CPU info:
    CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
    Hardware threads: 24
    Total Memory: 268381192 kB
-------------------------------------------------------------------
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\DNN\WriteCommand/cntk.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082728.104126\Speech\DNN_WriteCommand@release_cpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\DNN\WriteCommand OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082728.104126\Speech\DNN_WriteCommand@release_cpu DeviceId=-1 timestamping=true shareNodeValueMatrices=true
CNTK 2.0.beta6.0+ (HEAD 5f1fab, Dec 15 2016 06:29:34) on dphaim-26-new at 2016/12/15 08:29:56

C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe  configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\DNN\WriteCommand/cntk.cntk  currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data  RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082728.104126\Speech\DNN_WriteCommand@release_cpu  DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data  ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\DNN\WriteCommand  OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082728.104126\Speech\DNN_WriteCommand@release_cpu  DeviceId=-1  timestamping=true  shareNodeValueMatrices=true
Changed current directory to C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data

12/15/2016 08:29:56: ##############################################################################
12/15/2016 08:29:56: #                                                                            #
12/15/2016 08:29:56: # speechTrain command (train action)                                         #
12/15/2016 08:29:56: #                                                                            #
12/15/2016 08:29:56: ##############################################################################

parallelTrain option is not enabled. ParallelTrain config will be ignored.
12/15/2016 08:29:56: 
Creating virgin network.
SimpleNetworkBuilder Using CPU
reading script file glob_0000.scp ... 948 entries
total 132 state names in state list C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/state.list
htkmlfreader: reading MLF file C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/glob_0000.mlf ... total 948 entries
...............................................................................................feature set 0: 252734 frames in 948 out of 948 utterances
label set 0: 129 classes
minibatchutterancesource: 948 utterances grouped into 3 chunks, av. chunk size: 316.0 utterances, 84244.7 frames
12/15/2016 08:29:57: 
Model has 25 nodes. Using CPU.

12/15/2016 08:29:57: Training criterion:   CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
12/15/2016 08:29:57: Evaluation criterion: EvalClassificationError = ClassificationError


Allocating matrices for forward and/or backward propagation.

Memory Sharing: Out of 40 matrices, 21 are shared as 7, and 19 are not shared.

	{ H1 : [512 x 1 x *]
	  W0*features : [512 x *]
	  W0*features : [512 x *] (gradient) }
	{ B1 : [512 x 1] (gradient)
	  H2 : [512 x 1 x *] (gradient)
	  HLast : [132 x 1 x *] (gradient) }
	{ HLast : [132 x 1 x *]
	  W2 : [132 x 512] (gradient) }
	{ H2 : [512 x 1 x *]
	  W0 : [512 x 363] (gradient)
	  W0*features+B0 : [512 x 1 x *]
	  W0*features+B0 : [512 x 1 x *] (gradient)
	  W1*H1 : [512 x 1 x *]
	  W1*H1 : [512 x 1 x *] (gradient) }
	{ W1*H1+B1 : [512 x 1 x *]
	  W2*H1 : [132 x 1 x *] }
	{ W1 : [512 x 512] (gradient)
	  W1*H1+B1 : [512 x 1 x *] (gradient)
	  W2*H1 : [132 x 1 x *] (gradient) }
	{ B0 : [512 x 1] (gradient)
	  H1 : [512 x 1 x *] (gradient) }


12/15/2016 08:29:57: Training 516740 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:

12/15/2016 08:29:57: 	Node 'B0' (LearnableParameter operation) : [512 x 1]
12/15/2016 08:29:57: 	Node 'B1' (LearnableParameter operation) : [512 x 1]
12/15/2016 08:29:57: 	Node 'B2' (LearnableParameter operation) : [132 x 1]
12/15/2016 08:29:57: 	Node 'W0' (LearnableParameter operation) : [512 x 363]
12/15/2016 08:29:57: 	Node 'W1' (LearnableParameter operation) : [512 x 512]
12/15/2016 08:29:57: 	Node 'W2' (LearnableParameter operation) : [132 x 512]


12/15/2016 08:29:57: Precomputing --> 3 PreCompute nodes found.

