Added end-to-end test for CNTK speech workloads using AN4 dataset
This commit is contained in:
Vladimir Ivanov
Родитель
a0567f66ba
Коммит
746c2dbd1f
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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN">
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<html>
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<head>
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<title>AN4 License Terms</title>
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<meta http-equiv="content-type"
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content="text/html; charset=ISO-8859-1">
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</head>
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<body>
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<h2>AN4 License Terms</h2>
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<p>This audio database is free for use for any purpose (commercial or otherwise)
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subject to the restrictions detailed below.</p>
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<pre>
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/* ====================================================================
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* Copyright (c) 1991-2005 Carnegie Mellon University. All rights
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* reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* 1. Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in
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* the documentation and/or other materials provided with the
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* distribution.
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*
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* This work was supported in part by funding from the Defense Advanced
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* Research Projects Agency and the National Science Foundation of the
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* United States of America, and the CMU Sphinx Speech Consortium.
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*
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* THIS SOFTWARE IS PROVIDED BY CARNEGIE MELLON UNIVERSITY ``AS IS'' AND
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* ANY EXPRESSED OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
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* THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CARNEGIE MELLON UNIVERSITY
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* NOR ITS EMPLOYEES BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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* SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
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* LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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* ====================================================================
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*/
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</pre>
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</body>
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</html>
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Двоичный файл не отображается.
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Contents of this directory is a modified version of AN4 dataset pre-processed and optimized for CNTK end-to-end testing.
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AN4 dataset is a part of CMU audio databases located at http://www.speech.cs.cmu.edu/databases/an4
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This modified version of dataset is distributed under the terms of a AN4 license which can be found in AN4.LICENSE.html
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Разница между файлами не показана из-за своего большого размера
Загрузить разницу
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|
|
@ -0,0 +1,132 @@
|
|||
_ah_[2]
|
||||
_ah_[3]
|
||||
_ah_[4]
|
||||
_hmm_[2]
|
||||
_hmm_[3]
|
||||
_hmm_[4]
|
||||
_noise_[2]
|
||||
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|
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|
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aa_s2_1
|
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aa_s3_1
|
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aa_s4_1
|
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ae_s2_1
|
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ae_s3_1
|
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ae_s4_1
|
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ah_s2_1
|
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ah_s3_1
|
||||
ah_s4_1
|
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ao_s2_1
|
||||
ao_s3_1
|
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ao_s4_1
|
||||
aw_s2_1
|
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aw_s3_1
|
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aw_s4_1
|
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ax_s2_1
|
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ax_s3_1
|
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ax_s4_1
|
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ay_s2_1
|
||||
ay_s3_1
|
||||
ay_s4_1
|
||||
b_s2_1
|
||||
b_s3_1
|
||||
b_s4_1
|
||||
ch_s2_1
|
||||
ch_s3_1
|
||||
ch_s4_1
|
||||
d_s2_1
|
||||
d_s3_1
|
||||
d_s4_1
|
||||
dh_s2_1
|
||||
dh_s3_1
|
||||
dh_s4_1
|
||||
eh_s2_1
|
||||
eh_s3_1
|
||||
eh_s4_1
|
||||
er_s2_1
|
||||
er_s3_1
|
||||
er_s4_1
|
||||
ey_s2_1
|
||||
ey_s3_1
|
||||
ey_s4_1
|
||||
f_s2_1
|
||||
f_s3_1
|
||||
f_s4_1
|
||||
g_s2_1
|
||||
g_s3_1
|
||||
g_s4_1
|
||||
hh_s2_1
|
||||
hh_s3_1
|
||||
hh_s4_1
|
||||
ih_s2_1
|
||||
ih_s3_1
|
||||
ih_s4_1
|
||||
iy_s2_1
|
||||
iy_s3_1
|
||||
iy_s4_1
|
||||
jh_s2_1
|
||||
jh_s3_1
|
||||
jh_s4_1
|
||||
k_s2_1
|
||||
k_s3_1
|
||||
k_s4_1
|
||||
l_s2_1
|
||||
l_s3_1
|
||||
l_s4_1
|
||||
m_s2_1
|
||||
m_s3_1
|
||||
m_s4_1
|
||||
n_s2_1
|
||||
n_s3_1
|
||||
n_s4_1
|
||||
ng_s2_1
|
||||
ng_s3_1
|
||||
ng_s4_1
|
||||
ow_s2_1
|
||||
ow_s3_1
|
||||
ow_s4_1
|
||||
oy_s2_1
|
||||
oy_s3_1
|
||||
oy_s4_1
|
||||
p_s2_1
|
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p_s3_1
|
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p_s4_1
|
||||
r_s2_1
|
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r_s3_1
|
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r_s4_1
|
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s_s2_1
|
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s_s3_1
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s_s4_1
|
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sh_s2_1
|
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sh_s3_1
|
||||
sh_s4_1
|
||||
sil[2]
|
||||
sil[3]
|
||||
sil[4]
|
||||
t_s2_1
|
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t_s3_1
|
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t_s4_1
|
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th_s2_1
|
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th_s3_1
|
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th_s4_1
|
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uh_s2_1
|
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uh_s3_1
|
||||
uh_s4_1
|
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uw_s2_1
|
||||
uw_s3_1
|
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uw_s4_1
|
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v_s2_1
|
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v_s3_1
|
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v_s4_1
|
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w_s2_1
|
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w_s3_1
|
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w_s4_1
|
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y_s2_1
|
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y_s3_1
|
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y_s4_1
|
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z_s2_1
|
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z_s3_1
|
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z_s4_1
|
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zh_s2_1
|
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zh_s3_1
|
||||
zh_s4_1
|
||||
|
|
@ -0,0 +1,758 @@
|
|||
=== Running /home/vlivan/cntk/bin/x86_64.gpu.release.acml/cntk configFile=/home/vlivan/cntk/Tests/Speech/QuickE2E/cntk.config RunDir=/tmp/cntk-test-20150729191101.973007/Speech_QuickE2E@release_cpu DataDir=/home/vlivan/cntk/Tests/Speech/Data DeviceId=Auto
|
||||
running on localhost at 2015/07/29 19:11:01
|
||||
command line options:
|
||||
configFile=/home/vlivan/cntk/Tests/Speech/QuickE2E/cntk.config RunDir=/tmp/cntk-test-20150729191101.973007/Speech_QuickE2E@release_cpu DataDir=/home/vlivan/cntk/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=/tmp/cntk-test-20150729191101.973007/Speech_QuickE2E@release_cpu
|
||||
DataDir=/home/vlivan/cntk/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=/tmp/cntk-test-20150729191101.973007/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=/home/vlivan/cntk/Tests/Speech/Data/glob_0000.mlf
|
||||
labelMappingFile=/home/vlivan/cntk/Tests/Speech/Data/state.list
|
||||
labelDim=132
|
||||
labelType=Category
|
||||
]
|
||||
]
|
||||
]
|
||||
RunDir=/tmp/cntk-test-20150729191101.973007/Speech_QuickE2E@release_cpu
|
||||
DataDir=/home/vlivan/cntk/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=/home/vlivan/cntk/Tests/Speech/Data
|
||||
configparameters: cntk.config:deviceId=Auto
|
||||
configparameters: cntk.config:parallelTrain=false
|
||||
configparameters: cntk.config:precision=float
|
||||
configparameters: cntk.config:RunDir=/tmp/cntk-test-20150729191101.973007/Speech_QuickE2E@release_cpu
|
||||
configparameters: cntk.config:speechTrain=[
|
||||
action=train
|
||||
modelPath=/tmp/cntk-test-20150729191101.973007/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=/home/vlivan/cntk/Tests/Speech/Data/glob_0000.mlf
|
||||
labelMappingFile=/home/vlivan/cntk/Tests/Speech/Data/state.list
|
||||
labelDim=132
|
||||
labelType=Category
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
command: speechTrain
|
||||
precision = float
|
||||
lsof: WARNING: can't stat() ext4 file system /var/lib/docker/aufs
|
||||
Output information may be incomplete.
