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Added ability to run speech e2e tests on Windows (cygwin)

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Vladimir Ivanov 2015-08-11 18:17:20 -07:00 коммит произвёл unknown
Родитель 18077110dd
Коммит ca189d8e35
6 изменённых файлов: 1524 добавлений и 21 удалений
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1
.gitattributes поставляемый Normal file
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@ -0,0 +1 @@
run-test text eol=lf

3
MachineLearning/CNTK/CNTK.cpp
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@ -1476,7 +1476,8 @@ int wmain(int argc, wchar_t* argv[])
fcloseOrDie(fp); fcloseOrDie(fp);
} }
fprintf(stderr, "COMPLETED\n"); fprintf(stderr, "COMPLETED\n");
} fflush(stderr);
}
catch (const std::exception &err) catch (const std::exception &err)
{ {
fprintf(stderr, "EXCEPTION occurred: %s\n", err.what()); fprintf(stderr, "EXCEPTION occurred: %s\n", err.what());

738
Tests/Speech/QuickE2E/baseline.windows.cpu.txt Normal file
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=== Running /cygdrive/c/Users/svcphil/workspace.vlivan/CNTK-Build-Windows/x64/release/cntk.exe configFile=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\QuickE2E\cntk.config RunDir=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_cpu DataDir=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data DeviceId=Auto
-------------------------------------------------------------------
Build info:
Built time: Aug 11 2015 16:18:17
Last modified date: Tue Aug 11 16:16:08 2015
Built by svcphil on dphaim-26-new
Build Path: C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\MachineLearning\CNTK\
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0
Build Branch: master
Build SHA1: 397cc7cc16c00b1c12864d331c0729fde7a1bde3
-------------------------------------------------------------------
running on dphaim-26-new at 2015/08/11 17:47:10
command line options:
configFile=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\QuickE2E\cntk.config RunDir=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_cpu DataDir=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data DeviceId=Auto
>>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
precision=float
command=speechTrain
deviceId=$DeviceId$
parallelTrain=false
speechTrain=[
action=train
modelPath=$RunDir$/models/cntkSpeech.dnn
deviceId=$DeviceId$
traceLevel=1
SimpleNetworkBuilder=[
layerSizes=363:512:512:132
trainingCriterion=CrossEntropyWithSoftmax
evalCriterion=ErrorPrediction
layerTypes=Sigmoid
initValueScale=1.0
applyMeanVarNorm=true
uniformInit=true
needPrior=true
]
SGD=[
epochSize=20480
minibatchSize=64:256:1024:
learningRatesPerMB=1.0:0.5:0.1
numMBsToShowResult=10
momentumPerMB=0.9:0.656119
dropoutRate=0.0
maxEpochs=3
keepCheckPointFiles=true
AutoAdjust=[
reduceLearnRateIfImproveLessThan=0
loadBestModel=true
increaseLearnRateIfImproveMoreThan=1000000000
learnRateDecreaseFactor=0.5
learnRateIncreaseFactor=1.382
autoAdjustLR=AdjustAfterEpoch
]
clippingThresholdPerSample=1#INF
]
reader=[
readerType=HTKMLFReader
readMethod=blockRandomize
miniBatchMode=Partial
randomize=Auto
verbosity=0
features=[
dim=363
type=Real
scpFile=glob_0000.scp
]
labels=[
mlfFile=$DataDir$/glob_0000.mlf
labelMappingFile=$DataDir$/state.list
labelDim=132
labelType=Category
]
]
]
RunDir=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_cpu
DataDir=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data
DeviceId=Auto
<<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
>>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
precision=float
command=speechTrain
deviceId=Auto
parallelTrain=false
speechTrain=[
action=train
modelPath=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_cpu/models/cntkSpeech.dnn
deviceId=Auto
traceLevel=1
SimpleNetworkBuilder=[
layerSizes=363:512:512:132
trainingCriterion=CrossEntropyWithSoftmax
evalCriterion=ErrorPrediction
layerTypes=Sigmoid
initValueScale=1.0
applyMeanVarNorm=true
uniformInit=true
needPrior=true
]
SGD=[
epochSize=20480
minibatchSize=64:256:1024:
learningRatesPerMB=1.0:0.5:0.1
numMBsToShowResult=10
momentumPerMB=0.9:0.656119
dropoutRate=0.0
maxEpochs=3
keepCheckPointFiles=true
AutoAdjust=[
reduceLearnRateIfImproveLessThan=0
loadBestModel=true
increaseLearnRateIfImproveMoreThan=1000000000
learnRateDecreaseFactor=0.5
learnRateIncreaseFactor=1.382
autoAdjustLR=AdjustAfterEpoch
]
clippingThresholdPerSample=1#INF
]
reader=[
readerType=HTKMLFReader
readMethod=blockRandomize
miniBatchMode=Partial
randomize=Auto
verbosity=0
features=[
dim=363
type=Real
scpFile=glob_0000.scp
]
labels=[
mlfFile=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data/glob_0000.mlf
labelMappingFile=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data/state.list
labelDim=132
labelType=Category
]
]
]
RunDir=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_cpu
DataDir=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data
DeviceId=Auto
<<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
>>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
configparameters: cntk.config:command=speechTrain
configparameters: cntk.config:DataDir=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data
configparameters: cntk.config:deviceId=Auto
configparameters: cntk.config:parallelTrain=false
configparameters: cntk.config:precision=float
configparameters: cntk.config:RunDir=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_cpu
configparameters: cntk.config:speechTrain=[
action=train
modelPath=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_cpu/models/cntkSpeech.dnn
deviceId=Auto
traceLevel=1
SimpleNetworkBuilder=[
layerSizes=363:512:512:132
trainingCriterion=CrossEntropyWithSoftmax
evalCriterion=ErrorPrediction
layerTypes=Sigmoid
initValueScale=1.0
applyMeanVarNorm=true
uniformInit=true
needPrior=true
]
SGD=[
epochSize=20480
minibatchSize=64:256:1024:
learningRatesPerMB=1.0:0.5:0.1
numMBsToShowResult=10
momentumPerMB=0.9:0.656119
dropoutRate=0.0
maxEpochs=3
keepCheckPointFiles=true
AutoAdjust=[
reduceLearnRateIfImproveLessThan=0
loadBestModel=true
increaseLearnRateIfImproveMoreThan=1000000000
learnRateDecreaseFactor=0.5
learnRateIncreaseFactor=1.382
autoAdjustLR=AdjustAfterEpoch
]
clippingThresholdPerSample=1#INF
]
reader=[
readerType=HTKMLFReader
readMethod=blockRandomize
miniBatchMode=Partial
randomize=Auto
verbosity=0
features=[
dim=363
type=Real
scpFile=glob_0000.scp
]
labels=[
mlfFile=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data/glob_0000.mlf
labelMappingFile=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data/state.list
labelDim=132
labelType=Category
]
]
]
<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
command: speechTrain
precision = float
LockDevice: Capture device 1 and lock it for exclusive use
LockDevice: Capture device 2 and lock it for exclusive use
LockDevice: Capture device 3 and lock it for exclusive use
LockDevice: Capture device 0 and lock it for exclusive use
LockDevice: Capture device 1 and lock it for exclusive use
SimpleNetworkBuilder Using GPU 1
reading script file glob_0000.scp ... 948 entries
trainlayer: OOV-exclusion code enabled, but no unigram specified to derive the word set from, so you won't get OOV exclusion
total 132 state names in state list C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data/state.list
htkmlfreader: reading MLF file C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\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
GetTrainCriterionNodes ...
GetEvalCriterionNodes ...
Validating node CrossEntropyWithSoftmax
Validating --> labels = InputValue
Validating --> W2 = LearnableParameter
Validating --> W1 = LearnableParameter
Validating --> W0 = LearnableParameter
Validating --> features = InputValue
Validating --> MeanOfFeatures = Mean(features[363, 3])
Validating --> InvStdOfFeatures = InvStdDev(features[363, 3])
Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 3], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1])
Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 3])
Validating --> B0 = LearnableParameter
Validating --> W0*features+B0 = Plus(W0*features[512, 3], B0[512, 1])
Validating --> H1 = Sigmoid(W0*features+B0[512, 3])
Validating --> W1*H1 = Times(W1[512, 512], H1[512, 3])
Validating --> B1 = LearnableParameter
Validating --> W1*H1+B1 = Plus(W1*H1[512, 3], B1[512, 1])
Validating --> H2 = Sigmoid(W1*H1+B1[512, 3])
Validating --> W2*H1 = Times(W2[132, 512], H2[512, 3])
Validating --> B2 = LearnableParameter
Validating --> HLast = Plus(W2*H1[132, 3], B2[132, 1])
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, 3], HLast[132, 3])
Found 3 PreCompute nodes
NodeName: InvStdOfFeatures
NodeName: MeanOfFeatures
NodeName: Prior
minibatchiterator: epoch 0: frames [0..252734] (first utterance at frame 0) with 1 datapasses
requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms
Validating node InvStdOfFeatures
Validating --> features = InputValue
Validating --> InvStdOfFeatures = InvStdDev(features[363, 64])
Validating node MeanOfFeatures
Validating --> features = InputValue
Validating --> MeanOfFeatures = Mean(features[363, 64])
Validating node Prior
Validating --> labels = InputValue
Validating --> Prior = Mean(labels[132, 64])
Set Max Temp Mem Size For Convolution Nodes to 0 samples.
