: Node 'conv1.b' (LearnableParameter operation) : [1 x 1 x 64]
|
||||
08/16/2016 03:03:45: Node 'conv2.W' (LearnableParameter operation) : [192 x 1600]
|
||||
08/16/2016 03:03:45: Node 'conv2.b' (LearnableParameter operation) : [1 x 1 x 192]
|
||||
08/16/2016 03:03:45: Node 'conv3.W' (LearnableParameter operation) : [384 x 1728]
|
||||
08/16/2016 03:03:45: Node 'conv3.b' (LearnableParameter operation) : [1 x 1 x 384]
|
||||
08/16/2016 03:03:45: Node 'conv4.W' (LearnableParameter operation) : [256 x 3456]
|
||||
08/16/2016 03:03:45: Node 'conv4.b' (LearnableParameter operation) : [1 x 1 x 256]
|
||||
08/16/2016 03:03:45: Node 'conv5.W' (LearnableParameter operation) : [256 x 2304]
|
||||
08/16/2016 03:03:45: Node 'conv5.b' (LearnableParameter operation) : [1 x 1 x 256]
|
||||
08/16/2016 03:03:45: Node 'h1.W' (LearnableParameter operation) : [4096 x 6 x 6 x 256]
|
||||
08/16/2016 03:03:45: Node 'h1.b' (LearnableParameter operation) : [4096]
|
||||
08/16/2016 03:03:45: Node 'h2.W' (LearnableParameter operation) : [4096 x 4096]
|
||||
08/16/2016 03:03:45: Node 'h2.b' (LearnableParameter operation) : [4096]
|
||||
|
||||
08/16/2016 03:03:45: No PreCompute nodes found, or all already computed. Skipping pre-computation step.
|
||||
|
||||
08/16/2016 03:03:49: Starting Epoch 1: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..2999] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:03:49: Starting minibatch loop.
|
||||
08/16/2016 03:03:59: Epoch[ 1 of 3]-Minibatch[ 1- 100]: ce = 7.41005371 * 1600; err = 1.00000000 * 1600; time = 10.1500s; samplesPerSecond = 157.6
|
||||
08/16/2016 03:04:06: Finished Epoch[ 1 of 3]: [Training] ce = 7.23359609 * 2999; err = 1.00000000 * 2999; totalSamplesSeen = 2999; learningRatePerSample = 0.00062499999; epochTime=17.2906s
|
||||
08/16/2016 03:04:10: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet.1'
|
||||
|
||||
08/16/2016 03:04:14: Starting Epoch 2: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [2999..5998] (first sequence at sample 2999), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:04:14: Starting minibatch loop.
|
||||
08/16/2016 03:04:22: Epoch[ 2 of 3]-Minibatch[ 1- 100, 100.00%]: ce = 6.91799866 * 1600; err = 0.99937500 * 1600; time = 8.4264s; samplesPerSecond = 189.9
|
||||
08/16/2016 03:04:30: Finished Epoch[ 2 of 3]: [Training] ce = 6.91958452 * 2999; err = 0.99966656 * 2999; totalSamplesSeen = 5998; learningRatePerSample = 0.00062499999; epochTime=15.8522s
|
||||
08/16/2016 03:04:33: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet.2'
|
||||
|
||||
08/16/2016 03:04:37: Starting Epoch 3: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [5998..8997] (first sequence at sample 5998), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:04:37: Starting minibatch loop.
|
||||
08/16/2016 03:04:45: Epoch[ 3 of 3]-Minibatch[ 1- 100, 100.00%]: ce = 6.88781128 * 1600; err = 0.99687500 * 1600; time = 8.2882s; samplesPerSecond = 193.0
|
||||
08/16/2016 03:04:52: Finished Epoch[ 3 of 3]: [Training] ce = 6.88917725 * 2999; err = 0.99766589 * 2999; totalSamplesSeen = 8997; learningRatePerSample = 0.00062499999; epochTime=15.5577s
|
||||
08/16/2016 03:04:56: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet'
|
||||
08/16/2016 03:04:59: CNTKCommandTrainEnd: Train
|
||||
|
||||
08/16/2016 03:04:59: Action "train" complete.
|
||||
|
||||
|
||||
08/16/2016 03:04:59: ##############################################################################
|
||||
08/16/2016 03:04:59: # #
|
||||
08/16/2016 03:04:59: # Action "edit" #
|
||||
08/16/2016 03:04:59: # #
|
||||
08/16/2016 03:04:59: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
|
@ -592,27 +671,29 @@ Validating network. 30 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c: using GEMM convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using GEMM convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c: using GEMM convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using GEMM convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3.c: using GEMM convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv4.c: using GEMM convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv5.c: using GEMM convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool3: using GEMM convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
|
||||
18 out of 48 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
Node 'unnamed143' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.000000.
|
||||
Node 'unnamed143' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 5.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -672,8 +753,8 @@ Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [1000 x *1]
|
|||
Validating --> labels = InputValue() : -> [1000 x *1]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [1000 x *1], [1000 x *1] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, OutputNodes.z) : [1000 x *1], [1000 x *1] -> [1]
|
||||
Validating --> unnamed137 = LearnableParameter() : -> [1 x 1]
|
||||
Validating --> errTop5 = ErrorPrediction (labels, OutputNodes.z, unnamed137) : [1000 x *1], [1000 x *1], [1 x 1] -> [1]
|
||||
Validating --> unnamed143 = LearnableParameter() : -> [1 x 1]
|
||||
Validating --> errTop5 = ErrorPrediction (labels, OutputNodes.z, unnamed143) : [1000 x *1], [1000 x *1], [1 x 1] -> [1]
|
||||
|
||||
Validating network. 31 nodes to process in pass 2.
|
||||
|
||||
|
|
@ -687,28 +768,58 @@ Validating network, final pass.
|
|||
Post-processing network complete.
|
||||
|
||||
|
||||
05/03/2016 14:12:12: Action "edit" complete.
|
||||
08/16/2016 03:05:07: Action "edit" complete.
|
||||
|
||||
|
||||
05/03/2016 14:12:12: ##############################################################################
|
||||
05/03/2016 14:12:12: # #
|
||||
05/03/2016 14:12:12: # Action "test" #
|
||||
05/03/2016 14:12:12: # #
|
||||
05/03/2016 14:12:12: ##############################################################################
|
||||
08/16/2016 03:05:07: ##############################################################################
|
||||
08/16/2016 03:05:07: # #
|
||||
08/16/2016 03:05:07: # Action "test" #
|
||||
08/16/2016 03:05:07: # #
|
||||
08/16/2016 03:05:07: ##############################################################################
|
||||
|
||||
NDLBuilder Using GPU 0
|
||||
Node 'conv1.W' (LearnableParameter operation): Initializing Parameter[64 x 363] <- 0.000000.
|
||||
Node 'conv1.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv2.W' (LearnableParameter operation): Initializing Parameter[192 x 1600] <- 0.000000.
|
||||
Node 'conv2.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 192] <- 0.000000.
|
||||
Node 'conv3.W' (LearnableParameter operation): Initializing Parameter[384 x 1728] <- 0.000000.
|
||||
Node 'conv3.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 384] <- 0.000000.
|
||||
Node 'conv4.W' (LearnableParameter operation): Initializing Parameter[256 x 3456] <- 0.000000.
|
||||
Node 'conv4.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 0.000000.
|
||||
Node 'conv5.W' (LearnableParameter operation): Initializing Parameter[256 x 2304] <- 0.000000.
|
||||
Node 'conv5.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[4096 x 6 x 6 x 256] <- 0.000000.
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[4096] <- 0.000000.
|
||||
Node 'h2.b' (LearnableParameter operation): Initializing Parameter[4096] <- 0.000000.
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[1000] <- 0.000000.
|
||||
Node 'conv1.W' (LearnableParameter operation): Initializing Parameter[64 x 363] <- gaussian(seed=9, range=0.010497*0.950000, onCPU=false).
|
||||
Node 'conv1.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv2.W' (LearnableParameter operation): Initializing Parameter[192 x 1600] <- gaussian(seed=10, range=0.005000*2.000000, onCPU=false).
|
||||
Node 'conv2.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 192] <- 1.000000.
|
||||
Node 'conv3.W' (LearnableParameter operation): Initializing Parameter[384 x 1728] <- gaussian(seed=11, range=0.004811*2.070000, onCPU=false).
|
||||
Node 'conv3.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 384] <- 0.000000.
|
||||
Node 'conv4.W' (LearnableParameter operation): Initializing Parameter[256 x 3456] <- gaussian(seed=12, range=0.003402*2.900000, onCPU=false).
|
||||
Node 'conv4.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 1.000000.
|
||||
Node 'conv5.W' (LearnableParameter operation): Initializing Parameter[256 x 2304] <- gaussian(seed=13, range=0.004167*2.400000, onCPU=false).
|
||||
Node 'conv5.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 1.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[4096 x 6 x 6 x 256] <- gaussian(seed=14, range=0.002083*6.400000, onCPU=false).
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[4096] <- 1.000000.
|
||||
Node 'h2.W' (LearnableParameter operation): Initializating Parameter[4096 x 0] as gaussian later when dimensions are fully known.
|
||||
Node 'h2.b' (LearnableParameter operation): Initializing Parameter[4096] <- 1.000000.
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializating Parameter[1000 x 0] as gaussian later when dimensions are fully known.
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[1000] <- 1.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
4 roots:
|
||||
3 roots:
|
||||
OutputNodes.z = Plus()
|
||||
ce = CrossEntropyWithSoftmax()
|
||||
err = ErrorPrediction()
|
||||
errTop5 = ErrorPrediction()
|
||||
|
||||
Validating network. 50 nodes to process in pass 1.
|
||||
Validating network. 48 nodes to process in pass 1.
|
||||
|
||||
Validating --> OutputNodes.W = LearnableParameter() : -> [1000 x 4096]
|
||||
Validating --> h2.W = LearnableParameter() : -> [4096 x 4096]
|
||||
Validating --> OutputNodes.W = LearnableParameter() : -> [1000 x 0]
|
||||
Validating --> h2.W = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> h1.W = LearnableParameter() : -> [4096 x 6 x 6 x 256]
|
||||
Validating --> conv5.W = LearnableParameter() : -> [256 x 2304]
|
||||
Validating --> conv4.W = LearnableParameter() : -> [256 x 3456]
|
||||
|
|
@ -744,44 +855,46 @@ Validating --> h1.b = LearnableParameter() : -> [4096]
|
|||
Validating --> h1.z = Plus (h1.t, h1.b) : [4096 x *2], [4096] -> [4096 x *2]
|
||||
Validating --> h1.y = RectifiedLinear (h1.z) : [4096 x *2] -> [4096 x *2]
|
||||
Validating --> h1_d = Dropout (h1.y) : [4096 x *2] -> [4096 x *2]
|
||||
Node 'h2.W' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 4096].
|
||||
Node 'h2.W' (LearnableParameter operation): Initializing Parameter[4096 x 4096] <- gaussian(seed=15, range=0.003125*3.200000, onCPU=false).
|
||||
Validating --> h2.t = Times (h2.W, h1_d) : [4096 x 4096], [4096 x *2] -> [4096 x *2]
|
||||
Validating --> h2.b = LearnableParameter() : -> [4096]
|
||||
Validating --> h2.z = Plus (h2.t, h2.b) : [4096 x *2], [4096] -> [4096 x *2]
|
||||
Validating --> h2.y = RectifiedLinear (h2.z) : [4096 x *2] -> [4096 x *2]
|
||||
Validating --> h2_d = Dropout (h2.y) : [4096 x *2] -> [4096 x *2]
|
||||
Node 'OutputNodes.W' (LearnableParameter operation) operation: Tensor shape was inferred as [1000 x 4096].
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializing Parameter[1000 x 4096] <- gaussian(seed=16, range=0.003125*3.200000, onCPU=false).
|
||||
Validating --> OutputNodes.t = Times (OutputNodes.W, h2_d) : [1000 x 4096], [4096 x *2] -> [1000 x *2]
|
||||
Validating --> OutputNodes.b = LearnableParameter() : -> [1000]
|
||||
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [1000 x *2], [1000] -> [1000 x *2]
|
||||
Validating --> labels = InputValue() : -> [1000 x *2]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [1000 x *2], [1000 x *2] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, OutputNodes.z) : [1000 x *2], [1000 x *2] -> [1]
|
||||
Validating --> unnamed137 = LearnableParameter() : -> [1 x 1]
|
||||
Validating --> errTop5 = ErrorPrediction (labels, OutputNodes.z, unnamed137) : [1000 x *2], [1000 x *2], [1 x 1] -> [1]
|
||||
|
||||
Validating network. 31 nodes to process in pass 2.
|
||||
Validating network. 30 nodes to process in pass 2.
|
||||
|
||||
|
||||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c: using cuDNN convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c: using cuDNN convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv4.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv5.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool3: using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
|
||||
20 out of 50 nodes do not share the minibatch layout with the input data.
|
||||
18 out of 48 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
|
|
@ -790,62 +903,12 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 48 matrices, 0 are shared as 0, and 48 are not shared.
|
||||
|
||||
0000000000000000: {[OutputNodes.W Gradient[1000 x 4096]] [OutputNodes.b Gradient[1000]] [OutputNodes.t Gradient[1000 x *2]] [OutputNodes.z Gradient[1000 x *2]] [ce Gradient[1]] [conv1.W Gradient[64 x 363]] [conv1.b Gradient[1 x 1 x 64]] [conv1.c Gradient[56 x 56 x 64 x *2]] [conv1.y Gradient[56 x 56 x 64 x *2]] [conv1.z Gradient[56 x 56 x 64 x *2]] [conv2.W Gradient[192 x 1600]] [conv2.b Gradient[1 x 1 x 192]] [conv2.c Gradient[27 x 27 x 192 x *2]] [conv2.y Gradient[27 x 27 x 192 x *2]] [conv2.z Gradient[27 x 27 x 192 x *2]] [conv3.W Gradient[384 x 1728]] [conv3.b Gradient[1 x 1 x 384]] [conv3.c Gradient[13 x 13 x 384 x *2]] [conv3.y Gradient[13 x 13 x 384 x *2]] [conv3.z Gradient[13 x 13 x 384 x *2]] [conv4.W Gradient[256 x 3456]] [conv4.b Gradient[1 x 1 x 256]] [conv4.c Gradient[13 x 13 x 256 x *2]] [conv4.y Gradient[13 x 13 x 256 x *2]] [conv4.z Gradient[13 x 13 x 256 x *2]] [conv5.W Gradient[256 x 2304]] [conv5.b Gradient[1 x 1 x 256]] [conv5.c Gradient[13 x 13 x 256 x *2]] [conv5.y Gradient[13 x 13 x 256 x *2]] [conv5.z Gradient[13 x 13 x 256 x *2]] [err Gradient[1]] [errTop5 Gradient[1]] [features Gradient[224 x 224 x 3 x *2]] [h1.W Gradient[4096 x 6 x 6 x 256]] [h1.b Gradient[4096]] [h1.t Gradient[4096 x *2]] [h1.y Gradient[4096 x *2]] [h1.z Gradient[4096 x *2]] [h1_d Gradient[4096 x *2]] [h2.W Gradient[4096 x 4096]] [h2.b Gradient[4096]] [h2.t Gradient[4096 x *2]] [h2.y Gradient[4096 x *2]] [h2.z Gradient[4096 x *2]] [h2_d Gradient[4096 x *2]] [labels Gradient[1000 x *2]] [pool1 Gradient[27 x 27 x 64 x *2]] [pool2 Gradient[13 x 13 x 192 x *2]] [pool3 Gradient[6 x 6 x 256 x *2]] [unnamed137 Gradient[1 x 1]] }
|
||||
000000E28E168F70: {[conv3.W Value[384 x 1728]] }
|
||||
000000E28E1691F0: {[conv5.W Value[256 x 2304]] }
|
||||
000000E28E1693D0: {[conv4.b Value[1 x 1 x 256]] }
|
||||
000000E28E169510: {[conv4.W Value[256 x 3456]] }
|
||||
000000E28E169830: {[conv5.b Value[1 x 1 x 256]] }
|
||||
000000E28E1698D0: {[conv3.b Value[1 x 1 x 384]] }
|
||||
000000E36C778260: {[OutputNodes.b Value[1000]] }
|
||||
000000E36C7783A0: {[OutputNodes.W Value[1000 x 4096]] }
|
||||
000000E36C778440: {[labels Value[1000 x *2]] }
|
||||
000000E36C7786C0: {[features Value[224 x 224 x 3 x *2]] }
|
||||
000000E36C7788A0: {[h1.b Value[4096]] }
|
||||
000000E36C7789E0: {[h2.b Value[4096]] }
|
||||
000000E36C778B20: {[h2.W Value[4096 x 4096]] }
|
||||
000000E36C778DA0: {[h1.W Value[4096 x 6 x 6 x 256]] }
|
||||
000000E370969220: {[conv5.y Value[13 x 13 x 256 x *2]] }
|
||||
000000E370969360: {[h1.t Value[4096 x *2]] }
|
||||
000000E3709694A0: {[conv4.z Value[13 x 13 x 256 x *2]] }
|
||||
000000E370969540: {[conv4.c Value[13 x 13 x 256 x *2]] }
|
||||
000000E370969680: {[conv4.y Value[13 x 13 x 256 x *2]] }
|
||||
000000E370969720: {[conv5.z Value[13 x 13 x 256 x *2]] }
|
||||
000000E3709697C0: {[h1.z Value[4096 x *2]] }
|
||||
000000E370969860: {[h1_d Value[4096 x *2]] }
|
||||
000000E3709699A0: {[h2.t Value[4096 x *2]] }
|
||||
000000E370969A40: {[h2.z Value[4096 x *2]] }
|
||||
000000E370969AE0: {[h2.y Value[4096 x *2]] }
|
||||
000000E370969B80: {[h2_d Value[4096 x *2]] }
|
||||
000000E370969C20: {[conv3.y Value[13 x 13 x 384 x *2]] }
|
||||
000000E370969CC0: {[conv5.c Value[13 x 13 x 256 x *2]] }
|
||||
000000E370969D60: {[h1.y Value[4096 x *2]] }
|
||||
000000E370969EA0: {[OutputNodes.t Value[1000 x *2]] }
|
||||
000000E370969F40: {[pool3 Value[6 x 6 x 256 x *2]] }
|
||||
000000E37096A080: {[OutputNodes.z Value[1000 x *2]] }
|
||||
000000E3728E02A0: {[conv2.y Value[27 x 27 x 192 x *2]] }
|
||||
000000E3728E0340: {[conv1.c Value[56 x 56 x 64 x *2]] }
|
||||
000000E3728E03E0: {[err Value[1]] }
|
||||
000000E3728E0480: {[conv1.z Value[56 x 56 x 64 x *2]] }
|
||||
000000E3728E0700: {[pool2 Value[13 x 13 x 192 x *2]] }
|
||||
000000E3728E07A0: {[conv3.c Value[13 x 13 x 384 x *2]] }
|
||||
000000E3728E0980: {[errTop5 Value[1]] }
|
||||
000000E3728E0A20: {[conv3.z Value[13 x 13 x 384 x *2]] }
|
||||
000000E3728E0AC0: {[ce Value[1]] }
|
||||
000000E3728E0CA0: {[unnamed137 Value[1 x 1]] }
|
||||
000000E3728E0DE0: {[conv1.y Value[56 x 56 x 64 x *2]] }
|
||||
000000E3728E0E80: {[pool1 Value[27 x 27 x 64 x *2]] }
|
||||
000000E3728E0F20: {[conv2.c Value[27 x 27 x 192 x *2]] }
|
||||
000000E3728E1100: {[conv2.z Value[27 x 27 x 192 x *2]] }
|
||||
000000E372D9CB80: {[conv2.b Value[1 x 1 x 192]] }
|
||||
000000E372D9CE00: {[conv1.W Value[64 x 363]] }
|
||||
000000E372D9CFE0: {[conv2.W Value[192 x 1600]] }
|
||||
000000E372D9D120: {[conv1.b Value[1 x 1 x 64]] }
|
||||
|
||||
05/03/2016 14:12:19: Final Results: Minibatch[1-32]: err = 0.99800000 * 500; errTop5 = 0.99600000 * 500; ce = 6.94932878 * 500; perplexity = 1042.44978531
|
||||
08/16/2016 03:05:09: Minibatch[1-32]: err = 0.99800000 * 500; ce = 7.32805448 * 500
|
||||
08/16/2016 03:05:09: Final Results: Minibatch[1-32]: err = 0.99800000 * 500; ce = 7.32805448 * 500; perplexity = 1522.41699268
|
||||
|
||||
05/03/2016 14:12:19: Action "test" complete.
