CPU info:
    CPU Model Name: Intel(R) Xeon(R) CPU W3530 @ 2.80GHz
    Hardware threads: 4
    Total Memory: 12580404 kB
-------------------------------------------------------------------
=== Running C:\Program Files\Microsoft MPI\Bin\/mpiexec.exe -n 3 C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\DNN/cntk.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@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 OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@release_cpu DeviceId=-1 timestamping=true numCPUThreads=1 stderr=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@release_cpu/stderr
CNTK 2.0.beta6.0+ (HEAD 5f1fab, Dec 15 2016 06:29:34) on cntk-muc03 at 2016/12/15 08:31:16

C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe  configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\DNN/cntk.cntk  currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data  RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@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  OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@release_cpu  DeviceId=-1  timestamping=true  numCPUThreads=1  stderr=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@release_cpu/stderr
Changed current directory to C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data
requestnodes [MPIWrapper]: using 3 out of 3 MPI nodes on a single host (3 requested); we (1) are in (participating)
CNTK 2.0.beta6.0+ (HEAD 5f1fab, Dec 15 2016 06:29:34) on cntk-muc03 at 2016/12/15 08:31:16

C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe  configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\DNN/cntk.cntk  currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data  RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@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  OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@release_cpu  DeviceId=-1  timestamping=true  numCPUThreads=1  stderr=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@release_cpu/stderr
Changed current directory to C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data
requestnodes [MPIWrapper]: using 3 out of 3 MPI nodes on a single host (3 requested); we (2) are in (participating)
CNTK 2.0.beta6.0+ (HEAD 5f1fab, Dec 15 2016 06:29:34) on cntk-muc03 at 2016/12/15 08:31:16

C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe  configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\DNN/cntk.cntk  currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data  RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@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  OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@release_cpu  DeviceId=-1  timestamping=true  numCPUThreads=1  stderr=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@release_cpu/stderr
Changed current directory to C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data
requestnodes [MPIWrapper]: using 3 out of 3 MPI nodes on a single host (3 requested); we (0) are in (participating)
MPI Rank 0: 12/15/2016 08:31:16: Redirecting stderr to file C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@release_cpu/stderr_speechTrain.logrank0
MPI Rank 0: CNTK 2.0.beta6.0+ (HEAD 5f1fab, Dec 15 2016 06:29:34) on cntk-muc03 at 2016/12/15 08:31:16
MPI Rank 0: 
MPI Rank 0: C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe  configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\DNN/cntk.cntk  currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data  RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@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  OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@release_cpu  DeviceId=-1  timestamping=true  numCPUThreads=1  stderr=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@release_cpu/stderr
MPI Rank 0: 12/15/2016 08:31:16: Using 1 CPU threads.
MPI Rank 0: 
MPI Rank 0: 12/15/2016 08:31:16: ##############################################################################
MPI Rank 0: 12/15/2016 08:31:16: #                                                                            #
MPI Rank 0: 12/15/2016 08:31:16: # speechTrain command (train action)                                         #
MPI Rank 0: 12/15/2016 08:31:16: #                                                                            #
MPI Rank 0: 12/15/2016 08:31:16: ##############################################################################
MPI Rank 0: 
MPI Rank 0: 12/15/2016 08:31:16: 
MPI Rank 0: Creating virgin network.
MPI Rank 0: SimpleNetworkBuilder Using CPU
MPI Rank 0: reading script file glob_0000.scp ... 948 entries
MPI Rank 0: total 132 state names in state list C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/state.list
MPI Rank 0: htkmlfreader: reading MLF file C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/glob_0000.mlf ... total 948 entries
MPI Rank 0: ...............................................................................................feature set 0: 252734 frames in 948 out of 948 utterances
MPI Rank 0: label set 0: 129 classes
MPI Rank 0: minibatchutterancesource: 948 utterances grouped into 3 chunks, av. chunk size: 316.0 utterances, 84244.7 frames
MPI Rank 0: 12/15/2016 08:31:16: 
MPI Rank 0: Model has 25 nodes. Using CPU.
MPI Rank 0: 
MPI Rank 0: 12/15/2016 08:31:16: Training criterion:   CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
MPI Rank 0: 12/15/2016 08:31:16: Evaluation criterion: EvalClassificationError = ClassificationError
MPI Rank 0: 
MPI Rank 0: 
MPI Rank 0: Allocating matrices for forward and/or backward propagation.
MPI Rank 0: 
MPI Rank 0: Memory Sharing: Out of 40 matrices, 19 are shared as 8, and 21 are not shared.
MPI Rank 0: 
MPI Rank 0: 	{ B1 : [512 x 1] (gradient)
MPI Rank 0: 	  H2 : [512 x 1 x *] (gradient)
MPI Rank 0: 	  HLast : [132 x 1 x *] (gradient) }
MPI Rank 0: 	{ HLast : [132 x 1 x *]
MPI Rank 0: 	  W2 : [132 x 512] (gradient) }
MPI Rank 0: 	{ W1 : [512 x 512] (gradient)
MPI Rank 0: 	  W1*H1+B1 : [512 x 1 x *] }
MPI Rank 0: 	{ W0 : [512 x 363] (gradient)
MPI Rank 0: 	  W0*features+B0 : [512 x 1 x *] }
MPI Rank 0: 	{ H2 : [512 x 1 x *]
MPI Rank 0: 	  W1*H1 : [512 x 1 x *] (gradient) }
MPI Rank 0: 	{ H1 : [512 x 1 x *]
MPI Rank 0: 	  W0*features : [512 x *] (gradient) }
MPI Rank 0: 	{ W0*features+B0 : [512 x 1 x *] (gradient)
MPI Rank 0: 	  W1*H1 : [512 x 1 x *] }
MPI Rank 0: 	{ B0 : [512 x 1] (gradient)
MPI Rank 0: 	  H1 : [512 x 1 x *] (gradient)
MPI Rank 0: 	  W1*H1+B1 : [512 x 1 x *] (gradient)
MPI Rank 0: 	  W2*H1 : [132 x 1 x *] }
MPI Rank 0: 
MPI Rank 0: 
MPI Rank 0: 12/15/2016 08:31:16: Training 516740 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:
MPI Rank 0: 
MPI Rank 0: 12/15/2016 08:31:16: 	Node 'B0' (LearnableParameter operation) : [512 x 1]
MPI Rank 0: 12/15/2016 08:31:16: 	Node 'B1' (LearnableParameter operation) : [512 x 1]
MPI Rank 0: 12/15/2016 08:31:16: 	Node 'B2' (LearnableParameter operation) : [132 x 1]
MPI Rank 0: 12/15/2016 08:31:16: 	Node 'W0' (LearnableParameter operation) : [512 x 363]
MPI Rank 0: 12/15/2016 08:31:16: 	Node 'W1' (LearnableParameter operation) : [512 x 512]
MPI Rank 0: 12/15/2016 08:31:16: 	Node 'W2' (LearnableParameter operation) : [132 x 512]
MPI Rank 0: 
MPI Rank 0: Initializing dataParallelSGD with FP32 aggregation.
MPI Rank 0: 
MPI Rank 0: 12/15/2016 08:31:16: Precomputing --> 3 PreCompute nodes found.
MPI Rank 0: 
MPI Rank 0: 12/15/2016 08:31:16: 	MeanOfFeatures = Mean()
MPI Rank 0: 12/15/2016 08:31:16: 	InvStdOfFeatures = InvStdDev()
MPI Rank 0: 12/15/2016 08:31:16: 	Prior = Mean()
MPI Rank 0: minibatchiterator: epoch 0: frames [0..252734] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses
MPI Rank 0: requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms
MPI Rank 0: 
MPI Rank 0: 12/15/2016 08:31:19: Precomputing --> Completed.