Starting Epoch 1: learning rate per sample = 0.015625 momentum = 0.900000
minibatchiterator: epoch 0: frames [0..20480] (first utterance at frame 0) with 1 datapasses
Validating node EvalErrorPrediction
Validating --> labels = InputValue
Validating --> W2 = LearnableParameter
Validating --> W1 = LearnableParameter
Validating --> W0 = LearnableParameter
Validating --> features = InputValue
Validating --> MeanOfFeatures = Mean(features[363, 64])
Validating --> InvStdOfFeatures = InvStdDev(features[363, 64])
Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 64], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1])
Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 64])
Validating --> B0 = LearnableParameter
Validating --> W0*features+B0 = Plus(W0*features[512, 64], B0[512, 1])
Validating --> H1 = Sigmoid(W0*features+B0[512, 64])
Validating --> W1*H1 = Times(W1[512, 512], H1[512, 64])
Validating --> B1 = LearnableParameter
Validating --> W1*H1+B1 = Plus(W1*H1[512, 64], B1[512, 1])
Validating --> H2 = Sigmoid(W1*H1+B1[512, 64])
Validating --> W2*H1 = Times(W2[132, 512], H2[512, 64])
Validating --> B2 = LearnableParameter
Validating --> HLast = Plus(W2*H1[132, 64], B2[132, 1])
Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, 64], HLast[132, 64])
Epoch[ 1 of 3]-Minibatch[ 1- 10 of 320]: SamplesSeen = 640; TrainLossPerSample = 4.45646143; EvalErr[0]PerSample = 0.92500001; TotalTime = 0.01913s; TotalTimePerSample = 0.02988ms; SamplesPerSecond = 33462
Epoch[ 1 of 3]-Minibatch[ 11- 20 of 320]: SamplesSeen = 640; TrainLossPerSample = 4.22315693; EvalErr[0]PerSample = 0.90156251; TotalTime = 0.01453s; TotalTimePerSample = 0.02270ms; SamplesPerSecond = 44043
Epoch[ 1 of 3]-Minibatch[ 21- 30 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.95180511; EvalErr[0]PerSample = 0.84687501; TotalTime = 0.01459s; TotalTimePerSample = 0.02279ms; SamplesPerSecond = 43874
Epoch[ 1 of 3]-Minibatch[ 31- 40 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.94157934; EvalErr[0]PerSample = 0.89843750; TotalTime = 0.01459s; TotalTimePerSample = 0.02280ms; SamplesPerSecond = 43859
Epoch[ 1 of 3]-Minibatch[ 41- 50 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.85668945; EvalErr[0]PerSample = 0.91093749; TotalTime = 0.01456s; TotalTimePerSample = 0.02275ms; SamplesPerSecond = 43953
Epoch[ 1 of 3]-Minibatch[ 51- 60 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.72866368; EvalErr[0]PerSample = 0.89531249; TotalTime = 0.01450s; TotalTimePerSample = 0.02265ms; SamplesPerSecond = 44140
Epoch[ 1 of 3]-Minibatch[ 61- 70 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.51809072; EvalErr[0]PerSample = 0.82968748; TotalTime = 0.01453s; TotalTimePerSample = 0.02271ms; SamplesPerSecond = 44034
Epoch[ 1 of 3]-Minibatch[ 71- 80 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.48454905; EvalErr[0]PerSample = 0.80781251; TotalTime = 0.01452s; TotalTimePerSample = 0.02269ms; SamplesPerSecond = 44074
Epoch[ 1 of 3]-Minibatch[ 81- 90 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.33829641; EvalErr[0]PerSample = 0.76875001; TotalTime = 0.01453s; TotalTimePerSample = 0.02271ms; SamplesPerSecond = 44037
Epoch[ 1 of 3]-Minibatch[ 91- 100 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.50167227; EvalErr[0]PerSample = 0.79843748; TotalTime = 0.01447s; TotalTimePerSample = 0.02261ms; SamplesPerSecond = 44229
WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times.
Epoch[ 1 of 3]-Minibatch[ 101- 110 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.22861624; EvalErr[0]PerSample = 0.80000001; TotalTime = 0.01459s; TotalTimePerSample = 0.02279ms; SamplesPerSecond = 43874
Epoch[ 1 of 3]-Minibatch[ 111- 120 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.32616878; EvalErr[0]PerSample = 0.79062498; TotalTime = 0.01449s; TotalTimePerSample = 0.02264ms; SamplesPerSecond = 44174
Epoch[ 1 of 3]-Minibatch[ 121- 130 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.16897583; EvalErr[0]PerSample = 0.77968752; TotalTime = 0.01448s; TotalTimePerSample = 0.02262ms; SamplesPerSecond = 44201
Epoch[ 1 of 3]-Minibatch[ 131- 140 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.08891916; EvalErr[0]PerSample = 0.77656251; TotalTime = 0.01442s; TotalTimePerSample = 0.02253ms; SamplesPerSecond = 44385
Epoch[ 1 of 3]-Minibatch[ 141- 150 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.06004953; EvalErr[0]PerSample = 0.72968751; TotalTime = 0.01454s; TotalTimePerSample = 0.02271ms; SamplesPerSecond = 44031
Epoch[ 1 of 3]-Minibatch[ 151- 160 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.91128540; EvalErr[0]PerSample = 0.69531250; TotalTime = 0.01446s; TotalTimePerSample = 0.02259ms; SamplesPerSecond = 44272
Epoch[ 1 of 3]-Minibatch[ 161- 170 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.90172124; EvalErr[0]PerSample = 0.72968751; TotalTime = 0.01450s; TotalTimePerSample = 0.02266ms; SamplesPerSecond = 44128
Epoch[ 1 of 3]-Minibatch[ 171- 180 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.73261714; EvalErr[0]PerSample = 0.65312499; TotalTime = 0.01447s; TotalTimePerSample = 0.02261ms; SamplesPerSecond = 44232
Epoch[ 1 of 3]-Minibatch[ 181- 190 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.66515493; EvalErr[0]PerSample = 0.68437499; TotalTime = 0.01453s; TotalTimePerSample = 0.02270ms; SamplesPerSecond = 44061
Epoch[ 1 of 3]-Minibatch[ 191- 200 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.67383432; EvalErr[0]PerSample = 0.66406250; TotalTime = 0.01449s; TotalTimePerSample = 0.02264ms; SamplesPerSecond = 44165
Epoch[ 1 of 3]-Minibatch[ 201- 210 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.52869272; EvalErr[0]PerSample = 0.63593751; TotalTime = 0.01450s; TotalTimePerSample = 0.02266ms; SamplesPerSecond = 44134
Epoch[ 1 of 3]-Minibatch[ 211- 220 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.60032344; EvalErr[0]PerSample = 0.66718751; TotalTime = 0.01450s; TotalTimePerSample = 0.02266ms; SamplesPerSecond = 44128
Epoch[ 1 of 3]-Minibatch[ 221- 230 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.51134038; EvalErr[0]PerSample = 0.64843750; TotalTime = 0.01452s; TotalTimePerSample = 0.02268ms; SamplesPerSecond = 44086
Epoch[ 1 of 3]-Minibatch[ 231- 240 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.45362544; EvalErr[0]PerSample = 0.63749999; TotalTime = 0.01452s; TotalTimePerSample = 0.02269ms; SamplesPerSecond = 44068
Epoch[ 1 of 3]-Minibatch[ 241- 250 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.41640615; EvalErr[0]PerSample = 0.61562502; TotalTime = 0.01445s; TotalTimePerSample = 0.02258ms; SamplesPerSecond = 44287
Epoch[ 1 of 3]-Minibatch[ 251- 260 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.39745474; EvalErr[0]PerSample = 0.62812501; TotalTime = 0.01447s; TotalTimePerSample = 0.02261ms; SamplesPerSecond = 44229
Epoch[ 1 of 3]-Minibatch[ 261- 270 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.16415405; EvalErr[0]PerSample = 0.56718749; TotalTime = 0.01454s; TotalTimePerSample = 0.02272ms; SamplesPerSecond = 44013
Epoch[ 1 of 3]-Minibatch[ 271- 280 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.30347300; EvalErr[0]PerSample = 0.63593751; TotalTime = 0.01454s; TotalTimePerSample = 0.02272ms; SamplesPerSecond = 44016
Epoch[ 1 of 3]-Minibatch[ 281- 290 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.24398804; EvalErr[0]PerSample = 0.60937500; TotalTime = 0.01446s; TotalTimePerSample = 0.02260ms; SamplesPerSecond = 44253
Epoch[ 1 of 3]-Minibatch[ 291- 300 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.15322256; EvalErr[0]PerSample = 0.57968748; TotalTime = 0.01447s; TotalTimePerSample = 0.02262ms; SamplesPerSecond = 44214
Epoch[ 1 of 3]-Minibatch[ 301- 310 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.21664429; EvalErr[0]PerSample = 0.59531248; TotalTime = 0.01448s; TotalTimePerSample = 0.02262ms; SamplesPerSecond = 44208
Epoch[ 1 of 3]-Minibatch[ 311- 320 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.25246572; EvalErr[0]PerSample = 0.60156250; TotalTime = 0.01442s; TotalTimePerSample = 0.02253ms; SamplesPerSecond = 44392
Finished Epoch[1]: [Training Set] TrainLossPerSample = 3.0000031; EvalErrPerSample = 0.72836918; Ave LearnRatePerSample = 0.015625; EpochTime=0.4851
Starting Epoch 2: learning rate per sample = 0.001953 momentum = 0.656119
minibatchiterator: epoch 1: frames [20480..40960] (first utterance at frame 20480) with 1 datapasses
Epoch[ 2 of 3]-Minibatch[ 1- 10 of 80]: SamplesSeen = 2560; TrainLossPerSample = 2.08151960; EvalErr[0]PerSample = 0.55859375; TotalTime = 0.03149s; TotalTimePerSample = 0.01230ms; SamplesPerSecond = 81290