12/15/2016 08:29:57: 	MeanOfFeatures = Mean()
12/15/2016 08:29:57: 	InvStdOfFeatures = InvStdDev()
12/15/2016 08:29:57: 	Prior = Mean()
minibatchiterator: epoch 0: frames [0..252734] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses
requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms

12/15/2016 08:30:01: Precomputing --> Completed.


12/15/2016 08:30:01: Starting Epoch 1: learning rate per sample = 0.015625  effective momentum = 0.900000  momentum as time constant = 607.4 samples
minibatchiterator: epoch 0: frames [0..20480] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses

12/15/2016 08:30:01: Starting minibatch loop.
12/15/2016 08:30:01:  Epoch[ 1 of 3]-Minibatch[   1-  10, 3.13%]: CrossEntropyWithSoftmax = 4.59755211 * 640; EvalClassificationError = 0.93125000 * 640; time = 0.3174s; samplesPerSecond = 2016.6
12/15/2016 08:30:01:  Epoch[ 1 of 3]-Minibatch[  11-  20, 6.25%]: CrossEntropyWithSoftmax = 4.34610329 * 640; EvalClassificationError = 0.92031250 * 640; time = 0.0699s; samplesPerSecond = 9162.1
12/15/2016 08:30:01:  Epoch[ 1 of 3]-Minibatch[  21-  30, 9.38%]: CrossEntropyWithSoftmax = 3.98222656 * 640; EvalClassificationError = 0.89062500 * 640; time = 0.0572s; samplesPerSecond = 11185.3
12/15/2016 08:30:01:  Epoch[ 1 of 3]-Minibatch[  31-  40, 12.50%]: CrossEntropyWithSoftmax = 3.74152527 * 640; EvalClassificationError = 0.84531250 * 640; time = 0.0574s; samplesPerSecond = 11149.2
12/15/2016 08:30:01:  Epoch[ 1 of 3]-Minibatch[  41-  50, 15.63%]: CrossEntropyWithSoftmax = 3.83818665 * 640; EvalClassificationError = 0.86718750 * 640; time = 0.0572s; samplesPerSecond = 11190.0
12/15/2016 08:30:01:  Epoch[ 1 of 3]-Minibatch[  51-  60, 18.75%]: CrossEntropyWithSoftmax = 3.71641235 * 640; EvalClassificationError = 0.87500000 * 640; time = 0.0569s; samplesPerSecond = 11253.7
12/15/2016 08:30:01:  Epoch[ 1 of 3]-Minibatch[  61-  70, 21.88%]: CrossEntropyWithSoftmax = 3.41802368 * 640; EvalClassificationError = 0.79687500 * 640; time = 0.0589s; samplesPerSecond = 10860.3
12/15/2016 08:30:02:  Epoch[ 1 of 3]-Minibatch[  71-  80, 25.00%]: CrossEntropyWithSoftmax = 3.53833008 * 640; EvalClassificationError = 0.82812500 * 640; time = 0.2605s; samplesPerSecond = 2457.2
12/15/2016 08:30:02:  Epoch[ 1 of 3]-Minibatch[  81-  90, 28.13%]: CrossEntropyWithSoftmax = 3.50628052 * 640; EvalClassificationError = 0.81718750 * 640; time = 0.0567s; samplesPerSecond = 11277.9
12/15/2016 08:30:02:  Epoch[ 1 of 3]-Minibatch[  91- 100, 31.25%]: CrossEntropyWithSoftmax = 3.41477966 * 640; EvalClassificationError = 0.80781250 * 640; time = 0.0576s; samplesPerSecond = 11111.3
12/15/2016 08:30:02:  Epoch[ 1 of 3]-Minibatch[ 101- 110, 34.38%]: CrossEntropyWithSoftmax = 3.51030884 * 640; EvalClassificationError = 0.82812500 * 640; time = 0.0573s; samplesPerSecond = 11165.6
12/15/2016 08:30:02:  Epoch[ 1 of 3]-Minibatch[ 111- 120, 37.50%]: CrossEntropyWithSoftmax = 3.28365479 * 640; EvalClassificationError = 0.79375000 * 640; time = 0.0573s; samplesPerSecond = 11159.7
12/15/2016 08:30:02:  Epoch[ 1 of 3]-Minibatch[ 121- 130, 40.63%]: CrossEntropyWithSoftmax = 3.20931702 * 640; EvalClassificationError = 0.79531250 * 640; time = 0.2714s; samplesPerSecond = 2358.2
12/15/2016 08:30:02:  Epoch[ 1 of 3]-Minibatch[ 131- 140, 43.75%]: CrossEntropyWithSoftmax = 3.07460327 * 640; EvalClassificationError = 0.75468750 * 640; time = 0.0804s; samplesPerSecond = 7959.5
12/15/2016 08:30:02:  Epoch[ 1 of 3]-Minibatch[ 141- 150, 46.88%]: CrossEntropyWithSoftmax = 2.97528992 * 640; EvalClassificationError = 0.72031250 * 640; time = 0.0936s; samplesPerSecond = 6841.1