|
||||
LockDevice: Capture device 0 and lock it for exclusive use
|
||||
LockDevice: Capture device 0 and lock it for exclusive use
|
||||
SimpleNetworkBuilder Using GPU 0
|
||||
reading script file glob_0000.scp ... 948 entries
|
||||
total 132 state names in state list /home/vlivan/cntk/Tests/Speech/Data/state.list
|
||||
htkmlfreader: reading MLF file /home/vlivan/cntk/Tests/Speech/Data/glob_0000.mlf ...parse the line 55130
|
||||
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 = 4.3213539; EvalErr[0]PerSample = 0.89999998; TotalTime=0.064177; TotalTimePerSample=0.00010027656, SamplesPerSecond=9972
|
||||
Epoch[1 of 3]-Minibatch[11-20 of 320]: SamplesSeen = 640; TrainLossPerSample = 4.1507101; EvalErr[0]PerSample = 0.8671875; TotalTime=0.060664; TotalTimePerSample=9.47875e-05, SamplesPerSecond=10549
|
||||
Epoch[1 of 3]-Minibatch[21-30 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.9990096; EvalErr[0]PerSample = 0.87656248; TotalTime=0.062395; TotalTimePerSample=9.7492187e-05, SamplesPerSecond=10257
|
||||
Epoch[1 of 3]-Minibatch[31-40 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.8694596; EvalErr[0]PerSample = 0.87656248; TotalTime=0.058102; TotalTimePerSample=9.0784375e-05, SamplesPerSecond=11015
|
||||
Epoch[1 of 3]-Minibatch[41-50 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.8021927; EvalErr[0]PerSample = 0.87812501; TotalTime=0.058272; TotalTimePerSample=9.105e-05, SamplesPerSecond=10982
|
||||
Epoch[1 of 3]-Minibatch[51-60 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.7289093; EvalErr[0]PerSample = 0.86874998; TotalTime=0.056752; TotalTimePerSample=8.8675e-05, SamplesPerSecond=11277
|
||||
Epoch[1 of 3]-Minibatch[61-70 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.5618699; EvalErr[0]PerSample = 0.82343751; TotalTime=0.06015; TotalTimePerSample=9.3984375e-05, SamplesPerSecond=10640
|
||||
Epoch[1 of 3]-Minibatch[71-80 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.4279053; EvalErr[0]PerSample = 0.80781251; TotalTime=0.061573; TotalTimePerSample=9.6207812e-05, SamplesPerSecond=10394
|
||||
Epoch[1 of 3]-Minibatch[81-90 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.3392854; EvalErr[0]PerSample = 0.7734375; TotalTime=0.057831; TotalTimePerSample=9.0360938e-05, SamplesPerSecond=11066
|
||||
Epoch[1 of 3]-Minibatch[91-100 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.3639894; EvalErr[0]PerSample = 0.84375; TotalTime=0.05709; TotalTimePerSample=8.9203125e-05, SamplesPerSecond=11210
|
||||
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 = 3.2122345; EvalErr[0]PerSample = 0.75312501; TotalTime=0.061065; TotalTimePerSample=9.5414062e-05, SamplesPerSecond=10480
|
||||
Epoch[1 of 3]-Minibatch[111-120 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.3126526; EvalErr[0]PerSample = 0.78750002; TotalTime=0.058543; TotalTimePerSample=9.1473437e-05, SamplesPerSecond=10932
|
||||
Epoch[1 of 3]-Minibatch[121-130 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.1408203; EvalErr[0]PerSample = 0.74687499; TotalTime=0.0605; TotalTimePerSample=9.453125e-05, SamplesPerSecond=10578
|
||||
Epoch[1 of 3]-Minibatch[131-140 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.006897; EvalErr[0]PerSample = 0.69687498; TotalTime=0.054623; TotalTimePerSample=8.5348438e-05, SamplesPerSecond=11716
|
||||
Epoch[1 of 3]-Minibatch[141-150 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.0049591; EvalErr[0]PerSample = 0.72343749; TotalTime=0.059955; TotalTimePerSample=9.3679687e-05, SamplesPerSecond=10674
|
||||
Epoch[1 of 3]-Minibatch[151-160 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.9785829; EvalErr[0]PerSample = 0.73906249; TotalTime=0.060773; TotalTimePerSample=9.4957812e-05, SamplesPerSecond=10530
|
||||
Epoch[1 of 3]-Minibatch[161-170 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.8568604; EvalErr[0]PerSample = 0.70781249; TotalTime=0.060235; TotalTimePerSample=9.4117187e-05, SamplesPerSecond=10625
|
||||
Epoch[1 of 3]-Minibatch[171-180 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.6905334; EvalErr[0]PerSample = 0.671875; TotalTime=0.064974; TotalTimePerSample=0.00010152188, SamplesPerSecond=9850
|
||||
Epoch[1 of 3]-Minibatch[181-190 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.7865357; EvalErr[0]PerSample = 0.70468748; TotalTime=0.05438; TotalTimePerSample=8.496875e-05, SamplesPerSecond=11769
|
||||
Epoch[1 of 3]-Minibatch[191-200 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.5770202; EvalErr[0]PerSample = 0.6484375; TotalTime=0.063006; TotalTimePerSample=9.8446875e-05, SamplesPerSecond=10157
|
||||
Epoch[1 of 3]-Minibatch[201-210 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.6157165; EvalErr[0]PerSample = 0.6640625; TotalTime=0.058268; TotalTimePerSample=9.104375e-05, SamplesPerSecond=10983
|
||||
Epoch[1 of 3]-Minibatch[211-220 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.552362; EvalErr[0]PerSample = 0.65781248; TotalTime=0.059349; TotalTimePerSample=9.2732812e-05, SamplesPerSecond=10783
|
||||
Epoch[1 of 3]-Minibatch[221-230 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.4821167; EvalErr[0]PerSample = 0.625; TotalTime=0.061069; TotalTimePerSample=9.5420313e-05, SamplesPerSecond=10479
|
||||
Epoch[1 of 3]-Minibatch[231-240 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.3877869; EvalErr[0]PerSample = 0.62812501; TotalTime=0.055723; TotalTimePerSample=8.7067188e-05, SamplesPerSecond=11485
|
||||
Epoch[1 of 3]-Minibatch[241-250 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.3690064; EvalErr[0]PerSample = 0.6484375; TotalTime=0.061959; TotalTimePerSample=9.6810937e-05, SamplesPerSecond=10329
|
||||
Epoch[1 of 3]-Minibatch[251-260 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.4396729; EvalErr[0]PerSample = 0.6328125; TotalTime=0.062976; TotalTimePerSample=9.84e-05, SamplesPerSecond=10162
|
||||
Epoch[1 of 3]-Minibatch[261-270 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.3028197; EvalErr[0]PerSample = 0.61250001; TotalTime=0.060925; TotalTimePerSample=9.5195312e-05, SamplesPerSecond=10504
|
||||
Epoch[1 of 3]-Minibatch[271-280 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.1966858; EvalErr[0]PerSample = 0.55937499; TotalTime=0.060799; TotalTimePerSample=9.4998438e-05, SamplesPerSecond=10526
|
||||
Epoch[1 of 3]-Minibatch[281-290 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.2898011; EvalErr[0]PerSample = 0.60468751; TotalTime=0.055702; TotalTimePerSample=8.7034375e-05, SamplesPerSecond=11489
|
||||
Epoch[1 of 3]-Minibatch[291-300 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.1775086; EvalErr[0]PerSample = 0.62187499; TotalTime=0.061515; TotalTimePerSample=9.6117187e-05, SamplesPerSecond=10403
|
||||
Epoch[1 of 3]-Minibatch[301-310 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.2626343; EvalErr[0]PerSample = 0.59687501; TotalTime=0.059247; TotalTimePerSample=9.2573438e-05, SamplesPerSecond=10802
|
||||
Epoch[1 of 3]-Minibatch[311-320 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.1507263; EvalErr[0]PerSample = 0.5625; TotalTime=0.059464; TotalTimePerSample=9.29125e-05, SamplesPerSecond=10762
|
||||
Finished Epoch[1]: [Training Set] TrainLossPerSample = 2.9799569; EvalErrPerSample = 0.72216797; Ave LearnRatePerSample = 0.015625; EpochTime=1.913549
|
||||
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 = 2.0159853; EvalErr[0]PerSample = 0.54140627; TotalTime=0.100302; TotalTimePerSample=3.9180469e-05, SamplesPerSecond=25522
|
||||
Epoch[2 of 3]-Minibatch[11-20 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.9881856; EvalErr[0]PerSample = 0.54296875; TotalTime=0.093995; TotalTimePerSample=3.6716797e-05, SamplesPerSecond=27235
|
||||
Epoch[2 of 3]-Minibatch[21-30 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.9869812; EvalErr[0]PerSample = 0.54140627; TotalTime=0.09237; TotalTimePerSample=3.6082031e-05, SamplesPerSecond=27714
|
||||
Epoch[2 of 3]-Minibatch[31-40 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.9312614; EvalErr[0]PerSample = 0.5277344; TotalTime=0.092894; TotalTimePerSample=3.6286719e-05, SamplesPerSecond=27558
|
||||
Epoch[2 of 3]-Minibatch[41-50 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.9006774; EvalErr[0]PerSample = 0.52656251; TotalTime=0.08927; TotalTimePerSample=3.4871094e-05, SamplesPerSecond=28677
|
||||
Epoch[2 of 3]-Minibatch[51-60 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.9711578; EvalErr[0]PerSample = 0.54140627; TotalTime=0.091869; TotalTimePerSample=3.5886328e-05, SamplesPerSecond=27865
|
||||
Epoch[2 of 3]-Minibatch[61-70 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.8951813; EvalErr[0]PerSample = 0.52031249; TotalTime=0.092242; TotalTimePerSample=3.6032031e-05, SamplesPerSecond=27753
|
||||
Epoch[2 of 3]-Minibatch[71-80 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.904506; EvalErr[0]PerSample = 0.53164065; TotalTime=0.094062; TotalTimePerSample=3.6742969e-05, SamplesPerSecond=27216
|
||||
Finished Epoch[2]: [Training Set] TrainLossPerSample = 1.949242; EvalErrPerSample = 0.53417969; Ave LearnRatePerSample = 0.001953125; EpochTime=0.747962
|
||||
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 = 1.8735985; EvalErr[0]PerSample = 0.51933593; TotalTime=0.27124; TotalTimePerSample=2.6488281e-05, SamplesPerSecond=37752
|
||||
Epoch[3 of 3]-Minibatch[11-20 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.8665626; EvalErr[0]PerSample = 0.51748049; TotalTime=0.266098; TotalTimePerSample=2.5986133e-05, SamplesPerSecond=38482
|
||||
Finished Epoch[3]: [Training Set] TrainLossPerSample = 1.8700806; EvalErrPerSample = 0.51840824; Ave LearnRatePerSample = 9.765625146e-05; EpochTime=0.539342
|
||||
COMPLETED
|
||||
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
WARNING:
|
||||
|
||||
You should always run with libnvidia-ml.so that is installed with your
|
||||
NVIDIA Display Driver. By default it's installed in /usr/lib and /usr/lib64.
|
||||
libnvidia-ml.so in GDK package is a stub library that is attached only for
|
||||
build purposes (e.g. machine that you build your application doesn't have
|
||||
to have Display Driver installed).