Epoch[ 2 of 3]-Minibatch[ 11- 20 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.98395634; EvalErr[0]PerSample = 0.54257810; TotalTime = 0.02336s; TotalTimePerSample = 0.00913ms; SamplesPerSecond = 109570
Epoch[ 2 of 3]-Minibatch[ 21- 30 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.98575521; EvalErr[0]PerSample = 0.54492188; TotalTime = 0.02325s; TotalTimePerSample = 0.00908ms; SamplesPerSecond = 110116
Epoch[ 2 of 3]-Minibatch[ 31- 40 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.90484965; EvalErr[0]PerSample = 0.53164065; TotalTime = 0.02321s; TotalTimePerSample = 0.00906ms; SamplesPerSecond = 110316
Epoch[ 2 of 3]-Minibatch[ 41- 50 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.88324130; EvalErr[0]PerSample = 0.52539063; TotalTime = 0.02328s; TotalTimePerSample = 0.00909ms; SamplesPerSecond = 109975
Epoch[ 2 of 3]-Minibatch[ 51- 60 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.89109266; EvalErr[0]PerSample = 0.53359377; TotalTime = 0.02325s; TotalTimePerSample = 0.00908ms; SamplesPerSecond = 110093
Epoch[ 2 of 3]-Minibatch[ 61- 70 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.89496076; EvalErr[0]PerSample = 0.52890623; TotalTime = 0.02326s; TotalTimePerSample = 0.00909ms; SamplesPerSecond = 110055
Epoch[ 2 of 3]-Minibatch[ 71- 80 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.85944366; EvalErr[0]PerSample = 0.52265626; TotalTime = 0.02296s; TotalTimePerSample = 0.00897ms; SamplesPerSecond = 111473
Finished Epoch[2]: [Training Set] TrainLossPerSample = 1.9356024; EvalErrPerSample = 0.53603518; Ave LearnRatePerSample = 0.001953125; EpochTime=0.195263
Starting Epoch 3: learning rate per sample = 0.000098 momentum = 0.656119
minibatchiterator: epoch 2: frames [40960..61440] (first utterance at frame 40960) with 1 datapasses
Epoch[ 3 of 3]-Minibatch[ 1- 10 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.86752820; EvalErr[0]PerSample = 0.52177733; TotalTime = 0.08160s; TotalTimePerSample = 0.00797ms; SamplesPerSecond = 125485
Epoch[ 3 of 3]-Minibatch[ 11- 20 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.87358737; EvalErr[0]PerSample = 0.51542968; TotalTime = 0.05742s; TotalTimePerSample = 0.00561ms; SamplesPerSecond = 178319
Finished Epoch[3]: [Training Set] TrainLossPerSample = 1.8705578; EvalErrPerSample = 0.5186035; Ave LearnRatePerSample = 9.765625146e-005; EpochTime=0.142001
COMPLETED
=== Deleting last epoch data
==== Re-running from checkpoint
-------------------------------------------------------------------
Build info:
Built time: Aug 11 2015 16:18:17
Last modified date: Tue Aug 11 16:16:08 2015
Built by svcphil on dphaim-26-new
Build Path: C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\MachineLearning\CNTK\
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0
Build Branch: master
Build SHA1: 397cc7cc16c00b1c12864d331c0729fde7a1bde3
-------------------------------------------------------------------
running on dphaim-26-new at 2015/08/11 17:47:19
command line options:
configFile=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\QuickE2E\cntk.config RunDir=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_cpu DataDir=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data DeviceId=Auto
>>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
precision=float
command=speechTrain
deviceId=$DeviceId$
parallelTrain=false
speechTrain=[
action=train
modelPath=$RunDir$/models/cntkSpeech.dnn
deviceId=$DeviceId$
traceLevel=1
SimpleNetworkBuilder=[
layerSizes=363:512:512:132
trainingCriterion=CrossEntropyWithSoftmax
evalCriterion=ErrorPrediction
layerTypes=Sigmoid
initValueScale=1.0
applyMeanVarNorm=true
uniformInit=true
needPrior=true
]
SGD=[
epochSize=20480
minibatchSize=64:256:1024:
learningRatesPerMB=1.0:0.5:0.1
numMBsToShowResult=10
momentumPerMB=0.9:0.656119
dropoutRate=0.0
maxEpochs=3
keepCheckPointFiles=true
AutoAdjust=[
reduceLearnRateIfImproveLessThan=0
loadBestModel=true
increaseLearnRateIfImproveMoreThan=1000000000
learnRateDecreaseFactor=0.5
learnRateIncreaseFactor=1.382
autoAdjustLR=AdjustAfterEpoch
]
clippingThresholdPerSample=1#INF
]
reader=[
readerType=HTKMLFReader
readMethod=blockRandomize
miniBatchMode=Partial
randomize=Auto
verbosity=0
features=[
dim=363
type=Real
scpFile=glob_0000.scp
]
labels=[
mlfFile=$DataDir$/glob_0000.mlf
labelMappingFile=$DataDir$/state.list
labelDim=132
labelType=Category
]
]
]
RunDir=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_cpu
DataDir=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data
DeviceId=Auto
<<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
>>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
precision=float
command=speechTrain
deviceId=Auto
parallelTrain=false
speechTrain=[
action=train
modelPath=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_cpu/models/cntkSpeech.dnn
deviceId=Auto
traceLevel=1
SimpleNetworkBuilder=[
layerSizes=363:512:512:132
trainingCriterion=CrossEntropyWithSoftmax
evalCriterion=ErrorPrediction
layerTypes=Sigmoid
initValueScale=1.0
applyMeanVarNorm=true
uniformInit=true
needPrior=true
]
SGD=[
epochSize=20480
minibatchSize=64:256:1024:
learningRatesPerMB=1.0:0.5:0.1
numMBsToShowResult=10
momentumPerMB=0.9:0.656119
dropoutRate=0.0
maxEpochs=3
keepCheckPointFiles=true
AutoAdjust=[
reduceLearnRateIfImproveLessThan=0
loadBestModel=true
increaseLearnRateIfImproveMoreThan=1000000000
learnRateDecreaseFactor=0.5
learnRateIncreaseFactor=1.382
autoAdjustLR=AdjustAfterEpoch
]
clippingThresholdPerSample=1#INF
]
reader=[
readerType=HTKMLFReader
readMethod=blockRandomize
miniBatchMode=Partial
randomize=Auto
verbosity=0
features=[
dim=363
type=Real
scpFile=glob_0000.scp
]
labels=[
mlfFile=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data/glob_0000.mlf
labelMappingFile=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data/state.list
labelDim=132
labelType=Category
]
]
]
RunDir=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_cpu
DataDir=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data
DeviceId=Auto
<<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
>>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
configparameters: cntk.config:command=speechTrain
configparameters: cntk.config:DataDir=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data
configparameters: cntk.config:deviceId=Auto
configparameters: cntk.config:parallelTrain=false
configparameters: cntk.config:precision=float
configparameters: cntk.config:RunDir=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_cpu
configparameters: cntk.config:speechTrain=[
action=train
modelPath=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_cpu/models/cntkSpeech.dnn
deviceId=Auto
traceLevel=1
SimpleNetworkBuilder=[
layerSizes=363:512:512:132
trainingCriterion=CrossEntropyWithSoftmax
evalCriterion=ErrorPrediction
layerTypes=Sigmoid
initValueScale=1.0
applyMeanVarNorm=true
uniformInit=true
needPrior=true
]
SGD=[
epochSize=20480
minibatchSize=64:256:1024:
learningRatesPerMB=1.0:0.5:0.1
numMBsToShowResult=10
momentumPerMB=0.9:0.656119
dropoutRate=0.0
maxEpochs=3
keepCheckPointFiles=true
AutoAdjust=[
reduceLearnRateIfImproveLessThan=0
loadBestModel=true
increaseLearnRateIfImproveMoreThan=1000000000
learnRateDecreaseFactor=0.5
learnRateIncreaseFactor=1.382
autoAdjustLR=AdjustAfterEpoch
]
clippingThresholdPerSample=1#INF
]
reader=[
readerType=HTKMLFReader
readMethod=blockRandomize
miniBatchMode=Partial
randomize=Auto
verbosity=0
features=[
dim=363
type=Real
scpFile=glob_0000.scp
]
labels=[
mlfFile=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data/glob_0000.mlf
labelMappingFile=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data/state.list
labelDim=132
labelType=Category
]
]
]
<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
command: speechTrain
precision = float
LockDevice: Capture device 1 and lock it for exclusive use
LockDevice: Capture device 2 and lock it for exclusive use
LockDevice: Capture device 3 and lock it for exclusive use
LockDevice: Capture device 0 and lock it for exclusive use
LockDevice: Capture device 1 and lock it for exclusive use
SimpleNetworkBuilder Using GPU 1
reading script file glob_0000.scp ... 948 entries
trainlayer: OOV-exclusion code enabled, but no unigram specified to derive the word set from, so you won't get OOV exclusion
total 132 state names in state list C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data/state.list
htkmlfreader: reading MLF file C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\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
Starting from checkpoint. Load Network From File C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_cpu/models/cntkSpeech.dnn.2.
Printing Gradient Computation Node Order ...