12/15/2016 08:30:02:  Epoch[ 1 of 3]-Minibatch[ 151- 160, 50.00%]: CrossEntropyWithSoftmax = 3.11968384 * 640; EvalClassificationError = 0.74531250 * 640; time = 0.0660s; samplesPerSecond = 9696.7
12/15/2016 08:30:03:  Epoch[ 1 of 3]-Minibatch[ 161- 170, 53.13%]: CrossEntropyWithSoftmax = 2.84171753 * 640; EvalClassificationError = 0.71093750 * 640; time = 0.0747s; samplesPerSecond = 8564.3
12/15/2016 08:30:03:  Epoch[ 1 of 3]-Minibatch[ 171- 180, 56.25%]: CrossEntropyWithSoftmax = 2.74031372 * 640; EvalClassificationError = 0.66093750 * 640; time = 0.0769s; samplesPerSecond = 8325.4
12/15/2016 08:30:03:  Epoch[ 1 of 3]-Minibatch[ 181- 190, 59.38%]: CrossEntropyWithSoftmax = 2.83858032 * 640; EvalClassificationError = 0.72656250 * 640; time = 0.1709s; samplesPerSecond = 3744.4
12/15/2016 08:30:03:  Epoch[ 1 of 3]-Minibatch[ 191- 200, 62.50%]: CrossEntropyWithSoftmax = 2.74630737 * 640; EvalClassificationError = 0.69218750 * 640; time = 0.0569s; samplesPerSecond = 11242.7
12/15/2016 08:30:03:  Epoch[ 1 of 3]-Minibatch[ 201- 210, 65.63%]: CrossEntropyWithSoftmax = 2.61033325 * 640; EvalClassificationError = 0.66250000 * 640; time = 0.0572s; samplesPerSecond = 11190.4
12/15/2016 08:30:03:  Epoch[ 1 of 3]-Minibatch[ 211- 220, 68.75%]: CrossEntropyWithSoftmax = 2.61330566 * 640; EvalClassificationError = 0.65000000 * 640; time = 0.0573s; samplesPerSecond = 11166.4
12/15/2016 08:30:03:  Epoch[ 1 of 3]-Minibatch[ 221- 230, 71.88%]: CrossEntropyWithSoftmax = 2.54591064 * 640; EvalClassificationError = 0.66406250 * 640; time = 0.3391s; samplesPerSecond = 1887.4
12/15/2016 08:30:03:  Epoch[ 1 of 3]-Minibatch[ 231- 240, 75.00%]: CrossEntropyWithSoftmax = 2.57565918 * 640; EvalClassificationError = 0.66093750 * 640; time = 0.0616s; samplesPerSecond = 10389.9
12/15/2016 08:30:03:  Epoch[ 1 of 3]-Minibatch[ 241- 250, 78.13%]: CrossEntropyWithSoftmax = 2.49163818 * 640; EvalClassificationError = 0.63281250 * 640; time = 0.0569s; samplesPerSecond = 11240.1
12/15/2016 08:30:03:  Epoch[ 1 of 3]-Minibatch[ 251- 260, 81.25%]: CrossEntropyWithSoftmax = 2.39955444 * 640; EvalClassificationError = 0.62812500 * 640; time = 0.0565s; samplesPerSecond = 11328.2
12/15/2016 08:30:04:  Epoch[ 1 of 3]-Minibatch[ 261- 270, 84.38%]: CrossEntropyWithSoftmax = 2.27033691 * 640; EvalClassificationError = 0.59375000 * 640; time = 0.0576s; samplesPerSecond = 11118.4
12/15/2016 08:30:04:  Epoch[ 1 of 3]-Minibatch[ 271- 280, 87.50%]: CrossEntropyWithSoftmax = 2.52111816 * 640; EvalClassificationError = 0.66093750 * 640; time = 0.0567s; samplesPerSecond = 11294.1
12/15/2016 08:30:04:  Epoch[ 1 of 3]-Minibatch[ 281- 290, 90.63%]: CrossEntropyWithSoftmax = 2.27800293 * 640; EvalClassificationError = 0.59062500 * 640; time = 0.0577s; samplesPerSecond = 11087.1
12/15/2016 08:30:04:  Epoch[ 1 of 3]-Minibatch[ 291- 300, 93.75%]: CrossEntropyWithSoftmax = 2.26782837 * 640; EvalClassificationError = 0.61093750 * 640; time = 0.0575s; samplesPerSecond = 11134.3
12/15/2016 08:30:04:  Epoch[ 1 of 3]-Minibatch[ 301- 310, 96.88%]: CrossEntropyWithSoftmax = 2.24590454 * 640; EvalClassificationError = 0.58593750 * 640; time = 0.1586s; samplesPerSecond = 4034.8
12/15/2016 08:30:04:  Epoch[ 1 of 3]-Minibatch[ 311- 320, 100.00%]: CrossEntropyWithSoftmax = 2.24415283 * 640; EvalClassificationError = 0.59843750 * 640; time = 0.0583s; samplesPerSecond = 10970.0
12/15/2016 08:30:04: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 3.04696693 * 20480; EvalClassificationError = 0.73583984 * 20480; totalSamplesSeen = 20480; learningRatePerSample = 0.015625; epochTime=3.13287s
12/15/2016 08:30:04: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082728.104126\Speech\DNN_WriteCommand@release_cpu/models/cntkSpeech.dnn.1'