MPI Rank 0: 
MPI Rank 0: 
MPI Rank 0: 12/15/2016 08:31:20: Starting Epoch 1: learning rate per sample = 0.015625  effective momentum = 0.900000  momentum as time constant = 607.4 samples
MPI Rank 0: minibatchiterator: epoch 0: frames [0..20480] (first utterance at frame 0), data subset 0 of 3, with 1 datapasses
MPI Rank 0: 
MPI Rank 0: 12/15/2016 08:31:20: Starting minibatch loop, DataParallelSGD training (myRank = 0, numNodes = 3, numGradientBits = 32), distributed reading is ENABLED.
MPI Rank 0: 12/15/2016 08:31:20:  Epoch[ 1 of 3]-Minibatch[   1-  10, 3.13%]: CrossEntropyWithSoftmax = 4.59755209 * 640; EvalClassificationError = 0.93125000 * 640; time = 0.1225s; samplesPerSecond = 5225.1
MPI Rank 0: 12/15/2016 08:31:20:  Epoch[ 1 of 3]-Minibatch[  11-  20, 6.25%]: CrossEntropyWithSoftmax = 4.34610347 * 640; EvalClassificationError = 0.92031250 * 640; time = 0.1122s; samplesPerSecond = 5704.3
MPI Rank 0: 12/15/2016 08:31:20:  Epoch[ 1 of 3]-Minibatch[  21-  30, 9.38%]: CrossEntropyWithSoftmax = 3.98222493 * 640; EvalClassificationError = 0.89062500 * 640; time = 0.1140s; samplesPerSecond = 5613.7
MPI Rank 0: 12/15/2016 08:31:20:  Epoch[ 1 of 3]-Minibatch[  31-  40, 12.50%]: CrossEntropyWithSoftmax = 3.74152761 * 640; EvalClassificationError = 0.84531250 * 640; time = 0.1134s; samplesPerSecond = 5643.9
MPI Rank 0: 12/15/2016 08:31:20:  Epoch[ 1 of 3]-Minibatch[  41-  50, 15.63%]: CrossEntropyWithSoftmax = 3.83818495 * 640; EvalClassificationError = 0.86718750 * 640; time = 0.1116s; samplesPerSecond = 5737.0
MPI Rank 0: 12/15/2016 08:31:20:  Epoch[ 1 of 3]-Minibatch[  51-  60, 18.75%]: CrossEntropyWithSoftmax = 3.71641133 * 640; EvalClassificationError = 0.87500000 * 640; time = 0.1124s; samplesPerSecond = 5694.5
MPI Rank 0: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[  61-  70, 21.88%]: CrossEntropyWithSoftmax = 3.41802654 * 640; EvalClassificationError = 0.79687500 * 640; time = 0.1131s; samplesPerSecond = 5657.7
MPI Rank 0: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[  71-  80, 25.00%]: CrossEntropyWithSoftmax = 3.53832811 * 640; EvalClassificationError = 0.82812500 * 640; time = 0.1120s; samplesPerSecond = 5716.2
MPI Rank 0: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[  81-  90, 28.13%]: CrossEntropyWithSoftmax = 3.50627956 * 640; EvalClassificationError = 0.81718750 * 640; time = 0.1143s; samplesPerSecond = 5600.5
MPI Rank 0: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[  91- 100, 31.25%]: CrossEntropyWithSoftmax = 3.41478094 * 640; EvalClassificationError = 0.80781250 * 640; time = 0.1109s; samplesPerSecond = 5770.2
MPI Rank 0: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[ 101- 110, 34.38%]: CrossEntropyWithSoftmax = 3.51031055 * 640; EvalClassificationError = 0.82812500 * 640; time = 0.1135s; samplesPerSecond = 5638.0
MPI Rank 0: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[ 111- 120, 37.50%]: CrossEntropyWithSoftmax = 3.28365293 * 640; EvalClassificationError = 0.79375000 * 640; time = 0.1114s; samplesPerSecond = 5743.3
MPI Rank 0: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[ 121- 130, 40.63%]: CrossEntropyWithSoftmax = 3.20931888 * 640; EvalClassificationError = 0.79531250 * 640; time = 0.1114s; samplesPerSecond = 5747.3
MPI Rank 0: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[ 131- 140, 43.75%]: CrossEntropyWithSoftmax = 3.07460312 * 640; EvalClassificationError = 0.75468750 * 640; time = 0.1083s; samplesPerSecond = 5911.6
MPI Rank 0: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[ 141- 150, 46.88%]: CrossEntropyWithSoftmax = 2.97528860 * 640; EvalClassificationError = 0.72031250 * 640; time = 0.1105s; samplesPerSecond = 5791.6
MPI Rank 0: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 151- 160, 50.00%]: CrossEntropyWithSoftmax = 3.11968648 * 640; EvalClassificationError = 0.74531250 * 640; time = 0.1123s; samplesPerSecond = 5696.6
MPI Rank 0: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 161- 170, 53.13%]: CrossEntropyWithSoftmax = 2.84171867 * 640; EvalClassificationError = 0.71093750 * 640; time = 0.1130s; samplesPerSecond = 5664.7
MPI Rank 0: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 171- 180, 56.25%]: CrossEntropyWithSoftmax = 2.74031476 * 640; EvalClassificationError = 0.66093750 * 640; time = 0.1095s; samplesPerSecond = 5845.3
MPI Rank 0: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 181- 190, 59.38%]: CrossEntropyWithSoftmax = 2.83857843 * 640; EvalClassificationError = 0.72656250 * 640; time = 0.1108s; samplesPerSecond = 5774.1
MPI Rank 0: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 191- 200, 62.50%]: CrossEntropyWithSoftmax = 2.74632032 * 640; EvalClassificationError = 0.69218750 * 640; time = 0.1098s; samplesPerSecond = 5831.0
MPI Rank 0: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 201- 210, 65.63%]: CrossEntropyWithSoftmax = 2.61032974 * 640; EvalClassificationError = 0.66250000 * 640; time = 0.1097s; samplesPerSecond = 5831.9
MPI Rank 0: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 211- 220, 68.75%]: CrossEntropyWithSoftmax = 2.61330475 * 640; EvalClassificationError = 0.65000000 * 640; time = 0.1111s; samplesPerSecond = 5761.5
MPI Rank 0: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 221- 230, 71.88%]: CrossEntropyWithSoftmax = 2.54591535 * 640; EvalClassificationError = 0.66406250 * 640; time = 0.1095s; samplesPerSecond = 5843.4
MPI Rank 0: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 231- 240, 75.00%]: CrossEntropyWithSoftmax = 2.57566229 * 640; EvalClassificationError = 0.66093750 * 640; time = 0.1103s; samplesPerSecond = 5803.4
MPI Rank 0: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 241- 250, 78.13%]: CrossEntropyWithSoftmax = 2.49164700 * 640; EvalClassificationError = 0.63281250 * 640; time = 0.1096s; samplesPerSecond = 5838.5
MPI Rank 0: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 251- 260, 81.25%]: CrossEntropyWithSoftmax = 2.39954552 * 640; EvalClassificationError = 0.62812500 * 640; time = 0.1142s; samplesPerSecond = 5605.4
MPI Rank 0: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 261- 270, 84.38%]: CrossEntropyWithSoftmax = 2.27033979 * 640; EvalClassificationError = 0.59375000 * 640; time = 0.1105s; samplesPerSecond = 5792.2
MPI Rank 0: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 271- 280, 87.50%]: CrossEntropyWithSoftmax = 2.52112142 * 640; EvalClassificationError = 0.66093750 * 640; time = 0.1101s; samplesPerSecond = 5810.8
MPI Rank 0: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 281- 290, 90.63%]: CrossEntropyWithSoftmax = 2.27800742 * 640; EvalClassificationError = 0.59062500 * 640; time = 0.1102s; samplesPerSecond = 5809.0
MPI Rank 0: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 291- 300, 93.75%]: CrossEntropyWithSoftmax = 2.26783400 * 640; EvalClassificationError = 0.61093750 * 640; time = 0.1086s; samplesPerSecond = 5891.6
MPI Rank 0: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 301- 310, 96.88%]: CrossEntropyWithSoftmax = 2.24590123 * 640; EvalClassificationError = 0.58593750 * 640; time = 0.1105s; samplesPerSecond = 5791.8
MPI Rank 0: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 311- 320, 100.00%]: CrossEntropyWithSoftmax = 2.24415391 * 640; EvalClassificationError = 0.59843750 * 640; time = 0.1092s; samplesPerSecond = 5860.6
MPI Rank 0: 12/15/2016 08:31:23: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 3.04696796 * 20480; EvalClassificationError = 0.73583984 * 20480; totalSamplesSeen = 20480; learningRatePerSample = 0.015625; epochTime=3.58853s
MPI Rank 0: 12/15/2016 08:31:23: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@release_cpu/models/cntkSpeech.dnn.1'
MPI Rank 0: 
MPI Rank 0: 12/15/2016 08:31:23: Starting Epoch 2: learning rate per sample = 0.001953  effective momentum = 0.656119  momentum as time constant = 607.5 samples
MPI Rank 0: minibatchiterator: epoch 1: frames [20480..40960] (first utterance at frame 20480), data subset 0 of 3, with 1 datapasses
MPI Rank 0: 
MPI Rank 0: 12/15/2016 08:31:23: Starting minibatch loop, DataParallelSGD training (myRank = 0, numNodes = 3, numGradientBits = 32), distributed reading is ENABLED.