|
||||
08/16/2016 03:05:09: Action "test" complete.
|
||||
|
||||
05/03/2016 14:12:19: __COMPLETED__
|
||||
08/16/2016 03:05:09: __COMPLETED__
|
||||
Разница между файлами не показана из-за своего большого размера
Загрузить разницу
|
|
@ -1,49 +1,62 @@
|
|||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
|
||||
Hardware threads: 24
|
||||
Total Memory: 264172964 kB
|
||||
-------------------------------------------------------------------
|
||||
Copying test data to local directory
|
||||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/AlexNet.cntk currentDirectory=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/TestData RunDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu DataDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet OutputDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu DeviceId=0 timestamping=true
|
||||
=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/AlexNetCommon.cntk currentDirectory=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/TestData RunDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu DataDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet OutputDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu DeviceId=0 timestamping=true configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/AlexNet.cntk
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 3 2016 17:56:15
|
||||
Last modified date: Tue May 3 11:36:22 2016
|
||||
Built time: Aug 16 2016 09:41:56
|
||||
Last modified date: Fri Aug 12 07:32:43 2016
|
||||
Build type: release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: no
|
||||
Math lib: acml
|
||||
Math lib: mkl
|
||||
CUDA_PATH: /usr/local/cuda-7.5
|
||||
CUB_PATH: /usr/local/cub-1.4.1
|
||||
CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
Build Branch: HEAD
|
||||
Build SHA1: 571b092d60e131fd529081a5ed52af2dc815dc82
|
||||
Built by philly on 18750d26eb32
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by philly on f67b30a647de
|
||||
Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to /tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/TestData
|
||||
05/03/2016 18:06:53: -------------------------------------------------------------------
|
||||
05/03/2016 18:06:53: Build info:
|
||||
Changed current directory to /tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/TestData
|
||||
08/16/2016 09:55:24: -------------------------------------------------------------------
|
||||
08/16/2016 09:55:24: Build info:
|
||||
|
||||
05/03/2016 18:06:53: Built time: May 3 2016 17:56:15
|
||||
05/03/2016 18:06:53: Last modified date: Tue May 3 11:36:22 2016
|
||||
05/03/2016 18:06:53: Build type: release
|
||||
05/03/2016 18:06:53: Build target: GPU
|
||||
05/03/2016 18:06:53: With 1bit-SGD: no
|
||||
05/03/2016 18:06:53: Math lib: acml
|
||||
05/03/2016 18:06:53: CUDA_PATH: /usr/local/cuda-7.5
|
||||
05/03/2016 18:06:53: CUB_PATH: /usr/local/cub-1.4.1
|
||||
05/03/2016 18:06:53: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
05/03/2016 18:06:53: Build Branch: HEAD
|
||||
05/03/2016 18:06:53: Build SHA1: 571b092d60e131fd529081a5ed52af2dc815dc82
|
||||
05/03/2016 18:06:53: Built by philly on 18750d26eb32
|
||||
05/03/2016 18:06:53: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
05/03/2016 18:06:53: -------------------------------------------------------------------
|
||||
08/16/2016 09:55:24: Built time: Aug 16 2016 09:41:56
|
||||
08/16/2016 09:55:24: Last modified date: Fri Aug 12 07:32:43 2016
|
||||
08/16/2016 09:55:24: Build type: release
|
||||
08/16/2016 09:55:24: Build target: GPU
|
||||
08/16/2016 09:55:24: With 1bit-SGD: no
|
||||
08/16/2016 09:55:24: Math lib: mkl
|
||||
08/16/2016 09:55:24: CUDA_PATH: /usr/local/cuda-7.5
|
||||
08/16/2016 09:55:24: CUB_PATH: /usr/local/cub-1.4.1
|
||||
08/16/2016 09:55:24: CUDNN_PATH: /usr/local/cudnn-4.0
|
||||
08/16/2016 09:55:24: Build Branch: HEAD
|
||||
08/16/2016 09:55:24: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 09:55:24: Built by philly on f67b30a647de
|
||||
08/16/2016 09:55:24: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
|
||||
08/16/2016 09:55:24: -------------------------------------------------------------------
|
||||
08/16/2016 09:55:25: -------------------------------------------------------------------
|
||||
08/16/2016 09:55:25: GPU info:
|
||||
|
||||
05/03/2016 18:06:53: Running on localhost at 2016/05/03 18:06:53
|
||||
05/03/2016 18:06:53: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/AlexNet.cntk currentDirectory=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/TestData RunDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu DataDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet OutputDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu DeviceId=0 timestamping=true
|
||||
08/16/2016 09:55:25: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 09:55:25: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 09:55:25: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 09:55:25: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
|
||||
08/16/2016 09:55:25: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 09:55:25: Running on localhost at 2016/08/16 09:55:25
|
||||
08/16/2016 09:55:25: Command line:
|
||||
/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/AlexNetCommon.cntk currentDirectory=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/TestData RunDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu DataDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/TestData ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet OutputDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu DeviceId=0 timestamping=true configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/AlexNet.cntk
|
||||
|
||||
|
||||
|
||||
05/03/2016 18:06:53: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 18:06:53: ModelDir = "$RunDir$/models"
|
||||
08/16/2016 09:55:25: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 09:55:25: ModelDir = "$RunDir$/models"
|
||||
ndlMacros=$ConfigDir$/Macros.ndl
|
||||
precision=float
|
||||
deviceId=Auto
|
||||
|
|
@ -76,6 +89,29 @@ Train=[
|
|||
]
|
||||
numMBsToShowResult=100
|
||||
]
|
||||
]
|
||||
AddTop5Eval=[
|
||||
action=edit
|
||||
CurModel=$ModelDir$/AlexNet
|
||||
NewModel=$ModelDir$/AlexNet.Top5
|
||||
editPath=$ConfigDir$/add_top5_layer.mel
|
||||
]
|
||||
Test=[
|
||||
action=test
|
||||
modelPath=$ModelDir$/AlexNet.Top5
|
||||
minibatchSize=16
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=$ConfigDir$/AlexNet.ndl
|
||||
]
|
||||
]
|
||||
currentDirectory=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet
|
||||
OutputDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Train=[
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=$ConfigDir$/train_map.txt
|
||||
|
|
@ -95,19 +131,7 @@ Train=[
|
|||
]
|
||||
]
|
||||
]
|
||||
AddTop5Eval=[
|
||||
action=edit
|
||||
CurModel=$ModelDir$/AlexNet
|
||||
NewModel=$ModelDir$/AlexNet.Top5
|
||||
editPath=$ConfigDir$/add_top5_layer.mel
|
||||
]
|
||||
Test=[
|
||||
action=test
|
||||
modelPath=$ModelDir$/AlexNet.Top5
|
||||
minibatchSize=16
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=$ConfigDir$/AlexNet.ndl
|
||||
]
|
||||
Test=[
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=$ConfigDir$/val_map.txt
|
||||
|
|
@ -124,18 +148,11 @@ Test=[
|
|||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet
|
||||
OutputDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
|
||||
05/03/2016 18:06:53: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 09:55:25: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 18:06:53: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 18:06:53: ModelDir = "/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models"
|
||||
08/16/2016 09:55:25: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 09:55:25: ModelDir = "/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models"
|
||||
ndlMacros=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/Macros.ndl
|
||||
precision=float
|
||||
deviceId=Auto
|
||||
|
|
@ -145,7 +162,7 @@ traceLevel=1
|
|||
numMBsToShowResult=100
|
||||
Train=[
|
||||
action=train
|
||||
modelPath=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet
|
||||
modelPath=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/AlexNet.ndl
|
||||
]
|
||||
|
|
@ -168,6 +185,29 @@ Train=[
|
|||
]
|
||||
numMBsToShowResult=100
|
||||
]
|
||||
]
|
||||
AddTop5Eval=[
|
||||
action=edit
|
||||
CurModel=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet
|
||||
NewModel=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
editPath=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/add_top5_layer.mel
|
||||
]
|
||||
Test=[
|
||||
action=test
|
||||
modelPath=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
minibatchSize=16
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/AlexNet.ndl
|
||||
]
|
||||
]
|
||||
currentDirectory=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet
|
||||
OutputDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Train=[
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/train_map.txt
|
||||
|
|
@ -187,19 +227,7 @@ Train=[
|
|||
]
|
||||
]
|
||||
]
|
||||
AddTop5Eval=[
|
||||
action=edit
|
||||
CurModel=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet
|
||||
NewModel=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
editPath=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/add_top5_layer.mel
|
||||
]
|
||||
Test=[
|
||||
action=test
|
||||
modelPath=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
minibatchSize=16
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/AlexNet.ndl
|
||||
]
|
||||
Test=[
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/val_map.txt
|
||||
|
|
@ -216,43 +244,37 @@ Test=[
|
|||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/TestData
|
||||
RunDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu
|
||||
DataDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/TestData
|
||||
ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet
|
||||
OutputDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
|
||||
05/03/2016 18:06:53: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 09:55:25: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 18:06:53: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 09:55:25: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: AlexNet.cntk:AddTop5Eval=[
|
||||
action=edit
|
||||
CurModel=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet
|
||||
NewModel=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
CurModel=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet
|
||||
NewModel=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
editPath=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/add_top5_layer.mel
|
||||
]
|
||||
|
||||
configparameters: AlexNet.cntk:command=Train:AddTop5Eval:Test
|
||||
configparameters: AlexNet.cntk:ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet
|
||||
configparameters: AlexNet.cntk:currentDirectory=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/TestData
|
||||
configparameters: AlexNet.cntk:DataDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/TestData
|
||||
configparameters: AlexNet.cntk:currentDirectory=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/TestData
|
||||
configparameters: AlexNet.cntk:DataDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/TestData
|
||||
configparameters: AlexNet.cntk:deviceId=0
|
||||
configparameters: AlexNet.cntk:ModelDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models
|
||||
configparameters: AlexNet.cntk:ModelDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models
|
||||
configparameters: AlexNet.cntk:ndlMacros=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/Macros.ndl
|
||||
configparameters: AlexNet.cntk:numMBsToShowResult=100
|
||||
configparameters: AlexNet.cntk:OutputDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu
|
||||
configparameters: AlexNet.cntk:OutputDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu
|
||||
configparameters: AlexNet.cntk:parallelTrain=false
|
||||
configparameters: AlexNet.cntk:precision=float
|
||||
configparameters: AlexNet.cntk:RunDir=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu
|
||||
configparameters: AlexNet.cntk:RunDir=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu
|
||||
configparameters: AlexNet.cntk:Test=[
|
||||
action=test
|
||||
modelPath=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
modelPath=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
minibatchSize=16
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/AlexNet.ndl
|
||||
]
|
||||
] [
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/val_map.txt
|
||||
|
|
@ -274,7 +296,7 @@ configparameters: AlexNet.cntk:timestamping=true
|
|||
configparameters: AlexNet.cntk:traceLevel=1
|
||||
configparameters: AlexNet.cntk:Train=[
|
||||
action=train
|
||||
modelPath=/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet
|
||||
modelPath=/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/AlexNet.ndl
|
||||
]
|
||||
|
|
@ -297,6 +319,7 @@ configparameters: AlexNet.cntk:Train=[
|
|||
]
|
||||
numMBsToShowResult=100
|
||||
]
|
||||
] [
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Image/AlexNet/train_map.txt
|
||||
|
|
@ -317,24 +340,54 @@ configparameters: AlexNet.cntk:Train=[
|
|||
]
|
||||
]
|
||||
|
||||
05/03/2016 18:06:53: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/03/2016 18:06:53: Commands: Train AddTop5Eval Test
|
||||
05/03/2016 18:06:53: Precision = "float"
|
||||
05/03/2016 18:06:53: CNTKModelPath: /tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet
|
||||
05/03/2016 18:06:53: CNTKCommandTrainInfo: Train : 3
|
||||
05/03/2016 18:06:53: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
08/16/2016 09:55:25: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 09:55:25: Commands: Train AddTop5Eval Test
|
||||
08/16/2016 09:55:25: Precision = "float"
|
||||
08/16/2016 09:55:25: CNTKModelPath: /tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet
|
||||
08/16/2016 09:55:25: CNTKCommandTrainInfo: Train : 3
|
||||
08/16/2016 09:55:25: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
|
||||
05/03/2016 18:06:53: ##############################################################################
|
||||
05/03/2016 18:06:53: # #
|
||||
05/03/2016 18:06:53: # Action "train" #
|
||||
05/03/2016 18:06:53: # #
|
||||
05/03/2016 18:06:53: ##############################################################################
|
||||
08/16/2016 09:55:25: ##############################################################################
|
||||
08/16/2016 09:55:25: # #
|
||||
08/16/2016 09:55:25: # Action "train" #
|
||||
08/16/2016 09:55:25: # #
|
||||
08/16/2016 09:55:25: ##############################################################################
|
||||
|
||||
05/03/2016 18:06:53: CNTKCommandTrainBegin: Train
|
||||
08/16/2016 09:55:25: CNTKCommandTrainBegin: Train
|
||||
NDLBuilder Using GPU 0
|
||||
|
||||
05/03/2016 18:06:53: Creating virgin network.
|
||||
useParallelTrain option is not enabled. ParallelTrain config will be ignored.
|
||||
08/16/2016 09:55:25: Creating virgin network.
|
||||
Node 'conv1.W' (LearnableParameter operation): Initializing Parameter[64 x 363] <- 0.000000.
|
||||
Node 'conv1.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv2.W' (LearnableParameter operation): Initializing Parameter[192 x 1600] <- 0.000000.
|
||||
Node 'conv2.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 192] <- 0.000000.
|
||||
Node 'conv3.W' (LearnableParameter operation): Initializing Parameter[384 x 1728] <- 0.000000.
|
||||
Node 'conv3.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 384] <- 0.000000.
|
||||
Node 'conv4.W' (LearnableParameter operation): Initializing Parameter[256 x 3456] <- 0.000000.
|
||||
Node 'conv4.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 0.000000.
|
||||
Node 'conv5.W' (LearnableParameter operation): Initializing Parameter[256 x 2304] <- 0.000000.
|
||||
Node 'conv5.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[4096 x 6 x 6 x 256] <- 0.000000.