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
Linked to libnvidia-ml library at wrong path : /usr/src/gdk/nvml/lib/libnvidia-ml.so.1
|
||||
|
||||
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
WARNING:
|
||||
|
||||
You should always run with libnvidia-ml.so that is installed with your
|
||||
NVIDIA Display Driver. By default it's installed in /usr/lib and /usr/lib64.
|
||||
libnvidia-ml.so in GDK package is a stub library that is attached only for
|
||||
build purposes (e.g. machine that you build your application doesn't have
|
||||
to have Display Driver installed).
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
=== Deleting last epoch data
|
||||
==== Re-running from checkpoint
|
||||
running on localhost at 2015/07/29 19:11:07
|
||||
command line options:
|
||||
configFile=/home/vlivan/cntk/Tests/Speech/QuickE2E/cntk.config RunDir=/tmp/cntk-test-20150729191101.973007/Speech_QuickE2E@release_cpu DataDir=/home/vlivan/cntk/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=/tmp/cntk-test-20150729191101.973007/Speech_QuickE2E@release_cpu
|
||||
DataDir=/home/vlivan/cntk/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=/tmp/cntk-test-20150729191101.973007/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=/home/vlivan/cntk/Tests/Speech/Data/glob_0000.mlf
|
||||
labelMappingFile=/home/vlivan/cntk/Tests/Speech/Data/state.list
|
||||
labelDim=132
|
||||
labelType=Category
|
||||
]
|
||||
]
|
||||
]
|
||||
RunDir=/tmp/cntk-test-20150729191101.973007/Speech_QuickE2E@release_cpu
|
||||
DataDir=/home/vlivan/cntk/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=/home/vlivan/cntk/Tests/Speech/Data
|
||||
configparameters: cntk.config:deviceId=Auto
|
||||
configparameters: cntk.config:parallelTrain=false
|
||||
configparameters: cntk.config:precision=float
|
||||
configparameters: cntk.config:RunDir=/tmp/cntk-test-20150729191101.973007/Speech_QuickE2E@release_cpu
|
||||
configparameters: cntk.config:speechTrain=[
|
||||
action=train
|
||||
modelPath=/tmp/cntk-test-20150729191101.973007/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=/home/vlivan/cntk/Tests/Speech/Data/glob_0000.mlf
|
||||
labelMappingFile=/home/vlivan/cntk/Tests/Speech/Data/state.list
|
||||
labelDim=132
|
||||
labelType=Category
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
command: speechTrain
|
||||
precision = float
|
||||
lsof: WARNING: can't stat() ext4 file system /var/lib/docker/aufs
|
||||
Output information may be incomplete.
|
||||
LockDevice: Capture device 0 and lock it for exclusive use
|
||||
LockDevice: Capture device 0 and lock it for exclusive use
|
||||
SimpleNetworkBuilder Using GPU 0
|
||||
reading script file glob_0000.scp ... 948 entries
|
||||
total 132 state names in state list /home/vlivan/cntk/Tests/Speech/Data/state.list
|
||||
htkmlfreader: reading MLF file /home/vlivan/cntk/Tests/Speech/Data/glob_0000.mlf ...parse the line 55130
|
||||
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 /tmp/cntk-test-20150729191101.973007/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 = 1.8735985; EvalErr[0]PerSample = 0.51933593; TotalTime=0.390092; TotalTimePerSample=3.8094922e-05, SamplesPerSecond=26250
|
||||
Epoch[3 of 3]-Minibatch[11-20 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.8665626; EvalErr[0]PerSample = 0.51748049; TotalTime=0.261875; TotalTimePerSample=2.557373e-05, SamplesPerSecond=39102
|
||||
Finished Epoch[3]: [Training Set] TrainLossPerSample = 1.8700806; EvalErrPerSample = 0.51840824; Ave LearnRatePerSample = 9.765625146e-05; EpochTime=0.770276
|
||||
COMPLETED
|
||||
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
WARNING:
|
||||
|
||||
You should always run with libnvidia-ml.so that is installed with your
|
||||
NVIDIA Display Driver. By default it's installed in /usr/lib and /usr/lib64.
|
||||
libnvidia-ml.so in GDK package is a stub library that is attached only for
|
||||
build purposes (e.g. machine that you build your application doesn't have
|
||||
to have Display Driver installed).
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
Linked to libnvidia-ml library at wrong path : /usr/src/gdk/nvml/lib/libnvidia-ml.so.1
|
||||
|
||||
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
WARNING:
|
||||
|
||||
You should always run with libnvidia-ml.so that is installed with your
|
||||
NVIDIA Display Driver. By default it's installed in /usr/lib and /usr/lib64.
|
||||
libnvidia-ml.so in GDK package is a stub library that is attached only for
|
||||
build purposes (e.g. machine that you build your application doesn't have
|
||||
to have Display Driver installed).
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
|
|
@ -0,0 +1,758 @@
|
|||
=== Running /home/vlivan/cntk/bin/x86_64.gpu.release.acml/cntk configFile=/home/vlivan/cntk/Tests/Speech/QuickE2E/cntk.config RunDir=/tmp/cntk-test-20150729191101.973007/Speech_QuickE2E@release_gpu DataDir=/home/vlivan/cntk/Tests/Speech/Data DeviceId=Auto
|
||||
running on localhost at 2015/07/29 19:11:08
|
||||
command line options:
|
||||
configFile=/home/vlivan/cntk/Tests/Speech/QuickE2E/cntk.config RunDir=/tmp/cntk-test-20150729191101.973007/Speech_QuickE2E@release_gpu DataDir=/home/vlivan/cntk/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=/tmp/cntk-test-20150729191101.973007/Speech_QuickE2E@release_gpu
|
||||
DataDir=/home/vlivan/cntk/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=/tmp/cntk-test-20150729191101.973007/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=/home/vlivan/cntk/Tests/Speech/Data/glob_0000.mlf
|
||||
labelMappingFile=/home/vlivan/cntk/Tests/Speech/Data/state.list
|
||||
labelDim=132
|
||||
labelType=Category
|
||||
]
|
||||
]
|
||||
]
|
||||
RunDir=/tmp/cntk-test-20150729191101.973007/Speech_QuickE2E@release_gpu
|
||||
DataDir=/home/vlivan/cntk/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=/home/vlivan/cntk/Tests/Speech/Data
|
||||
configparameters: cntk.config:deviceId=Auto
|
||||
configparameters: cntk.config:parallelTrain=false
|
||||
configparameters: cntk.config:precision=float
|
||||
configparameters: cntk.config:RunDir=/tmp/cntk-test-20150729191101.973007/Speech_QuickE2E@release_gpu
|
||||
configparameters: cntk.config:speechTrain=[
|
||||
action=train
|
||||
modelPath=/tmp/cntk-test-20150729191101.973007/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=/home/vlivan/cntk/Tests/Speech/Data/glob_0000.mlf
|
||||
labelMappingFile=/home/vlivan/cntk/Tests/Speech/Data/state.list
|
||||
labelDim=132
|
||||
labelType=Category
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
command: speechTrain
|
||||
precision = float
|
||||
lsof: WARNING: can't stat() ext4 file system /var/lib/docker/aufs
|
||||
Output information may be incomplete.