MPI Rank 0: 12/15/2016 08:31:24:  Epoch[ 2 of 3]-Minibatch[   1-  10, 12.50%]: CrossEntropyWithSoftmax = 2.14624175 * 2560; EvalClassificationError = 0.56953125 * 2560; time = 0.2418s; samplesPerSecond = 10587.8
MPI Rank 0: 12/15/2016 08:31:24:  Epoch[ 2 of 3]-Minibatch[  11-  20, 25.00%]: CrossEntropyWithSoftmax = 2.06174128 * 2560; EvalClassificationError = 0.55742187 * 2560; time = 0.2326s; samplesPerSecond = 11004.5
MPI Rank 0: 12/15/2016 08:31:24:  Epoch[ 2 of 3]-Minibatch[  21-  30, 37.50%]: CrossEntropyWithSoftmax = 2.04994338 * 2560; EvalClassificationError = 0.55351562 * 2560; time = 0.2293s; samplesPerSecond = 11166.7
MPI Rank 0: 12/15/2016 08:31:24:  Epoch[ 2 of 3]-Minibatch[  31-  40, 50.00%]: CrossEntropyWithSoftmax = 2.03695538 * 2560; EvalClassificationError = 0.56132812 * 2560; time = 0.2298s; samplesPerSecond = 11138.5
MPI Rank 0: 12/15/2016 08:31:25:  Epoch[ 2 of 3]-Minibatch[  41-  50, 62.50%]: CrossEntropyWithSoftmax = 2.03086227 * 2560; EvalClassificationError = 0.55664063 * 2560; time = 0.2347s; samplesPerSecond = 10907.4
MPI Rank 0: 12/15/2016 08:31:25:  Epoch[ 2 of 3]-Minibatch[  51-  60, 75.00%]: CrossEntropyWithSoftmax = 1.97306193 * 2560; EvalClassificationError = 0.53671875 * 2560; time = 0.2221s; samplesPerSecond = 11524.3
MPI Rank 0: 12/15/2016 08:31:25:  Epoch[ 2 of 3]-Minibatch[  61-  70, 87.50%]: CrossEntropyWithSoftmax = 1.96746064 * 2560; EvalClassificationError = 0.53164062 * 2560; time = 0.2241s; samplesPerSecond = 11425.0
MPI Rank 0: 12/15/2016 08:31:25:  Epoch[ 2 of 3]-Minibatch[  71-  80, 100.00%]: CrossEntropyWithSoftmax = 1.95498165 * 2560; EvalClassificationError = 0.53750000 * 2560; time = 0.2250s; samplesPerSecond = 11378.8
MPI Rank 0: 12/15/2016 08:31:25: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 2.02765603 * 20480; EvalClassificationError = 0.55053711 * 20480; totalSamplesSeen = 40960; learningRatePerSample = 0.001953125; epochTime=1.84777s
MPI Rank 0: 12/15/2016 08:31:25: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@release_cpu/models/cntkSpeech.dnn.2'
MPI Rank 0: 
MPI Rank 0: 12/15/2016 08:31:25: Starting Epoch 3: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 0: minibatchiterator: epoch 2: frames [40960..61440] (first utterance at frame 40960), data subset 0 of 3, with 1 datapasses
MPI Rank 0: 
MPI Rank 0: 12/15/2016 08:31:25: Starting minibatch loop, DataParallelSGD training (myRank = 0, numNodes = 3, numGradientBits = 32), distributed reading is ENABLED.
MPI Rank 0: 12/15/2016 08:31:26:  Epoch[ 3 of 3]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.95358449 * 10240; EvalClassificationError = 0.53603516 * 10240; time = 0.7052s; samplesPerSecond = 14520.5
MPI Rank 0: 12/15/2016 08:31:27:  Epoch[ 3 of 3]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.97540911 * 10240; EvalClassificationError = 0.55253906 * 10240; time = 0.6838s; samplesPerSecond = 14975.6
MPI Rank 0: 12/15/2016 08:31:27: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 1.96449680 * 20480; EvalClassificationError = 0.54428711 * 20480; totalSamplesSeen = 61440; learningRatePerSample = 9.7656251e-005; epochTime=1.39589s
MPI Rank 0: 12/15/2016 08:31:27: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@release_cpu/models/cntkSpeech.dnn'
MPI Rank 0: 
MPI Rank 0: 12/15/2016 08:31:27: Action "train" complete.
MPI Rank 0: 
MPI Rank 0: 12/15/2016 08:31:27: __COMPLETED__
MPI Rank 1: 12/15/2016 08:31:17: Redirecting stderr to file C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@release_cpu/stderr_speechTrain.logrank1
MPI Rank 1: CNTK 2.0.beta6.0+ (HEAD 5f1fab, Dec 15 2016 06:29:34) on cntk-muc03 at 2016/12/15 08:31:16
MPI Rank 1: 
MPI Rank 1: C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe  configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\DNN/cntk.cntk  currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data  RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@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  OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@release_cpu  DeviceId=-1  timestamping=true  numCPUThreads=1  stderr=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@release_cpu/stderr
MPI Rank 1: 12/15/2016 08:31:17: Using 1 CPU threads.
MPI Rank 1: 
MPI Rank 1: 12/15/2016 08:31:17: ##############################################################################
MPI Rank 1: 12/15/2016 08:31:17: #                                                                            #
MPI Rank 1: 12/15/2016 08:31:17: # speechTrain command (train action)                                         #
MPI Rank 1: 12/15/2016 08:31:17: #                                                                            #
MPI Rank 1: 12/15/2016 08:31:17: ##############################################################################
MPI Rank 1: 
MPI Rank 1: 12/15/2016 08:31:17: 
MPI Rank 1: Creating virgin network.