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[4096] <- 0.000000.
|
||||
Node 'h2.b' (LearnableParameter operation): Initializing Parameter[4096] <- 0.000000.
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[1000] <- 0.000000.
|
||||
Node 'conv1.W' (LearnableParameter operation): Initializing Parameter[64 x 363] <- gaussian(seed=1, range=0.010497*0.950000, onCPU=false).
|
||||
SetGaussianRandomValue (GPU): creating curand object with seed 1, sizeof(ElemType)==4
|
||||
Node 'conv1.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv2.W' (LearnableParameter operation): Initializing Parameter[192 x 1600] <- gaussian(seed=2, range=0.005000*2.000000, onCPU=false).
|
||||
Node 'conv2.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 192] <- 1.000000.
|
||||
Node 'conv3.W' (LearnableParameter operation): Initializing Parameter[384 x 1728] <- gaussian(seed=3, range=0.004811*2.070000, onCPU=false).
|
||||
Node 'conv3.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 384] <- 0.000000.
|
||||
Node 'conv4.W' (LearnableParameter operation): Initializing Parameter[256 x 3456] <- gaussian(seed=4, range=0.003402*2.900000, onCPU=false).
|
||||
Node 'conv4.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 1.000000.
|
||||
Node 'conv5.W' (LearnableParameter operation): Initializing Parameter[256 x 2304] <- gaussian(seed=5, range=0.004167*2.400000, onCPU=false).
|
||||
Node 'conv5.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 1.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[4096 x 6 x 6 x 256] <- gaussian(seed=6, range=0.002083*6.400000, onCPU=false).
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[4096] <- 1.000000.
|
||||
Node 'h2.W' (LearnableParameter operation): Initializating Parameter[4096 x 0] as gaussian later when dimensions are fully known.
|
||||
Node 'h2.b' (LearnableParameter operation): Initializing Parameter[4096] <- 1.000000.
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializating Parameter[1000 x 0] as gaussian later when dimensions are fully known.
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[1000] <- 1.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -345,8 +398,8 @@ Post-processing network...
|
|||
|
||||
Validating network. 48 nodes to process in pass 1.
|
||||
|
||||
Validating --> OutputNodes.W = LearnableParameter() : -> [1000 x 4096]
|
||||
Validating --> h2.W = LearnableParameter() : -> [4096 x 4096]
|
||||
Validating --> OutputNodes.W = LearnableParameter() : -> [1000 x 0]
|
||||
Validating --> h2.W = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> h1.W = LearnableParameter() : -> [4096 x 6 x 6 x 256]
|
||||
Validating --> conv5.W = LearnableParameter() : -> [256 x 2304]
|
||||
Validating --> conv4.W = LearnableParameter() : -> [256 x 3456]
|
||||
|
|
@ -382,11 +435,15 @@ Validating --> h1.b = LearnableParameter() : -> [4096]
|
|||
Validating --> h1.z = Plus (h1.t, h1.b) : [4096 x *], [4096] -> [4096 x *]
|
||||
Validating --> h1.y = RectifiedLinear (h1.z) : [4096 x *] -> [4096 x *]
|
||||
Validating --> h1_d = Dropout (h1.y) : [4096 x *] -> [4096 x *]
|
||||
Node 'h2.W' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 4096].
|
||||
Node 'h2.W' (LearnableParameter operation): Initializing Parameter[4096 x 4096] <- gaussian(seed=7, range=0.003125*3.200000, onCPU=false).
|
||||
Validating --> h2.t = Times (h2.W, h1_d) : [4096 x 4096], [4096 x *] -> [4096 x *]
|
||||
Validating --> h2.b = LearnableParameter() : -> [4096]
|
||||
Validating --> h2.z = Plus (h2.t, h2.b) : [4096 x *], [4096] -> [4096 x *]
|
||||
Validating --> h2.y = RectifiedLinear (h2.z) : [4096 x *] -> [4096 x *]
|
||||
Validating --> h2_d = Dropout (h2.y) : [4096 x *] -> [4096 x *]
|
||||
Node 'OutputNodes.W' (LearnableParameter operation) operation: Tensor shape was inferred as [1000 x 4096].
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializing Parameter[1000 x 4096] <- gaussian(seed=8, range=0.003125*3.200000, onCPU=false).
|
||||
Validating --> OutputNodes.t = Times (OutputNodes.W, h2_d) : [1000 x 4096], [4096 x *] -> [1000 x *]
|
||||
Validating --> OutputNodes.b = LearnableParameter() : -> [1000]
|
||||
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [1000 x *], [1000] -> [1000 x *]
|
||||
|
|
@ -400,134 +457,157 @@ Validating network. 30 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c: using cuDNN convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c: using cuDNN convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv4.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv5.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool3: using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
|
||||
18 out of 48 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
05/03/2016 18:06:53: Created model with 48 nodes on GPU 0.
|
||||
08/16/2016 09:55:25: Created model with 48 nodes on GPU 0.
|
||||
|
||||
05/03/2016 18:06:53: Training criterion node(s):
|
||||
05/03/2016 18:06:53: ce = CrossEntropyWithSoftmax
|
||||
08/16/2016 09:55:25: Training criterion node(s):
|
||||
08/16/2016 09:55:25: ce = CrossEntropyWithSoftmax
|
||||
|
||||
05/03/2016 18:06:53: Evaluation criterion node(s):
|
||||
|
||||
05/03/2016 18:06:53: err = ErrorPrediction
|
||||
08/16/2016 09:55:25: Evaluation criterion node(s):
|
||||
08/16/2016 09:55:25: err = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 93 matrices, 61 are shared as 27, and 32 are not shared.
|
||||
|
||||
(nil): {[err Gradient[1]] [features Gradient[224 x 224 x 3 x *]] [labels Gradient[1000 x *]] }
|
||||
0x1eb05c8: {[features Value[224 x 224 x 3 x *]] }
|
||||
0x27d0c58: {[conv1.W Value[64 x 363]] }
|
||||
0x27d1a38: {[conv1.b Value[1 x 1 x 64]] }
|
||||
0x27d32a8: {[conv2.W Value[192 x 1600]] }
|
||||
0x27d49b8: {[conv2.b Value[1 x 1 x 192]] }
|
||||
0x27d5c88: {[conv3.W Value[384 x 1728]] }
|
||||
0x27d7378: {[conv3.b Value[1 x 1 x 384]] }
|
||||
0x27d8698: {[conv4.W Value[256 x 3456]] }
|
||||
0x27d9798: {[OutputNodes.b Value[1000]] }
|
||||
0x27d9b88: {[conv4.b Value[1 x 1 x 256]] }
|
||||
0x27dadf8: {[conv5.W Value[256 x 2304]] }
|
||||
0x27dbff8: {[conv5.b Value[1 x 1 x 256]] }
|
||||
0x27dd778: {[h1.W Value[4096 x 6 x 6 x 256]] }
|
||||
0x27de688: {[h1.b Value[4096]] }
|
||||
0x2c0cab8: {[labels Value[1000 x *]] }
|
||||
0x2ea6e78: {[h2.W Value[4096 x 4096]] }
|
||||
0x2ea7c18: {[h2.b Value[4096]] }
|
||||
0x2ea8838: {[OutputNodes.W Value[1000 x 4096]] }
|
||||
0x7f47b2c352e8: {[conv1.c Gradient[56 x 56 x 64 x *]] [conv1.y Value[56 x 56 x 64 x *]] }
|
||||
0x7f47b2c35448: {[conv1.W Gradient[64 x 363]] [conv1.z Value[56 x 56 x 64 x *]] }
|
||||
0x7f47b2c35648: {[conv1.z Gradient[56 x 56 x 64 x *]] [pool1 Value[27 x 27 x 64 x *]] }
|
||||
0x7f47b2c35948: {[conv1.c Value[56 x 56 x 64 x *]] }
|
||||
0x7f47b2e95948: {[conv1.b Gradient[1 x 1 x 64]] [conv1.y Gradient[56 x 56 x 64 x *]] }
|
||||
0x7f47b2e95b08: {[conv2.W Gradient[192 x 1600]] [conv2.z Value[27 x 27 x 192 x *]] }
|
||||
0x7f47b2e95cc8: {[conv2.c Gradient[27 x 27 x 192 x *]] [conv2.y Value[27 x 27 x 192 x *]] }
|
||||
0x7f47b2e95e88: {[conv2.z Gradient[27 x 27 x 192 x *]] [pool1 Gradient[27 x 27 x 64 x *]] [pool2 Value[13 x 13 x 192 x *]] }
|
||||
0x7f47b2e96048: {[conv3.c Value[13 x 13 x 384 x *]] }
|
||||
0x7f47b2e96208: {[conv2.b Gradient[1 x 1 x 192]] [conv2.y Gradient[27 x 27 x 192 x *]] }
|
||||
0x7f47b2e963c8: {[conv3.W Gradient[384 x 1728]] [conv3.z Value[13 x 13 x 384 x *]] }
|
||||
0x7f47b2e96588: {[conv3.c Gradient[13 x 13 x 384 x *]] [conv3.y Value[13 x 13 x 384 x *]] }
|
||||
0x7f47b2e96748: {[conv4.c Value[13 x 13 x 256 x *]] }
|
||||
0x7f47b2e96908: {[conv3.z Gradient[13 x 13 x 384 x *]] [pool2 Gradient[13 x 13 x 192 x *]] }
|
||||
0x7f47b2e96ac8: {[conv4.W Gradient[256 x 3456]] [conv4.z Value[13 x 13 x 256 x *]] }
|
||||
0x7f47b2e96c88: {[conv4.c Gradient[13 x 13 x 256 x *]] [conv4.y Value[13 x 13 x 256 x *]] }
|
||||
0x7f47b2e96e48: {[conv5.c Value[13 x 13 x 256 x *]] }
|
||||
0x7f47b2e97008: {[conv3.b Gradient[1 x 1 x 384]] [conv3.y Gradient[13 x 13 x 384 x *]] [conv4.z Gradient[13 x 13 x 256 x *]] }
|
||||
0x7f47b2e971c8: {[conv5.W Gradient[256 x 2304]] [conv5.z Value[13 x 13 x 256 x *]] }
|
||||
0x7f47b2e97388: {[conv5.c Gradient[13 x 13 x 256 x *]] [conv5.y Value[13 x 13 x 256 x *]] }
|
||||
0x7f47b2e97548: {[conv4.b Gradient[1 x 1 x 256]] [conv4.y Gradient[13 x 13 x 256 x *]] [conv5.z Gradient[13 x 13 x 256 x *]] [pool3 Value[6 x 6 x 256 x *]] }
|
||||
0x7f47b2e97708: {[conv5.b Gradient[1 x 1 x 256]] [conv5.y Gradient[13 x 13 x 256 x *]] [h1.t Value[4096 x *]] }
|
||||
0x7f47b2e978c8: {[h1.W Gradient[4096 x 6 x 6 x 256]] [h1.z Value[4096 x *]] }
|
||||
0x7f47b2e97a88: {[h1.t Gradient[4096 x *]] [h1.y Value[4096 x *]] }
|
||||
0x7f47b2e97c48: {[h1_d Value[4096 x *]] }
|
||||
0x7f47b2e97e08: {[h1.z Gradient[4096 x *]] [pool3 Gradient[6 x 6 x 256 x *]] }
|
||||
0x7f47b2e97fc8: {[h1.b Gradient[4096]] [h1.y Gradient[4096 x *]] [h2.t Value[4096 x *]] }
|
||||
0x7f47b2e98188: {[h2.W Gradient[4096 x 4096]] [h2.z Value[4096 x *]] }
|
||||
0x7f47b2e98348: {[h2.t Gradient[4096 x *]] [h2.y Value[4096 x *]] }
|
||||
0x7f47b2e98508: {[h2_d Value[4096 x *]] }
|
||||
0x7f47b2e986c8: {[h1_d Gradient[4096 x *]] [h2.z Gradient[4096 x *]] }
|
||||
0x7f47b2e98888: {[OutputNodes.t Value[1000 x *]] [h2.b Gradient[4096]] [h2.y Gradient[4096 x *]] }
|
||||
0x7f47b2e99428: {[ce Gradient[1]] }
|
||||
0x7f47b2e995e8: {[OutputNodes.W Gradient[1000 x 4096]] [OutputNodes.z Gradient[1000 x *]] }
|
||||
0x7f47b2e997a8: {[OutputNodes.t Gradient[1000 x *]] }
|
||||
0x7f47b2e99968: {[OutputNodes.b Gradient[1000]] }
|
||||
0x7f47b2e99b28: {[h2_d Gradient[4096 x *]] }
|
||||
0x7f47b2e9aa08: {[OutputNodes.z Value[1000 x *]] }
|
||||
0x7f47b2e9abc8: {[ce Value[1]] }
|
||||
0x7f47b2e9b2f8: {[conv2.c Value[27 x 27 x 192 x *]] }
|
||||
0x7f47b2ef4ce8: {[err Value[1]] }
|
||||
|
||||
05/03/2016 18:06:53: No PreCompute nodes found, skipping PreCompute step.
|
||||
|
||||
05/03/2016 18:06:55: Starting Epoch 1: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
|
||||
05/03/2016 18:06:55: Starting minibatch loop.
|
||||
05/03/2016 18:07:02: Epoch[ 1 of 3]-Minibatch[ 1- 100]: ce = 7.41642395 * 1600; err = 1.00000000 * 1600; time = 7.0425s; samplesPerSecond = 227.2
|
||||
05/03/2016 18:07:08: Finished Epoch[ 1 of 3]: [Training] ce = 7.22737918 * 2999; err = 0.99966656 * 2999; totalSamplesSeen = 2999; learningRatePerSample = 0.00062499999; epochTime=12.9259s
|
||||
05/03/2016 18:07:10: SGD: Saving checkpoint model '/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet.1'
|
||||
|
||||
05/03/2016 18:07:13: Starting Epoch 2: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
|
||||
05/03/2016 18:07:13: Starting minibatch loop.
|
||||
05/03/2016 18:07:19: Epoch[ 2 of 3]-Minibatch[ 1- 100, 100.00%]: ce = 6.90983215 * 1600; err = 1.00000000 * 1600; time = 6.2320s; samplesPerSecond = 256.7
|
||||
05/03/2016 18:07:25: Finished Epoch[ 2 of 3]: [Training] ce = 6.91963923 * 2999; err = 0.99866622 * 2999; totalSamplesSeen = 5998; learningRatePerSample = 0.00062499999; epochTime=12.2905s
|
||||
05/03/2016 18:07:27: SGD: Saving checkpoint model '/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet.2'
|
||||
|
||||
05/03/2016 18:07:29: Starting Epoch 3: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
|
||||
05/03/2016 18:07:29: Starting minibatch loop.
|
||||
05/03/2016 18:07:36: Epoch[ 3 of 3]-Minibatch[ 1- 100, 100.00%]: ce = 6.87519836 * 1600; err = 0.99937500 * 1600; time = 6.4714s; samplesPerSecond = 247.2
|
||||
05/03/2016 18:07:42: Finished Epoch[ 3 of 3]: [Training] ce = 6.88608052 * 2999; err = 0.99833278 * 2999; totalSamplesSeen = 8997; learningRatePerSample = 0.00062499999; epochTime=12.1425s
|
||||
05/03/2016 18:07:44: SGD: Saving checkpoint model '/tmp/cntk-test-20160503180555.960884/Image_AlexNet@release_gpu/models/AlexNet'
|
||||
05/03/2016 18:07:46: CNTKCommandTrainEnd: Train
|
||||
|
||||
05/03/2016 18:07:46: Action "train" complete.