|
||||
LockDevice: Capture device 0 and lock it for exclusive use
|
||||
LockDevice: Capture device 0 and lock it for exclusive use
|
||||
SimpleNetworkBuilder Using GPU 0
|
||||
reading script file glob_0000.scp ... 948 entries
|
||||
total 132 state names in state list /home/vlivan/cntk/Tests/Speech/Data/state.list
|
||||
htkmlfreader: reading MLF file /home/vlivan/cntk/Tests/Speech/Data/glob_0000.mlf ...parse the line 55130
|
||||
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 = 4.3213539; EvalErr[0]PerSample = 0.89999998; TotalTime=0.064294; TotalTimePerSample=0.00010045938, SamplesPerSecond=9954
|
||||
Epoch[1 of 3]-Minibatch[11-20 of 320]: SamplesSeen = 640; TrainLossPerSample = 4.1507101; EvalErr[0]PerSample = 0.8671875; TotalTime=0.055813; TotalTimePerSample=8.7207812e-05, SamplesPerSecond=11466
|
||||
Epoch[1 of 3]-Minibatch[21-30 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.9990096; EvalErr[0]PerSample = 0.87656248; TotalTime=0.062703; TotalTimePerSample=9.7973437e-05, SamplesPerSecond=10206
|
||||
Epoch[1 of 3]-Minibatch[31-40 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.8694596; EvalErr[0]PerSample = 0.87656248; TotalTime=0.059923; TotalTimePerSample=9.3629687e-05, SamplesPerSecond=10680
|
||||
Epoch[1 of 3]-Minibatch[41-50 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.8021927; EvalErr[0]PerSample = 0.87812501; TotalTime=0.061061; TotalTimePerSample=9.5407812e-05, SamplesPerSecond=10481
|
||||
Epoch[1 of 3]-Minibatch[51-60 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.7289093; EvalErr[0]PerSample = 0.86874998; TotalTime=0.062101; TotalTimePerSample=9.7032813e-05, SamplesPerSecond=10305
|
||||
Epoch[1 of 3]-Minibatch[61-70 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.5618699; EvalErr[0]PerSample = 0.82343751; TotalTime=0.056094; TotalTimePerSample=8.7646875e-05, SamplesPerSecond=11409
|
||||
Epoch[1 of 3]-Minibatch[71-80 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.4279053; EvalErr[0]PerSample = 0.80781251; TotalTime=0.063459; TotalTimePerSample=9.9154687e-05, SamplesPerSecond=10085
|
||||
Epoch[1 of 3]-Minibatch[81-90 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.3392854; EvalErr[0]PerSample = 0.7734375; TotalTime=0.062265; TotalTimePerSample=9.7289063e-05, SamplesPerSecond=10278
|
||||
Epoch[1 of 3]-Minibatch[91-100 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.3639894; EvalErr[0]PerSample = 0.84375; TotalTime=0.059843; TotalTimePerSample=9.3504687e-05, SamplesPerSecond=10694
|
||||
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 = 3.2122345; EvalErr[0]PerSample = 0.75312501; TotalTime=0.062375; TotalTimePerSample=9.7460937e-05, SamplesPerSecond=10260
|
||||
Epoch[1 of 3]-Minibatch[111-120 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.3126526; EvalErr[0]PerSample = 0.78750002; TotalTime=0.061085; TotalTimePerSample=9.5445313e-05, SamplesPerSecond=10477
|
||||
Epoch[1 of 3]-Minibatch[121-130 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.1408203; EvalErr[0]PerSample = 0.74687499; TotalTime=0.064562; TotalTimePerSample=0.00010087812, SamplesPerSecond=9912
|
||||
Epoch[1 of 3]-Minibatch[131-140 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.006897; EvalErr[0]PerSample = 0.69687498; TotalTime=0.0575; TotalTimePerSample=8.984375e-05, SamplesPerSecond=11130
|
||||
Epoch[1 of 3]-Minibatch[141-150 of 320]: SamplesSeen = 640; TrainLossPerSample = 3.0049591; EvalErr[0]PerSample = 0.72343749; TotalTime=0.058338; TotalTimePerSample=9.1153125e-05, SamplesPerSecond=10970
|
||||
Epoch[1 of 3]-Minibatch[151-160 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.9785829; EvalErr[0]PerSample = 0.73906249; TotalTime=0.064603; TotalTimePerSample=0.00010094219, SamplesPerSecond=9906
|
||||
Epoch[1 of 3]-Minibatch[161-170 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.8568604; EvalErr[0]PerSample = 0.70781249; TotalTime=0.060368; TotalTimePerSample=9.4325e-05, SamplesPerSecond=10601
|
||||
Epoch[1 of 3]-Minibatch[171-180 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.6905334; EvalErr[0]PerSample = 0.671875; TotalTime=0.059125; TotalTimePerSample=9.2382812e-05, SamplesPerSecond=10824
|
||||
Epoch[1 of 3]-Minibatch[181-190 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.7865357; EvalErr[0]PerSample = 0.70468748; TotalTime=0.056113; TotalTimePerSample=8.7676563e-05, SamplesPerSecond=11405
|
||||
Epoch[1 of 3]-Minibatch[191-200 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.5770202; EvalErr[0]PerSample = 0.6484375; TotalTime=0.060745; TotalTimePerSample=9.4914062e-05, SamplesPerSecond=10535
|
||||
Epoch[1 of 3]-Minibatch[201-210 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.6157165; EvalErr[0]PerSample = 0.6640625; TotalTime=0.059709; TotalTimePerSample=9.3295312e-05, SamplesPerSecond=10718
|
||||
Epoch[1 of 3]-Minibatch[211-220 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.552362; EvalErr[0]PerSample = 0.65781248; TotalTime=0.061917; TotalTimePerSample=9.6745313e-05, SamplesPerSecond=10336
|
||||
Epoch[1 of 3]-Minibatch[221-230 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.4821167; EvalErr[0]PerSample = 0.625; TotalTime=0.053813; TotalTimePerSample=8.4082813e-05, SamplesPerSecond=11893
|
||||
Epoch[1 of 3]-Minibatch[231-240 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.3877869; EvalErr[0]PerSample = 0.62812501; TotalTime=0.061932; TotalTimePerSample=9.676875e-05, SamplesPerSecond=10333
|
||||
Epoch[1 of 3]-Minibatch[241-250 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.3690064; EvalErr[0]PerSample = 0.6484375; TotalTime=0.059294; TotalTimePerSample=9.2646875e-05, SamplesPerSecond=10793
|
||||
Epoch[1 of 3]-Minibatch[251-260 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.4396729; EvalErr[0]PerSample = 0.6328125; TotalTime=0.060513; TotalTimePerSample=9.4551562e-05, SamplesPerSecond=10576
|
||||
Epoch[1 of 3]-Minibatch[261-270 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.3028197; EvalErr[0]PerSample = 0.61250001; TotalTime=0.06037; TotalTimePerSample=9.4328125e-05, SamplesPerSecond=10601
|
||||
Epoch[1 of 3]-Minibatch[271-280 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.1966858; EvalErr[0]PerSample = 0.55937499; TotalTime=0.056485; TotalTimePerSample=8.8257812e-05, SamplesPerSecond=11330
|
||||
Epoch[1 of 3]-Minibatch[281-290 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.2898011; EvalErr[0]PerSample = 0.60468751; TotalTime=0.059356; TotalTimePerSample=9.274375e-05, SamplesPerSecond=10782
|
||||
Epoch[1 of 3]-Minibatch[291-300 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.1775086; EvalErr[0]PerSample = 0.62187499; TotalTime=0.059501; TotalTimePerSample=9.2970312e-05, SamplesPerSecond=10756
|
||||
Epoch[1 of 3]-Minibatch[301-310 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.2626343; EvalErr[0]PerSample = 0.59687501; TotalTime=0.064342; TotalTimePerSample=0.00010053437, SamplesPerSecond=9946
|
||||
Epoch[1 of 3]-Minibatch[311-320 of 320]: SamplesSeen = 640; TrainLossPerSample = 2.1507263; EvalErr[0]PerSample = 0.5625; TotalTime=0.064522; TotalTimePerSample=0.00010081563, SamplesPerSecond=9919
|
||||
Finished Epoch[1]: [Training Set] TrainLossPerSample = 2.9799569; EvalErrPerSample = 0.72216797; Ave LearnRatePerSample = 0.015625; EpochTime=1.935613
|
||||
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 = 2.0159853; EvalErr[0]PerSample = 0.54140627; TotalTime=0.102487; TotalTimePerSample=4.0033984e-05, SamplesPerSecond=24978
|
||||
Epoch[2 of 3]-Minibatch[11-20 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.9881856; EvalErr[0]PerSample = 0.54296875; TotalTime=0.09473; TotalTimePerSample=3.7003906e-05, SamplesPerSecond=27024
|
||||
Epoch[2 of 3]-Minibatch[21-30 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.9869812; EvalErr[0]PerSample = 0.54140627; TotalTime=0.091318; TotalTimePerSample=3.5671094e-05, SamplesPerSecond=28033
|
||||
Epoch[2 of 3]-Minibatch[31-40 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.9312614; EvalErr[0]PerSample = 0.5277344; TotalTime=0.092408; TotalTimePerSample=3.6096875e-05, SamplesPerSecond=27703
|
||||
Epoch[2 of 3]-Minibatch[41-50 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.9006774; EvalErr[0]PerSample = 0.52656251; TotalTime=0.098698; TotalTimePerSample=3.8553906e-05, SamplesPerSecond=25937
|
||||
Epoch[2 of 3]-Minibatch[51-60 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.9711578; EvalErr[0]PerSample = 0.54140627; TotalTime=0.0896; TotalTimePerSample=3.5e-05, SamplesPerSecond=28571
|
||||
Epoch[2 of 3]-Minibatch[61-70 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.8951813; EvalErr[0]PerSample = 0.52031249; TotalTime=0.092477; TotalTimePerSample=3.6123828e-05, SamplesPerSecond=27682
|
||||
Epoch[2 of 3]-Minibatch[71-80 of 80]: SamplesSeen = 2560; TrainLossPerSample = 1.904506; EvalErr[0]PerSample = 0.53164065; TotalTime=0.091179; TotalTimePerSample=3.5616797e-05, SamplesPerSecond=28076
|
||||
Finished Epoch[2]: [Training Set] TrainLossPerSample = 1.949242; EvalErrPerSample = 0.53417969; Ave LearnRatePerSample = 0.001953125; EpochTime=0.753703
|
||||
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 = 1.8735985; EvalErr[0]PerSample = 0.51933593; TotalTime=0.27395; TotalTimePerSample=2.675293e-05, SamplesPerSecond=37379
|
||||
Epoch[3 of 3]-Minibatch[11-20 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.8665626; EvalErr[0]PerSample = 0.51748049; TotalTime=0.261453; TotalTimePerSample=2.553252e-05, SamplesPerSecond=39165
|
||||
Finished Epoch[3]: [Training Set] TrainLossPerSample = 1.8700806; EvalErrPerSample = 0.51840824; Ave LearnRatePerSample = 9.765625146e-05; EpochTime=0.537273
|
||||
COMPLETED
|
||||
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
WARNING:
|
||||
|
||||
You should always run with libnvidia-ml.so that is installed with your
|
||||
NVIDIA Display Driver. By default it's installed in /usr/lib and /usr/lib64.