MPI Rank 1: SimpleNetworkBuilder Using CPU
MPI Rank 1: reading script file glob_0000.scp ... 948 entries
MPI Rank 1: total 132 state names in state list C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/state.list
MPI Rank 1: htkmlfreader: reading MLF file C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/glob_0000.mlf ... total 948 entries
MPI Rank 1: ...............................................................................................feature set 0: 252734 frames in 948 out of 948 utterances
MPI Rank 1: label set 0: 129 classes
MPI Rank 1: minibatchutterancesource: 948 utterances grouped into 3 chunks, av. chunk size: 316.0 utterances, 84244.7 frames
MPI Rank 1: 12/15/2016 08:31:17: 
MPI Rank 1: Model has 25 nodes. Using CPU.
MPI Rank 1: 
MPI Rank 1: 12/15/2016 08:31:17: Training criterion:   CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
MPI Rank 1: 12/15/2016 08:31:17: Evaluation criterion: EvalClassificationError = ClassificationError
MPI Rank 1: 
MPI Rank 1: 
MPI Rank 1: Allocating matrices for forward and/or backward propagation.
MPI Rank 1: 
MPI Rank 1: Memory Sharing: Out of 40 matrices, 19 are shared as 8, and 21 are not shared.
MPI Rank 1: 
MPI Rank 1: 	{ HLast : [132 x 1 x *]
MPI Rank 1: 	  W2 : [132 x 512] (gradient) }
MPI Rank 1: 	{ H1 : [512 x 1 x *]
MPI Rank 1: 	  W0*features : [512 x *] (gradient) }
MPI Rank 1: 	{ H2 : [512 x 1 x *]
MPI Rank 1: 	  W1*H1 : [512 x 1 x *] (gradient) }
MPI Rank 1: 	{ W0*features+B0 : [512 x 1 x *] (gradient)
MPI Rank 1: 	  W1*H1 : [512 x 1 x *] }
MPI Rank 1: 	{ B0 : [512 x 1] (gradient)
MPI Rank 1: 	  H1 : [512 x 1 x *] (gradient)
MPI Rank 1: 	  W1*H1+B1 : [512 x 1 x *] (gradient)
MPI Rank 1: 	  W2*H1 : [132 x 1 x *] }
MPI Rank 1: 	{ B1 : [512 x 1] (gradient)
MPI Rank 1: 	  H2 : [512 x 1 x *] (gradient)
MPI Rank 1: 	  HLast : [132 x 1 x *] (gradient) }
MPI Rank 1: 	{ W1 : [512 x 512] (gradient)
MPI Rank 1: 	  W1*H1+B1 : [512 x 1 x *] }
MPI Rank 1: 	{ W0 : [512 x 363] (gradient)
MPI Rank 1: 	  W0*features+B0 : [512 x 1 x *] }
MPI Rank 1: 
MPI Rank 1: 
MPI Rank 1: 12/15/2016 08:31:17: Training 516740 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:
MPI Rank 1: 
MPI Rank 1: 12/15/2016 08:31:17: 	Node 'B0' (LearnableParameter operation) : [512 x 1]
MPI Rank 1: 12/15/2016 08:31:17: 	Node 'B1' (LearnableParameter operation) : [512 x 1]
MPI Rank 1: 12/15/2016 08:31:17: 	Node 'B2' (LearnableParameter operation) : [132 x 1]
MPI Rank 1: 12/15/2016 08:31:17: 	Node 'W0' (LearnableParameter operation) : [512 x 363]
MPI Rank 1: 12/15/2016 08:31:17: 	Node 'W1' (LearnableParameter operation) : [512 x 512]
MPI Rank 1: 12/15/2016 08:31:17: 	Node 'W2' (LearnableParameter operation) : [132 x 512]
MPI Rank 1: 
MPI Rank 1: Initializing dataParallelSGD with FP32 aggregation.
MPI Rank 1: 
MPI Rank 1: 12/15/2016 08:31:17: Precomputing --> 3 PreCompute nodes found.
MPI Rank 1: 
MPI Rank 1: 12/15/2016 08:31:17: 	MeanOfFeatures = Mean()
MPI Rank 1: 12/15/2016 08:31:17: 	InvStdOfFeatures = InvStdDev()
MPI Rank 1: 12/15/2016 08:31:17: 	Prior = Mean()
MPI Rank 1: minibatchiterator: epoch 0: frames [0..252734] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses
MPI Rank 1: requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms
MPI Rank 1: 
MPI Rank 1: 12/15/2016 08:31:19: Precomputing --> Completed.
MPI Rank 1: 
MPI Rank 1: 
MPI Rank 1: 12/15/2016 08:31:20: Starting Epoch 1: learning rate per sample = 0.015625  effective momentum = 0.900000  momentum as time constant = 607.4 samples
MPI Rank 1: minibatchiterator: epoch 0: frames [0..20480] (first utterance at frame 0), data subset 1 of 3, with 1 datapasses
MPI Rank 1: 
MPI Rank 1: 12/15/2016 08:31:20: Starting minibatch loop, DataParallelSGD training (myRank = 1, numNodes = 3, numGradientBits = 32), distributed reading is ENABLED.
MPI Rank 1: 12/15/2016 08:31:20:  Epoch[ 1 of 3]-Minibatch[   1-  10, 3.13%]: CrossEntropyWithSoftmax = 4.59755209 * 640; EvalClassificationError = 0.93125000 * 640; time = 0.1266s; samplesPerSecond = 5054.1
MPI Rank 1: 12/15/2016 08:31:20:  Epoch[ 1 of 3]-Minibatch[  11-  20, 6.25%]: CrossEntropyWithSoftmax = 4.34610347 * 640; EvalClassificationError = 0.92031250 * 640; time = 0.1122s; samplesPerSecond = 5704.8
MPI Rank 1: 12/15/2016 08:31:20:  Epoch[ 1 of 3]-Minibatch[  21-  30, 9.38%]: CrossEntropyWithSoftmax = 3.98222493 * 640; EvalClassificationError = 0.89062500 * 640; time = 0.1140s; samplesPerSecond = 5613.1
MPI Rank 1: 12/15/2016 08:31:20:  Epoch[ 1 of 3]-Minibatch[  31-  40, 12.50%]: CrossEntropyWithSoftmax = 3.74152761 * 640; EvalClassificationError = 0.84531250 * 640; time = 0.1135s; samplesPerSecond = 5641.1
MPI Rank 1: 12/15/2016 08:31:20:  Epoch[ 1 of 3]-Minibatch[  41-  50, 15.63%]: CrossEntropyWithSoftmax = 3.83818495 * 640; EvalClassificationError = 0.86718750 * 640; time = 0.1115s; samplesPerSecond = 5738.9
MPI Rank 1: 12/15/2016 08:31:20:  Epoch[ 1 of 3]-Minibatch[  51-  60, 18.75%]: CrossEntropyWithSoftmax = 3.71641133 * 640; EvalClassificationError = 0.87500000 * 640; time = 0.1124s; samplesPerSecond = 5694.4
MPI Rank 1: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[  61-  70, 21.88%]: CrossEntropyWithSoftmax = 3.41802654 * 640; EvalClassificationError = 0.79687500 * 640; time = 0.1131s; samplesPerSecond = 5658.0
MPI Rank 1: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[  71-  80, 25.00%]: CrossEntropyWithSoftmax = 3.53832811 * 640; EvalClassificationError = 0.82812500 * 640; time = 0.1120s; samplesPerSecond = 5716.0
MPI Rank 1: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[  81-  90, 28.13%]: CrossEntropyWithSoftmax = 3.50627956 * 640; EvalClassificationError = 0.81718750 * 640; time = 0.1143s; samplesPerSecond = 5601.3