|
||||
{ conv2.b : [1 x 1 x 192] (gradient)
|
||||
conv2.y : [27 x 27 x 192 x *] (gradient) }
|
||||
{ conv3.W : [384 x 1728] (gradient)
|
||||
conv3.z : [13 x 13 x 384 x *] }
|
||||
{ conv3.c : [13 x 13 x 384 x *] (gradient)
|
||||
conv3.y : [13 x 13 x 384 x *] }
|
||||
{ conv3.z : [13 x 13 x 384 x *] (gradient)
|
||||
pool2 : [13 x 13 x 192 x *] (gradient) }
|
||||
{ conv4.W : [256 x 3456] (gradient)
|
||||
conv4.z : [13 x 13 x 256 x *] }
|
||||
{ conv4.c : [13 x 13 x 256 x *] (gradient)
|
||||
conv4.y : [13 x 13 x 256 x *] }
|
||||
{ conv3.b : [1 x 1 x 384] (gradient)
|
||||
conv3.y : [13 x 13 x 384 x *] (gradient)
|
||||
conv4.z : [13 x 13 x 256 x *] (gradient) }
|
||||
{ conv5.W : [256 x 2304] (gradient)
|
||||
conv5.z : [13 x 13 x 256 x *] }
|
||||
{ conv5.c : [13 x 13 x 256 x *] (gradient)
|
||||
conv5.y : [13 x 13 x 256 x *] }
|
||||
{ conv4.b : [1 x 1 x 256] (gradient)
|
||||
conv4.y : [13 x 13 x 256 x *] (gradient)
|
||||
conv5.z : [13 x 13 x 256 x *] (gradient)
|
||||
pool3 : [6 x 6 x 256 x *] }
|
||||
{ conv5.b : [1 x 1 x 256] (gradient)
|
||||
conv5.y : [13 x 13 x 256 x *] (gradient)
|
||||
h1.t : [4096 x *] }
|
||||
{ h1.W : [4096 x 6 x 6 x 256] (gradient)
|
||||
h1.z : [4096 x *] }
|
||||
{ h1.t : [4096 x *] (gradient)
|
||||
h1.y : [4096 x *] }
|
||||
{ h1.z : [4096 x *] (gradient)
|
||||
pool3 : [6 x 6 x 256 x *] (gradient) }
|
||||
{ h1.b : [4096] (gradient)
|
||||
h1.y : [4096 x *] (gradient)
|
||||
h2.t : [4096 x *] }
|
||||
{ h2.W : [4096 x 4096] (gradient)
|
||||
h2.z : [4096 x *] }
|
||||
{ h2.t : [4096 x *] (gradient)
|
||||
h2.y : [4096 x *] }
|
||||
{ h1_d : [4096 x *] (gradient)
|
||||
h2.z : [4096 x *] (gradient) }
|
||||
{ OutputNodes.t : [1000 x *]
|
||||
h2.b : [4096] (gradient)
|
||||
h2.y : [4096 x *] (gradient) }
|
||||
{ OutputNodes.W : [1000 x 4096] (gradient)
|
||||
OutputNodes.z : [1000 x *] (gradient) }
|
||||
{ conv1.z : [56 x 56 x 64 x *] (gradient)
|
||||
pool1 : [27 x 27 x 64 x *] }
|
||||
{ conv1.c : [56 x 56 x 64 x *] (gradient)
|
||||
conv1.y : [56 x 56 x 64 x *] }
|
||||
{ conv1.W : [64 x 363] (gradient)
|
||||
conv1.z : [56 x 56 x 64 x *] }
|
||||
{ conv2.c : [27 x 27 x 192 x *] (gradient)
|
||||
conv2.y : [27 x 27 x 192 x *] }
|
||||
{ conv2.z : [27 x 27 x 192 x *] (gradient)
|
||||
pool1 : [27 x 27 x 64 x *] (gradient)
|
||||
pool2 : [13 x 13 x 192 x *] }
|
||||
{ conv1.b : [1 x 1 x 64] (gradient)
|
||||
conv1.y : [56 x 56 x 64 x *] (gradient) }
|
||||
{ conv2.W : [192 x 1600] (gradient)
|
||||
conv2.z : [27 x 27 x 192 x *] }
|
||||
|
||||
|
||||
05/03/2016 18:07:46: ##############################################################################
|
||||
05/03/2016 18:07:46: # #
|
||||
05/03/2016 18:07:46: # Action "edit" #
|
||||
05/03/2016 18:07:46: # #
|
||||
05/03/2016 18:07:46: ##############################################################################
|
||||
08/16/2016 09:55:25: Training 61100840 parameters in 16 out of 16 parameter tensors and 45 nodes with gradient:
|
||||
|
||||
08/16/2016 09:55:25: Node 'OutputNodes.W' (LearnableParameter operation) : [1000 x 4096]
|
||||
08/16/2016 09:55:25: Node 'OutputNodes.b' (LearnableParameter operation) : [1000]
|
||||
08/16/2016 09:55:25: Node 'conv1.W' (LearnableParameter operation) : [64 x 363]
|
||||
08/16/2016 09:55:25: Node 'conv1.b' (LearnableParameter operation) : [1 x 1 x 64]
|
||||
08/16/2016 09:55:25: Node 'conv2.W' (LearnableParameter operation) : [192 x 1600]
|
||||
08/16/2016 09:55:25: Node 'conv2.b' (LearnableParameter operation) : [1 x 1 x 192]
|
||||
08/16/2016 09:55:25: Node 'conv3.W' (LearnableParameter operation) : [384 x 1728]
|
||||
08/16/2016 09:55:25: Node 'conv3.b' (LearnableParameter operation) : [1 x 1 x 384]
|
||||
08/16/2016 09:55:25: Node 'conv4.W' (LearnableParameter operation) : [256 x 3456]
|
||||
08/16/2016 09:55:25: Node 'conv4.b' (LearnableParameter operation) : [1 x 1 x 256]
|
||||
08/16/2016 09:55:25: Node 'conv5.W' (LearnableParameter operation) : [256 x 2304]
|
||||
08/16/2016 09:55:25: Node 'conv5.b' (LearnableParameter operation) : [1 x 1 x 256]
|
||||
08/16/2016 09:55:25: Node 'h1.W' (LearnableParameter operation) : [4096 x 6 x 6 x 256]
|
||||
08/16/2016 09:55:25: Node 'h1.b' (LearnableParameter operation) : [4096]
|
||||
08/16/2016 09:55:25: Node 'h2.W' (LearnableParameter operation) : [4096 x 4096]
|
||||
08/16/2016 09:55:25: Node 'h2.b' (LearnableParameter operation) : [4096]
|
||||
|
||||
08/16/2016 09:55:25: No PreCompute nodes found, or all already computed. Skipping pre-computation step.
|
||||
|
||||
08/16/2016 09:55:27: Starting Epoch 1: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..2999] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
08/16/2016 09:55:27: Starting minibatch loop.
|
||||
08/16/2016 09:55:36: Epoch[ 1 of 3]-Minibatch[ 1- 100]: ce = 7.41094299 * 1600; err = 0.99937500 * 1600; time = 8.3724s; samplesPerSecond = 191.1
|
||||
08/16/2016 09:55:42: Finished Epoch[ 1 of 3]: [Training] ce = 7.23292074 * 2999; err = 0.99899967 * 2999; totalSamplesSeen = 2999; learningRatePerSample = 0.00062499999; epochTime=14.0535s
|
||||
08/16/2016 09:55:44: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet.1'
|
||||
|
||||
08/16/2016 09:55:46: Starting Epoch 2: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [2999..5998] (first sequence at sample 2999), data subset 0 of 1
|
||||
|
||||
08/16/2016 09:55:46: Starting minibatch loop.
|
||||
08/16/2016 09:55:53: Epoch[ 2 of 3]-Minibatch[ 1- 100, 100.00%]: ce = 6.91068848 * 1600; err = 0.99875000 * 1600; time = 7.2054s; samplesPerSecond = 222.1
|
||||
08/16/2016 09:56:00: Finished Epoch[ 2 of 3]: [Training] ce = 6.91553955 * 2999; err = 0.99933311 * 2999; totalSamplesSeen = 5998; learningRatePerSample = 0.00062499999; epochTime=13.8615s
|
||||
08/16/2016 09:56:03: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet.2'
|
||||
|
||||
08/16/2016 09:56:05: Starting Epoch 3: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [5998..8997] (first sequence at sample 5998), data subset 0 of 1
|
||||
|
||||
08/16/2016 09:56:05: Starting minibatch loop.
|
||||
08/16/2016 09:56:12: Epoch[ 3 of 3]-Minibatch[ 1- 100, 100.00%]: ce = 6.88422668 * 1600; err = 0.99687500 * 1600; time = 7.1340s; samplesPerSecond = 224.3
|
||||
08/16/2016 09:56:19: Finished Epoch[ 3 of 3]: [Training] ce = 6.88836513 * 2999; err = 0.99766589 * 2999; totalSamplesSeen = 8997; learningRatePerSample = 0.00062499999; epochTime=13.7378s
|
||||
08/16/2016 09:56:21: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095522.619074/Image_AlexNet@release_gpu/models/AlexNet'
|
||||
08/16/2016 09:56:25: CNTKCommandTrainEnd: Train
|
||||
|
||||
08/16/2016 09:56:25: Action "train" complete.
|
||||
|
||||
|
||||
08/16/2016 09:56:25: ##############################################################################
|
||||
08/16/2016 09:56:25: # #
|
||||
08/16/2016 09:56:25: # Action "edit" #
|
||||
08/16/2016 09:56:25: # #
|
||||
08/16/2016 09:56:25: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
|
@ -594,27 +674,29 @@ Validating network. 30 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c: using GEMM convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using GEMM convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c: using GEMM convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using GEMM convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3.c: using GEMM convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv4.c: using GEMM convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv5.c: using GEMM convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool3: using GEMM convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
|
||||
18 out of 48 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
Node 'unnamed143' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.000000.
|
||||
Node 'unnamed143' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 5.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -674,8 +756,8 @@ Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [1000 x *1]
|
|||
Validating --> labels = InputValue() : -> [1000 x *1]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [1000 x *1], [1000 x *1] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, OutputNodes.z) : [1000 x *1], [1000 x *1] -> [1]
|
||||
Validating --> unnamed137 = LearnableParameter() : -> [1 x 1]
|
||||
Validating --> errTop5 = ErrorPrediction (labels, OutputNodes.z, unnamed137) : [1000 x *1], [1000 x *1], [1 x 1] -> [1]
|
||||
Validating --> unnamed143 = LearnableParameter() : -> [1 x 1]
|
||||
Validating --> errTop5 = ErrorPrediction (labels, OutputNodes.z, unnamed143) : [1000 x *1], [1000 x *1], [1 x 1] -> [1]
|
||||
|
||||
Validating network. 31 nodes to process in pass 2.
|
||||
|
||||
|
|
@ -689,28 +771,58 @@ Validating network, final pass.
|
|||
Post-processing network complete.
|
||||
|
||||
|
||||
05/03/2016 18:07:51: Action "edit" complete.
|
||||
08/16/2016 09:56:31: Action "edit" complete.
|
||||
|
||||
|
||||
05/03/2016 18:07:51: ##############################################################################
|
||||
05/03/2016 18:07:51: # #
|
||||
05/03/2016 18:07:51: # Action "test" #
|
||||
05/03/2016 18:07:51: # #
|
||||
05/03/2016 18:07:51: ##############################################################################
|
||||
08/16/2016 09:56:31: ##############################################################################
|
||||
08/16/2016 09:56:31: # #
|
||||
08/16/2016 09:56:31: # Action "test" #
|
||||
08/16/2016 09:56:31: # #
|
||||
08/16/2016 09:56:31: ##############################################################################
|
||||
|
||||
NDLBuilder Using GPU 0
|
||||
Node 'conv1.W' (LearnableParameter operation): Initializing Parameter[64 x 363] <- 0.000000.
|
||||
Node 'conv1.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv2.W' (LearnableParameter operation): Initializing Parameter[192 x 1600] <- 0.000000.
|
||||
Node 'conv2.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 192] <- 0.000000.
|
||||
Node 'conv3.W' (LearnableParameter operation): Initializing Parameter[384 x 1728] <- 0.000000.
|
||||
Node 'conv3.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 384] <- 0.000000.
|
||||
Node 'conv4.W' (LearnableParameter operation): Initializing Parameter[256 x 3456] <- 0.000000.
|
||||
Node 'conv4.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 0.000000.
|
||||
Node 'conv5.W' (LearnableParameter operation): Initializing Parameter[256 x 2304] <- 0.000000.
|
||||
Node 'conv5.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[4096 x 6 x 6 x 256] <- 0.000000.
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[4096] <- 0.000000.
|
||||
Node 'h2.b' (LearnableParameter operation): Initializing Parameter[4096] <- 0.000000.
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[1000] <- 0.000000.
|
||||
Node 'conv1.W' (LearnableParameter operation): Initializing Parameter[64 x 363] <- gaussian(seed=9, range=0.010497*0.950000, onCPU=false).
|
||||
Node 'conv1.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv2.W' (LearnableParameter operation): Initializing Parameter[192 x 1600] <- gaussian(seed=10, range=0.005000*2.000000, onCPU=false).
|
||||
Node 'conv2.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 192] <- 1.000000.
|
||||
Node 'conv3.W' (LearnableParameter operation): Initializing Parameter[384 x 1728] <- gaussian(seed=11, range=0.004811*2.070000, onCPU=false).
|
||||
Node 'conv3.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 384] <- 0.000000.
|
||||
Node 'conv4.W' (LearnableParameter operation): Initializing Parameter[256 x 3456] <- gaussian(seed=12, range=0.003402*2.900000, onCPU=false).
|
||||
Node 'conv4.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 1.000000.
|
||||
Node 'conv5.W' (LearnableParameter operation): Initializing Parameter[256 x 2304] <- gaussian(seed=13, range=0.004167*2.400000, onCPU=false).
|
||||
Node 'conv5.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 1.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[4096 x 6 x 6 x 256] <- gaussian(seed=14, range=0.002083*6.400000, onCPU=false).
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[4096] <- 1.000000.
|
||||
Node 'h2.W' (LearnableParameter operation): Initializating Parameter[4096 x 0] as gaussian later when dimensions are fully known.
|
||||
Node 'h2.b' (LearnableParameter operation): Initializing Parameter[4096] <- 1.000000.
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializating Parameter[1000 x 0] as gaussian later when dimensions are fully known.
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[1000] <- 1.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
4 roots:
|
||||
3 roots:
|
||||
OutputNodes.z = Plus()
|
||||
ce = CrossEntropyWithSoftmax()
|
||||
err = ErrorPrediction()
|
||||
errTop5 = ErrorPrediction()
|
||||
|
||||
Validating network. 50 nodes to process in pass 1.
|
||||
Validating network. 48 nodes to process in pass 1.
|
||||
|
||||
Validating --> OutputNodes.W = LearnableParameter() : -> [1000 x 4096]
|
||||
Validating --> h2.W = LearnableParameter() : -> [4096 x 4096]
|
||||
Validating --> OutputNodes.W = LearnableParameter() : -> [1000 x 0]
|
||||
Validating --> h2.W = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> h1.W = LearnableParameter() : -> [4096 x 6 x 6 x 256]
|
||||
Validating --> conv5.W = LearnableParameter() : -> [256 x 2304]
|
||||
Validating --> conv4.W = LearnableParameter() : -> [256 x 3456]
|
||||
|
|
@ -746,44 +858,46 @@ Validating --> h1.b = LearnableParameter() : -> [4096]
|
|||
Validating --> h1.z = Plus (h1.t, h1.b) : [4096 x *2], [4096] -> [4096 x *2]
|
||||
Validating --> h1.y = RectifiedLinear (h1.z) : [4096 x *2] -> [4096 x *2]
|
||||
Validating --> h1_d = Dropout (h1.y) : [4096 x *2] -> [4096 x *2]
|
||||
Node 'h2.W' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 4096].
|
||||
Node 'h2.W' (LearnableParameter operation): Initializing Parameter[4096 x 4096] <- gaussian(seed=15, range=0.003125*3.200000, onCPU=false).
|
||||
Validating --> h2.t = Times (h2.W, h1_d) : [4096 x 4096], [4096 x *2] -> [4096 x *2]
|
||||
Validating --> h2.b = LearnableParameter() : -> [4096]
|
||||
Validating --> h2.z = Plus (h2.t, h2.b) : [4096 x *2], [4096] -> [4096 x *2]
|
||||
Validating --> h2.y = RectifiedLinear (h2.z) : [4096 x *2] -> [4096 x *2]
|
||||
Validating --> h2_d = Dropout (h2.y) : [4096 x *2] -> [4096 x *2]
|
||||
Node 'OutputNodes.W' (LearnableParameter operation) operation: Tensor shape was inferred as [1000 x 4096].