CrossEntropyWithSoftmax[0, 0] = CrossEntropyWithSoftmax(labels[132, 256], HLast[0, 0])
HLast[0, 0] = Plus(W2*H1[0, 0], B2[132, 1])
B2[132, 1] = LearnableParameter
W2*H1[0, 0] = Times(W2[132, 512], H2[0, 0])
H2[0, 0] = Sigmoid(W1*H1+B1[0, 0])
W1*H1+B1[0, 0] = Plus(W1*H1[0, 0], B1[512, 1])
B1[512, 1] = LearnableParameter
W1*H1[0, 0] = Times(W1[512, 512], H1[0, 0])
H1[0, 0] = Sigmoid(W0*features+B0[0, 0])
W0*features+B0[0, 0] = Plus(W0*features[0, 0], B0[512, 1])
B0[512, 1] = LearnableParameter
W0*features[0, 0] = Times(W0[512, 363], MVNormalizedFeatures[0, 0])
MVNormalizedFeatures[0, 0] = PerDimMeanVarNormalization(features[363, 256], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1])
InvStdOfFeatures[363, 1] = InvStdDev(features[363, 256])
MeanOfFeatures[363, 1] = Mean(features[363, 256])
features[363, 256] = InputValue
W0[512, 363] = LearnableParameter
W1[512, 512] = LearnableParameter
W2[132, 512] = LearnableParameter
labels[132, 256] = InputValue
Validating node CrossEntropyWithSoftmax
Validating --> labels = InputValue
Validating --> W2 = LearnableParameter
Validating --> W1 = LearnableParameter
Validating --> W0 = LearnableParameter
Validating --> features = InputValue
Validating --> MeanOfFeatures = Mean(features[363, 256])
Validating --> InvStdOfFeatures = InvStdDev(features[363, 256])
Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 256], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1])
Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 256])
Validating --> B0 = LearnableParameter
Validating --> W0*features+B0 = Plus(W0*features[512, 256], B0[512, 1])
Validating --> H1 = Sigmoid(W0*features+B0[512, 256])
Validating --> W1*H1 = Times(W1[512, 512], H1[512, 256])
Validating --> B1 = LearnableParameter
Validating --> W1*H1+B1 = Plus(W1*H1[512, 256], B1[512, 1])
Validating --> H2 = Sigmoid(W1*H1+B1[512, 256])
Validating --> W2*H1 = Times(W2[132, 512], H2[512, 256])
Validating --> B2 = LearnableParameter
Validating --> HLast = Plus(W2*H1[132, 256], B2[132, 1])
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, 256], HLast[132, 256])
Validating node ScaledLogLikelihood
Validating --> W2 = LearnableParameter
Validating --> W1 = LearnableParameter
Validating --> W0 = LearnableParameter
Validating --> features = InputValue
Validating --> MeanOfFeatures = Mean(features[363, 256])
Validating --> InvStdOfFeatures = InvStdDev(features[363, 256])
Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 256], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1])
Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 256])
Validating --> B0 = LearnableParameter
Validating --> W0*features+B0 = Plus(W0*features[512, 256], B0[512, 1])
Validating --> H1 = Sigmoid(W0*features+B0[512, 256])
Validating --> W1*H1 = Times(W1[512, 512], H1[512, 256])
Validating --> B1 = LearnableParameter
Validating --> W1*H1+B1 = Plus(W1*H1[512, 256], B1[512, 1])
Validating --> H2 = Sigmoid(W1*H1+B1[512, 256])
Validating --> W2*H1 = Times(W2[132, 512], H2[512, 256])
Validating --> B2 = LearnableParameter
Validating --> HLast = Plus(W2*H1[132, 256], B2[132, 1])
Validating --> labels = InputValue
Validating --> Prior = Mean(labels[132, 256])
Validating --> LogOfPrior = Log(Prior[132, 1])
Validating --> ScaledLogLikelihood = Minus(HLast[132, 256], LogOfPrior[132, 1])
Validating node EvalErrorPrediction
Validating --> labels = InputValue
Validating --> W2 = LearnableParameter
Validating --> W1 = LearnableParameter
Validating --> W0 = LearnableParameter
Validating --> features = InputValue
Validating --> MeanOfFeatures = Mean(features[363, 256])
Validating --> InvStdOfFeatures = InvStdDev(features[363, 256])
Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 256], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1])
Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 256])
Validating --> B0 = LearnableParameter
Validating --> W0*features+B0 = Plus(W0*features[512, 256], B0[512, 1])
Validating --> H1 = Sigmoid(W0*features+B0[512, 256])
Validating --> W1*H1 = Times(W1[512, 512], H1[512, 256])
Validating --> B1 = LearnableParameter
Validating --> W1*H1+B1 = Plus(W1*H1[512, 256], B1[512, 1])
Validating --> H2 = Sigmoid(W1*H1+B1[512, 256])
Validating --> W2*H1 = Times(W2[132, 512], H2[512, 256])
Validating --> B2 = LearnableParameter
Validating --> HLast = Plus(W2*H1[132, 256], B2[132, 1])
Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, 256], HLast[132, 256])
GetTrainCriterionNodes ...
GetEvalCriterionNodes ...
Validating node CrossEntropyWithSoftmax
Validating --> labels = InputValue
Validating --> W2 = LearnableParameter
Validating --> W1 = LearnableParameter
Validating --> W0 = LearnableParameter
Validating --> features = InputValue
Validating --> MeanOfFeatures = Mean(features[363, 256])
Validating --> InvStdOfFeatures = InvStdDev(features[363, 256])
Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 256], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1])
Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 256])
Validating --> B0 = LearnableParameter
Validating --> W0*features+B0 = Plus(W0*features[512, 256], B0[512, 1])
Validating --> H1 = Sigmoid(W0*features+B0[512, 256])
Validating --> W1*H1 = Times(W1[512, 512], H1[512, 256])
Validating --> B1 = LearnableParameter
Validating --> W1*H1+B1 = Plus(W1*H1[512, 256], B1[512, 1])
Validating --> H2 = Sigmoid(W1*H1+B1[512, 256])
Validating --> W2*H1 = Times(W2[132, 512], H2[512, 256])
Validating --> B2 = LearnableParameter
Validating --> HLast = Plus(W2*H1[132, 256], B2[132, 1])
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, 256], HLast[132, 256])
No PreCompute nodes found, skipping PreCompute step
Set Max Temp Mem Size For Convolution Nodes to 0 samples.
Starting Epoch 3: learning rate per sample = 0.000098 momentum = 0.656119
minibatchiterator: epoch 2: frames [40960..61440] (first utterance at frame 40960) with 1 datapasses
requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms
Validating node EvalErrorPrediction
Validating --> labels = InputValue
Validating --> W2 = LearnableParameter
Validating --> W1 = LearnableParameter
Validating --> W0 = LearnableParameter
Validating --> features = InputValue
Validating --> MeanOfFeatures = Mean(features[363, 1024])
Validating --> InvStdOfFeatures = InvStdDev(features[363, 1024])
Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 1024], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1])
Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 1024])
Validating --> B0 = LearnableParameter
Validating --> W0*features+B0 = Plus(W0*features[512, 1024], B0[512, 1])
Validating --> H1 = Sigmoid(W0*features+B0[512, 1024])
Validating --> W1*H1 = Times(W1[512, 512], H1[512, 1024])
Validating --> B1 = LearnableParameter
Validating --> W1*H1+B1 = Plus(W1*H1[512, 1024], B1[512, 1])
Validating --> H2 = Sigmoid(W1*H1+B1[512, 1024])
Validating --> W2*H1 = Times(W2[132, 512], H2[512, 1024])
Validating --> B2 = LearnableParameter
Validating --> HLast = Plus(W2*H1[132, 1024], B2[132, 1])
Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, 1024], HLast[132, 1024])
Epoch[ 3 of 3]-Minibatch[ 1- 10 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.86752820; EvalErr[0]PerSample = 0.52177733; TotalTime = 0.40600s; TotalTimePerSample = 0.03965ms; SamplesPerSecond = 25221
Epoch[ 3 of 3]-Minibatch[ 11- 20 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.87358737; EvalErr[0]PerSample = 0.51542968; TotalTime = 0.05538s; TotalTimePerSample = 0.00541ms; SamplesPerSecond = 184900
Finished Epoch[3]: [Training Set] TrainLossPerSample = 1.8705578; EvalErrPerSample = 0.5186035; Ave LearnRatePerSample = 9.765625146e-005; EpochTime=0.692077
COMPLETED

738
Tests/Speech/QuickE2E/baseline.windows.gpu.txt Normal file
Просмотреть файл

@ -0,0 +1,738 @@
=== Running /cygdrive/c/Users/svcphil/workspace.vlivan/CNTK-Build-Windows/x64/release/cntk.exe configFile=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\QuickE2E\cntk.config RunDir=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_gpu DataDir=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data DeviceId=Auto
-------------------------------------------------------------------
Build info:
Built time: Aug 11 2015 16:18:17
Last modified date: Tue Aug 11 16:16:08 2015
Built by svcphil on dphaim-26-new
Build Path: C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\MachineLearning\CNTK\
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0
Build Branch: master
Build SHA1: 397cc7cc16c00b1c12864d331c0729fde7a1bde3
-------------------------------------------------------------------
running on dphaim-26-new at 2015/08/11 17:47:26
command line options:
configFile=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\QuickE2E\cntk.config RunDir=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_gpu DataDir=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data DeviceId=Auto
>>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
precision=float
command=speechTrain
deviceId=$DeviceId$
parallelTrain=false
speechTrain=[
action=train
modelPath=$RunDir$/models/cntkSpeech.dnn
deviceId=$DeviceId$
traceLevel=1
SimpleNetworkBuilder=[
layerSizes=363:512:512:132
trainingCriterion=CrossEntropyWithSoftmax
evalCriterion=ErrorPrediction
layerTypes=Sigmoid
initValueScale=1.0
applyMeanVarNorm=true
uniformInit=true
needPrior=true
]
SGD=[
epochSize=20480
minibatchSize=64:256:1024:
learningRatesPerMB=1.0:0.5:0.1
numMBsToShowResult=10
momentumPerMB=0.9:0.656119
dropoutRate=0.0
maxEpochs=3
keepCheckPointFiles=true
AutoAdjust=[
reduceLearnRateIfImproveLessThan=0
loadBestModel=true
increaseLearnRateIfImproveMoreThan=1000000000
learnRateDecreaseFactor=0.5
learnRateIncreaseFactor=1.382
autoAdjustLR=AdjustAfterEpoch
]
clippingThresholdPerSample=1#INF
]
reader=[
readerType=HTKMLFReader
readMethod=blockRandomize
miniBatchMode=Partial
randomize=Auto
verbosity=0
features=[
dim=363
type=Real
scpFile=glob_0000.scp
]
labels=[
mlfFile=$DataDir$/glob_0000.mlf
labelMappingFile=$DataDir$/state.list
labelDim=132
labelType=Category
]
]
]
RunDir=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_gpu
DataDir=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data
DeviceId=Auto
<<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
>>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
precision=float
command=speechTrain
deviceId=Auto
parallelTrain=false
speechTrain=[
action=train
modelPath=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_gpu/models/cntkSpeech.dnn
deviceId=Auto
traceLevel=1
SimpleNetworkBuilder=[
layerSizes=363:512:512:132
trainingCriterion=CrossEntropyWithSoftmax
evalCriterion=ErrorPrediction
layerTypes=Sigmoid
initValueScale=1.0
applyMeanVarNorm=true
uniformInit=true
needPrior=true
]
SGD=[
epochSize=20480
minibatchSize=64:256:1024:
learningRatesPerMB=1.0:0.5:0.1
numMBsToShowResult=10
momentumPerMB=0.9:0.656119
dropoutRate=0.0
maxEpochs=3
keepCheckPointFiles=true
AutoAdjust=[
reduceLearnRateIfImproveLessThan=0
loadBestModel=true
increaseLearnRateIfImproveMoreThan=1000000000
learnRateDecreaseFactor=0.5
learnRateIncreaseFactor=1.382
autoAdjustLR=AdjustAfterEpoch
]
clippingThresholdPerSample=1#INF
]
reader=[
readerType=HTKMLFReader
readMethod=blockRandomize
miniBatchMode=Partial
randomize=Auto
verbosity=0
features=[
dim=363
type=Real
scpFile=glob_0000.scp
]
labels=[
mlfFile=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data/glob_0000.mlf
labelMappingFile=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data/state.list
labelDim=132
labelType=Category
]
]
]
RunDir=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_gpu
DataDir=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data
DeviceId=Auto
<<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
>>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
configparameters: cntk.config:command=speechTrain
configparameters: cntk.config:DataDir=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data
configparameters: cntk.config:deviceId=Auto
configparameters: cntk.config:parallelTrain=false
configparameters: cntk.config:precision=float
configparameters: cntk.config:RunDir=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_gpu
configparameters: cntk.config:speechTrain=[
action=train
modelPath=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_gpu/models/cntkSpeech.dnn
deviceId=Auto
traceLevel=1
SimpleNetworkBuilder=[
layerSizes=363:512:512:132
trainingCriterion=CrossEntropyWithSoftmax
evalCriterion=ErrorPrediction
layerTypes=Sigmoid
initValueScale=1.0
applyMeanVarNorm=true
uniformInit=true
needPrior=true
]
SGD=[
epochSize=20480
minibatchSize=64:256:1024:
learningRatesPerMB=1.0:0.5:0.1
numMBsToShowResult=10
momentumPerMB=0.9:0.656119
dropoutRate=0.0
maxEpochs=3
keepCheckPointFiles=true
AutoAdjust=[
reduceLearnRateIfImproveLessThan=0
loadBestModel=true
increaseLearnRateIfImproveMoreThan=1000000000
learnRateDecreaseFactor=0.5
learnRateIncreaseFactor=1.382
autoAdjustLR=AdjustAfterEpoch
]
clippingThresholdPerSample=1#INF
]
reader=[
readerType=HTKMLFReader
readMethod=blockRandomize
miniBatchMode=Partial
randomize=Auto
verbosity=0
features=[
dim=363
type=Real
scpFile=glob_0000.scp
]
labels=[
mlfFile=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data/glob_0000.mlf
labelMappingFile=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data/state.list
labelDim=132
labelType=Category
]
]
]
<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
command: speechTrain
precision = float
LockDevice: Capture device 1 and lock it for exclusive use
LockDevice: Capture device 2 and lock it for exclusive use
LockDevice: Capture device 3 and lock it for exclusive use
LockDevice: Capture device 0 and lock it for exclusive use
LockDevice: Capture device 1 and lock it for exclusive use
SimpleNetworkBuilder Using GPU 1
reading script file glob_0000.scp ... 948 entries
trainlayer: OOV-exclusion code enabled, but no unigram specified to derive the word set from, so you won't get OOV exclusion
total 132 state names in state list C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data/state.list
htkmlfreader: reading MLF file C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\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
GetTrainCriterionNodes ...