MPI Rank 1: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[  91- 100, 31.25%]: CrossEntropyWithSoftmax = 3.41478094 * 640; EvalClassificationError = 0.80781250 * 640; time = 0.1109s; samplesPerSecond = 5770.2
MPI Rank 1: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[ 101- 110, 34.38%]: CrossEntropyWithSoftmax = 3.51031055 * 640; EvalClassificationError = 0.82812500 * 640; time = 0.1135s; samplesPerSecond = 5637.7
MPI Rank 1: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[ 111- 120, 37.50%]: CrossEntropyWithSoftmax = 3.28365293 * 640; EvalClassificationError = 0.79375000 * 640; time = 0.1114s; samplesPerSecond = 5743.2
MPI Rank 1: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[ 121- 130, 40.63%]: CrossEntropyWithSoftmax = 3.20931888 * 640; EvalClassificationError = 0.79531250 * 640; time = 0.1114s; samplesPerSecond = 5747.1
MPI Rank 1: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[ 131- 140, 43.75%]: CrossEntropyWithSoftmax = 3.07460312 * 640; EvalClassificationError = 0.75468750 * 640; time = 0.1083s; samplesPerSecond = 5910.5
MPI Rank 1: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[ 141- 150, 46.88%]: CrossEntropyWithSoftmax = 2.97528860 * 640; EvalClassificationError = 0.72031250 * 640; time = 0.1105s; samplesPerSecond = 5791.5
MPI Rank 1: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 151- 160, 50.00%]: CrossEntropyWithSoftmax = 3.11968648 * 640; EvalClassificationError = 0.74531250 * 640; time = 0.1123s; samplesPerSecond = 5697.8
MPI Rank 1: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 161- 170, 53.13%]: CrossEntropyWithSoftmax = 2.84171867 * 640; EvalClassificationError = 0.71093750 * 640; time = 0.1130s; samplesPerSecond = 5664.4
MPI Rank 1: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 171- 180, 56.25%]: CrossEntropyWithSoftmax = 2.74031476 * 640; EvalClassificationError = 0.66093750 * 640; time = 0.1095s; samplesPerSecond = 5845.0
MPI Rank 1: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 181- 190, 59.38%]: CrossEntropyWithSoftmax = 2.83857843 * 640; EvalClassificationError = 0.72656250 * 640; time = 0.1108s; samplesPerSecond = 5773.8
MPI Rank 1: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 191- 200, 62.50%]: CrossEntropyWithSoftmax = 2.74632032 * 640; EvalClassificationError = 0.69218750 * 640; time = 0.1097s; samplesPerSecond = 5832.7
MPI Rank 1: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 201- 210, 65.63%]: CrossEntropyWithSoftmax = 2.61032974 * 640; EvalClassificationError = 0.66250000 * 640; time = 0.1098s; samplesPerSecond = 5829.6
MPI Rank 1: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 211- 220, 68.75%]: CrossEntropyWithSoftmax = 2.61330475 * 640; EvalClassificationError = 0.65000000 * 640; time = 0.1111s; samplesPerSecond = 5761.0
MPI Rank 1: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 221- 230, 71.88%]: CrossEntropyWithSoftmax = 2.54591535 * 640; EvalClassificationError = 0.66406250 * 640; time = 0.1096s; samplesPerSecond = 5841.4
MPI Rank 1: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 231- 240, 75.00%]: CrossEntropyWithSoftmax = 2.57566229 * 640; EvalClassificationError = 0.66093750 * 640; time = 0.1103s; samplesPerSecond = 5800.9
MPI Rank 1: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 241- 250, 78.13%]: CrossEntropyWithSoftmax = 2.49164700 * 640; EvalClassificationError = 0.63281250 * 640; time = 0.1097s; samplesPerSecond = 5835.1
MPI Rank 1: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 251- 260, 81.25%]: CrossEntropyWithSoftmax = 2.39954552 * 640; EvalClassificationError = 0.62812500 * 640; time = 0.1141s; samplesPerSecond = 5607.5
MPI Rank 1: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 261- 270, 84.38%]: CrossEntropyWithSoftmax = 2.27033979 * 640; EvalClassificationError = 0.59375000 * 640; time = 0.1105s; samplesPerSecond = 5792.1
MPI Rank 1: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 271- 280, 87.50%]: CrossEntropyWithSoftmax = 2.52112142 * 640; EvalClassificationError = 0.66093750 * 640; time = 0.1102s; samplesPerSecond = 5809.7
MPI Rank 1: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 281- 290, 90.63%]: CrossEntropyWithSoftmax = 2.27800742 * 640; EvalClassificationError = 0.59062500 * 640; time = 0.1102s; samplesPerSecond = 5809.4
MPI Rank 1: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 291- 300, 93.75%]: CrossEntropyWithSoftmax = 2.26783400 * 640; EvalClassificationError = 0.61093750 * 640; time = 0.1086s; samplesPerSecond = 5891.6
MPI Rank 1: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 301- 310, 96.88%]: CrossEntropyWithSoftmax = 2.24590123 * 640; EvalClassificationError = 0.58593750 * 640; time = 0.1105s; samplesPerSecond = 5791.0
MPI Rank 1: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 311- 320, 100.00%]: CrossEntropyWithSoftmax = 2.24415391 * 640; EvalClassificationError = 0.59843750 * 640; time = 0.1092s; samplesPerSecond = 5860.4
MPI Rank 1: 12/15/2016 08:31:23: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 3.04696796 * 20480; EvalClassificationError = 0.73583984 * 20480; totalSamplesSeen = 20480; learningRatePerSample = 0.015625; epochTime=3.59272s
MPI Rank 1: 
MPI Rank 1: 12/15/2016 08:31:23: Starting Epoch 2: learning rate per sample = 0.001953  effective momentum = 0.656119  momentum as time constant = 607.5 samples
MPI Rank 1: minibatchiterator: epoch 1: frames [20480..40960] (first utterance at frame 20480), data subset 1 of 3, with 1 datapasses
MPI Rank 1: 
MPI Rank 1: 12/15/2016 08:31:23: Starting minibatch loop, DataParallelSGD training (myRank = 1, numNodes = 3, numGradientBits = 32), distributed reading is ENABLED.