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializing Parameter[1000 x 4096] <- gaussian(seed=16, range=0.003125*3.200000, onCPU=false).
|
||||
Validating --> OutputNodes.t = Times (OutputNodes.W, h2_d) : [1000 x 4096], [4096 x *2] -> [1000 x *2]
|
||||
Validating --> OutputNodes.b = LearnableParameter() : -> [1000]
|
||||
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [1000 x *2], [1000] -> [1000 x *2]
|
||||
Validating --> labels = InputValue() : -> [1000 x *2]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [1000 x *2], [1000 x *2] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, OutputNodes.z) : [1000 x *2], [1000 x *2] -> [1]
|
||||
Validating --> unnamed137 = LearnableParameter() : -> [1 x 1]
|
||||
Validating --> errTop5 = ErrorPrediction (labels, OutputNodes.z, unnamed137) : [1000 x *2], [1000 x *2], [1 x 1] -> [1]
|
||||
|
||||
Validating network. 31 nodes to process in pass 2.
|
||||
Validating network. 30 nodes to process in pass 2.
|
||||
|
||||
|
||||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c: using cuDNN convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c: using cuDNN convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv4.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv5.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool3: using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
|
||||
20 out of 50 nodes do not share the minibatch layout with the input data.
|
||||
18 out of 48 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
|
|
@ -792,62 +906,12 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 48 matrices, 0 are shared as 0, and 48 are not shared.
|
||||
|
||||
(nil): {[OutputNodes.W Gradient[1000 x 4096]] [OutputNodes.b Gradient[1000]] [OutputNodes.t Gradient[1000 x *2]] [OutputNodes.z Gradient[1000 x *2]] [ce Gradient[1]] [conv1.W Gradient[64 x 363]] [conv1.b Gradient[1 x 1 x 64]] [conv1.c Gradient[56 x 56 x 64 x *2]] [conv1.y Gradient[56 x 56 x 64 x *2]] [conv1.z Gradient[56 x 56 x 64 x *2]] [conv2.W Gradient[192 x 1600]] [conv2.b Gradient[1 x 1 x 192]] [conv2.c Gradient[27 x 27 x 192 x *2]] [conv2.y Gradient[27 x 27 x 192 x *2]] [conv2.z Gradient[27 x 27 x 192 x *2]] [conv3.W Gradient[384 x 1728]] [conv3.b Gradient[1 x 1 x 384]] [conv3.c Gradient[13 x 13 x 384 x *2]] [conv3.y Gradient[13 x 13 x 384 x *2]] [conv3.z Gradient[13 x 13 x 384 x *2]] [conv4.W Gradient[256 x 3456]] [conv4.b Gradient[1 x 1 x 256]] [conv4.c Gradient[13 x 13 x 256 x *2]] [conv4.y Gradient[13 x 13 x 256 x *2]] [conv4.z Gradient[13 x 13 x 256 x *2]] [conv5.W Gradient[256 x 2304]] [conv5.b Gradient[1 x 1 x 256]] [conv5.c Gradient[13 x 13 x 256 x *2]] [conv5.y Gradient[13 x 13 x 256 x *2]] [conv5.z Gradient[13 x 13 x 256 x *2]] [err Gradient[1]] [errTop5 Gradient[1]] [features Gradient[224 x 224 x 3 x *2]] [h1.W Gradient[4096 x 6 x 6 x 256]] [h1.b Gradient[4096]] [h1.t Gradient[4096 x *2]] [h1.y Gradient[4096 x *2]] [h1.z Gradient[4096 x *2]] [h1_d Gradient[4096 x *2]] [h2.W Gradient[4096 x 4096]] [h2.b Gradient[4096]] [h2.t Gradient[4096 x *2]] [h2.y Gradient[4096 x *2]] [h2.z Gradient[4096 x *2]] [h2_d Gradient[4096 x *2]] [labels Gradient[1000 x *2]] [pool1 Gradient[27 x 27 x 64 x *2]] [pool2 Gradient[13 x 13 x 192 x *2]] [pool3 Gradient[6 x 6 x 256 x *2]] [unnamed137 Gradient[1 x 1]] }
|
||||
0x7f479db02088: {[conv1.b Value[1 x 1 x 64]] }
|
||||
0x7f479db2c418: {[conv1.W Value[64 x 363]] }
|
||||
0x7f479db2d7a8: {[conv2.W Value[192 x 1600]] }
|
||||
0x7f479db2dae8: {[conv2.b Value[1 x 1 x 192]] }
|
||||
0x7f479db2fdd8: {[conv3.W Value[384 x 1728]] }
|
||||
0x7f479db30118: {[conv3.b Value[1 x 1 x 384]] }
|
||||
0x7f479db30908: {[conv4.b Value[1 x 1 x 256]] }
|
||||
0x7f479db33f08: {[conv4.W Value[256 x 3456]] }
|
||||
0x7f479db35358: {[conv5.b Value[1 x 1 x 256]] }
|
||||
0x7f479db36608: {[conv5.W Value[256 x 2304]] }
|
||||
0x7f479db37d68: {[features Value[224 x 224 x 3 x *2]] }
|
||||
0x7f479db38858: {[h1.W Value[4096 x 6 x 6 x 256]] }
|
||||
0x7f479db38b98: {[h1.b Value[4096]] }
|
||||
0x7f479db3aa98: {[h2.b Value[4096]] }
|
||||
0x7f479db3b5d8: {[h2.W Value[4096 x 4096]] }
|
||||
0x7f479db3ca98: {[labels Value[1000 x *2]] }
|
||||
0x7f479db3de18: {[OutputNodes.b Value[1000]] }
|
||||
0x7f479db3e628: {[OutputNodes.W Value[1000 x 4096]] }
|
||||
0x7f479db40748: {[unnamed137 Value[1 x 1]] }
|
||||
0x7f479db413e8: {[errTop5 Value[1]] }
|
||||
0x7f479db42138: {[ce Value[1]] }
|
||||
0x7f479db48378: {[err Value[1]] }
|
||||
0x7f479db53e18: {[pool3 Value[6 x 6 x 256 x *2]] }
|
||||
0x7f479db53fd8: {[h1.t Value[4096 x *2]] }
|
||||
0x7f479db54198: {[h1.z Value[4096 x *2]] }
|
||||
0x7f479db54358: {[h1.y Value[4096 x *2]] }
|
||||
0x7f479db54518: {[h1_d Value[4096 x *2]] }
|
||||
0x7f479db54898: {[h2.t Value[4096 x *2]] }
|
||||
0x7f479db54a58: {[h2.z Value[4096 x *2]] }
|
||||
0x7f479db54c18: {[h2.y Value[4096 x *2]] }
|
||||
0x7f479db54dd8: {[h2_d Value[4096 x *2]] }
|
||||
0x7f479db55158: {[OutputNodes.t Value[1000 x *2]] }
|
||||
0x7f479db55318: {[OutputNodes.z Value[1000 x *2]] }
|
||||
0x7f47a644f258: {[conv1.z Value[56 x 56 x 64 x *2]] }
|
||||
0x7f47a644f558: {[conv1.c Value[56 x 56 x 64 x *2]] }
|
||||
0x7f47a6450068: {[conv1.y Value[56 x 56 x 64 x *2]] }
|
||||
0x7f47a64506b8: {[pool1 Value[27 x 27 x 64 x *2]] }
|
||||
0x7f47a6450878: {[conv2.c Value[27 x 27 x 192 x *2]] }
|
||||
0x7f47a6450bf8: {[conv2.z Value[27 x 27 x 192 x *2]] }
|
||||
0x7f47a6450db8: {[conv2.y Value[27 x 27 x 192 x *2]] }
|
||||
0x7f47a6450f78: {[pool2 Value[13 x 13 x 192 x *2]] }
|
||||
0x7f47a6451138: {[conv3.c Value[13 x 13 x 384 x *2]] }
|
||||
0x7f47a64514b8: {[conv3.z Value[13 x 13 x 384 x *2]] }
|
||||
0x7f47a6451678: {[conv3.y Value[13 x 13 x 384 x *2]] }
|
||||
0x7f47a6451838: {[conv4.c Value[13 x 13 x 256 x *2]] }
|
||||
0x7f47a6451bb8: {[conv4.z Value[13 x 13 x 256 x *2]] }
|
||||
0x7f47a6451d78: {[conv4.y Value[13 x 13 x 256 x *2]] }
|
||||
0x7f47a6451f38: {[conv5.c Value[13 x 13 x 256 x *2]] }
|
||||
0x7f47a64522b8: {[conv5.z Value[13 x 13 x 256 x *2]] }
|
||||
0x7f47a6452478: {[conv5.y Value[13 x 13 x 256 x *2]] }
|
||||
|
||||
05/03/2016 18:07:55: Final Results: Minibatch[1-32]: err = 0.99800000 * 500; errTop5 = 0.99400000 * 500; ce = 6.96324823 * 500; perplexity = 1057.06156985
|
||||
08/16/2016 09:56:33: Minibatch[1-32]: err = 0.99800000 * 500; ce = 7.32804733 * 500
|
||||
08/16/2016 09:56:33: Final Results: Minibatch[1-32]: err = 0.99800000 * 500; ce = 7.32804733 * 500; perplexity = 1522.40611516
|
||||
|
||||
05/03/2016 18:07:55: Action "test" complete.
|
||||
08/16/2016 09:56:33: Action "test" complete.
|
||||
|
||||
05/03/2016 18:07:55: __COMPLETED__
|
||||
08/16/2016 09:56:33: __COMPLETED__
|
||||
Разница между файлами не показана из-за своего большого размера
Загрузить разницу
|
|
@ -1,47 +1,59 @@
|
|||
CPU info:
|
||||
CPU Model Name: Intel(R) Xeon(R) CPU W3530 @ 2.80GHz
|
||||
Hardware threads: 4
|
||||
Total Memory: 12580404 kB
|
||||
-------------------------------------------------------------------
|
||||
Copying test data to local directory
|
||||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/AlexNet.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu DeviceId=0 timestamping=true
|
||||
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/AlexNetCommon.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu DeviceId=0 timestamping=true configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/AlexNet.cntk
|
||||
-------------------------------------------------------------------
|
||||
Build info:
|
||||
|
||||
Built time: May 3 2016 13:23:06
|
||||
Last modified date: Mon Apr 18 00:00:12 2016
|
||||
Built time: Aug 16 2016 02:54:53
|
||||
Last modified date: Fri Aug 12 05:31:21 2016
|
||||
Build type: Release
|
||||
Build target: GPU
|
||||
With 1bit-SGD: no
|
||||
Math lib: mkl
|
||||
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
CUB_PATH: C:\src\cub-1.4.1
|
||||
CUB_PATH: c:\src\cub-1.4.1
|
||||
CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
Build Branch: HEAD
|
||||
Build SHA1: af96f7cce6c3c78a4f1e9315e061291c79360e12
|
||||
Built by svcphil on LIANA-09-w
|
||||
Build Path: c:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
Built by svcphil on Philly-Pool3
|
||||
Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
-------------------------------------------------------------------
|
||||
Changed current directory to C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu\TestData
|
||||
05/03/2016 14:11:01: -------------------------------------------------------------------
|
||||
05/03/2016 14:11:01: Build info:
|
||||
Changed current directory to C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu\TestData
|
||||
08/16/2016 03:03:44: -------------------------------------------------------------------
|
||||
08/16/2016 03:03:44: Build info:
|
||||
|
||||
05/03/2016 14:11:01: Built time: May 3 2016 13:23:06
|
||||
05/03/2016 14:11:01: Last modified date: Mon Apr 18 00:00:12 2016
|
||||
05/03/2016 14:11:01: Build type: Release
|
||||
05/03/2016 14:11:01: Build target: GPU
|
||||
05/03/2016 14:11:01: With 1bit-SGD: no
|
||||
05/03/2016 14:11:01: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
05/03/2016 14:11:01: CUB_PATH: C:\src\cub-1.4.1
|
||||
05/03/2016 14:11:01: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
05/03/2016 14:11:01: Build Branch: HEAD
|
||||
05/03/2016 14:11:01: Build SHA1: af96f7cce6c3c78a4f1e9315e061291c79360e12
|
||||
05/03/2016 14:11:01: Built by svcphil on LIANA-09-w
|
||||
05/03/2016 14:11:01: Build Path: c:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
05/03/2016 14:11:01: -------------------------------------------------------------------
|
||||
08/16/2016 03:03:44: Built time: Aug 16 2016 02:54:53
|
||||
08/16/2016 03:03:44: Last modified date: Fri Aug 12 05:31:21 2016
|
||||
08/16/2016 03:03:44: Build type: Release
|
||||
08/16/2016 03:03:44: Build target: GPU
|
||||
08/16/2016 03:03:44: With 1bit-SGD: no
|
||||
08/16/2016 03:03:44: Math lib: mkl
|
||||
08/16/2016 03:03:44: CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
|
||||
08/16/2016 03:03:44: CUB_PATH: c:\src\cub-1.4.1
|
||||
08/16/2016 03:03:44: CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
|
||||
08/16/2016 03:03:44: Build Branch: HEAD
|
||||
08/16/2016 03:03:44: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
|
||||
08/16/2016 03:03:44: Built by svcphil on Philly-Pool3
|
||||
08/16/2016 03:03:44: Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
|
||||
08/16/2016 03:03:44: -------------------------------------------------------------------
|
||||
08/16/2016 03:03:45: -------------------------------------------------------------------
|
||||
08/16/2016 03:03:45: GPU info:
|
||||
|
||||
05/03/2016 14:11:01: Running on DPHAIM-25 at 2016/05/03 14:11:01
|
||||
05/03/2016 14:11:01: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/AlexNet.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu DeviceId=0 timestamping=true
|
||||
08/16/2016 03:03:45: Device[0]: cores = 2496; computeCapability = 5.2; type = "Quadro M4000"; memory = 8090 MB
|
||||
08/16/2016 03:03:45: -------------------------------------------------------------------
|
||||
|
||||
08/16/2016 03:03:45: Running on cntk-muc00 at 2016/08/16 03:03:45
|
||||
08/16/2016 03:03:45: Command line:
|
||||
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/AlexNetCommon.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu DeviceId=0 timestamping=true configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/AlexNet.cntk
|
||||
|
||||
|
||||
|
||||
05/03/2016 14:11:01: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 14:11:01: ModelDir = "$RunDir$/models"
|
||||
08/16/2016 03:03:45: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:03:45: ModelDir = "$RunDir$/models"
|
||||
ndlMacros=$ConfigDir$/Macros.ndl
|
||||
precision=float
|
||||
deviceId=Auto
|
||||
|
|
@ -74,6 +86,29 @@ Train=[
|
|||
]
|
||||
numMBsToShowResult=100
|
||||
]
|
||||
]
|
||||
AddTop5Eval=[
|
||||
action=edit
|
||||
CurModel=$ModelDir$/AlexNet
|
||||
NewModel=$ModelDir$/AlexNet.Top5
|
||||
editPath=$ConfigDir$/add_top5_layer.mel
|
||||
]
|
||||
Test=[
|
||||
action=test
|
||||
modelPath=$ModelDir$/AlexNet.Top5
|
||||
minibatchSize=16
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=$ConfigDir$/AlexNet.ndl
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu\TestData
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Train=[
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=$ConfigDir$/train_map.txt
|
||||
|
|
@ -93,19 +128,7 @@ Train=[
|
|||
]
|
||||
]
|
||||
]
|
||||
AddTop5Eval=[
|
||||
action=edit
|
||||
CurModel=$ModelDir$/AlexNet
|
||||
NewModel=$ModelDir$/AlexNet.Top5
|
||||
editPath=$ConfigDir$/add_top5_layer.mel
|
||||
]
|
||||
Test=[
|
||||
action=test
|
||||
modelPath=$ModelDir$/AlexNet.Top5
|
||||
minibatchSize=16
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=$ConfigDir$/AlexNet.ndl
|
||||
]
|
||||
Test=[
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=$ConfigDir$/val_map.txt
|
||||
|
|
@ -122,18 +145,11 @@ Test=[
|
|||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu\TestData
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
|
||||
05/03/2016 14:11:01: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:03:45: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 14:11:01: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
05/03/2016 14:11:01: ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models"
|
||||
08/16/2016 03:03:45: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:03:45: ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models"
|
||||
ndlMacros=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/Macros.ndl
|
||||
precision=float
|
||||
deviceId=Auto
|
||||
|
|
@ -143,7 +159,7 @@ traceLevel=1
|
|||
numMBsToShowResult=100
|
||||
Train=[
|
||||
action=train
|
||||
modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet
|
||||
modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/AlexNet.ndl
|
||||
]
|
||||
|
|
@ -166,6 +182,29 @@ Train=[
|
|||
]
|
||||
numMBsToShowResult=100
|
||||
]
|
||||
]
|
||||
AddTop5Eval=[
|
||||
action=edit
|
||||
CurModel=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet
|
||||
NewModel=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
editPath=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/add_top5_layer.mel
|
||||
]
|
||||
Test=[
|
||||
action=test
|
||||
modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
minibatchSize=16
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/AlexNet.ndl
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu\TestData
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
Train=[
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/train_map.txt
|
||||
|
|
@ -185,19 +224,7 @@ Train=[
|
|||
]
|
||||
]
|
||||
]
|
||||
AddTop5Eval=[
|
||||
action=edit
|
||||
CurModel=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet
|
||||
NewModel=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
editPath=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/add_top5_layer.mel
|
||||
]
|
||||
Test=[
|
||||
action=test
|
||||
modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
minibatchSize=16
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/AlexNet.ndl
|
||||
]
|
||||
Test=[
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/val_map.txt
|
||||
|
|
@ -214,43 +241,37 @@ Test=[
|
|||
]
|
||||
]
|
||||
]
|
||||
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu\TestData
|
||||
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu
|
||||
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu\TestData
|
||||
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet
|
||||
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu
|
||||
DeviceId=0
|
||||
timestamping=true
|
||||
|
||||
05/03/2016 14:11:01: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:03:45: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
05/03/2016 14:11:01: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
08/16/2016 03:03:45: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
|
||||
configparameters: AlexNet.cntk:AddTop5Eval=[
|
||||
action=edit
|
||||
CurModel=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet
|
||||
NewModel=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
CurModel=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet
|
||||
NewModel=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
editPath=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/add_top5_layer.mel
|
||||
]
|
||||
|
||||
configparameters: AlexNet.cntk:command=Train:AddTop5Eval:Test
|
||||
configparameters: AlexNet.cntk:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet
|
||||
configparameters: AlexNet.cntk:currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu\TestData
|
||||
configparameters: AlexNet.cntk:DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu\TestData
|
||||
configparameters: AlexNet.cntk:currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu\TestData
|
||||
configparameters: AlexNet.cntk:DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu\TestData
|
||||
configparameters: AlexNet.cntk:deviceId=0
|
||||
configparameters: AlexNet.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models
|
||||
configparameters: AlexNet.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models
|
||||
configparameters: AlexNet.cntk:ndlMacros=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/Macros.ndl
|
||||
configparameters: AlexNet.cntk:numMBsToShowResult=100
|
||||
configparameters: AlexNet.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu
|
||||
configparameters: AlexNet.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu
|
||||
configparameters: AlexNet.cntk:parallelTrain=false
|
||||
configparameters: AlexNet.cntk:precision=float
|
||||
configparameters: AlexNet.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu
|
||||
configparameters: AlexNet.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu
|
||||
configparameters: AlexNet.cntk:Test=[
|
||||
action=test
|
||||
modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet.Top5
|
||||
minibatchSize=16
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/AlexNet.ndl
|
||||
]
|
||||
] [
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/val_map.txt
|
||||
|
|
@ -272,7 +293,7 @@ configparameters: AlexNet.cntk:timestamping=true
|
|||
configparameters: AlexNet.cntk:traceLevel=1
|
||||
configparameters: AlexNet.cntk:Train=[
|
||||
action=train
|
||||
modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet
|
||||
modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet
|
||||
NDLNetworkBuilder=[
|
||||
networkDescription=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/AlexNet.ndl
|
||||
]
|
||||
|
|
@ -295,6 +316,7 @@ configparameters: AlexNet.cntk:Train=[
|
|||
]
|
||||
numMBsToShowResult=100
|
||||
]
|
||||
] [
|
||||
reader=[
|
||||
readerType=ImageReader
|
||||
file=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Image\AlexNet/train_map.txt
|
||||
|
|
@ -315,24 +337,54 @@ configparameters: AlexNet.cntk:Train=[
|
|||
]
|
||||
]
|
||||
|
||||
05/03/2016 14:11:01: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
05/03/2016 14:11:01: Commands: Train AddTop5Eval Test
|
||||
05/03/2016 14:11:01: Precision = "float"
|
||||
05/03/2016 14:11:01: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet
|
||||
05/03/2016 14:11:01: CNTKCommandTrainInfo: Train : 3
|
||||
05/03/2016 14:11:01: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
08/16/2016 03:03:45: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
08/16/2016 03:03:45: Commands: Train AddTop5Eval Test
|
||||
08/16/2016 03:03:45: Precision = "float"
|
||||
08/16/2016 03:03:45: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet
|
||||
08/16/2016 03:03:45: CNTKCommandTrainInfo: Train : 3
|
||||
08/16/2016 03:03:45: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
|
||||
|
||||
05/03/2016 14:11:01: ##############################################################################
|
||||
05/03/2016 14:11:01: # #
|
||||
05/03/2016 14:11:01: # Action "train" #
|
||||
05/03/2016 14:11:01: # #
|
||||
05/03/2016 14:11:01: ##############################################################################
|
||||
08/16/2016 03:03:45: ##############################################################################
|
||||
08/16/2016 03:03:45: # #
|
||||
08/16/2016 03:03:45: # Action "train" #
|
||||
08/16/2016 03:03:45: # #
|
||||
08/16/2016 03:03:45: ##############################################################################
|
||||
|
||||
05/03/2016 14:11:01: CNTKCommandTrainBegin: Train
|
||||
08/16/2016 03:03:45: CNTKCommandTrainBegin: Train
|
||||
NDLBuilder Using GPU 0
|
||||
|
||||
05/03/2016 14:11:01: Creating virgin network.