|
||||
libnvidia-ml.so in GDK package is a stub library that is attached only for
|
||||
build purposes (e.g. machine that you build your application doesn't have
|
||||
to have Display Driver installed).
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
Linked to libnvidia-ml library at wrong path : /usr/src/gdk/nvml/lib/libnvidia-ml.so.1
|
||||
|
||||
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
WARNING:
|
||||
|
||||
You should always run with libnvidia-ml.so that is installed with your
|
||||
NVIDIA Display Driver. By default it's installed in /usr/lib and /usr/lib64.
|
||||
libnvidia-ml.so in GDK package is a stub library that is attached only for
|
||||
build purposes (e.g. machine that you build your application doesn't have
|
||||
to have Display Driver installed).
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
=== Deleting last epoch data
|
||||
==== Re-running from checkpoint
|
||||
running on localhost at 2015/07/29 19:11:14
|
||||
command line options:
|
||||
configFile=/home/vlivan/cntk/Tests/Speech/QuickE2E/cntk.config RunDir=/tmp/cntk-test-20150729191101.973007/Speech_QuickE2E@release_gpu DataDir=/home/vlivan/cntk/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=/tmp/cntk-test-20150729191101.973007/Speech_QuickE2E@release_gpu
|
||||
DataDir=/home/vlivan/cntk/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=/tmp/cntk-test-20150729191101.973007/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=/home/vlivan/cntk/Tests/Speech/Data/glob_0000.mlf
|
||||
labelMappingFile=/home/vlivan/cntk/Tests/Speech/Data/state.list
|
||||
labelDim=132
|
||||
labelType=Category
|
||||
]
|
||||
]
|
||||
]
|
||||
RunDir=/tmp/cntk-test-20150729191101.973007/Speech_QuickE2E@release_gpu
|
||||
DataDir=/home/vlivan/cntk/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=/home/vlivan/cntk/Tests/Speech/Data
|
||||
configparameters: cntk.config:deviceId=Auto
|
||||
configparameters: cntk.config:parallelTrain=false
|
||||
configparameters: cntk.config:precision=float
|
||||
configparameters: cntk.config:RunDir=/tmp/cntk-test-20150729191101.973007/Speech_QuickE2E@release_gpu
|
||||
configparameters: cntk.config:speechTrain=[
|
||||
action=train
|
||||
modelPath=/tmp/cntk-test-20150729191101.973007/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=/home/vlivan/cntk/Tests/Speech/Data/glob_0000.mlf
|
||||
labelMappingFile=/home/vlivan/cntk/Tests/Speech/Data/state.list
|
||||
labelDim=132
|
||||
labelType=Category
|
||||
]
|
||||
]
|
||||
]
|
||||
|
||||
<<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
|
||||
command: speechTrain
|
||||
precision = float
|
||||
lsof: WARNING: can't stat() ext4 file system /var/lib/docker/aufs
|
||||
Output information may be incomplete.
|
||||
LockDevice: Capture device 0 and lock it for exclusive use
|
||||
LockDevice: Capture device 0 and lock it for exclusive use
|
||||
SimpleNetworkBuilder Using GPU 0
|
||||
reading script file glob_0000.scp ... 948 entries
|
||||
total 132 state names in state list /home/vlivan/cntk/Tests/Speech/Data/state.list
|
||||
htkmlfreader: reading MLF file /home/vlivan/cntk/Tests/Speech/Data/glob_0000.mlf ...parse the line 55130
|
||||
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 /tmp/cntk-test-20150729191101.973007/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 = 1.8735985; EvalErr[0]PerSample = 0.51933593; TotalTime=0.430752; TotalTimePerSample=4.2065625e-05, SamplesPerSecond=23772
|
||||
Epoch[3 of 3]-Minibatch[11-20 of 20]: SamplesSeen = 10240; TrainLossPerSample = 1.8665626; EvalErr[0]PerSample = 0.51748049; TotalTime=0.2702; TotalTimePerSample=2.6386719e-05, SamplesPerSecond=37897
|
||||
Finished Epoch[3]: [Training Set] TrainLossPerSample = 1.8700806; EvalErrPerSample = 0.51840824; Ave LearnRatePerSample = 9.765625146e-05; EpochTime=0.868162
|
||||
COMPLETED
|
||||
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
WARNING:
|
||||
|
||||
You should always run with libnvidia-ml.so that is installed with your
|
||||
NVIDIA Display Driver. By default it's installed in /usr/lib and /usr/lib64.
|
||||
libnvidia-ml.so in GDK package is a stub library that is attached only for
|
||||
build purposes (e.g. machine that you build your application doesn't have
|
||||
to have Display Driver installed).
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
Linked to libnvidia-ml library at wrong path : /usr/src/gdk/nvml/lib/libnvidia-ml.so.1
|
||||
|
||||
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
WARNING:
|
||||
|
||||
You should always run with libnvidia-ml.so that is installed with your
|
||||
NVIDIA Display Driver. By default it's installed in /usr/lib and /usr/lib64.
|
||||
libnvidia-ml.so in GDK package is a stub library that is attached only for
|
||||
build purposes (e.g. machine that you build your application doesn't have
|
||||
to have Display Driver installed).
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
|
|
@ -0,0 +1,63 @@
|
|||
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
|
||||
]
|
||||
]
|
||||
]
|
||||
|
|
@ -0,0 +1,17 @@
|
|||
#!/bin/bash
|
||||
CNTK_BINARY=$TEST_BUILD_LOCATION/x86_64.gpu.$TEST_FLAVOR.acml/cntk
|
||||
if [ "$TEST_DEVICE" == "CPU" ]; then
|
||||
CNTK_DEVICE_ID=-1
|
||||
else
|
||||
CNTK_DEVICE_ID=Auto
|
||||
fi
|
||||
CNTK_ARGS="configFile=$TEST_DIR/cntk.config RunDir=$TEST_RUN_DIR DataDir=$TEST_DATA_DIR DeviceId=$CNTK_DEVICE_ID"
|
||||
MODELS_DIR=$TEST_RUN_DIR/models
|
||||
[ -d $MODELS_DIR ] && rm -rf $MODELS_DIR
|
||||
mkdir -p $MODELS_DIR || exit $?
|
||||
echo === Running $CNTK_BINARY $CNTK_ARGS
|
||||
$CNTK_BINARY $CNTK_ARGS || exit $?
|
||||
echo === Deleting last epoch data
|
||||
rm $TEST_RUN_DIR/models/*.dnn
|
||||
echo ==== Re-running from checkpoint
|
||||
$CNTK_BINARY $CNTK_ARGS || exit $?