MPI Rank 1: 12/15/2016 08:31:24:  Epoch[ 2 of 3]-Minibatch[   1-  10, 12.50%]: CrossEntropyWithSoftmax = 2.14624175 * 2560; EvalClassificationError = 0.56953125 * 2560; time = 0.2419s; samplesPerSecond = 10583.8
MPI Rank 1: 12/15/2016 08:31:24:  Epoch[ 2 of 3]-Minibatch[  11-  20, 25.00%]: CrossEntropyWithSoftmax = 2.06174128 * 2560; EvalClassificationError = 0.55742187 * 2560; time = 0.2327s; samplesPerSecond = 11002.6
MPI Rank 1: 12/15/2016 08:31:24:  Epoch[ 2 of 3]-Minibatch[  21-  30, 37.50%]: CrossEntropyWithSoftmax = 2.04994338 * 2560; EvalClassificationError = 0.55351562 * 2560; time = 0.2293s; samplesPerSecond = 11164.1
MPI Rank 1: 12/15/2016 08:31:24:  Epoch[ 2 of 3]-Minibatch[  31-  40, 50.00%]: CrossEntropyWithSoftmax = 2.03695538 * 2560; EvalClassificationError = 0.56132812 * 2560; time = 0.2298s; samplesPerSecond = 11140.6
MPI Rank 1: 12/15/2016 08:31:25:  Epoch[ 2 of 3]-Minibatch[  41-  50, 62.50%]: CrossEntropyWithSoftmax = 2.03086227 * 2560; EvalClassificationError = 0.55664063 * 2560; time = 0.2347s; samplesPerSecond = 10907.3
MPI Rank 1: 12/15/2016 08:31:25:  Epoch[ 2 of 3]-Minibatch[  51-  60, 75.00%]: CrossEntropyWithSoftmax = 1.97306193 * 2560; EvalClassificationError = 0.53671875 * 2560; time = 0.2222s; samplesPerSecond = 11522.8
MPI Rank 1: 12/15/2016 08:31:25:  Epoch[ 2 of 3]-Minibatch[  61-  70, 87.50%]: CrossEntropyWithSoftmax = 1.96746064 * 2560; EvalClassificationError = 0.53164062 * 2560; time = 0.2240s; samplesPerSecond = 11427.0
MPI Rank 1: 12/15/2016 08:31:25:  Epoch[ 2 of 3]-Minibatch[  71-  80, 100.00%]: CrossEntropyWithSoftmax = 1.95498165 * 2560; EvalClassificationError = 0.53750000 * 2560; time = 0.2250s; samplesPerSecond = 11378.7
MPI Rank 1: 12/15/2016 08:31:25: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 2.02765603 * 20480; EvalClassificationError = 0.55053711 * 20480; totalSamplesSeen = 40960; learningRatePerSample = 0.001953125; epochTime=1.84776s
MPI Rank 1: 
MPI Rank 1: 12/15/2016 08:31:25: Starting Epoch 3: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 1: minibatchiterator: epoch 2: frames [40960..61440] (first utterance at frame 40960), data subset 1 of 3, with 1 datapasses
MPI Rank 1: 
MPI Rank 1: 12/15/2016 08:31:25: Starting minibatch loop, DataParallelSGD training (myRank = 1, numNodes = 3, numGradientBits = 32), distributed reading is ENABLED.
MPI Rank 1: 12/15/2016 08:31:26:  Epoch[ 3 of 3]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.95358449 * 10240; EvalClassificationError = 0.53603516 * 10240; time = 0.7053s; samplesPerSecond = 14519.2
MPI Rank 1: 12/15/2016 08:31:27:  Epoch[ 3 of 3]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.97540911 * 10240; EvalClassificationError = 0.55253906 * 10240; time = 0.6838s; samplesPerSecond = 14975.4
MPI Rank 1: 12/15/2016 08:31:27: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 1.96449680 * 20480; EvalClassificationError = 0.54428711 * 20480; totalSamplesSeen = 61440; learningRatePerSample = 9.7656251e-005; epochTime=1.39588s
MPI Rank 1: 
MPI Rank 1: 12/15/2016 08:31:27: Action "train" complete.
MPI Rank 1: 
MPI Rank 1: 12/15/2016 08:31:27: __COMPLETED__
MPI Rank 2: 12/15/2016 08:31:17: Redirecting stderr to file C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@release_cpu/stderr_speechTrain.logrank2
MPI Rank 2: CNTK 2.0.beta6.0+ (HEAD 5f1fab, Dec 15 2016 06:29:34) on cntk-muc03 at 2016/12/15 08:31:16
MPI Rank 2: 
MPI Rank 2: C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe  configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\DNN/cntk.cntk  currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data  RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@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  OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@release_cpu  DeviceId=-1  timestamping=true  numCPUThreads=1  stderr=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161215082748.614918\Speech\DNN_ParallelNoQuantization@release_cpu/stderr
MPI Rank 2: 12/15/2016 08:31:17: Using 1 CPU threads.
MPI Rank 2: 
MPI Rank 2: 12/15/2016 08:31:17: ##############################################################################
MPI Rank 2: 12/15/2016 08:31:17: #                                                                            #
MPI Rank 2: 12/15/2016 08:31:17: # speechTrain command (train action)                                         #
MPI Rank 2: 12/15/2016 08:31:17: #                                                                            #
MPI Rank 2: 12/15/2016 08:31:17: ##############################################################################
MPI Rank 2: 
MPI Rank 2: 12/15/2016 08:31:17: 
MPI Rank 2: Creating virgin network.
MPI Rank 2: SimpleNetworkBuilder Using CPU
MPI Rank 2: reading script file glob_0000.scp ... 948 entries
MPI Rank 2: total 132 state names in state list C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/state.list
MPI Rank 2: htkmlfreader: reading MLF file C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/glob_0000.mlf ... total 948 entries
MPI Rank 2: ...............................................................................................feature set 0: 252734 frames in 948 out of 948 utterances
MPI Rank 2: label set 0: 129 classes
MPI Rank 2: minibatchutterancesource: 948 utterances grouped into 3 chunks, av. chunk size: 316.0 utterances, 84244.7 frames
MPI Rank 2: 12/15/2016 08:31:17: 
MPI Rank 2: Model has 25 nodes. Using CPU.
MPI Rank 2: 
MPI Rank 2: 12/15/2016 08:31:17: Training criterion:   CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
MPI Rank 2: 12/15/2016 08:31:17: Evaluation criterion: EvalClassificationError = ClassificationError
MPI Rank 2: 
MPI Rank 2: 
MPI Rank 2: Allocating matrices for forward and/or backward propagation.
MPI Rank 2: 
MPI Rank 2: Memory Sharing: Out of 40 matrices, 19 are shared as 8, and 21 are not shared.
MPI Rank 2: 
MPI Rank 2: 	{ W1 : [512 x 512] (gradient)
MPI Rank 2: 	  W1*H1+B1 : [512 x 1 x *] }
MPI Rank 2: 	{ B0 : [512 x 1] (gradient)
MPI Rank 2: 	  H1 : [512 x 1 x *] (gradient)
MPI Rank 2: 	  W1*H1+B1 : [512 x 1 x *] (gradient)
MPI Rank 2: 	  W2*H1 : [132 x 1 x *] }
MPI Rank 2: 	{ B1 : [512 x 1] (gradient)
MPI Rank 2: 	  H2 : [512 x 1 x *] (gradient)
MPI Rank 2: 	  HLast : [132 x 1 x *] (gradient) }
MPI Rank 2: 	{ HLast : [132 x 1 x *]
MPI Rank 2: 	  W2 : [132 x 512] (gradient) }
MPI Rank 2: 	{ H2 : [512 x 1 x *]
MPI Rank 2: 	  W1*H1 : [512 x 1 x *] (gradient) }
MPI Rank 2: 	{ H1 : [512 x 1 x *]
MPI Rank 2: 	  W0*features : [512 x *] (gradient) }
MPI Rank 2: 	{ W0 : [512 x 363] (gradient)
MPI Rank 2: 	  W0*features+B0 : [512 x 1 x *] }
MPI Rank 2: 	{ W0*features+B0 : [512 x 1 x *] (gradient)
MPI Rank 2: 	  W1*H1 : [512 x 1 x *] }
MPI Rank 2: 
MPI Rank 2: 
MPI Rank 2: 12/15/2016 08:31:17: Training 516740 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:
MPI Rank 2: 
MPI Rank 2: 12/15/2016 08:31:17: 	Node 'B0' (LearnableParameter operation) : [512 x 1]
MPI Rank 2: 12/15/2016 08:31:17: 	Node 'B1' (LearnableParameter operation) : [512 x 1]
MPI Rank 2: 12/15/2016 08:31:17: 	Node 'B2' (LearnableParameter operation) : [132 x 1]
MPI Rank 2: 12/15/2016 08:31:17: 	Node 'W0' (LearnableParameter operation) : [512 x 363]
MPI Rank 2: 12/15/2016 08:31:17: 	Node 'W1' (LearnableParameter operation) : [512 x 512]
MPI Rank 2: 12/15/2016 08:31:17: 	Node 'W2' (LearnableParameter operation) : [132 x 512]
MPI Rank 2: 
MPI Rank 2: Initializing dataParallelSGD with FP32 aggregation.