|
||||
useParallelTrain option is not enabled. ParallelTrain config will be ignored.
|
||||
08/16/2016 03:03:45: Creating virgin network.
|
||||
Node 'conv1.W' (LearnableParameter operation): Initializing Parameter[64 x 363] <- 0.000000.
|
||||
Node 'conv1.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv2.W' (LearnableParameter operation): Initializing Parameter[192 x 1600] <- 0.000000.
|
||||
Node 'conv2.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 192] <- 0.000000.
|
||||
Node 'conv3.W' (LearnableParameter operation): Initializing Parameter[384 x 1728] <- 0.000000.
|
||||
Node 'conv3.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 384] <- 0.000000.
|
||||
Node 'conv4.W' (LearnableParameter operation): Initializing Parameter[256 x 3456] <- 0.000000.
|
||||
Node 'conv4.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 0.000000.
|
||||
Node 'conv5.W' (LearnableParameter operation): Initializing Parameter[256 x 2304] <- 0.000000.
|
||||
Node 'conv5.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[4096 x 6 x 6 x 256] <- 0.000000.
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[4096] <- 0.000000.
|
||||
Node 'h2.b' (LearnableParameter operation): Initializing Parameter[4096] <- 0.000000.
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[1000] <- 0.000000.
|
||||
Node 'conv1.W' (LearnableParameter operation): Initializing Parameter[64 x 363] <- gaussian(seed=1, range=0.010497*0.950000, onCPU=false).
|
||||
Microsoft::MSR::CNTK::GPUMatrix<ElemType>::SetGaussianRandomValue (GPU): creating curand object with seed 1, sizeof(ElemType)==4
|
||||
Node 'conv1.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv2.W' (LearnableParameter operation): Initializing Parameter[192 x 1600] <- gaussian(seed=2, range=0.005000*2.000000, onCPU=false).
|
||||
Node 'conv2.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 192] <- 1.000000.
|
||||
Node 'conv3.W' (LearnableParameter operation): Initializing Parameter[384 x 1728] <- gaussian(seed=3, range=0.004811*2.070000, onCPU=false).
|
||||
Node 'conv3.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 384] <- 0.000000.
|
||||
Node 'conv4.W' (LearnableParameter operation): Initializing Parameter[256 x 3456] <- gaussian(seed=4, range=0.003402*2.900000, onCPU=false).
|
||||
Node 'conv4.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 1.000000.
|
||||
Node 'conv5.W' (LearnableParameter operation): Initializing Parameter[256 x 2304] <- gaussian(seed=5, range=0.004167*2.400000, onCPU=false).
|
||||
Node 'conv5.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 1.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[4096 x 6 x 6 x 256] <- gaussian(seed=6, range=0.002083*6.400000, onCPU=false).
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[4096] <- 1.000000.
|
||||
Node 'h2.W' (LearnableParameter operation): Initializating Parameter[4096 x 0] as gaussian later when dimensions are fully known.
|
||||
Node 'h2.b' (LearnableParameter operation): Initializing Parameter[4096] <- 1.000000.
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializating Parameter[1000 x 0] as gaussian later when dimensions are fully known.
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[1000] <- 1.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -343,8 +395,8 @@ Post-processing network...
|
|||
|
||||
Validating network. 48 nodes to process in pass 1.
|
||||
|
||||
Validating --> OutputNodes.W = LearnableParameter() : -> [1000 x 4096]
|
||||
Validating --> h2.W = LearnableParameter() : -> [4096 x 4096]
|
||||
Validating --> OutputNodes.W = LearnableParameter() : -> [1000 x 0]
|
||||
Validating --> h2.W = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> h1.W = LearnableParameter() : -> [4096 x 6 x 6 x 256]
|
||||
Validating --> conv5.W = LearnableParameter() : -> [256 x 2304]
|
||||
Validating --> conv4.W = LearnableParameter() : -> [256 x 3456]
|
||||
|
|
@ -380,11 +432,15 @@ Validating --> h1.b = LearnableParameter() : -> [4096]
|
|||
Validating --> h1.z = Plus (h1.t, h1.b) : [4096 x *], [4096] -> [4096 x *]
|
||||
Validating --> h1.y = RectifiedLinear (h1.z) : [4096 x *] -> [4096 x *]
|
||||
Validating --> h1_d = Dropout (h1.y) : [4096 x *] -> [4096 x *]
|
||||
Node 'h2.W' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 4096].
|
||||
Node 'h2.W' (LearnableParameter operation): Initializing Parameter[4096 x 4096] <- gaussian(seed=7, range=0.003125*3.200000, onCPU=false).
|
||||
Validating --> h2.t = Times (h2.W, h1_d) : [4096 x 4096], [4096 x *] -> [4096 x *]
|
||||
Validating --> h2.b = LearnableParameter() : -> [4096]
|
||||
Validating --> h2.z = Plus (h2.t, h2.b) : [4096 x *], [4096] -> [4096 x *]
|
||||
Validating --> h2.y = RectifiedLinear (h2.z) : [4096 x *] -> [4096 x *]
|
||||
Validating --> h2_d = Dropout (h2.y) : [4096 x *] -> [4096 x *]
|
||||
Node 'OutputNodes.W' (LearnableParameter operation) operation: Tensor shape was inferred as [1000 x 4096].
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializing Parameter[1000 x 4096] <- gaussian(seed=8, range=0.003125*3.200000, onCPU=false).
|
||||
Validating --> OutputNodes.t = Times (OutputNodes.W, h2_d) : [1000 x 4096], [4096 x *] -> [1000 x *]
|
||||
Validating --> OutputNodes.b = LearnableParameter() : -> [1000]
|
||||
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [1000 x *], [1000] -> [1000 x *]
|
||||
|
|
@ -398,134 +454,157 @@ Validating network. 30 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c: using cuDNN convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c: using cuDNN convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv4.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv5.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool3: using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
|
||||
18 out of 48 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
05/03/2016 14:11:02: Created model with 48 nodes on GPU 0.
|
||||
08/16/2016 03:03:45: Created model with 48 nodes on GPU 0.
|
||||
|
||||
05/03/2016 14:11:02: Training criterion node(s):
|
||||
05/03/2016 14:11:02: ce = CrossEntropyWithSoftmax
|
||||
08/16/2016 03:03:45: Training criterion node(s):
|
||||
08/16/2016 03:03:45: ce = CrossEntropyWithSoftmax
|
||||
|
||||
05/03/2016 14:11:02: Evaluation criterion node(s):
|
||||
|
||||
05/03/2016 14:11:02: err = ErrorPrediction
|
||||
08/16/2016 03:03:45: Evaluation criterion node(s):
|
||||
08/16/2016 03:03:45: err = ErrorPrediction
|
||||
|
||||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 93 matrices, 61 are shared as 27, and 32 are not shared.
|
||||
|
||||
0000000000000000: {[err Gradient[1]] [features Gradient[224 x 224 x 3 x *]] [labels Gradient[1000 x *]] }
|
||||
000000E290039200: {[conv2.W Value[192 x 1600]] }
|
||||
000000E290039340: {[conv1.W Value[64 x 363]] }
|
||||
000000E290039480: {[conv1.b Value[1 x 1 x 64]] }
|
||||
000000E290039520: {[conv2.b Value[1 x 1 x 192]] }
|
||||
000000E29003A060: {[features Value[224 x 224 x 3 x *]] }
|
||||
000000E29003A240: {[labels Value[1000 x *]] }
|
||||
000000E2A80AE1D0: {[OutputNodes.b Value[1000]] }
|
||||
000000E2A80AE270: {[conv3.W Value[384 x 1728]] }
|
||||
000000E2A80AE310: {[h1.W Value[4096 x 6 x 6 x 256]] }
|
||||
000000E2A80AE950: {[conv5.b Value[1 x 1 x 256]] }
|
||||
000000E2A80AEC70: {[h1.b Value[4096]] }
|
||||
000000E2A80AF350: {[h2.W Value[4096 x 4096]] }
|
||||
000000E2A80AF530: {[conv3.b Value[1 x 1 x 384]] }
|
||||
000000E2A80AF710: {[conv4.b Value[1 x 1 x 256]] }
|
||||
000000E2A80AFA30: {[h2.b Value[4096]] }
|
||||
000000E2A80AFDF0: {[conv5.W Value[256 x 2304]] }
|
||||
000000E2A80AFE90: {[conv4.W Value[256 x 3456]] }
|
||||
000000E2A80AFF30: {[OutputNodes.W Value[1000 x 4096]] }
|
||||
000000E2AE0BA220: {[conv4.c Value[13 x 13 x 256 x *]] }
|
||||
000000E2AE0BA2C0: {[h2.W Gradient[4096 x 4096]] [h2.z Value[4096 x *]] }
|
||||
000000E2AE0BA360: {[conv5.c Gradient[13 x 13 x 256 x *]] [conv5.y Value[13 x 13 x 256 x *]] }
|
||||
000000E2AE0BA400: {[OutputNodes.t Value[1000 x *]] [h2.b Gradient[4096]] [h2.y Gradient[4096 x *]] }
|
||||
000000E2AE0BA720: {[err Value[1]] }
|
||||
000000E2AE0BA7C0: {[conv3.b Gradient[1 x 1 x 384]] [conv3.y Gradient[13 x 13 x 384 x *]] [conv4.z Gradient[13 x 13 x 256 x *]] }
|
||||
000000E2AE0BA860: {[conv1.c Gradient[56 x 56 x 64 x *]] [conv1.y Value[56 x 56 x 64 x *]] }
|
||||
000000E2AE0BA900: {[conv1.b Gradient[1 x 1 x 64]] [conv1.y Gradient[56 x 56 x 64 x *]] }
|
||||
000000E2AE0BA9A0: {[conv1.z Gradient[56 x 56 x 64 x *]] [pool1 Value[27 x 27 x 64 x *]] }
|
||||
000000E2AE0BAA40: {[conv3.z Gradient[13 x 13 x 384 x *]] [pool2 Gradient[13 x 13 x 192 x *]] }
|
||||
000000E2AE0BAAE0: {[conv5.W Gradient[256 x 2304]] [conv5.z Value[13 x 13 x 256 x *]] }
|
||||
000000E2AE0BAB80: {[h1_d Value[4096 x *]] }
|
||||
000000E2AE0BACC0: {[conv3.c Gradient[13 x 13 x 384 x *]] [conv3.y Value[13 x 13 x 384 x *]] }
|
||||
000000E2AE0BAE00: {[conv3.c Value[13 x 13 x 384 x *]] }
|
||||
000000E2AE0BAEA0: {[conv4.W Gradient[256 x 3456]] [conv4.z Value[13 x 13 x 256 x *]] }
|
||||
000000E2AE0BAFE0: {[h2_d Value[4096 x *]] }
|
||||
000000E2AE0BB080: {[conv4.c Gradient[13 x 13 x 256 x *]] [conv4.y Value[13 x 13 x 256 x *]] }
|
||||
000000E2AE0BB120: {[h1.W Gradient[4096 x 6 x 6 x 256]] [h1.z Value[4096 x *]] }
|
||||
000000E2AE0BB1C0: {[ce Gradient[1]] }
|
||||
000000E2AE0BB260: {[OutputNodes.b Gradient[1000]] }
|
||||
000000E2AE0BB3A0: {[conv2.W Gradient[192 x 1600]] [conv2.z Value[27 x 27 x 192 x *]] }
|
||||
000000E2AE0BB4E0: {[conv1.W Gradient[64 x 363]] [conv1.z Value[56 x 56 x 64 x *]] }
|
||||
000000E2AE0BB800: {[conv2.b Gradient[1 x 1 x 192]] [conv2.y Gradient[27 x 27 x 192 x *]] }
|
||||
000000E2AE0BB940: {[h1.z Gradient[4096 x *]] [pool3 Gradient[6 x 6 x 256 x *]] }
|
||||
000000E2AE0BB9E0: {[h1.b Gradient[4096]] [h1.y Gradient[4096 x *]] [h2.t Value[4096 x *]] }
|
||||
000000E2AE0BBB20: {[OutputNodes.t Gradient[1000 x *]] }
|
||||
000000E2AE0BBBC0: {[conv4.b Gradient[1 x 1 x 256]] [conv4.y Gradient[13 x 13 x 256 x *]] [conv5.z Gradient[13 x 13 x 256 x *]] [pool3 Value[6 x 6 x 256 x *]] }
|
||||
000000E2AE0BBD00: {[ce Value[1]] }
|
||||
000000E2AE0BBDA0: {[conv2.c Value[27 x 27 x 192 x *]] }
|
||||
000000E2AE0BBE40: {[conv1.c Value[56 x 56 x 64 x *]] }
|
||||
000000E2AE0BBF80: {[conv2.c Gradient[27 x 27 x 192 x *]] [conv2.y Value[27 x 27 x 192 x *]] }
|
||||
000000E2AE0BC020: {[h2.t Gradient[4096 x *]] [h2.y Value[4096 x *]] }
|
||||
000000E2AE0BC160: {[conv5.c Value[13 x 13 x 256 x *]] }
|
||||
000000E2AE0BC200: {[conv2.z Gradient[27 x 27 x 192 x *]] [pool1 Gradient[27 x 27 x 64 x *]] [pool2 Value[13 x 13 x 192 x *]] }
|
||||
000000E2AE0BC2A0: {[OutputNodes.z Value[1000 x *]] }
|
||||
000000E2AE0BC340: {[h1_d Gradient[4096 x *]] [h2.z Gradient[4096 x *]] }
|
||||
000000E2AE0BC480: {[OutputNodes.W Gradient[1000 x 4096]] [OutputNodes.z Gradient[1000 x *]] }
|
||||
000000E2AE0BC520: {[h2_d Gradient[4096 x *]] }
|
||||
000000E2AE0BC840: {[conv3.W Gradient[384 x 1728]] [conv3.z Value[13 x 13 x 384 x *]] }
|
||||
000000E2AE0BC8E0: {[conv5.b Gradient[1 x 1 x 256]] [conv5.y Gradient[13 x 13 x 256 x *]] [h1.t Value[4096 x *]] }
|
||||
000000E2AE0BC980: {[h1.t Gradient[4096 x *]] [h1.y Value[4096 x *]] }
|
||||
|
||||
05/03/2016 14:11:02: No PreCompute nodes found, skipping PreCompute step.