12/15/2016 08:30:04: Starting Epoch 2: learning rate per sample = 0.001953  effective momentum = 0.656119  momentum as time constant = 607.5 samples
minibatchiterator: epoch 1: frames [20480..40960] (first utterance at frame 20480), data subset 0 of 1, with 1 datapasses

12/15/2016 08:30:04: Starting minibatch loop.
12/15/2016 08:30:04:  Epoch[ 2 of 3]-Minibatch[   1-  10, 12.50%]: CrossEntropyWithSoftmax = 2.14624214 * 2560; EvalClassificationError = 0.56953125 * 2560; time = 0.3299s; samplesPerSecond = 7760.1
12/15/2016 08:30:05:  Epoch[ 2 of 3]-Minibatch[  11-  20, 25.00%]: CrossEntropyWithSoftmax = 2.06174126 * 2560; EvalClassificationError = 0.55742187 * 2560; time = 0.1590s; samplesPerSecond = 16098.7
12/15/2016 08:30:05:  Epoch[ 2 of 3]-Minibatch[  21-  30, 37.50%]: CrossEntropyWithSoftmax = 2.04994278 * 2560; EvalClassificationError = 0.55351562 * 2560; time = 0.1582s; samplesPerSecond = 16185.7
12/15/2016 08:30:05:  Epoch[ 2 of 3]-Minibatch[  31-  40, 50.00%]: CrossEntropyWithSoftmax = 2.03695602 * 2560; EvalClassificationError = 0.56132812 * 2560; time = 0.3331s; samplesPerSecond = 7686.5
12/15/2016 08:30:05:  Epoch[ 2 of 3]-Minibatch[  41-  50, 62.50%]: CrossEntropyWithSoftmax = 2.03086166 * 2560; EvalClassificationError = 0.55664063 * 2560; time = 0.1550s; samplesPerSecond = 16512.2
12/15/2016 08:30:05:  Epoch[ 2 of 3]-Minibatch[  51-  60, 75.00%]: CrossEntropyWithSoftmax = 1.97306137 * 2560; EvalClassificationError = 0.53671875 * 2560; time = 0.1497s; samplesPerSecond = 17097.0
12/15/2016 08:30:06:  Epoch[ 2 of 3]-Minibatch[  61-  70, 87.50%]: CrossEntropyWithSoftmax = 1.96746063 * 2560; EvalClassificationError = 0.53164062 * 2560; time = 0.4665s; samplesPerSecond = 5487.9
12/15/2016 08:30:06:  Epoch[ 2 of 3]-Minibatch[  71-  80, 100.00%]: CrossEntropyWithSoftmax = 1.95498352 * 2560; EvalClassificationError = 0.53750000 * 2560; time = 0.2591s; samplesPerSecond = 9879.4
12/15/2016 08:30:06: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 2.02765617 * 20480; EvalClassificationError = 0.55053711 * 20480; totalSamplesSeen = 40960; learningRatePerSample = 0.001953125; epochTime=2.01267s
12/15/2016 08:30:06: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082728.104126\Speech\DNN_WriteCommand@release_cpu/models/cntkSpeech.dnn.2'