MPI Rank 2: 
MPI Rank 2: 12/15/2016 08:31:17: Precomputing --> 3 PreCompute nodes found.
MPI Rank 2: 
MPI Rank 2: 12/15/2016 08:31:17: 	MeanOfFeatures = Mean()
MPI Rank 2: 12/15/2016 08:31:17: 	InvStdOfFeatures = InvStdDev()
MPI Rank 2: 12/15/2016 08:31:17: 	Prior = Mean()
MPI Rank 2: minibatchiterator: epoch 0: frames [0..252734] (first utterance at frame 0), data subset 0 of 1, with 1 datapasses
MPI Rank 2: requiredata: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms
MPI Rank 2: 
MPI Rank 2: 12/15/2016 08:31:20: Precomputing --> Completed.
MPI Rank 2: 
MPI Rank 2: 
MPI Rank 2: 12/15/2016 08:31:20: Starting Epoch 1: learning rate per sample = 0.015625  effective momentum = 0.900000  momentum as time constant = 607.4 samples
MPI Rank 2: minibatchiterator: epoch 0: frames [0..20480] (first utterance at frame 0), data subset 2 of 3, with 1 datapasses
MPI Rank 2: 
MPI Rank 2: 12/15/2016 08:31:20: Starting minibatch loop, DataParallelSGD training (myRank = 2, numNodes = 3, numGradientBits = 32), distributed reading is ENABLED.
MPI Rank 2: 12/15/2016 08:31:20:  Epoch[ 1 of 3]-Minibatch[   1-  10, 3.13%]: CrossEntropyWithSoftmax = 4.59755209 * 640; EvalClassificationError = 0.93125000 * 640; time = 0.1259s; samplesPerSecond = 5082.3
MPI Rank 2: 12/15/2016 08:31:20:  Epoch[ 1 of 3]-Minibatch[  11-  20, 6.25%]: CrossEntropyWithSoftmax = 4.34610347 * 640; EvalClassificationError = 0.92031250 * 640; time = 0.1121s; samplesPerSecond = 5707.5
MPI Rank 2: 12/15/2016 08:31:20:  Epoch[ 1 of 3]-Minibatch[  21-  30, 9.38%]: CrossEntropyWithSoftmax = 3.98222493 * 640; EvalClassificationError = 0.89062500 * 640; time = 0.1140s; samplesPerSecond = 5613.8
MPI Rank 2: 12/15/2016 08:31:20:  Epoch[ 1 of 3]-Minibatch[  31-  40, 12.50%]: CrossEntropyWithSoftmax = 3.74152761 * 640; EvalClassificationError = 0.84531250 * 640; time = 0.1133s; samplesPerSecond = 5647.3
MPI Rank 2: 12/15/2016 08:31:20:  Epoch[ 1 of 3]-Minibatch[  41-  50, 15.63%]: CrossEntropyWithSoftmax = 3.83818495 * 640; EvalClassificationError = 0.86718750 * 640; time = 0.1115s; samplesPerSecond = 5739.4
MPI Rank 2: 12/15/2016 08:31:20:  Epoch[ 1 of 3]-Minibatch[  51-  60, 18.75%]: CrossEntropyWithSoftmax = 3.71641133 * 640; EvalClassificationError = 0.87500000 * 640; time = 0.1124s; samplesPerSecond = 5694.7
MPI Rank 2: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[  61-  70, 21.88%]: CrossEntropyWithSoftmax = 3.41802654 * 640; EvalClassificationError = 0.79687500 * 640; time = 0.1131s; samplesPerSecond = 5659.7
MPI Rank 2: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[  71-  80, 25.00%]: CrossEntropyWithSoftmax = 3.53832811 * 640; EvalClassificationError = 0.82812500 * 640; time = 0.1119s; samplesPerSecond = 5718.0
MPI Rank 2: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[  81-  90, 28.13%]: CrossEntropyWithSoftmax = 3.50627956 * 640; EvalClassificationError = 0.81718750 * 640; time = 0.1142s; samplesPerSecond = 5601.8
MPI Rank 2: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[  91- 100, 31.25%]: CrossEntropyWithSoftmax = 3.41478094 * 640; EvalClassificationError = 0.80781250 * 640; time = 0.1109s; samplesPerSecond = 5770.7
MPI Rank 2: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[ 101- 110, 34.38%]: CrossEntropyWithSoftmax = 3.51031055 * 640; EvalClassificationError = 0.82812500 * 640; time = 0.1135s; samplesPerSecond = 5641.1
MPI Rank 2: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[ 111- 120, 37.50%]: CrossEntropyWithSoftmax = 3.28365293 * 640; EvalClassificationError = 0.79375000 * 640; time = 0.1114s; samplesPerSecond = 5744.4
MPI Rank 2: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[ 121- 130, 40.63%]: CrossEntropyWithSoftmax = 3.20931888 * 640; EvalClassificationError = 0.79531250 * 640; time = 0.1113s; samplesPerSecond = 5748.1
MPI Rank 2: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[ 131- 140, 43.75%]: CrossEntropyWithSoftmax = 3.07460312 * 640; EvalClassificationError = 0.75468750 * 640; time = 0.1082s; samplesPerSecond = 5914.6
MPI Rank 2: 12/15/2016 08:31:21:  Epoch[ 1 of 3]-Minibatch[ 141- 150, 46.88%]: CrossEntropyWithSoftmax = 2.97528860 * 640; EvalClassificationError = 0.72031250 * 640; time = 0.1105s; samplesPerSecond = 5792.4
MPI Rank 2: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 151- 160, 50.00%]: CrossEntropyWithSoftmax = 3.11968648 * 640; EvalClassificationError = 0.74531250 * 640; time = 0.1123s; samplesPerSecond = 5699.5
MPI Rank 2: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 161- 170, 53.13%]: CrossEntropyWithSoftmax = 2.84171867 * 640; EvalClassificationError = 0.71093750 * 640; time = 0.1130s; samplesPerSecond = 5665.8
MPI Rank 2: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 171- 180, 56.25%]: CrossEntropyWithSoftmax = 2.74031476 * 640; EvalClassificationError = 0.66093750 * 640; time = 0.1094s; samplesPerSecond = 5850.0
MPI Rank 2: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 181- 190, 59.38%]: CrossEntropyWithSoftmax = 2.83857843 * 640; EvalClassificationError = 0.72656250 * 640; time = 0.1108s; samplesPerSecond = 5775.1
MPI Rank 2: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 191- 200, 62.50%]: CrossEntropyWithSoftmax = 2.74632032 * 640; EvalClassificationError = 0.69218750 * 640; time = 0.1097s; samplesPerSecond = 5834.4
MPI Rank 2: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 201- 210, 65.63%]: CrossEntropyWithSoftmax = 2.61032974 * 640; EvalClassificationError = 0.66250000 * 640; time = 0.1098s; samplesPerSecond = 5831.3
MPI Rank 2: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 211- 220, 68.75%]: CrossEntropyWithSoftmax = 2.61330475 * 640; EvalClassificationError = 0.65000000 * 640; time = 0.1110s; samplesPerSecond = 5763.8
MPI Rank 2: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 221- 230, 71.88%]: CrossEntropyWithSoftmax = 2.54591535 * 640; EvalClassificationError = 0.66406250 * 640; time = 0.1096s; samplesPerSecond = 5841.9