|
||||
|
||||
05/03/2016 14:11:05: Starting Epoch 1: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
|
||||
05/03/2016 14:11:05: Starting minibatch loop.
|
||||
05/03/2016 14:11:14: Epoch[ 1 of 3]-Minibatch[ 1- 100]: ce = 7.43287354 * 1600; err = 0.99937500 * 1600; time = 8.8275s; samplesPerSecond = 181.3
|
||||
05/03/2016 14:11:20: Finished Epoch[ 1 of 3]: [Training] ce = 7.24222462 * 2999; err = 0.99933311 * 2999; totalSamplesSeen = 2999; learningRatePerSample = 0.00062499999; epochTime=14.8733s
|
||||
05/03/2016 14:11:24: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet.1'
|
||||
|
||||
05/03/2016 14:11:27: Starting Epoch 2: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
|
||||
05/03/2016 14:11:27: Starting minibatch loop.
|
||||
05/03/2016 14:11:34: Epoch[ 2 of 3]-Minibatch[ 1- 100, 100.00%]: ce = 6.90465576 * 1600; err = 0.99937500 * 1600; time = 6.9523s; samplesPerSecond = 230.1
|
||||
05/03/2016 14:11:40: Finished Epoch[ 2 of 3]: [Training] ce = 6.91868774 * 2999; err = 0.99899967 * 2999; totalSamplesSeen = 5998; learningRatePerSample = 0.00062499999; epochTime=12.9929s
|
||||
05/03/2016 14:11:43: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet.2'
|
||||
|
||||
05/03/2016 14:11:46: Starting Epoch 3: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
|
||||
05/03/2016 14:11:46: Starting minibatch loop.
|
||||
05/03/2016 14:11:53: Epoch[ 3 of 3]-Minibatch[ 1- 100, 100.00%]: ce = 6.87353699 * 1600; err = 0.99750000 * 1600; time = 7.0845s; samplesPerSecond = 225.8
|
||||
05/03/2016 14:11:59: Finished Epoch[ 3 of 3]: [Training] ce = 6.88654161 * 2999; err = 0.99799933 * 2999; totalSamplesSeen = 8997; learningRatePerSample = 0.00062499999; epochTime=13.0423s
|
||||
05/03/2016 14:12:03: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160503141032.133212\Image_AlexNet@release_gpu/models/AlexNet'
|
||||
05/03/2016 14:12:06: CNTKCommandTrainEnd: Train
|
||||
|
||||
05/03/2016 14:12:06: Action "train" complete.
|
||||
{ conv1.W : [64 x 363] (gradient)
|
||||
conv1.z : [56 x 56 x 64 x *] }
|
||||
{ conv1.c : [56 x 56 x 64 x *] (gradient)
|
||||
conv1.y : [56 x 56 x 64 x *] }
|
||||
{ conv2.c : [27 x 27 x 192 x *] (gradient)
|
||||
conv2.y : [27 x 27 x 192 x *] }
|
||||
{ conv2.z : [27 x 27 x 192 x *] (gradient)
|
||||
pool1 : [27 x 27 x 64 x *] (gradient)
|
||||
pool2 : [13 x 13 x 192 x *] }
|
||||
{ conv3.W : [384 x 1728] (gradient)
|
||||
conv3.z : [13 x 13 x 384 x *] }
|
||||
{ conv1.z : [56 x 56 x 64 x *] (gradient)
|
||||
pool1 : [27 x 27 x 64 x *] }
|
||||
{ conv3.c : [13 x 13 x 384 x *] (gradient)
|
||||
conv3.y : [13 x 13 x 384 x *] }
|
||||
{ conv2.b : [1 x 1 x 192] (gradient)
|
||||
conv2.y : [27 x 27 x 192 x *] (gradient) }
|
||||
{ conv1.b : [1 x 1 x 64] (gradient)
|
||||
conv1.y : [56 x 56 x 64 x *] (gradient) }
|
||||
{ conv2.W : [192 x 1600] (gradient)
|
||||
conv2.z : [27 x 27 x 192 x *] }
|
||||
{ conv5.b : [1 x 1 x 256] (gradient)
|
||||
conv5.y : [13 x 13 x 256 x *] (gradient)
|
||||
h1.t : [4096 x *] }
|
||||
{ h1_d : [4096 x *] (gradient)
|
||||
h2.z : [4096 x *] (gradient) }
|
||||
{ h1.W : [4096 x 6 x 6 x 256] (gradient)
|
||||
h1.z : [4096 x *] }
|
||||
{ h1.z : [4096 x *] (gradient)
|
||||
pool3 : [6 x 6 x 256 x *] (gradient) }
|
||||
{ OutputNodes.t : [1000 x *]
|
||||
h2.b : [4096] (gradient)
|
||||
h2.y : [4096 x *] (gradient) }
|
||||
{ conv4.b : [1 x 1 x 256] (gradient)
|
||||
conv4.y : [13 x 13 x 256 x *] (gradient)
|
||||
conv5.z : [13 x 13 x 256 x *] (gradient)
|
||||
pool3 : [6 x 6 x 256 x *] }
|
||||
{ conv5.c : [13 x 13 x 256 x *] (gradient)
|
||||
conv5.y : [13 x 13 x 256 x *] }
|
||||
{ OutputNodes.W : [1000 x 4096] (gradient)
|
||||
OutputNodes.z : [1000 x *] (gradient) }
|
||||
{ conv3.b : [1 x 1 x 384] (gradient)
|
||||
conv3.y : [13 x 13 x 384 x *] (gradient)
|
||||
conv4.z : [13 x 13 x 256 x *] (gradient) }
|
||||
{ h1.t : [4096 x *] (gradient)
|
||||
h1.y : [4096 x *] }
|
||||
{ conv4.c : [13 x 13 x 256 x *] (gradient)
|
||||
conv4.y : [13 x 13 x 256 x *] }
|
||||
{ h2.W : [4096 x 4096] (gradient)
|
||||
h2.z : [4096 x *] }
|
||||
{ h2.t : [4096 x *] (gradient)
|
||||
h2.y : [4096 x *] }
|
||||
{ h1.b : [4096] (gradient)
|
||||
h1.y : [4096 x *] (gradient)
|
||||
h2.t : [4096 x *] }
|
||||
{ conv5.W : [256 x 2304] (gradient)
|
||||
conv5.z : [13 x 13 x 256 x *] }
|
||||
{ conv3.z : [13 x 13 x 384 x *] (gradient)
|
||||
pool2 : [13 x 13 x 192 x *] (gradient) }
|
||||
{ conv4.W : [256 x 3456] (gradient)
|
||||
conv4.z : [13 x 13 x 256 x *] }
|
||||
|
||||
|
||||
05/03/2016 14:12:06: ##############################################################################
|
||||
05/03/2016 14:12:06: # #
|
||||
05/03/2016 14:12:06: # Action "edit" #
|
||||
05/03/2016 14:12:06: # #
|
||||
05/03/2016 14:12:06: ##############################################################################
|
||||
08/16/2016 03:03:45: Training 61100840 parameters in 16 out of 16 parameter tensors and 45 nodes with gradient:
|
||||
|
||||
08/16/2016 03:03:45: Node 'OutputNodes.W' (LearnableParameter operation) : [1000 x 4096]
|
||||
08/16/2016 03:03:45: Node 'OutputNodes.b' (LearnableParameter operation) : [1000]
|
||||
08/16/2016 03:03:45: Node 'conv1.W' (LearnableParameter operation) : [64 x 363]
|
||||
08/16/2016 03:03:45: Node 'conv1.b' (LearnableParameter operation) : [1 x 1 x 64]
|
||||
08/16/2016 03:03:45: Node 'conv2.W' (LearnableParameter operation) : [192 x 1600]
|
||||
08/16/2016 03:03:45: Node 'conv2.b' (LearnableParameter operation) : [1 x 1 x 192]
|
||||
08/16/2016 03:03:45: Node 'conv3.W' (LearnableParameter operation) : [384 x 1728]
|
||||
08/16/2016 03:03:45: Node 'conv3.b' (LearnableParameter operation) : [1 x 1 x 384]
|
||||
08/16/2016 03:03:45: Node 'conv4.W' (LearnableParameter operation) : [256 x 3456]
|
||||
08/16/2016 03:03:45: Node 'conv4.b' (LearnableParameter operation) : [1 x 1 x 256]
|
||||
08/16/2016 03:03:45: Node 'conv5.W' (LearnableParameter operation) : [256 x 2304]
|
||||
08/16/2016 03:03:45: Node 'conv5.b' (LearnableParameter operation) : [1 x 1 x 256]
|
||||
08/16/2016 03:03:45: Node 'h1.W' (LearnableParameter operation) : [4096 x 6 x 6 x 256]
|
||||
08/16/2016 03:03:45: Node 'h1.b' (LearnableParameter operation) : [4096]
|
||||
08/16/2016 03:03:45: Node 'h2.W' (LearnableParameter operation) : [4096 x 4096]
|
||||
08/16/2016 03:03:45: Node 'h2.b' (LearnableParameter operation) : [4096]
|
||||
|
||||
08/16/2016 03:03:45: No PreCompute nodes found, or all already computed. Skipping pre-computation step.
|
||||
|
||||
08/16/2016 03:03:49: Starting Epoch 1: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
BlockRandomizer::StartEpoch: epoch 0: frames [0..2999] (first sequence at sample 0), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:03:49: Starting minibatch loop.
|
||||
08/16/2016 03:03:59: Epoch[ 1 of 3]-Minibatch[ 1- 100]: ce = 7.41005371 * 1600; err = 1.00000000 * 1600; time = 10.1500s; samplesPerSecond = 157.6
|
||||
08/16/2016 03:04:06: Finished Epoch[ 1 of 3]: [Training] ce = 7.23359609 * 2999; err = 1.00000000 * 2999; totalSamplesSeen = 2999; learningRatePerSample = 0.00062499999; epochTime=17.2906s
|
||||
08/16/2016 03:04:10: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet.1'
|
||||
|
||||
08/16/2016 03:04:14: Starting Epoch 2: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
BlockRandomizer::StartEpoch: epoch 1: frames [2999..5998] (first sequence at sample 2999), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:04:14: Starting minibatch loop.
|
||||
08/16/2016 03:04:22: Epoch[ 2 of 3]-Minibatch[ 1- 100, 100.00%]: ce = 6.91799866 * 1600; err = 0.99937500 * 1600; time = 8.4264s; samplesPerSecond = 189.9
|
||||
08/16/2016 03:04:30: Finished Epoch[ 2 of 3]: [Training] ce = 6.91958452 * 2999; err = 0.99966656 * 2999; totalSamplesSeen = 5998; learningRatePerSample = 0.00062499999; epochTime=15.8522s
|
||||
08/16/2016 03:04:33: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet.2'
|
||||
|
||||
08/16/2016 03:04:37: Starting Epoch 3: learning rate per sample = 0.000625 effective momentum = 0.900000 momentum as time constant = 151.9 samples
|
||||
BlockRandomizer::StartEpoch: epoch 2: frames [5998..8997] (first sequence at sample 5998), data subset 0 of 1
|
||||
|
||||
08/16/2016 03:04:37: Starting minibatch loop.
|
||||
08/16/2016 03:04:45: Epoch[ 3 of 3]-Minibatch[ 1- 100, 100.00%]: ce = 6.88781128 * 1600; err = 0.99687500 * 1600; time = 8.2882s; samplesPerSecond = 193.0
|
||||
08/16/2016 03:04:52: Finished Epoch[ 3 of 3]: [Training] ce = 6.88917725 * 2999; err = 0.99766589 * 2999; totalSamplesSeen = 8997; learningRatePerSample = 0.00062499999; epochTime=15.5577s
|
||||
08/16/2016 03:04:56: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030158.863578\Image_AlexNet@release_gpu/models/AlexNet'
|
||||
08/16/2016 03:04:59: CNTKCommandTrainEnd: Train
|
||||
|
||||
08/16/2016 03:04:59: Action "train" complete.
|
||||
|
||||
|
||||
08/16/2016 03:04:59: ##############################################################################
|
||||
08/16/2016 03:04:59: # #
|
||||
08/16/2016 03:04:59: # Action "edit" #
|
||||
08/16/2016 03:04:59: # #
|
||||
08/16/2016 03:04:59: ##############################################################################
|
||||
|
||||
|
||||
Post-processing network...
|
||||
|
|
@ -592,27 +671,29 @@ Validating network. 30 nodes to process in pass 2.
|
|||
Validating network, final pass.
|
||||
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c: using GEMM convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using GEMM convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c: using GEMM convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using GEMM convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3.c: using GEMM convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv4.c: using GEMM convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv5.c: using GEMM convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using GEMM convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool3: using GEMM convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
|
||||
18 out of 48 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
Node 'unnamed143' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.000000.
|
||||
Node 'unnamed143' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 5.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
|
|
@ -672,8 +753,8 @@ Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [1000 x *1]
|
|||
Validating --> labels = InputValue() : -> [1000 x *1]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [1000 x *1], [1000 x *1] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, OutputNodes.z) : [1000 x *1], [1000 x *1] -> [1]
|
||||
Validating --> unnamed137 = LearnableParameter() : -> [1 x 1]
|
||||
Validating --> errTop5 = ErrorPrediction (labels, OutputNodes.z, unnamed137) : [1000 x *1], [1000 x *1], [1 x 1] -> [1]
|
||||
Validating --> unnamed143 = LearnableParameter() : -> [1 x 1]
|
||||
Validating --> errTop5 = ErrorPrediction (labels, OutputNodes.z, unnamed143) : [1000 x *1], [1000 x *1], [1 x 1] -> [1]
|
||||
|
||||
Validating network. 31 nodes to process in pass 2.
|
||||
|
||||
|
|
@ -687,28 +768,58 @@ Validating network, final pass.
|
|||
Post-processing network complete.
|
||||
|
||||
|
||||
05/03/2016 14:12:12: Action "edit" complete.
|
||||
08/16/2016 03:05:07: Action "edit" complete.
|
||||
|
||||
|
||||
05/03/2016 14:12:12: ##############################################################################
|
||||
05/03/2016 14:12:12: # #
|
||||
05/03/2016 14:12:12: # Action "test" #
|
||||
05/03/2016 14:12:12: # #
|
||||
05/03/2016 14:12:12: ##############################################################################
|
||||
08/16/2016 03:05:07: ##############################################################################
|
||||
08/16/2016 03:05:07: # #
|
||||
08/16/2016 03:05:07: # Action "test" #
|
||||
08/16/2016 03:05:07: # #
|
||||
08/16/2016 03:05:07: ##############################################################################
|
||||
|
||||
NDLBuilder Using GPU 0
|
||||
Node 'conv1.W' (LearnableParameter operation): Initializing Parameter[64 x 363] <- 0.000000.
|
||||
Node 'conv1.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv2.W' (LearnableParameter operation): Initializing Parameter[192 x 1600] <- 0.000000.
|
||||
Node 'conv2.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 192] <- 0.000000.
|
||||
Node 'conv3.W' (LearnableParameter operation): Initializing Parameter[384 x 1728] <- 0.000000.
|
||||
Node 'conv3.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 384] <- 0.000000.
|
||||
Node 'conv4.W' (LearnableParameter operation): Initializing Parameter[256 x 3456] <- 0.000000.
|
||||
Node 'conv4.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 0.000000.
|
||||
Node 'conv5.W' (LearnableParameter operation): Initializing Parameter[256 x 2304] <- 0.000000.
|
||||
Node 'conv5.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 0.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[4096 x 6 x 6 x 256] <- 0.000000.