GetEvalCriterionNodes ...
Validating node CrossEntropyWithSoftmax
Validating --> labels = InputValue
Validating --> W2 = LearnableParameter
Validating --> W1 = LearnableParameter
Validating --> W0 = LearnableParameter
Validating --> features = InputValue
Validating --> MeanOfFeatures = Mean(features[363, 3])
Validating --> InvStdOfFeatures = InvStdDev(features[363, 3])
Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 3], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1])
Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 3])
Validating --> B0 = LearnableParameter
Validating --> W0*features+B0 = Plus(W0*features[512, 3], B0[512, 1])
Validating --> H1 = Sigmoid(W0*features+B0[512, 3])
Validating --> W1*H1 = Times(W1[512, 512], H1[512, 3])
Validating --> B1 = LearnableParameter
Validating --> W1*H1+B1 = Plus(W1*H1[512, 3], B1[512, 1])
Validating --> H2 = Sigmoid(W1*H1+B1[512, 3])
Validating --> W2*H1 = Times(W2[132, 512], H2[512, 3])
Validating --> B2 = LearnableParameter
Validating --> HLast = Plus(W2*H1[132, 3], B2[132, 1])
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, 3], HLast[132, 3])
Found 3 PreCompute nodes
NodeName: InvStdOfFeatures
NodeName: MeanOfFeatures
NodeName: Prior
minibatchiterator: epoch 0: frames [0..252734] (first utterance at frame 0) with 1 datapasses
requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms
Validating node InvStdOfFeatures
Validating --> features = InputValue
Validating --> InvStdOfFeatures = InvStdDev(features[363, 64])
Validating node MeanOfFeatures
Validating --> features = InputValue
Validating --> MeanOfFeatures = Mean(features[363, 64])
Validating node Prior
Validating --> labels = InputValue
Validating --> Prior = Mean(labels[132, 64])
Set Max Temp Mem Size For Convolution Nodes to 0 samples.
Starting Epoch 1: learning rate per sample = 0.015625 momentum = 0.900000
minibatchiterator: epoch 0: frames [0..20480] (first utterance at frame 0) with 1 datapasses
Validating node EvalErrorPrediction
Validating --> labels = InputValue
Validating --> W2 = LearnableParameter
Validating --> W1 = LearnableParameter
Validating --> W0 = LearnableParameter
Validating --> features = InputValue
Validating --> MeanOfFeatures = Mean(features[363, 64])
Validating --> InvStdOfFeatures = InvStdDev(features[363, 64])
Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 64], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1])
Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 64])
Validating --> B0 = LearnableParameter
Validating --> W0*features+B0 = Plus(W0*features[512, 64], B0[512, 1])
Validating --> H1 = Sigmoid(W0*features+B0[512, 64])
Validating --> W1*H1 = Times(W1[512, 512], H1[512, 64])
Validating --> B1 = LearnableParameter
Validating --> W1*H1+B1 = Plus(W1*H1[512, 64], B1[512, 1])
Validating --> H2 = Sigmoid(W1*H1+B1[512, 64])
Validating --> W2*H1 = Times(W2[132, 512], H2[512, 64])
Validating --> B2 = LearnableParameter
Validating --> HLast = Plus(W2*H1[132, 64], B2[132, 1])
Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, 64], HLast[132, 64])
Epoch[ 1 of 3]-Minibatch[ 1- 10 of 320]: SamplesSeen = 640; TrainLossPerSample = 4.45646143; EvalErr[0]PerSample = 0.92500001; TotalTime = 0.03190s; TotalTimePerSample = 0.04985ms; SamplesPerSecond = 20061
Epoch[ 1 of 3]-Minibatch[ 11- 20 of 320]: SamplesSeen = 640; TrainLossPerSample = 4.22315693; EvalErr[0]PerSample = 0.90156251; TotalTime = 0.02454s; TotalTimePerSample = 0.03835ms; SamplesPerSecond = 26075
Epoch[ 1 of 3]-Minibatch[ 21- 30 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.95180511; EvalErr[0]PerSample = 0.84687501; TotalTime = 0.02438s; TotalTimePerSample = 0.03809ms; SamplesPerSecond = 26254
Epoch[ 1 of 3]-Minibatch[ 31- 40 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.94157934; EvalErr[0]PerSample = 0.89843750; TotalTime = 0.02445s; TotalTimePerSample = 0.03820ms; SamplesPerSecond = 26181
Epoch[ 1 of 3]-Minibatch[ 41- 50 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.85668945; EvalErr[0]PerSample = 0.91093749; TotalTime = 0.02429s; TotalTimePerSample = 0.03795ms; SamplesPerSecond = 26352
Epoch[ 1 of 3]-Minibatch[ 51- 60 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.72866368; EvalErr[0]PerSample = 0.89531249; TotalTime = 0.02445s; TotalTimePerSample = 0.03820ms; SamplesPerSecond = 26178
Epoch[ 1 of 3]-Minibatch[ 61- 70 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.51809072; EvalErr[0]PerSample = 0.82968748; TotalTime = 0.02423s; TotalTimePerSample = 0.03786ms; SamplesPerSecond = 26415
Epoch[ 1 of 3]-Minibatch[ 71- 80 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.48454905; EvalErr[0]PerSample = 0.80781251; TotalTime = 0.02249s; TotalTimePerSample = 0.03514ms; SamplesPerSecond = 28457
Epoch[ 1 of 3]-Minibatch[ 81- 90 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.33829641; EvalErr[0]PerSample = 0.76875001; TotalTime = 0.02169s; TotalTimePerSample = 0.03390ms; SamplesPerSecond = 29501
Epoch[ 1 of 3]-Minibatch[ 91- 100 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.50167227; EvalErr[0]PerSample = 0.79843748; TotalTime = 0.02178s; TotalTimePerSample = 0.03403ms; SamplesPerSecond = 29386
WARNING: The same matrix with dim [1, 1] has been transferred between different devices for 20 times.