MPI Rank 2: 12/15/2016 08:31:22:  Epoch[ 1 of 3]-Minibatch[ 231- 240, 75.00%]: CrossEntropyWithSoftmax = 2.57566229 * 640; EvalClassificationError = 0.66093750 * 640; time = 0.1103s; samplesPerSecond = 5803.0
MPI Rank 2: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 241- 250, 78.13%]: CrossEntropyWithSoftmax = 2.49164700 * 640; EvalClassificationError = 0.63281250 * 640; time = 0.1096s; samplesPerSecond = 5837.8
MPI Rank 2: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 251- 260, 81.25%]: CrossEntropyWithSoftmax = 2.39954552 * 640; EvalClassificationError = 0.62812500 * 640; time = 0.1141s; samplesPerSecond = 5609.5
MPI Rank 2: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 261- 270, 84.38%]: CrossEntropyWithSoftmax = 2.27033979 * 640; EvalClassificationError = 0.59375000 * 640; time = 0.1105s; samplesPerSecond = 5793.8
MPI Rank 2: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 271- 280, 87.50%]: CrossEntropyWithSoftmax = 2.52112142 * 640; EvalClassificationError = 0.66093750 * 640; time = 0.1102s; samplesPerSecond = 5810.0
MPI Rank 2: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 281- 290, 90.63%]: CrossEntropyWithSoftmax = 2.27800742 * 640; EvalClassificationError = 0.59062500 * 640; time = 0.1101s; samplesPerSecond = 5812.0
MPI Rank 2: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 291- 300, 93.75%]: CrossEntropyWithSoftmax = 2.26783400 * 640; EvalClassificationError = 0.61093750 * 640; time = 0.1086s; samplesPerSecond = 5892.2
MPI Rank 2: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 301- 310, 96.88%]: CrossEntropyWithSoftmax = 2.24590123 * 640; EvalClassificationError = 0.58593750 * 640; time = 0.1105s; samplesPerSecond = 5793.1
MPI Rank 2: 12/15/2016 08:31:23:  Epoch[ 1 of 3]-Minibatch[ 311- 320, 100.00%]: CrossEntropyWithSoftmax = 2.24415391 * 640; EvalClassificationError = 0.59843750 * 640; time = 0.1091s; samplesPerSecond = 5863.6
MPI Rank 2: 12/15/2016 08:31:23: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 3.04696796 * 20480; EvalClassificationError = 0.73583984 * 20480; totalSamplesSeen = 20480; learningRatePerSample = 0.015625; epochTime=3.59201s
MPI Rank 2: 
MPI Rank 2: 12/15/2016 08:31:23: Starting Epoch 2: learning rate per sample = 0.001953  effective momentum = 0.656119  momentum as time constant = 607.5 samples
MPI Rank 2: minibatchiterator: epoch 1: frames [20480..40960] (first utterance at frame 20480), data subset 2 of 3, with 1 datapasses
MPI Rank 2: 
MPI Rank 2: 12/15/2016 08:31:23: Starting minibatch loop, DataParallelSGD training (myRank = 2, numNodes = 3, numGradientBits = 32), distributed reading is ENABLED.
MPI Rank 2: 12/15/2016 08:31:24:  Epoch[ 2 of 3]-Minibatch[   1-  10, 12.50%]: CrossEntropyWithSoftmax = 2.14624175 * 2560; EvalClassificationError = 0.56953125 * 2560; time = 0.2319s; samplesPerSecond = 11037.6
MPI Rank 2: 12/15/2016 08:31:24:  Epoch[ 2 of 3]-Minibatch[  11-  20, 25.00%]: CrossEntropyWithSoftmax = 2.06174128 * 2560; EvalClassificationError = 0.55742187 * 2560; time = 0.2326s; samplesPerSecond = 11006.3
MPI Rank 2: 12/15/2016 08:31:24:  Epoch[ 2 of 3]-Minibatch[  21-  30, 37.50%]: CrossEntropyWithSoftmax = 2.04994338 * 2560; EvalClassificationError = 0.55351562 * 2560; time = 0.2292s; samplesPerSecond = 11167.0
MPI Rank 2: 12/15/2016 08:31:24:  Epoch[ 2 of 3]-Minibatch[  31-  40, 50.00%]: CrossEntropyWithSoftmax = 2.03695538 * 2560; EvalClassificationError = 0.56132812 * 2560; time = 0.2298s; samplesPerSecond = 11140.3
MPI Rank 2: 12/15/2016 08:31:25:  Epoch[ 2 of 3]-Minibatch[  41-  50, 62.50%]: CrossEntropyWithSoftmax = 2.03086227 * 2560; EvalClassificationError = 0.55664063 * 2560; time = 0.2347s; samplesPerSecond = 10908.7
MPI Rank 2: 12/15/2016 08:31:25:  Epoch[ 2 of 3]-Minibatch[  51-  60, 75.00%]: CrossEntropyWithSoftmax = 1.97306193 * 2560; EvalClassificationError = 0.53671875 * 2560; time = 0.2221s; samplesPerSecond = 11524.1
MPI Rank 2: 12/15/2016 08:31:25:  Epoch[ 2 of 3]-Minibatch[  61-  70, 87.50%]: CrossEntropyWithSoftmax = 1.96746064 * 2560; EvalClassificationError = 0.53164062 * 2560; time = 0.2240s; samplesPerSecond = 11428.0
MPI Rank 2: 12/15/2016 08:31:25:  Epoch[ 2 of 3]-Minibatch[  71-  80, 100.00%]: CrossEntropyWithSoftmax = 1.95498165 * 2560; EvalClassificationError = 0.53750000 * 2560; time = 0.2250s; samplesPerSecond = 11377.8
MPI Rank 2: 12/15/2016 08:31:25: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 2.02765603 * 20480; EvalClassificationError = 0.55053711 * 20480; totalSamplesSeen = 40960; learningRatePerSample = 0.001953125; epochTime=1.83772s
MPI Rank 2: 
MPI Rank 2: 12/15/2016 08:31:25: Starting Epoch 3: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 2: minibatchiterator: epoch 2: frames [40960..61440] (first utterance at frame 40960), data subset 2 of 3, with 1 datapasses
MPI Rank 2: 
MPI Rank 2: 12/15/2016 08:31:25: Starting minibatch loop, DataParallelSGD training (myRank = 2, numNodes = 3, numGradientBits = 32), distributed reading is ENABLED.
MPI Rank 2: 12/15/2016 08:31:26:  Epoch[ 3 of 3]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.95358449 * 10240; EvalClassificationError = 0.53603516 * 10240; time = 0.6863s; samplesPerSecond = 14920.6
MPI Rank 2: 12/15/2016 08:31:27:  Epoch[ 3 of 3]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.97540911 * 10240; EvalClassificationError = 0.55253906 * 10240; time = 0.6837s; samplesPerSecond = 14977.5
MPI Rank 2: 12/15/2016 08:31:27: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 1.96449680 * 20480; EvalClassificationError = 0.54428711 * 20480; totalSamplesSeen = 61440; learningRatePerSample = 9.7656251e-005; epochTime=1.37654s
MPI Rank 2: 
MPI Rank 2: 12/15/2016 08:31:27: Action "train" complete.
MPI Rank 2: 
MPI Rank 2: 12/15/2016 08:31:27: __COMPLETED__