12/15/2016 08:30:06: Starting Epoch 3: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
minibatchiterator: epoch 2: frames [40960..61440] (first utterance at frame 40960), data subset 0 of 1, with 1 datapasses

12/15/2016 08:30:06: Starting minibatch loop.
12/15/2016 08:30:07:  Epoch[ 3 of 3]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.95358448 * 10240; EvalClassificationError = 0.53603516 * 10240; time = 0.7321s; samplesPerSecond = 13987.2
12/15/2016 08:30:08:  Epoch[ 3 of 3]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.97540913 * 10240; EvalClassificationError = 0.55253906 * 10240; time = 0.7902s; samplesPerSecond = 12958.6
12/15/2016 08:30:08: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 1.96449680 * 20480; EvalClassificationError = 0.54428711 * 20480; totalSamplesSeen = 61440; learningRatePerSample = 9.7656251e-005; epochTime=1.52866s
12/15/2016 08:30:08: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082728.104126\Speech\DNN_WriteCommand@release_cpu/models/cntkSpeech.dnn'

12/15/2016 08:30:08: Action "train" complete.


12/15/2016 08:30:08: ##############################################################################
12/15/2016 08:30:08: #                                                                            #
12/15/2016 08:30:08: # write command (write action)                                               #
12/15/2016 08:30:08: #                                                                            #
12/15/2016 08:30:08: ##############################################################################

reading script file glob_0000.write.scp ... 10 entries

Post-processing network...

7 roots:
	CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
	EvalClassificationError = ClassificationError()
	InvStdOfFeatures = InvStdDev()
	MeanOfFeatures = Mean()
	PosteriorProb = Softmax()
	Prior = Mean()
	ScaledLogLikelihood = Minus()

Validating network. 25 nodes to process in pass 1.