Epoch[ 1 of 3]-Minibatch[ 101- 110 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.22861624; EvalErr[0]PerSample = 0.80000001; TotalTime = 0.02166s; TotalTimePerSample = 0.03385ms; SamplesPerSecond = 29546
Epoch[ 1 of 3]-Minibatch[ 111- 120 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.32616878; EvalErr[0]PerSample = 0.79062498; TotalTime = 0.02063s; TotalTimePerSample = 0.03224ms; SamplesPerSecond = 31018
Epoch[ 1 of 3]-Minibatch[ 121- 130 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.16897583; EvalErr[0]PerSample = 0.77968752; TotalTime = 0.01950s; TotalTimePerSample = 0.03048ms; SamplesPerSecond = 32813
Epoch[ 1 of 3]-Minibatch[ 131- 140 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.08891916; EvalErr[0]PerSample = 0.77656251; TotalTime = 0.01961s; TotalTimePerSample = 0.03063ms; SamplesPerSecond = 32644
Epoch[ 1 of 3]-Minibatch[ 141- 150 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.06004953; EvalErr[0]PerSample = 0.72968751; TotalTime = 0.01950s; TotalTimePerSample = 0.03046ms; SamplesPerSecond = 32825
Epoch[ 1 of 3]-Minibatch[ 151- 160 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.91128540; EvalErr[0]PerSample = 0.69531250; TotalTime = 0.01965s; TotalTimePerSample = 0.03070ms; SamplesPerSecond = 32571
Epoch[ 1 of 3]-Minibatch[ 161- 170 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.90172124; EvalErr[0]PerSample = 0.72968751; TotalTime = 0.01828s; TotalTimePerSample = 0.02857ms; SamplesPerSecond = 35003
Epoch[ 1 of 3]-Minibatch[ 171- 180 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.73261714; EvalErr[0]PerSample = 0.65312499; TotalTime = 0.01799s; TotalTimePerSample = 0.02811ms; SamplesPerSecond = 35569
Epoch[ 1 of 3]-Minibatch[ 181- 190 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.66515493; EvalErr[0]PerSample = 0.68437499; TotalTime = 0.01789s; TotalTimePerSample = 0.02796ms; SamplesPerSecond = 35766
Epoch[ 1 of 3]-Minibatch[ 191- 200 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.67383432; EvalErr[0]PerSample = 0.66406250; TotalTime = 0.01792s; TotalTimePerSample = 0.02800ms; SamplesPerSecond = 35708
Epoch[ 1 of 3]-Minibatch[ 201- 210 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.52869272; EvalErr[0]PerSample = 0.63593751; TotalTime = 0.01805s; TotalTimePerSample = 0.02821ms; SamplesPerSecond = 35451
Epoch[ 1 of 3]-Minibatch[ 211- 220 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.60032344; EvalErr[0]PerSample = 0.66718751; TotalTime = 0.01696s; TotalTimePerSample = 0.02650ms; SamplesPerSecond = 37738
Epoch[ 1 of 3]-Minibatch[ 221- 230 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.51134038; EvalErr[0]PerSample = 0.64843750; TotalTime = 0.01658s; TotalTimePerSample = 0.02591ms; SamplesPerSecond = 38598
Epoch[ 1 of 3]-Minibatch[ 231- 240 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.45362544; EvalErr[0]PerSample = 0.63749999; TotalTime = 0.01663s; TotalTimePerSample = 0.02598ms; SamplesPerSecond = 38491
Epoch[ 1 of 3]-Minibatch[ 241- 250 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.41640615; EvalErr[0]PerSample = 0.61562502; TotalTime = 0.01670s; TotalTimePerSample = 0.02610ms; SamplesPerSecond = 38321
Epoch[ 1 of 3]-Minibatch[ 251- 260 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.39745474; EvalErr[0]PerSample = 0.62812501; TotalTime = 0.01672s; TotalTimePerSample = 0.02612ms; SamplesPerSecond = 38279
Epoch[ 1 of 3]-Minibatch[ 261- 270 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.16415405; EvalErr[0]PerSample = 0.56718749; TotalTime = 0.01621s; TotalTimePerSample = 0.02533ms; SamplesPerSecond = 39481
Epoch[ 1 of 3]-Minibatch[ 271- 280 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.30347300; EvalErr[0]PerSample = 0.63593751; TotalTime = 0.01583s; TotalTimePerSample = 0.02474ms; SamplesPerSecond = 40427
Epoch[ 1 of 3]-Minibatch[ 281- 290 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.24398804; EvalErr[0]PerSample = 0.60937500; TotalTime = 0.01579s; TotalTimePerSample = 0.02467ms; SamplesPerSecond = 40542
Epoch[ 1 of 3]-Minibatch[ 291- 300 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.15322256; EvalErr[0]PerSample = 0.57968748; TotalTime = 0.01582s; TotalTimePerSample = 0.02472ms; SamplesPerSecond = 40447
Epoch[ 1 of 3]-Minibatch[ 301- 310 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.21664429; EvalErr[0]PerSample = 0.59531248; TotalTime = 0.01570s; TotalTimePerSample = 0.02453ms; SamplesPerSecond = 40761
Epoch[ 1 of 3]-Minibatch[ 311- 320 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.25246572; EvalErr[0]PerSample = 0.60156250; TotalTime = 0.01556s; TotalTimePerSample = 0.02431ms; SamplesPerSecond = 41139
Finished Epoch[1]: [Training Set] TrainLossPerSample = 3.0000031; EvalErrPerSample = 0.72836918; Ave LearnRatePerSample = 0.015625; EpochTime=0.657568
Starting Epoch 2: learning rate per sample = 0.001953 momentum = 0.656119
minibatchiterator: epoch 1: frames [20480..40960] (first utterance at frame 20480) with 1 datapasses
Epoch[ 2 of 3]-Minibatch[ 1- 10 of 80]: SamplesSeen = 2560; TrainLossPerSample = 2.08151960; EvalErr[0]PerSample = 0.55859375; TotalTime = 0.03143s; TotalTimePerSample = 0.01228ms; SamplesPerSecond = 81456
Epoch[ 2 of 3]-Minibatch[ 11- 20 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.98395634; EvalErr[0]PerSample = 0.54257810; TotalTime = 0.02295s; TotalTimePerSample = 0.00896ms; SamplesPerSecond = 111561
Epoch[ 2 of 3]-Minibatch[ 21- 30 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.98575521; EvalErr[0]PerSample = 0.54492188; TotalTime = 0.02287s; TotalTimePerSample = 0.00893ms; SamplesPerSecond = 111951
Epoch[ 2 of 3]-Minibatch[ 31- 40 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.90484965; EvalErr[0]PerSample = 0.53164065; TotalTime = 0.02284s; TotalTimePerSample = 0.00892ms; SamplesPerSecond = 112069
Epoch[ 2 of 3]-Minibatch[ 41- 50 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.88324130; EvalErr[0]PerSample = 0.52539063; TotalTime = 0.02277s; TotalTimePerSample = 0.00889ms; SamplesPerSecond = 112448
Epoch[ 2 of 3]-Minibatch[ 51- 60 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.89109266; EvalErr[0]PerSample = 0.53359377; TotalTime = 0.02287s; TotalTimePerSample = 0.00894ms; SamplesPerSecond = 111917
Epoch[ 2 of 3]-Minibatch[ 61- 70 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.89496076; EvalErr[0]PerSample = 0.52890623; TotalTime = 0.02279s; TotalTimePerSample = 0.00890ms; SamplesPerSecond = 112325
Epoch[ 2 of 3]-Minibatch[ 71- 80 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.85944366; EvalErr[0]PerSample = 0.52265626; TotalTime = 0.02265s; TotalTimePerSample = 0.00885ms; SamplesPerSecond = 113044
Finished Epoch[2]: [Training Set] TrainLossPerSample = 1.9356024; EvalErrPerSample = 0.53603518; Ave LearnRatePerSample = 0.001953125; EpochTime=0.192318
Starting Epoch 3: learning rate per sample = 0.000098 momentum = 0.656119
minibatchiterator: epoch 2: frames [40960..61440] (first utterance at frame 40960) with 1 datapasses
Epoch[ 3 of 3]-Minibatch[ 1- 10 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.86752820; EvalErr[0]PerSample = 0.52177733; TotalTime = 0.08080s; TotalTimePerSample = 0.00789ms; SamplesPerSecond = 126735
Epoch[ 3 of 3]-Minibatch[ 11- 20 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.87358737; EvalErr[0]PerSample = 0.51542968; TotalTime = 0.05544s; TotalTimePerSample = 0.00541ms; SamplesPerSecond = 184694
Finished Epoch[3]: [Training Set] TrainLossPerSample = 1.8705578; EvalErrPerSample = 0.5186035; Ave LearnRatePerSample = 9.765625146e-005; EpochTime=0.139063
COMPLETED
=== Deleting last epoch data
==== Re-running from checkpoint
-------------------------------------------------------------------
Build info:
Built time: Aug 11 2015 16:18:17
Last modified date: Tue Aug 11 16:16:08 2015
Built by svcphil on dphaim-26-new
Build Path: C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\MachineLearning\CNTK\
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0
Build Branch: master
Build SHA1: 397cc7cc16c00b1c12864d331c0729fde7a1bde3
-------------------------------------------------------------------
running on dphaim-26-new at 2015/08/11 17:47:34
command line options:
configFile=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\QuickE2E\cntk.config RunDir=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_gpu DataDir=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data DeviceId=Auto
>>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
precision=float
command=speechTrain
deviceId=$DeviceId$
parallelTrain=false
speechTrain=[
action=train
modelPath=$RunDir$/models/cntkSpeech.dnn
deviceId=$DeviceId$
traceLevel=1
SimpleNetworkBuilder=[
layerSizes=363:512:512:132
trainingCriterion=CrossEntropyWithSoftmax
evalCriterion=ErrorPrediction
layerTypes=Sigmoid
initValueScale=1.0
applyMeanVarNorm=true
uniformInit=true
needPrior=true
]
SGD=[
epochSize=20480
minibatchSize=64:256:1024:
learningRatesPerMB=1.0:0.5:0.1
numMBsToShowResult=10
momentumPerMB=0.9:0.656119
dropoutRate=0.0
maxEpochs=3
keepCheckPointFiles=true
AutoAdjust=[
reduceLearnRateIfImproveLessThan=0
loadBestModel=true
increaseLearnRateIfImproveMoreThan=1000000000
learnRateDecreaseFactor=0.5
learnRateIncreaseFactor=1.382
autoAdjustLR=AdjustAfterEpoch
]
clippingThresholdPerSample=1#INF
]
reader=[
readerType=HTKMLFReader
readMethod=blockRandomize
miniBatchMode=Partial
randomize=Auto
verbosity=0
features=[
dim=363
type=Real
scpFile=glob_0000.scp
]
labels=[
mlfFile=$DataDir$/glob_0000.mlf
labelMappingFile=$DataDir$/state.list
labelDim=132
labelType=Category
]
]
]
RunDir=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_gpu
DataDir=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data
DeviceId=Auto
<<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
>>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
precision=float
command=speechTrain
deviceId=Auto
parallelTrain=false
speechTrain=[
action=train
modelPath=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_gpu/models/cntkSpeech.dnn
deviceId=Auto
traceLevel=1
SimpleNetworkBuilder=[
layerSizes=363:512:512:132
trainingCriterion=CrossEntropyWithSoftmax
evalCriterion=ErrorPrediction
layerTypes=Sigmoid
initValueScale=1.0
applyMeanVarNorm=true
uniformInit=true
needPrior=true
]
SGD=[
epochSize=20480
minibatchSize=64:256:1024:
learningRatesPerMB=1.0:0.5:0.1
numMBsToShowResult=10
momentumPerMB=0.9:0.656119
dropoutRate=0.0
maxEpochs=3
keepCheckPointFiles=true
AutoAdjust=[
reduceLearnRateIfImproveLessThan=0
loadBestModel=true
increaseLearnRateIfImproveMoreThan=1000000000
learnRateDecreaseFactor=0.5
learnRateIncreaseFactor=1.382
autoAdjustLR=AdjustAfterEpoch
]
clippingThresholdPerSample=1#INF
]
reader=[
readerType=HTKMLFReader
readMethod=blockRandomize
miniBatchMode=Partial
randomize=Auto
verbosity=0
features=[
dim=363
type=Real
scpFile=glob_0000.scp
]
labels=[
mlfFile=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data/glob_0000.mlf
labelMappingFile=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data/state.list
labelDim=132
labelType=Category
]
]
]
RunDir=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_gpu
DataDir=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data
DeviceId=Auto
<<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
>>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
configparameters: cntk.config:command=speechTrain
configparameters: cntk.config:DataDir=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data
configparameters: cntk.config:deviceId=Auto
configparameters: cntk.config:parallelTrain=false
configparameters: cntk.config:precision=float
configparameters: cntk.config:RunDir=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_gpu
configparameters: cntk.config:speechTrain=[
action=train
modelPath=C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_gpu/models/cntkSpeech.dnn
deviceId=Auto
traceLevel=1
SimpleNetworkBuilder=[
layerSizes=363:512:512:132
trainingCriterion=CrossEntropyWithSoftmax
evalCriterion=ErrorPrediction
layerTypes=Sigmoid
initValueScale=1.0
applyMeanVarNorm=true
uniformInit=true
needPrior=true
]
SGD=[
epochSize=20480
minibatchSize=64:256:1024:
learningRatesPerMB=1.0:0.5:0.1
numMBsToShowResult=10
momentumPerMB=0.9:0.656119
dropoutRate=0.0
maxEpochs=3
keepCheckPointFiles=true
AutoAdjust=[
reduceLearnRateIfImproveLessThan=0
loadBestModel=true
increaseLearnRateIfImproveMoreThan=1000000000
learnRateDecreaseFactor=0.5
learnRateIncreaseFactor=1.382
autoAdjustLR=AdjustAfterEpoch
]
clippingThresholdPerSample=1#INF
]
reader=[
readerType=HTKMLFReader
readMethod=blockRandomize
miniBatchMode=Partial
randomize=Auto
verbosity=0
features=[
dim=363
type=Real
scpFile=glob_0000.scp
]
labels=[
mlfFile=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data/glob_0000.mlf
labelMappingFile=C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data/state.list
labelDim=132
labelType=Category
]
]
]
<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
command: speechTrain
precision = float
LockDevice: Capture device 1 and lock it for exclusive use
LockDevice: Capture device 2 and lock it for exclusive use
LockDevice: Capture device 3 and lock it for exclusive use
LockDevice: Capture device 0 and lock it for exclusive use
LockDevice: Capture device 1 and lock it for exclusive use
SimpleNetworkBuilder Using GPU 1
reading script file glob_0000.scp ... 948 entries
trainlayer: OOV-exclusion code enabled, but no unigram specified to derive the word set from, so you won't get OOV exclusion
total 132 state names in state list C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\Data/state.list
htkmlfreader: reading MLF file C:\Users\svcphil\workspace.vlivan\CNTK-Build-Windows\Tests\Speech\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
Starting from checkpoint. Load Network From File C:\Users\svcphil\AppData\Local\Temp\2\cntk-test-20150811174551.851046\Speech_QuickE2E@release_gpu/models/cntkSpeech.dnn.2.