|
||||
|
|
@ -0,0 +1,27 @@
|
|||
dataDir: ../Data
|
||||
|
||||
testCases:
|
||||
CNTK Run must be completed:
|
||||
patterns:
|
||||
- ^COMPLETED
|
||||
|
||||
Must train epochs in exactly same order and parameters:
|
||||
patterns:
|
||||
- ^Starting Epoch {{integer}}
|
||||
- learning rate per sample = {{float}}
|
||||
- momentum = {{float}}
|
||||
|
||||
Epochs must be finished with expected results:
|
||||
patterns:
|
||||
- ^Finished Epoch[{{integer}}]
|
||||
- TrainLossPerSample = {{float,tolerance=1%}}
|
||||
- EvalErrPerSample = {{float,tolerance=1%}}
|
||||
- Ave LearnRatePerSample = {{float,tolerance=1%}}
|
||||
|
||||
Per-minibatch training results must match:
|
||||
patterns:
|
||||
- ^ Epoch[{{integer}} of {{integer}}]-Minibatch[{{integer}}-{{integer}} of {{integer}}]
|
||||
- SamplesSeen = {{integer}}
|
||||
- TrainLossPerSample = {{float,tolerance=1%}}
|
||||
- EvalErr[0]PerSample = {{float,tolerance=1%}}
|
||||
|
||||
|
|
@ -0,0 +1,551 @@
|
|||
#!/usr/bin/env python
|
||||
# ----------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# ---------------------------------------------------------
|
||||
# This is a test driver for running end-to-end CNTK tests
|
||||
#
|
||||
# ----- Running a test and/or updating baselines ------
|
||||
# For instructions see:
|
||||
# ./TestDriver.py --help
|
||||
#
|
||||
# ---- Adding the tests: -------
|
||||
# File system organization:
|
||||
# Each test suite (e.g. Speech) has its own directory inside Tests
|
||||
# Each test (e.g. QuickE2E) has its own directory within test suite
|
||||
#
|
||||
# Each test directory has a following components:
|
||||
# - testcases.yml - main test confuguration file, whcih defines all test cases
|
||||
# - run-test - (run-test) script
|
||||
# - baseline*.txt - baseline files whith a captured expected output of run-test script
|
||||
#
|
||||
# ----- testcases.yml format -------
|
||||
# dataDir: <path> #<relative-path-to the data directory
|
||||
#
|
||||
# testCases:
|
||||
# <name of the testcase 1>:
|
||||
# patterns:
|
||||
# - <pattern 1> # see pattern language
|
||||
# - <pattern 2>
|
||||
# - .....
|
||||
#
|
||||
# <name of the testcase 2>:
|
||||
# patterns:
|
||||
# - <pattern 1>
|
||||
# - <pattern 2>
|
||||
# - .....
|
||||
# .....
|
||||
#
|
||||
# ----- pattern language --------
|
||||
# Multpile patterns of the same testcase are matching a *single* line of text
|
||||
# Pattern is essentiually a substring which has to be found in a line
|
||||
# if pattern starts with ^ then matching is constrained to look only at the beginning of the line
|
||||
#
|
||||
# pattern can have one or multiple placelohders wrapped with double-curly braces: {{...}}
|
||||
# this placeholders can match any text conforming to the type constraint. Available placeholders
|
||||
# {{integer}} - matches any (positive or negative integer) value
|
||||
# {{float}} - matches any float value
|
||||
# {{float,tolerance=0.00001}} - matches float value with given absolute tolerance: 0.00001 in this example
|
||||
# {{float,tolerance=2%}} - matches float value with relative tolerance, 2% in this example
|
||||
#
|
||||
# At runtime patterns are compiled by TestDriver.py to regular expressions
|
||||
#
|
||||
# ---- Baseline files ----
|
||||
# Order of searching baseline files, depends on the current mode for a given test:
|
||||
#
|
||||
# 1. baseline.<flavor>.<device>.txt
|
||||
# 2. baseline.<flavor>.txt
|
||||
# 3. baseline.<device>.txt
|
||||
# 4. baseline.txt
|
||||
# where <flavor> = { debug | release }
|
||||
# <device> = { cpu | gpu }
|
||||
#
|
||||
# ----- Algorithm ------
|
||||
# Baseline verification:
|
||||
# For each testcase
|
||||
# - filter all lines which matches
|
||||
# - if no lines found then abord with an error - since either baseline and/or pattern are invalid
|
||||
# Running test:
|
||||
# Run test script (run-test) and capture output:
|
||||
#
|
||||
# For each testcase
|
||||
# - filter all matching lines from baseline
|
||||
# - filter all matching lines from test output
|
||||
# - compare filtered lines one by one, ensuring that substrings defined by patterns are matching
|
||||
#
|
||||
# In practice, TestDriver performs 1 pass through the output of run-test performing a real-time
|
||||
# matching against all test-cases/pattern simulteneously
|
||||
#
|
||||
|
||||
import sys, os, argparse, traceback, yaml, subprocess, random, re, time
|
||||
|
||||
thisDir = os.path.dirname(os.path.realpath(__file__))
|
||||
|
||||
# This class encapsulates an instance of the test
|
||||
class Test:
|
||||
# "Suite/TestName" => instance of Test
|
||||
allTestsIndexedByFullName = {}
|
||||
|
||||
# suite - name of the test suite
|
||||
# name - name of the test
|
||||
# path to the testcases.yml file
|
||||
def __init__(self, suite, name, pathToYmlFile):
|
||||
self.suite = suite
|
||||
self.name = name
|
||||
self.fullName = suite + "/" + name
|
||||
# computing location of test directory (yml file directory)
|
||||
self.testDir = os.path.dirname(pathToYmlFile)
|
||||
# parsing yml file with testcases
|
||||
with open(pathToYmlFile, "r") as f:
|
||||
self.rawYamlData = yaml.safe_load(f.read())
|
||||
|
||||
# finding location of data directory
|
||||
if self.rawYamlData["dataDir"]:
|
||||
self.dataDir = os.path.realpath(os.path.join(self.testDir, self.rawYamlData["dataDir"]))
|
||||
else:
|
||||
self.dataDir = self.testDir
|
||||
|
||||
testCasesYaml = self.rawYamlData["testCases"]
|
||||
self.testCases = []
|
||||
for name in testCasesYaml.keys():
|
||||
try:
|
||||
self.testCases.append(TestCase(name, testCasesYaml[name]))
|
||||
except Exception as e:
|
||||
print >>sys.stderr, "ERROR registering test case: " + name
|
||||
raise
|
||||
|
||||
# Populates Tests.allTestsIndexedByFullName by scanning directory tree
|
||||
# and finding all testcases.yml files
|
||||
@staticmethod
|
||||
def discoverAllTests():
|
||||
for dirName, subdirList, fileList in os.walk(thisDir):
|
||||
if 'testcases.yml' in fileList:
|
||||
testDir = dirName
|
||||
testName = os.path.basename(dirName)
|
||||
suiteDir = os.path.dirname(dirName)
|
||||
# sute name will be derived from the path components
|
||||
suiteName = os.path.relpath(suiteDir, thisDir).replace('\\', '/')
|
||||
try:
|
||||
test = Test(suiteName, testName, dirName + "/testcases.yml")
|
||||
Test.allTestsIndexedByFullName[test.fullName.lower()] = test
|
||||
except Exception as e:
|
||||
print >>sys.stderr, "ERROR registering test: " + dirName
|
||||
traceback.print_exc()
|
||||
sys.exit(1)
|
||||
|
||||
# Runs this test
|
||||
# flavor - "debug" or "release"
|
||||
# device - "cpu" or "gpu"
|
||||
# args - command line arguments from argparse
|
||||
# returns an instance of TestRunResult
|
||||
def run(self, flavor, device, args):
|
||||
# Locating and reading baseline file
|
||||
baselineFile = self.findBaselineFile(flavor, device)
|
||||
if baselineFile == None:
|
||||
return TestRunResult.fatalError("Baseline file sanity check", "Can't find baseline file")
|
||||
|
||||
with open(baselineFile, "r") as f:
|
||||
baseline = f.read().split("\n")
|
||||
if args.verbose:
|
||||
print "Baseline:", baselineFile
|
||||
|
||||
# Before running the test, pre-creating TestCaseRunResult object for each test case
|
||||
# and compute filtered lines from baseline file.
|
||||
# Note: some test cases might fail at this time if baseline and/or patterns are inconsistant
|
||||
result = TestRunResult()
|
||||
result.succeeded = True
|
||||
if not args.update_baseline:
|
||||
for testCase in self.testCases:
|
||||
testCaseRunResult = testCase.processBaseline(baseline)
|
||||
if not testCaseRunResult.succeeded:
|
||||
result.succeeded = False
|
||||
result.testCaseRunResults.append(testCaseRunResult)
|
||||
|
||||
# preparing run directory
|
||||
runDir = os.path.join(args.run_dir, "{0}_{1}@{2}_{3}".format(self.suite, self.name, flavor, device))
|
||||
if not os.path.isdir(runDir):
|
||||
os.makedirs(runDir)
|
||||
|
||||
# preparing environment for the test script
|
||||
os.environ["TEST_FLAVOR"] = flavor
|
||||
os.environ["TEST_DEVICE"] = device
|
||||
os.environ["TEST_BUILD_LOCATION"] = args.build_location
|
||||
os.environ["TEST_DIR"] = self.testDir
|
||||
os.environ["TEST_DATA_DIR"] = self.dataDir
|
||||
os.environ["TEST_RUN_DIR"] = runDir
|
||||
# WORKAROUND: changing current dir to the dataDir so relative paths in SCP files work as expected
|
||||
os.chdir(self.dataDir)
|
||||
# Running test script
|
||||
#TODO:port this properly to windows
|
||||
# Writing standard output to the file and to the console (if --verbose)
|
||||
logFile = os.path.join(runDir, "output.txt")
|
||||
allLines = []
|
||||
if args.verbose:
|
||||
print self.fullName + ":>" + logFile
|
||||
with open(logFile, "w") as output:
|
||||
cmdLine = ["bash", "-c", self.testDir + "/run-test 2>&1"]
|
||||
process = subprocess.Popen(cmdLine, stdout=subprocess.PIPE)
|
||||
|
||||
while True:
|
||||
line = process.stdout.readline()
|
||||
if not line:
|
||||
break
|
||||
|
||||
if len(line)>0 and line[-1]=='\n':
|
||||
line=line[:len(line)-1]
|
||||
|
||||
if args.verbose:
|
||||
print self.fullName + ": " + line
|
||||
|
||||
print >>output, line
|
||||
allLines.append(line)
|
||||
output.flush()
|
||||
for testCaseRunResult in result.testCaseRunResults:
|
||||
testCaseRunResult.testCase.processLine(line, testCaseRunResult, args.verbose)
|
||||
|
||||
exitCode = process.wait()
|
||||
success = True
|
||||
|
||||
# checking exit code
|
||||
if exitCode != 0:
|
||||
return TestRunResult.fatalError("Exit code must be 0", "==> got exit code {0} when running: {1}".format(exitCode, " ".join(cmdLine)), logFile = logFile)
|
||||
|
||||
# saving log file path, so it can be reported later
|
||||
result.logFile = logFile
|
||||
|
||||
# finalizing verification - need to check whether we have any unmatched lines
|
||||
for testCaseRunResult in result.testCaseRunResults:
|
||||
testCaseRunResult.testCase.finalize(testCaseRunResult)
|
||||
if not testCaseRunResult.succeeded:
|
||||
result.succeeded = False
|
||||
|
||||
if args.update_baseline and result.succeeded:
|
||||
# When running in --update-baseline mode
|
||||
# verifying that new output is succesfully matching every pattern in the testcases.yml
|
||||
# If this is not the case then baseline update will be rejected
|
||||
for testCase in self.testCases:
|
||||
testCaseRunResult = testCase.processBaseline(allLines)
|
||||
if not testCaseRunResult.succeeded:
|
||||
result.succeeded = False
|
||||
result.testCaseRunResults.append(testCaseRunResult)
|
||||
|
||||
if result.succeeded:
|
||||
if args.verbose:
|
||||
print "Updating baseline file", baselineFile
|
||||
with open(baselineFile, "w") as f:
|
||||
f.write("\n".join(allLines))
|
||||
|
||||
return result
|
||||
|
||||
# Finds a location of a baseline file by probing different names in the following order:
|
||||
# baseline.$flavor.$device.txt
|
||||
# baseline.$flavor.txt
|
||||
# baseline.$device.txt
|
||||
# baseline.txt
|
||||
def findBaselineFile(self, flavor, device):
|
||||
for f in ["." + flavor.lower(), ""]:
|
||||
for d in ["." + device.lower(), ""]:
|
||||
candidateName = "baseline" + f + d + ".txt";
|
||||
fullPath = os.path.join(self.testDir, candidateName)
|
||||
if os.path.isfile(fullPath):
|
||||
return fullPath
|
||||
return None
|
||||
|
||||
# This class encapsulates one testcase (in testcases.yml file)
|
||||
class TestCase:
|
||||
def __init__(self, name, yamlNode):
|
||||
self.name = name
|
||||
self.patterns = []
|
||||
if "patterns" in yamlNode:
|
||||
for pattern in yamlNode["patterns"]:
|
||||
try:
|
||||
self.patterns.append(TestPattern(pattern))
|
||||
except Exception as e:
|
||||
print >>sys.stderr, "ERROR registering pattern: " + pattern
|
||||
raise
|
||||
|
||||
# Processes the baseline file and return an instance of TestCaseRunResult
|
||||
# which is ready to be passed into processLine
|
||||
def processBaseline(self, baseline):
|
||||
result = TestCaseRunResult(self.name, True)
|
||||
result.diagnostics = ""
|
||||
result.testCase = self
|
||||
|
||||
# filter all lines of baseline file leaving only those which match ALL the patterns
|
||||
filteredLines = []
|
||||
for line in baseline:
|
||||
if all([p.match(line) for p in self.patterns]):
|
||||
filteredLines.append(line)
|
||||
if len(filteredLines) == 0:
|
||||
result.succeeded = False
|
||||
result.diagnostics+="Baseline file doesn't have any lines matching all patterns defined in the test case.\n"\
|
||||
"Possible cause: patterns are wrong and/or baseline file doesn't have required line"
|
||||
result.expectedLines = filteredLines
|
||||
return result
|
||||
|
||||
# Runs this test case and report result into TestCaseRunResult
|
||||
def processLine(self, line, result, verbose):
|
||||
if all([p.match(line) for p in self.patterns]):
|
||||
if len(result.expectedLines) > 0:
|
||||
# we have mathed line in the output and at leat one remaining unmatched in a baseline
|
||||
expected = result.expectedLines[0]
|
||||
# running comparison logic for each pattern
|
||||
failedPatterns = []
|
||||
for p in self.patterns:
|
||||
if not p.compare(expected, line):
|
||||
result.succeeded = False
|
||||
failedPatterns.append(p)
|
||||
|
||||
# in the case of failure - reporting mismatched lines
|
||||
if len(failedPatterns)>0:
|
||||
result.diagnostics+=("Baseline: {0}\n"+
|
||||
"Output: {1}\n"
|
||||
).format(expected, line)
|
||||
if verbose:
|
||||
print "[FAILED]: Testcase", self.name
|
||||
print "Baseline:", expected
|
||||
|
||||
# also show all failed patterns
|
||||
for p in failedPatterns:
|
||||
msg = "Failed pattern: " + p.patternText
|
||||
if verbose:
|
||||
print msg
|
||||
result.diagnostics+=msg+"\n"
|
||||
# removing this line, since we already matched it (whether succesfully or not - doesn't matter)
|
||||
del result.expectedLines[0]
|
||||
else:
|
||||
# we have matched line in the output - but don't have any remaining unmatched in a baseline
|
||||
result.succeeded = False
|
||||
result.diagnostics+=("Unexpected (extra) line in the output which matches the pattern, but doesn't appear in baseline file.\n"+
|
||||
"Extra line: {0}"
|
||||
).format(line)
|
||||
|
||||
# called once for each TestCaseRunResult at the end to check for unmatched patterns
|
||||
def finalize(self, result):
|
||||
if len(result.expectedLines) > 0:
|
||||
result.succeeded = False
|
||||
result.diagnostics+=("{0} expected lines weren't observed in the output.\n"+
|
||||
"First unmatched: {1}"
|
||||
).format(len(result.expectedLines), result.expectedLines[0])
|
||||
|
||||
# This encapsulates parsing and evaluation of a test patterns occurring in testcases.yml file
|
||||
class TestPattern:
|
||||
# maps a type (specified in {{...}} expressions) to a regular expression
|
||||
typeTable = {
|
||||
"integer" : r"\s*-?[0-9]+",
|
||||
"float" : r"\s*-?([0-9]*\.[0-9]+|[0-9]+)(e[+-]?[0-9]+)?"
|
||||
}
|
||||
def __init__(self, patternText):
|
||||
self.patternText = str(patternText)
|
||||
if len(patternText) == 0:
|
||||
raise Exception("Empty pattern")
|
||||
if patternText[0]=='^':
|
||||
patternText = patternText[1:]
|
||||
prefix = "^"
|
||||
else:
|
||||
prefix = ".*?"
|
||||
|
||||
# After parsing this will be a list of tuples (dataType, tolerance) for each {{...}} section from left to right
|
||||
self.groupInfo = []
|
||||
|
||||
# Transforming our pattern into a sigle regular expression
|
||||
# processing {{...}} fragments and escaping all regex special characters
|
||||
self.regexText = prefix + re.sub(r"(\{\{[^}]+\}\}|[\[\]\.\*\+\{\}\(\)\$\^\\\|\?])", self.patternParse, patternText)
|
||||
# Compiling it to perform a check (fail-fast) and for faster matching later
|
||||
self.regex = re.compile(self.regexText)
|
||||
|
||||
|
||||
# this is a callback method passed to re.sub call above - it performs the core parsing logic
|
||||
def patternParse(self, match):
|
||||
fragment = match.group(1)
|
||||
if len(fragment) == 1:
|
||||
# this is a spexcial character of regex
|
||||
return "\\" + fragment;
|
||||
else:
|
||||
# parsing {{...}} expressions
|
||||
m = re.match(r"{{(integer|float)(,tolerance=([-0-9\.e]*)(%?))?}}", fragment)
|
||||
dataType = m.group(1)
|
||||
if m.group(3):
|
||||
tolerance = float(m.group(3))
|
||||
if m.group(4) == "%":
|
||||
# using minus sign to indicate that it is a relative value
|
||||
tolerance = - tolerance/100.0;
|
||||
else:
|
||||
tolerance = 0.0
|
||||
# saving information about data type and tolerance
|
||||
self.groupInfo.append((dataType, tolerance))