Validating --> labels = InputValue() :  -> [132 x *1]
Validating --> W2 = LearnableParameter() :  -> [132 x 512]
Validating --> W1 = LearnableParameter() :  -> [512 x 512]
Validating --> W0 = LearnableParameter() :  -> [512 x 363]
Validating --> features = InputValue() :  -> [363 x *1]
Validating --> MeanOfFeatures = Mean (features) : [363 x *1] -> [363]
Validating --> InvStdOfFeatures = InvStdDev (features) : [363 x *1] -> [363]
Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization (features, MeanOfFeatures, InvStdOfFeatures) : [363 x *1], [363], [363] -> [363 x *1]
Validating --> W0*features = Times (W0, MVNormalizedFeatures) : [512 x 363], [363 x *1] -> [512 x *1]
Validating --> B0 = LearnableParameter() :  -> [512 x 1]
Validating --> W0*features+B0 = Plus (W0*features, B0) : [512 x *1], [512 x 1] -> [512 x 1 x *1]
Validating --> H1 = Sigmoid (W0*features+B0) : [512 x 1 x *1] -> [512 x 1 x *1]
Validating --> W1*H1 = Times (W1, H1) : [512 x 512], [512 x 1 x *1] -> [512 x 1 x *1]
Validating --> B1 = LearnableParameter() :  -> [512 x 1]
Validating --> W1*H1+B1 = Plus (W1*H1, B1) : [512 x 1 x *1], [512 x 1] -> [512 x 1 x *1]
Validating --> H2 = Sigmoid (W1*H1+B1) : [512 x 1 x *1] -> [512 x 1 x *1]
Validating --> W2*H1 = Times (W2, H2) : [132 x 512], [512 x 1 x *1] -> [132 x 1 x *1]
Validating --> B2 = LearnableParameter() :  -> [132 x 1]
Validating --> HLast = Plus (W2*H1, B2) : [132 x 1 x *1], [132 x 1] -> [132 x 1 x *1]
Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [132 x *1], [132 x 1 x *1] -> [1]
Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [132 x *1], [132 x 1 x *1] -> [1]
Validating --> PosteriorProb = Softmax (HLast) : [132 x 1 x *1] -> [132 x 1 x *1]
Validating --> Prior = Mean (labels) : [132 x *1] -> [132]
Validating --> LogOfPrior = Log (Prior) : [132] -> [132]
Validating --> ScaledLogLikelihood = Minus (HLast, LogOfPrior) : [132 x 1 x *1], [132] -> [132 x 1 x *1]

Validating network. 17 nodes to process in pass 2.


Validating network, final pass.




Post-processing network complete.



Allocating matrices for forward and/or backward propagation.

Memory Sharing: Out of 25 matrices, 12 are shared as 3, and 13 are not shared.

	{ CrossEntropyWithSoftmax : [1]
	  EvalClassificationError : [1]
	  PosteriorProb : [132 x 1 x *1] }
	{ H1 : [512 x 1 x *1]
	  W0*features : [512 x *1]
	  W1*H1+B1 : [512 x 1 x *1]
	  W2*H1 : [132 x 1 x *1] }
	{ H2 : [512 x 1 x *1]
	  HLast : [132 x 1 x *1]
	  MVNormalizedFeatures : [363 x *1]
	  W0*features+B0 : [512 x 1 x *1]
	  W1*H1 : [512 x 1 x *1] }

evaluate: reading 368 frames of An4/71/71/cen5-fjam-b.mfc
Minibatch[0]: ActualMBSize = 368
evaluate: reading 438 frames of An4/213/213/cen4-fsaf2-b.mfc
Minibatch[1]: ActualMBSize = 438
evaluate: reading 368 frames of An4/513/513/cen7-mgah-b.mfc
Minibatch[2]: ActualMBSize = 368
evaluate: reading 248 frames of An4/614/614/cen7-mkdb-b.mfc
Minibatch[3]: ActualMBSize = 248
evaluate: reading 248 frames of An4/507/507/cen1-mgah-b.mfc
Minibatch[4]: ActualMBSize = 248
evaluate: reading 358 frames of An4/693/693/cen8-mmkw-b.mfc
Minibatch[5]: ActualMBSize = 358
evaluate: reading 308 frames of An4/918/918/cen4-mtos-b.mfc
Minibatch[6]: ActualMBSize = 308
evaluate: reading 608 frames of An4/477/477/an257-mewl-b.mfc
Minibatch[7]: ActualMBSize = 608
evaluate: reading 78 frames of An4/454/454/an70-meht-b.mfc
Minibatch[8]: ActualMBSize = 78
evaluate: reading 228 frames of An4/254/254/cen6-ftmj-b.mfc
Minibatch[9]: ActualMBSize = 228
Written to C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082728.104126\Speech\DNN_WriteCommand@release_cpu/Output*
Total Samples Evaluated = 3250

12/15/2016 08:30:09: Action "write" complete.

12/15/2016 08:30:09: __COMPLETED__
Error: Output of write command does not match baseline output within specified tolerance. See /cygdrive/c/Users/svcphil/AppData/Local/Temp/cntk-test-20161215082728.104126/Speech/DNN_WriteCommand@release_cpu/Output.ScaledLogLikelihood.diff