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[4096] <- 0.000000.
|
||||
Node 'h2.b' (LearnableParameter operation): Initializing Parameter[4096] <- 0.000000.
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[1000] <- 0.000000.
|
||||
Node 'conv1.W' (LearnableParameter operation): Initializing Parameter[64 x 363] <- gaussian(seed=9, range=0.010497*0.950000, onCPU=false).
|
||||
Node 'conv1.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
|
||||
Node 'conv2.W' (LearnableParameter operation): Initializing Parameter[192 x 1600] <- gaussian(seed=10, range=0.005000*2.000000, onCPU=false).
|
||||
Node 'conv2.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 192] <- 1.000000.
|
||||
Node 'conv3.W' (LearnableParameter operation): Initializing Parameter[384 x 1728] <- gaussian(seed=11, range=0.004811*2.070000, onCPU=false).
|
||||
Node 'conv3.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 384] <- 0.000000.
|
||||
Node 'conv4.W' (LearnableParameter operation): Initializing Parameter[256 x 3456] <- gaussian(seed=12, range=0.003402*2.900000, onCPU=false).
|
||||
Node 'conv4.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 1.000000.
|
||||
Node 'conv5.W' (LearnableParameter operation): Initializing Parameter[256 x 2304] <- gaussian(seed=13, range=0.004167*2.400000, onCPU=false).
|
||||
Node 'conv5.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 256] <- 1.000000.
|
||||
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[4096 x 6 x 6 x 256] <- gaussian(seed=14, range=0.002083*6.400000, onCPU=false).
|
||||
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[4096] <- 1.000000.
|
||||
Node 'h2.W' (LearnableParameter operation): Initializating Parameter[4096 x 0] as gaussian later when dimensions are fully known.
|
||||
Node 'h2.b' (LearnableParameter operation): Initializing Parameter[4096] <- 1.000000.
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializating Parameter[1000 x 0] as gaussian later when dimensions are fully known.
|
||||
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[1000] <- 1.000000.
|
||||
|
||||
Post-processing network...
|
||||
|
||||
4 roots:
|
||||
3 roots:
|
||||
OutputNodes.z = Plus()
|
||||
ce = CrossEntropyWithSoftmax()
|
||||
err = ErrorPrediction()
|
||||
errTop5 = ErrorPrediction()
|
||||
|
||||
Validating network. 50 nodes to process in pass 1.
|
||||
Validating network. 48 nodes to process in pass 1.
|
||||
|
||||
Validating --> OutputNodes.W = LearnableParameter() : -> [1000 x 4096]
|
||||
Validating --> h2.W = LearnableParameter() : -> [4096 x 4096]
|
||||
Validating --> OutputNodes.W = LearnableParameter() : -> [1000 x 0]
|
||||
Validating --> h2.W = LearnableParameter() : -> [4096 x 0]
|
||||
Validating --> h1.W = LearnableParameter() : -> [4096 x 6 x 6 x 256]
|
||||
Validating --> conv5.W = LearnableParameter() : -> [256 x 2304]
|
||||
Validating --> conv4.W = LearnableParameter() : -> [256 x 3456]
|
||||
|
|
@ -744,44 +855,46 @@ Validating --> h1.b = LearnableParameter() : -> [4096]
|
|||
Validating --> h1.z = Plus (h1.t, h1.b) : [4096 x *2], [4096] -> [4096 x *2]
|
||||
Validating --> h1.y = RectifiedLinear (h1.z) : [4096 x *2] -> [4096 x *2]
|
||||
Validating --> h1_d = Dropout (h1.y) : [4096 x *2] -> [4096 x *2]
|
||||
Node 'h2.W' (LearnableParameter operation) operation: Tensor shape was inferred as [4096 x 4096].
|
||||
Node 'h2.W' (LearnableParameter operation): Initializing Parameter[4096 x 4096] <- gaussian(seed=15, range=0.003125*3.200000, onCPU=false).
|
||||
Validating --> h2.t = Times (h2.W, h1_d) : [4096 x 4096], [4096 x *2] -> [4096 x *2]
|
||||
Validating --> h2.b = LearnableParameter() : -> [4096]
|
||||
Validating --> h2.z = Plus (h2.t, h2.b) : [4096 x *2], [4096] -> [4096 x *2]
|
||||
Validating --> h2.y = RectifiedLinear (h2.z) : [4096 x *2] -> [4096 x *2]
|
||||
Validating --> h2_d = Dropout (h2.y) : [4096 x *2] -> [4096 x *2]
|
||||
Node 'OutputNodes.W' (LearnableParameter operation) operation: Tensor shape was inferred as [1000 x 4096].
|
||||
Node 'OutputNodes.W' (LearnableParameter operation): Initializing Parameter[1000 x 4096] <- gaussian(seed=16, range=0.003125*3.200000, onCPU=false).
|
||||
Validating --> OutputNodes.t = Times (OutputNodes.W, h2_d) : [1000 x 4096], [4096 x *2] -> [1000 x *2]
|
||||
Validating --> OutputNodes.b = LearnableParameter() : -> [1000]
|
||||
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [1000 x *2], [1000] -> [1000 x *2]
|
||||
Validating --> labels = InputValue() : -> [1000 x *2]
|
||||
Validating --> ce = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [1000 x *2], [1000 x *2] -> [1]
|
||||
Validating --> err = ErrorPrediction (labels, OutputNodes.z) : [1000 x *2], [1000 x *2] -> [1]
|
||||
Validating --> unnamed137 = LearnableParameter() : -> [1 x 1]
|
||||
Validating --> errTop5 = ErrorPrediction (labels, OutputNodes.z, unnamed137) : [1000 x *2], [1000 x *2], [1 x 1] -> [1]
|
||||
|
||||
Validating network. 31 nodes to process in pass 2.
|
||||
Validating network. 30 nodes to process in pass 2.
|
||||
|
||||
|
||||
Validating network, final pass.
|
||||
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv1.c: using cuDNN convolution engine for geometry: Input: 224 x 224 x 3, Output: 56 x 56 x 64, Kernel: 11 x 11 x 3, Map: 1 x 1 x 64, Stride: 4 x 4 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool1: using cuDNN convolution engine for geometry: Input: 56 x 56 x 64, Output: 27 x 27 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv2.c: using cuDNN convolution engine for geometry: Input: 27 x 27 x 64, Output: 27 x 27 x 192, Kernel: 5 x 5 x 64, Map: 1 x 1 x 192, Stride: 1 x 1 x 64, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool2: using cuDNN convolution engine for geometry: Input: 27 x 27 x 192, Output: 13 x 13 x 192, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv3.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 192, Output: 13 x 13 x 384, Kernel: 3 x 3 x 192, Map: 1 x 1 x 384, Stride: 1 x 1 x 192, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv4.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 384, Output: 13 x 13 x 256, Kernel: 3 x 3 x 384, Map: 1 x 1 x 256, Stride: 1 x 1 x 384, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
conv5.c: using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 13 x 13 x 256, Kernel: 3 x 3 x 256, Map: 1 x 1 x 256, Stride: 1 x 1 x 256, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
Using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
pool3: using cuDNN convolution engine for geometry: Input: 13 x 13 x 256, Output: 6 x 6 x 256, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.
|
||||
|
||||
|
||||
20 out of 50 nodes do not share the minibatch layout with the input data.
|
||||
18 out of 48 nodes do not share the minibatch layout with the input data.
|
||||
|
||||
Post-processing network complete.
|
||||
|
||||
|
|
@ -790,62 +903,12 @@ evalNodeNames are not specified, using all the default evalnodes and training cr
|
|||
|
||||
Allocating matrices for forward and/or backward propagation.
|
||||
|
||||
Memory Sharing Structure:
|
||||
Memory Sharing: Out of 48 matrices, 0 are shared as 0, and 48 are not shared.
|
||||
|
||||
0000000000000000: {[OutputNodes.W Gradient[1000 x 4096]] [OutputNodes.b Gradient[1000]] [OutputNodes.t Gradient[1000 x *2]] [OutputNodes.z Gradient[1000 x *2]] [ce Gradient[1]] [conv1.W Gradient[64 x 363]] [conv1.b Gradient[1 x 1 x 64]] [conv1.c Gradient[56 x 56 x 64 x *2]] [conv1.y Gradient[56 x 56 x 64 x *2]] [conv1.z Gradient[56 x 56 x 64 x *2]] [conv2.W Gradient[192 x 1600]] [conv2.b Gradient[1 x 1 x 192]] [conv2.c Gradient[27 x 27 x 192 x *2]] [conv2.y Gradient[27 x 27 x 192 x *2]] [conv2.z Gradient[27 x 27 x 192 x *2]] [conv3.W Gradient[384 x 1728]] [conv3.b Gradient[1 x 1 x 384]] [conv3.c Gradient[13 x 13 x 384 x *2]] [conv3.y Gradient[13 x 13 x 384 x *2]] [conv3.z Gradient[13 x 13 x 384 x *2]] [conv4.W Gradient[256 x 3456]] [conv4.b Gradient[1 x 1 x 256]] [conv4.c Gradient[13 x 13 x 256 x *2]] [conv4.y Gradient[13 x 13 x 256 x *2]] [conv4.z Gradient[13 x 13 x 256 x *2]] [conv5.W Gradient[256 x 2304]] [conv5.b Gradient[1 x 1 x 256]] [conv5.c Gradient[13 x 13 x 256 x *2]] [conv5.y Gradient[13 x 13 x 256 x *2]] [conv5.z Gradient[13 x 13 x 256 x *2]] [err Gradient[1]] [errTop5 Gradient[1]] [features Gradient[224 x 224 x 3 x *2]] [h1.W Gradient[4096 x 6 x 6 x 256]] [h1.b Gradient[4096]] [h1.t Gradient[4096 x *2]] [h1.y Gradient[4096 x *2]] [h1.z Gradient[4096 x *2]] [h1_d Gradient[4096 x *2]] [h2.W Gradient[4096 x 4096]] [h2.b Gradient[4096]] [h2.t Gradient[4096 x *2]] [h2.y Gradient[4096 x *2]] [h2.z Gradient[4096 x *2]] [h2_d Gradient[4096 x *2]] [labels Gradient[1000 x *2]] [pool1 Gradient[27 x 27 x 64 x *2]] [pool2 Gradient[13 x 13 x 192 x *2]] [pool3 Gradient[6 x 6 x 256 x *2]] [unnamed137 Gradient[1 x 1]] }
|
||||
000000E28E168F70: {[conv3.W Value[384 x 1728]] }
|
||||
000000E28E1691F0: {[conv5.W Value[256 x 2304]] }
|
||||
000000E28E1693D0: {[conv4.b Value[1 x 1 x 256]] }
|
||||
000000E28E169510: {[conv4.W Value[256 x 3456]] }
|
||||
000000E28E169830: {[conv5.b Value[1 x 1 x 256]] }
|
||||
000000E28E1698D0: {[conv3.b Value[1 x 1 x 384]] }
|
||||
000000E36C778260: {[OutputNodes.b Value[1000]] }
|
||||
000000E36C7783A0: {[OutputNodes.W Value[1000 x 4096]] }
|
||||
000000E36C778440: {[labels Value[1000 x *2]] }
|
||||
000000E36C7786C0: {[features Value[224 x 224 x 3 x *2]] }
|
||||
000000E36C7788A0: {[h1.b Value[4096]] }
|
||||
000000E36C7789E0: {[h2.b Value[4096]] }
|
||||
000000E36C778B20: {[h2.W Value[4096 x 4096]] }
|
||||
000000E36C778DA0: {[h1.W Value[4096 x 6 x 6 x 256]] }
|
||||
000000E370969220: {[conv5.y Value[13 x 13 x 256 x *2]] }
|
||||
000000E370969360: {[h1.t Value[4096 x *2]] }
|
||||
000000E3709694A0: {[conv4.z Value[13 x 13 x 256 x *2]] }
|
||||
000000E370969540: {[conv4.c Value[13 x 13 x 256 x *2]] }
|
||||
000000E370969680: {[conv4.y Value[13 x 13 x 256 x *2]] }
|
||||
000000E370969720: {[conv5.z Value[13 x 13 x 256 x *2]] }
|
||||
000000E3709697C0: {[h1.z Value[4096 x *2]] }
|
||||
000000E370969860: {[h1_d Value[4096 x *2]] }
|
||||
000000E3709699A0: {[h2.t Value[4096 x *2]] }
|
||||
000000E370969A40: {[h2.z Value[4096 x *2]] }
|
||||
000000E370969AE0: {[h2.y Value[4096 x *2]] }
|
||||
000000E370969B80: {[h2_d Value[4096 x *2]] }
|
||||
000000E370969C20: {[conv3.y Value[13 x 13 x 384 x *2]] }
|
||||
000000E370969CC0: {[conv5.c Value[13 x 13 x 256 x *2]] }
|
||||
000000E370969D60: {[h1.y Value[4096 x *2]] }
|
||||
000000E370969EA0: {[OutputNodes.t Value[1000 x *2]] }
|
||||
000000E370969F40: {[pool3 Value[6 x 6 x 256 x *2]] }
|
||||
000000E37096A080: {[OutputNodes.z Value[1000 x *2]] }
|
||||
000000E3728E02A0: {[conv2.y Value[27 x 27 x 192 x *2]] }
|
||||
000000E3728E0340: {[conv1.c Value[56 x 56 x 64 x *2]] }
|
||||
000000E3728E03E0: {[err Value[1]] }
|
||||
000000E3728E0480: {[conv1.z Value[56 x 56 x 64 x *2]] }
|
||||
000000E3728E0700: {[pool2 Value[13 x 13 x 192 x *2]] }
|
||||
000000E3728E07A0: {[conv3.c Value[13 x 13 x 384 x *2]] }
|
||||
000000E3728E0980: {[errTop5 Value[1]] }
|
||||
000000E3728E0A20: {[conv3.z Value[13 x 13 x 384 x *2]] }
|
||||
000000E3728E0AC0: {[ce Value[1]] }
|
||||
000000E3728E0CA0: {[unnamed137 Value[1 x 1]] }
|
||||
000000E3728E0DE0: {[conv1.y Value[56 x 56 x 64 x *2]] }
|
||||
000000E3728E0E80: {[pool1 Value[27 x 27 x 64 x *2]] }
|
||||
000000E3728E0F20: {[conv2.c Value[27 x 27 x 192 x *2]] }
|
||||
000000E3728E1100: {[conv2.z Value[27 x 27 x 192 x *2]] }
|
||||
000000E372D9CB80: {[conv2.b Value[1 x 1 x 192]] }
|
||||
000000E372D9CE00: {[conv1.W Value[64 x 363]] }
|
||||
000000E372D9CFE0: {[conv2.W Value[192 x 1600]] }
|
||||
000000E372D9D120: {[conv1.b Value[1 x 1 x 64]] }
|
||||
|
||||
05/03/2016 14:12:19: Final Results: Minibatch[1-32]: err = 0.99800000 * 500; errTop5 = 0.99600000 * 500; ce = 6.94932878 * 500; perplexity = 1042.44978531
|
||||
08/16/2016 03:05:09: Minibatch[1-32]: err = 0.99800000 * 500; ce = 7.32805448 * 500
|
||||
08/16/2016 03:05:09: Final Results: Minibatch[1-32]: err = 0.99800000 * 500; ce = 7.32805448 * 500; perplexity = 1522.41699268
|
||||
|
||||
05/03/2016 14:12:19: Action "test" complete.
|
||||
08/16/2016 03:05:09: Action "test" complete.
|
||||
|
||||
05/03/2016 14:12:19: __COMPLETED__
|
||||
08/16/2016 03:05:09: __COMPLETED__
|
||||
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|
|
@ -89,6 +89,7 @@ reader = [
|
|||
miniBatchMode = "partial"
|
||||
randomize = "auto"
|
||||
verbosity = 0
|
||||
useMersenneTwisterRand=true
|
||||
|
||||
features = [
|
||||
dim = 363
|
||||
|
|
|
|||
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