Printing Gradient Computation Node Order ...
CrossEntropyWithSoftmax[0, 0] = CrossEntropyWithSoftmax(labels[132, 256], HLast[0, 0])
HLast[0, 0] = Plus(W2*H1[0, 0], B2[132, 1])
B2[132, 1] = LearnableParameter
W2*H1[0, 0] = Times(W2[132, 512], H2[0, 0])
H2[0, 0] = Sigmoid(W1*H1+B1[0, 0])
W1*H1+B1[0, 0] = Plus(W1*H1[0, 0], B1[512, 1])
B1[512, 1] = LearnableParameter
W1*H1[0, 0] = Times(W1[512, 512], H1[0, 0])
H1[0, 0] = Sigmoid(W0*features+B0[0, 0])
W0*features+B0[0, 0] = Plus(W0*features[0, 0], B0[512, 1])
B0[512, 1] = LearnableParameter
W0*features[0, 0] = Times(W0[512, 363], MVNormalizedFeatures[0, 0])
MVNormalizedFeatures[0, 0] = PerDimMeanVarNormalization(features[363, 256], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1])
InvStdOfFeatures[363, 1] = InvStdDev(features[363, 256])
MeanOfFeatures[363, 1] = Mean(features[363, 256])
features[363, 256] = InputValue
W0[512, 363] = LearnableParameter
W1[512, 512] = LearnableParameter
W2[132, 512] = LearnableParameter
labels[132, 256] = InputValue
Validating node CrossEntropyWithSoftmax
Validating --> labels = InputValue
Validating --> W2 = LearnableParameter
Validating --> W1 = LearnableParameter
Validating --> W0 = LearnableParameter
Validating --> features = InputValue
Validating --> MeanOfFeatures = Mean(features[363, 256])
Validating --> InvStdOfFeatures = InvStdDev(features[363, 256])
Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 256], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1])
Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 256])
Validating --> B0 = LearnableParameter
Validating --> W0*features+B0 = Plus(W0*features[512, 256], B0[512, 1])
Validating --> H1 = Sigmoid(W0*features+B0[512, 256])
Validating --> W1*H1 = Times(W1[512, 512], H1[512, 256])
Validating --> B1 = LearnableParameter
Validating --> W1*H1+B1 = Plus(W1*H1[512, 256], B1[512, 1])
Validating --> H2 = Sigmoid(W1*H1+B1[512, 256])
Validating --> W2*H1 = Times(W2[132, 512], H2[512, 256])
Validating --> B2 = LearnableParameter
Validating --> HLast = Plus(W2*H1[132, 256], B2[132, 1])
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, 256], HLast[132, 256])
Validating node ScaledLogLikelihood
Validating --> W2 = LearnableParameter
Validating --> W1 = LearnableParameter
Validating --> W0 = LearnableParameter
Validating --> features = InputValue
Validating --> MeanOfFeatures = Mean(features[363, 256])
Validating --> InvStdOfFeatures = InvStdDev(features[363, 256])
Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 256], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1])
Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 256])
Validating --> B0 = LearnableParameter
Validating --> W0*features+B0 = Plus(W0*features[512, 256], B0[512, 1])
Validating --> H1 = Sigmoid(W0*features+B0[512, 256])
Validating --> W1*H1 = Times(W1[512, 512], H1[512, 256])
Validating --> B1 = LearnableParameter
Validating --> W1*H1+B1 = Plus(W1*H1[512, 256], B1[512, 1])
Validating --> H2 = Sigmoid(W1*H1+B1[512, 256])
Validating --> W2*H1 = Times(W2[132, 512], H2[512, 256])
Validating --> B2 = LearnableParameter
Validating --> HLast = Plus(W2*H1[132, 256], B2[132, 1])
Validating --> labels = InputValue
Validating --> Prior = Mean(labels[132, 256])
Validating --> LogOfPrior = Log(Prior[132, 1])
Validating --> ScaledLogLikelihood = Minus(HLast[132, 256], LogOfPrior[132, 1])
Validating node EvalErrorPrediction
Validating --> labels = InputValue
Validating --> W2 = LearnableParameter
Validating --> W1 = LearnableParameter
Validating --> W0 = LearnableParameter
Validating --> features = InputValue
Validating --> MeanOfFeatures = Mean(features[363, 256])
Validating --> InvStdOfFeatures = InvStdDev(features[363, 256])
Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 256], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1])
Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 256])
Validating --> B0 = LearnableParameter
Validating --> W0*features+B0 = Plus(W0*features[512, 256], B0[512, 1])
Validating --> H1 = Sigmoid(W0*features+B0[512, 256])
Validating --> W1*H1 = Times(W1[512, 512], H1[512, 256])
Validating --> B1 = LearnableParameter
Validating --> W1*H1+B1 = Plus(W1*H1[512, 256], B1[512, 1])
Validating --> H2 = Sigmoid(W1*H1+B1[512, 256])
Validating --> W2*H1 = Times(W2[132, 512], H2[512, 256])
Validating --> B2 = LearnableParameter
Validating --> HLast = Plus(W2*H1[132, 256], B2[132, 1])
Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, 256], HLast[132, 256])
GetTrainCriterionNodes ...
GetEvalCriterionNodes ...
Validating node CrossEntropyWithSoftmax
Validating --> labels = InputValue
Validating --> W2 = LearnableParameter
Validating --> W1 = LearnableParameter
Validating --> W0 = LearnableParameter
Validating --> features = InputValue
Validating --> MeanOfFeatures = Mean(features[363, 256])
Validating --> InvStdOfFeatures = InvStdDev(features[363, 256])
Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 256], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1])
Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 256])
Validating --> B0 = LearnableParameter
Validating --> W0*features+B0 = Plus(W0*features[512, 256], B0[512, 1])
Validating --> H1 = Sigmoid(W0*features+B0[512, 256])
Validating --> W1*H1 = Times(W1[512, 512], H1[512, 256])
Validating --> B1 = LearnableParameter
Validating --> W1*H1+B1 = Plus(W1*H1[512, 256], B1[512, 1])
Validating --> H2 = Sigmoid(W1*H1+B1[512, 256])
Validating --> W2*H1 = Times(W2[132, 512], H2[512, 256])
Validating --> B2 = LearnableParameter
Validating --> HLast = Plus(W2*H1[132, 256], B2[132, 1])
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax(labels[132, 256], HLast[132, 256])
No PreCompute nodes found, skipping PreCompute step
Set Max Temp Mem Size For Convolution Nodes to 0 samples.