|
||||
# converting this to regex which mathes specific type
|
||||
# All {{...}} sections are converted to regex groups named as G0, G1, G2...
|
||||
return "(?P<G{0}>{1})".format(len(self.groupInfo)-1, TestPattern.typeTable[dataType])
|
||||
|
||||
# Checks wether given line matches this pattern
|
||||
# returns True or False
|
||||
def match(self, line):
|
||||
return self.regex.match(line) != None
|
||||
|
||||
# Compares a line from baseline log and a line from real output against this pattern
|
||||
# return true or false
|
||||
def compare(self, expected, actual):
|
||||
em = self.regex.match(expected)
|
||||
am = self.regex.match(actual)
|
||||
if em == None and am == None:
|
||||
return True
|
||||
if em == None or am == None:
|
||||
return False
|
||||
|
||||
for i in range(0, len(self.groupInfo)):
|
||||
dataType, tolerance = self.groupInfo[i]
|
||||
groupId = "G"+str(i)
|
||||
expectedText = em.group(groupId).strip()
|
||||
actualText = am.group(groupId).strip()
|
||||
if dataType=="integer":
|
||||
return int(expectedText) == int(actualText)
|
||||
elif dataType=="float":
|
||||
epsilon = tolerance if tolerance > 0 else abs(float(expectedText)*tolerance)
|
||||
return abs(float(expectedText)-float(actualText)) <= epsilon
|
||||
else:
|
||||
return False;
|
||||
return True
|
||||
|
||||
class TestRunResult:
|
||||
def __init__(self):
|
||||
self.succeeded = False;
|
||||
self.testCaseRunResults = [] # list of TestCaseRunResult
|
||||
|
||||
@staticmethod
|
||||
def fatalError(name, diagnostics, logFile = None):
|
||||
r = TestRunResult()
|
||||
r.testCaseRunResults.append(TestCaseRunResult(name, False, diagnostics))
|
||||
r.logFile = logFile
|
||||
return r
|
||||
|
||||
class TestCaseRunResult:
|
||||
def __init__(self, testCaseName, succeeded, diagnostics = None):
|
||||
self.testCaseName = testCaseName
|
||||
self.succeeded = succeeded
|
||||
self.diagnostics = diagnostics
|
||||
self.expectedLines = [] # list of remaining unmatched expected lines from the baseline file for this test case run
|
||||
|
||||
# Lists all available tests
|
||||
def listCommand(args):
|
||||
for t in Test.allTestsIndexedByFullName.values():
|
||||
print t.fullName
|
||||
|
||||
# Runs given test(s) or all tests
|
||||
def runCommand(args):
|
||||
if len(args.test) > 0:
|
||||
testsToRun = []
|
||||
for name in args.test:
|
||||
if name.lower() in Test.allTestsIndexedByFullName:
|
||||
testsToRun.append(Test.allTestsIndexedByFullName[name.lower()])
|
||||
else:
|
||||
print >>sys.stderr, "ERROR: test not found", name
|
||||
return 1
|
||||
else:
|
||||
testsToRun = Test.allTestsIndexedByFullName.values()
|
||||
devices = ["cpu", "gpu"]
|
||||
if (args.device):
|
||||
args.device = args.device.lower()
|
||||
if not args.device in devices:
|
||||
print >>sys.stderr, "--device must be one of", devices
|
||||
return 1
|
||||
devices = [args.device]
|
||||
|
||||
flavors = ["debug", "release"]
|
||||
if (args.flavor):
|
||||
args.flavor = args.flavor.lower()
|
||||
if not args.flavor in flavors:
|
||||
print >>sys.stderr, "--flavor must be one of", flavors
|
||||
return 1
|
||||
flavors = [args.flavor]
|
||||
|
||||
print "CNTK Test Driver is started"
|
||||
print "Running tests: ", " ".join([y.fullName for y in testsToRun])
|
||||
print "Build location: ", args.build_location
|
||||
print "Run location: ", args.run_dir
|
||||
print "Flavors: ", " ".join(flavors)
|
||||
print "Devices: ", " ".join(devices)
|
||||
if (args.update_baseline):
|
||||
print "*** Running in automatic baseline update mode ***"
|
||||
print ""
|
||||
succeededCount, totalCount = 0, 0
|
||||
for test in testsToRun:
|
||||
for flavor in flavors:
|
||||
for device in devices:
|
||||
totalCount = totalCount + 1
|
||||
# Printing the test which is about to run (without terminating the line)
|
||||
sys.stdout.write("Running test {0} ({1} {2}) - ".format(test.fullName, flavor, device));
|
||||
# in verbose mode, terminate the line, since there will be a lot of output
|
||||
if args.verbose:
|
||||
sys.stdout.write("\n");
|
||||
sys.stdout.flush()
|
||||
# Running the test and collecting a run results
|
||||
result = test.run(flavor, device, args)
|
||||
if args.verbose:
|
||||
# writing the test name one more time (after possibly long verbose output)
|
||||
sys.stdout.write("Test finished {0} ({1} {2}) - ".format(test.fullName, flavor, device));
|
||||
if result.succeeded:
|
||||
succeededCount = succeededCount + 1
|
||||
# in no-verbose mode this will be printed in the same line as 'Running test...'
|
||||
print "[OK]"
|
||||
else:
|
||||
print "[FAILED]"
|
||||
# Showing per-test-case results:
|
||||
for testCaseRunResult in result.testCaseRunResults:
|
||||
if testCaseRunResult.succeeded:
|
||||
# Printing 'OK' test cases only in verbose mode
|
||||
if (args.verbose):
|
||||
print(" [OK] " + testCaseRunResult.testCaseName);
|
||||
else:
|
||||
# 'FAILED' + detailed diagnostics with proper indendtation
|
||||
print(" [FAILED] " + testCaseRunResult.testCaseName);
|
||||
if testCaseRunResult.diagnostics:
|
||||
for line in testCaseRunResult.diagnostics.split('\n'):
|
||||
print " " + line;
|
||||
# In non-verbose mode log wasn't piped to the stdout, showing log file path for conveniencce
|
||||
|
||||
if not result.succeeded and not args.verbose and result.logFile:
|
||||
print " See log file for details:", result.logFile
|
||||
|
||||
if args.update_baseline:
|
||||
print "{0}/{1} baselines updated, {2} failed".format(succeededCount, totalCount, totalCount - succeededCount)
|
||||
else:
|
||||
print "{0}/{1} tests passed, {2} failed".format(succeededCount, totalCount, totalCount - succeededCount)
|
||||
if succeededCount != totalCount:
|
||||
sys.exit(10)
|
||||
|
||||
# ======================= Entry point =======================
|
||||
parser = argparse.ArgumentParser(description="TestDriver - CNTK Test Driver")
|
||||
subparsers = parser.add_subparsers(help="command to execute. Run TestDriver.py <command> --help for command-specific help")
|
||||
runSubparser = subparsers.add_parser("run", help="run test(s)")
|
||||
runSubparser.add_argument("test", nargs="*",
|
||||
help="optional test name(s) to run, specified as Suite/TestName. "
|
||||
"Use list command to list available tests. "
|
||||
"If not specified then all tests will be run.")
|
||||
#TODO: port paths to Windows
|
||||
defaultBuildLocation=os.path.realpath(os.path.join(thisDir, "..", "bin"))
|
||||
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("-f", "--flavor", help="release|debug - run only a specific flavor")
|
||||
#TODO: port paths to Windows
|
||||
defaultRunDir=os.path.join("/tmp", "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("--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.set_defaults(func=runCommand)
|
||||
|
||||
listSubparser = subparsers.add_parser("list", help="list available tests")
|
||||
listSubparser.set_defaults(func=listCommand)
|
||||
|
||||
if len(sys.argv)==1:
|
||||
parser.print_help()
|
||||
sys.exit(1)
|
||||
|
||||
args = parser.parse_args(sys.argv[1:])
|
||||
|
||||
# discover all the tests
|
||||
Test.discoverAllTests()
|
||||
|
||||
# execute the command
|
||||
args.func(args)
|
||||
|
||||
|
|
@ -0,0 +1,42 @@
|
|||
=== AN4 dataset ====
|
||||
|
||||
Contents of Tests/Speech/Data directory is a modified version of AN4 dataset pre-processed and optimized for CNTK end-to-end testing.
|
||||
AN4 dataset is a part of CMU audio databases located at http://www.speech.cs.cmu.edu/databases/an4
|
||||
This modified version of dataset is distributed under the terms of a AN4 license:
|
||||
|
||||
/* ====================================================================
|
||||
* Copyright (c) 1991-2005 Carnegie Mellon University. All rights
|
||||
* reserved.
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without
|
||||
* modification, are permitted provided that the following conditions
|
||||
* are met:
|
||||
*
|
||||
* 1. Redistributions of source code must retain the above copyright
|
||||
* notice, this list of conditions and the following disclaimer.
|
||||
*
|
||||
* 2. Redistributions in binary form must reproduce the above copyright
|
||||
* notice, this list of conditions and the following disclaimer in
|
||||
* the documentation and/or other materials provided with the
|
||||
* distribution.
|
||||
*
|
||||
* This work was supported in part by funding from the Defense Advanced
|
||||
* Research Projects Agency and the National Science Foundation of the
|
||||
* United States of America, and the CMU Sphinx Speech Consortium.
|
||||
*
|
||||
* THIS SOFTWARE IS PROVIDED BY CARNEGIE MELLON UNIVERSITY ``AS IS'' AND
|
||||
* ANY EXPRESSED OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
|
||||
* THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
||||
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CARNEGIE MELLON UNIVERSITY
|
||||
* NOR ITS EMPLOYEES BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
|
||||
* SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
|
||||
* LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
|
||||
* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
|
||||
* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
|
||||
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*
|
||||
* ====================================================================
|
||||
*/
|
||||
|
||||
|
||||
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