Starting Epoch 3: learning rate per sample = 0.000098 momentum = 0.656119
minibatchiterator: epoch 2: frames [40960..61440] (first utterance at frame 40960) with 1 datapasses
requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms
Validating node EvalErrorPrediction
Validating --> labels = InputValue
Validating --> W2 = LearnableParameter
Validating --> W1 = LearnableParameter
Validating --> W0 = LearnableParameter
Validating --> features = InputValue
Validating --> MeanOfFeatures = Mean(features[363, 1024])
Validating --> InvStdOfFeatures = InvStdDev(features[363, 1024])
Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization(features[363, 1024], MeanOfFeatures[363, 1], InvStdOfFeatures[363, 1])
Validating --> W0*features = Times(W0[512, 363], MVNormalizedFeatures[363, 1024])
Validating --> B0 = LearnableParameter
Validating --> W0*features+B0 = Plus(W0*features[512, 1024], B0[512, 1])
Validating --> H1 = Sigmoid(W0*features+B0[512, 1024])
Validating --> W1*H1 = Times(W1[512, 512], H1[512, 1024])
Validating --> B1 = LearnableParameter
Validating --> W1*H1+B1 = Plus(W1*H1[512, 1024], B1[512, 1])
Validating --> H2 = Sigmoid(W1*H1+B1[512, 1024])
Validating --> W2*H1 = Times(W2[132, 512], H2[512, 1024])
Validating --> B2 = LearnableParameter
Validating --> HLast = Plus(W2*H1[132, 1024], B2[132, 1])
Validating --> EvalErrorPrediction = ErrorPrediction(labels[132, 1024], HLast[132, 1024])
Epoch[ 3 of 3]-Minibatch[ 1- 10 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.86752820; EvalErr[0]PerSample = 0.52177733; TotalTime = 0.42093s; TotalTimePerSample = 0.04111ms; SamplesPerSecond = 24327
Epoch[ 3 of 3]-Minibatch[ 11- 20 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.87358737; EvalErr[0]PerSample = 0.51542968; TotalTime = 0.05521s; TotalTimePerSample = 0.00539ms; SamplesPerSecond = 185480
Finished Epoch[3]: [Training Set] TrainLossPerSample = 1.8705578; EvalErrPerSample = 0.5186035; Ave LearnRatePerSample = 9.765625146e-005; EpochTime=0.690137
COMPLETED

21
Tests/Speech/QuickE2E/run-test
Просмотреть файл

@ -1,17 +1,28 @@
#!/bin/bash #!/bin/bash
CNTK_BINARY=$TEST_BUILD_LOCATION/$TEST_FLAVOR/bin/cntk
if [ "$TEST_DEVICE" == "CPU" ]; then if [ "$TEST_DEVICE" == "CPU" ]; then
CNTK_DEVICE_ID=-1 CNTK_DEVICE_ID=-1
else else
CNTK_DEVICE_ID=Auto CNTK_DEVICE_ID=Auto
fi fi
CNTK_ARGS="configFile=$TEST_DIR/cntk.config RunDir=$TEST_RUN_DIR DataDir=$TEST_DATA_DIR DeviceId=$CNTK_DEVICE_ID"
configFile=$TEST_DIR/cntk.config
RunDir=$TEST_RUN_DIR
DataDir=$TEST_DATA_DIR
if [ "$OS" == "Windows_NT" ]; then
# When running on cygwin translating /cygdrive/xxx paths to proper windows paths:
configFile=$(cygpath -aw $configFile)
RunDir=$(cygpath -aw $RunDir)
DataDir=$(cygpath -aw $DataDir)
fi
CNTK_ARGS="configFile=$configFile RunDir=$RunDir DataDir=$DataDir DeviceId=$CNTK_DEVICE_ID"
MODELS_DIR=$TEST_RUN_DIR/models MODELS_DIR=$TEST_RUN_DIR/models
[ -d $MODELS_DIR ] && rm -rf $MODELS_DIR [ -d $MODELS_DIR ] && rm -rf $MODELS_DIR
mkdir -p $MODELS_DIR || exit $? mkdir -p $MODELS_DIR || exit $?
echo === Running $CNTK_BINARY $CNTK_ARGS echo === Running $TEST_CNTK_BINARY $CNTK_ARGS
$CNTK_BINARY $CNTK_ARGS || exit $? $TEST_CNTK_BINARY $CNTK_ARGS || exit $?
echo === Deleting last epoch data echo === Deleting last epoch data
rm $TEST_RUN_DIR/models/*.dnn rm $TEST_RUN_DIR/models/*.dnn
echo ==== Re-running from checkpoint echo ==== Re-running from checkpoint
$CNTK_BINARY $CNTK_ARGS || exit $? $TEST_CNTK_BINARY $CNTK_ARGS || exit $?

44
Tests/TestDriver.py
Просмотреть файл

@ -15,7 +15,7 @@
# #
# Each test directory has a following components: # Each test directory has a following components:
# - testcases.yml - main test confuguration file, whcih defines all test cases # - testcases.yml - main test confuguration file, whcih defines all test cases
# - run-test - (run-test) script # - run-test - (run-test) script
# - baseline*.txt - baseline files whith a captured expected output of run-test script # - baseline*.txt - baseline files whith a captured expected output of run-test script
# #
# ----- testcases.yml format ------- # ----- testcases.yml format -------
@ -52,10 +52,14 @@
# ---- Baseline files ---- # ---- Baseline files ----
# Order of searching baseline files, depends on the current mode for a given test: # Order of searching baseline files, depends on the current mode for a given test:
# #
# 1. baseline.<flavor>.<device>.txt # 1. baseline.<os>.<flavor>.<device>.txt
# 2. baseline.<flavor>.txt # 2. baseline.<os>.<flavor>.txt
# 3. baseline.<device>.txt # 3. baseline.<os>.<device>.txt
# 4. baseline.txt # 4. baseline.<os>.txt
# 5. baseline.<flavor>.<device>.txt
# 6. baseline.<flavor>.txt
# 7. baseline.<device>.txt
# 8. baseline.txt
# where <flavor> = { debug | release } # where <flavor> = { debug | release }
# <device> = { cpu | gpu } # <device> = { cpu | gpu }
# #
@ -79,6 +83,7 @@
import sys, os, argparse, traceback, yaml, subprocess, random, re, time import sys, os, argparse, traceback, yaml, subprocess, random, re, time
thisDir = os.path.dirname(os.path.realpath(__file__)) thisDir = os.path.dirname(os.path.realpath(__file__))
windows = os.getenv("OS")=="Windows_NT"
# This class encapsulates an instance of the test # This class encapsulates an instance of the test
class Test: class Test:
@ -169,6 +174,10 @@ class Test:
os.environ["TEST_FLAVOR"] = flavor os.environ["TEST_FLAVOR"] = flavor
os.environ["TEST_DEVICE"] = device os.environ["TEST_DEVICE"] = device
os.environ["TEST_BUILD_LOCATION"] = args.build_location os.environ["TEST_BUILD_LOCATION"] = args.build_location
if windows:
os.environ["TEST_CNTK_BINARY"] = os.path.join(args.build_location, flavor, "cntk.exe")
else:
os.environ["TEST_CNTK_BINARY"] = os.path.join(args.build_location, flavor, "bin", "cntk")
os.environ["TEST_DIR"] = self.testDir os.environ["TEST_DIR"] = self.testDir
os.environ["TEST_DATA_DIR"] = self.dataDir os.environ["TEST_DATA_DIR"] = self.dataDir
os.environ["TEST_RUN_DIR"] = runDir os.environ["TEST_RUN_DIR"] = runDir
@ -237,17 +246,22 @@ class Test:
return result return result
# Finds a location of a baseline file by probing different names in the following order: # Finds a location of a baseline file by probing different names in the following order:
# baseline.$os.$flavor.$device.txt
# baseline.$os.$flavor.txt
# baseline.$os.$device.txt
# baseline.$os.txt
# baseline.$flavor.$device.txt # baseline.$flavor.$device.txt
# baseline.$flavor.txt # baseline.$flavor.txt
# baseline.$device.txt # baseline.$device.txt
# baseline.txt # baseline.txt
def findBaselineFile(self, flavor, device): def findBaselineFile(self, flavor, device):
for f in ["." + flavor.lower(), ""]: for o in ["." + ("windows" if windows else "linux"), ""]:
for d in ["." + device.lower(), ""]: for f in ["." + flavor.lower(), ""]:
candidateName = "baseline" + f + d + ".txt"; for d in ["." + device.lower(), ""]:
fullPath = os.path.join(self.testDir, candidateName) candidateName = "baseline" + o + f + d + ".txt"
if os.path.isfile(fullPath): fullPath = os.path.join(self.testDir, candidateName)
return fullPath if os.path.isfile(fullPath):
return fullPath
return None return None
# This class encapsulates one testcase (in testcases.yml file) # This class encapsulates one testcase (in testcases.yml file)
@ -521,13 +535,13 @@ runSubparser.add_argument("test", nargs="*",
help="optional test name(s) to run, specified as Suite/TestName. " help="optional test name(s) to run, specified as Suite/TestName. "
"Use list command to list available tests. " "Use list command to list available tests. "
"If not specified then all tests will be run.") "If not specified then all tests will be run.")
#TODO: port paths to Windows defaultBuildLocation=os.path.realpath(os.path.join(thisDir, "..", "x64" if windows else "build"))
defaultBuildLocation=os.path.realpath(os.path.join(thisDir, "..", "build"))
runSubparser.add_argument("-b", "--build-location", default=defaultBuildLocation, help="location of the CNTK build to run") runSubparser.add_argument("-b", "--build-location", default=defaultBuildLocation, help="location of the CNTK build to run")
runSubparser.add_argument("-d", "--device", help="cpu|gpu - run on a specific device") runSubparser.add_argument("-d", "--device", help="cpu|gpu - run on a specific device")
runSubparser.add_argument("-f", "--flavor", help="release|debug - run only a specific flavor") runSubparser.add_argument("-f", "--flavor", help="release|debug - run only a specific flavor")
#TODO: port paths to Windows tmpDir = os.getenv("TEMP") if windows else "/tmp"
defaultRunDir=os.path.join("/tmp", "cntk-test-{0}.{1}".format(time.strftime("%Y%m%d%H%M%S"), random.randint(0,1000000))) defaultRunDir=os.path.join(tmpDir, "cntk-test-{0}.{1}".format(time.strftime("%Y%m%d%H%M%S"), random.randint(0,1000000)))
runSubparser.add_argument("-r", "--run-dir", default=defaultRunDir, help="directory where to store test output, default: a random dir within /tmp") runSubparser.add_argument("-r", "--run-dir", default=defaultRunDir, help="directory where to store test output, default: a random dir within /tmp")
runSubparser.add_argument("--update-baseline", action='store_true', help="update baseline file(s) instead of matching them") runSubparser.add_argument("--update-baseline", action='store_true', help="update baseline file(s) instead of matching them")
runSubparser.add_argument("-v", "--verbose", action='store_true', help="verbose output - dump all output of test script") runSubparser.add_argument("-v", "--verbose", action='store_true', help="verbose output - dump all output of test script")