CPU info:
    CPU Model Name: Intel(R) Xeon(R) CPU E5-2690 v3 @ 2.60GHz
    Hardware threads: 6
    Total Memory: 58719796 kB
-------------------------------------------------------------------
=== Running c:\local\msmpi-7.0.12437.6\Bin/mpiexec.exe -n 2 C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\DNN\SaveBestModelPerCriterion/cntkcv.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data RunDir=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@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\SaveBestModelPerCriterion OutputDir=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu DeviceId=-1 timestamping=true numCPUThreads=3 shareNodeValueMatrices=true saveBestModelPerCriterion=true stderr=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/stderr
CNTK 2.3.1+ (HEAD db192c, Jan 10 2018 22:59:43) at 2018/01/11 08:55:13

C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe  configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\DNN\SaveBestModelPerCriterion/cntkcv.cntk  currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data  RunDir=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@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\SaveBestModelPerCriterion  OutputDir=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu  DeviceId=-1  timestamping=true  numCPUThreads=3  shareNodeValueMatrices=true  saveBestModelPerCriterion=true  stderr=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/stderr
Changed current directory to C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data
ping [requestnodes (before change)]: 2 nodes pinging each other
CNTK 2.3.1+ (HEAD db192c, Jan 10 2018 22:59:43) at 2018/01/11 08:55:13

C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe  configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\DNN\SaveBestModelPerCriterion/cntkcv.cntk  currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data  RunDir=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@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\SaveBestModelPerCriterion  OutputDir=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu  DeviceId=-1  timestamping=true  numCPUThreads=3  shareNodeValueMatrices=true  saveBestModelPerCriterion=true  stderr=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/stderr
Changed current directory to C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data
ping [requestnodes (before change)]: 2 nodes pinging each other
ping [requestnodes (after change)]: 2 nodes pinging each other
ping [requestnodes (after change)]: 2 nodes pinging each other
requestnodes [MPIWrapperMpi]: using 2 out of 2 MPI nodes on a single host (2 requested); we (1) are in (participating)
requestnodes [MPIWrapperMpi]: using 2 out of 2 MPI nodes on a single host (2 requested); we (0) are in (participating)
ping [mpihelper]: 2 nodes pinging each other
ping [mpihelper]: 2 nodes pinging each other
MPI Rank 0: 01/11/2018 08:55:13: Redirecting stderr to file C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/stderr_speechTrain.logrank0
MPI Rank 0: CNTK 2.3.1+ (HEAD db192c, Jan 10 2018 22:59:43) at 2018/01/11 08:55:13
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\SaveBestModelPerCriterion/cntkcv.cntk  currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data  RunDir=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@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\SaveBestModelPerCriterion  OutputDir=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu  DeviceId=-1  timestamping=true  numCPUThreads=3  shareNodeValueMatrices=true  saveBestModelPerCriterion=true  stderr=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/stderr
MPI Rank 0: -------------------------------------------------------------------
MPI Rank 0: Build info: 
MPI Rank 0: 
MPI Rank 0: 		Built time: Jan 10 2018 22:47:38
MPI Rank 0: 		Last modified date: Wed Jan 10 22:18:32 2018
MPI Rank 0: 		Build type: Release
MPI Rank 0: 		Build target: GPU
MPI Rank 0: 		With ASGD: yes
MPI Rank 0: 		Math lib: mkl
MPI Rank 0: 		CUDA version: 9.0.0
MPI Rank 0: 		CUDNN version: 7.0.5
MPI Rank 0: 		Build Branch: HEAD
MPI Rank 0: 		Build SHA1: db192cd3cb9ac688cae719c41e5930a4e3f628ea
MPI Rank 0: 		MPI distribution: Microsoft MPI
MPI Rank 0: 		MPI version: 7.0.12437.6
MPI Rank 0: -------------------------------------------------------------------
MPI Rank 0: -------------------------------------------------------------------
MPI Rank 0: GPU info:
MPI Rank 0: 
MPI Rank 0: 		Device[0]: cores = 3072; computeCapability = 5.2; type = "Tesla M60"; total memory = 8124 MB; free memory = 8001 MB
MPI Rank 0: -------------------------------------------------------------------
MPI Rank 0: 01/11/2018 08:55:13: Using 3 CPU threads.
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:13: ##############################################################################
MPI Rank 0: 01/11/2018 08:55:13: #                                                                            #
MPI Rank 0: 01/11/2018 08:55:13: # speechTrain command (train action)                                         #
MPI Rank 0: 01/11/2018 08:55:13: #                                                                            #
MPI Rank 0: 01/11/2018 08:55:13: ##############################################################################
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:13: 
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: HTKDeserializer: selected '948' utterances grouped into '3' chunks, average chunk size: 316.0 utterances, 84244.7 frames (for I/O: 316.0 utterances, 84244.7 frames)
MPI Rank 0: HTKDeserializer: determined feature kind as '33'-dimensional 'USER' with frame shift 10.0 ms
MPI Rank 0: Total (133) state names in state list 'C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/state.list'
MPI Rank 0: MLFDeserializer: '948' utterances with '252734' frames
MPI Rank 0: Reading script file glob_0000.cv.scp ... 300 entries
MPI Rank 0: HTKDeserializer: selected '300' utterances grouped into '1' chunks, average chunk size: 300.0 utterances, 83050.0 frames (for I/O: 300.0 utterances, 83050.0 frames)
MPI Rank 0: HTKDeserializer: determined feature kind as '33'-dimensional 'USER' with frame shift 10.0 ms
MPI Rank 0: Total (133) state names in state list 'C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/state.list'
MPI Rank 0: MLFDeserializer: '948' utterances with '252734' frames
MPI Rank 0: 01/11/2018 08:55:13: 
MPI Rank 0: Model has 25 nodes. Using CPU.
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:13: Training criterion:   CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
MPI Rank 0: 01/11/2018 08:55:13: 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: Gradient Memory Aliasing: 4 are aliased.
MPI Rank 0: 	W1*H1 (gradient) reuses W1*H1+B1 (gradient)
MPI Rank 0: 	W2*H1 (gradient) reuses HLast (gradient)
MPI Rank 0: 
MPI Rank 0: Memory Sharing: Out of 40 matrices, 20 are shared as 5, and 20 are not shared.
MPI Rank 0: 
MPI Rank 0: Here are the ones that share memory:
MPI Rank 0: 	{ PosteriorProb : [132 x 1 x *]
MPI Rank 0: 	  ScaledLogLikelihood : [132 x 1 x *] }
MPI Rank 0: 	{ H1 : [512 x 1 x *] (gradient)
MPI Rank 0: 	  H2 : [512 x 1 x *] (gradient)
MPI Rank 0: 	  HLast : [132 x 1 x *]
MPI Rank 0: 	  W0*features : [512 x *] (gradient)
MPI Rank 0: 	  W1*H1+B1 : [512 x 1 x *] }
MPI Rank 0: 	{ H1 : [512 x 1 x *]
MPI Rank 0: 	  W0 : [512 x 363] (gradient)
MPI Rank 0: 	  W0*features : [512 x *] }
MPI Rank 0: 	{ HLast : [132 x 1 x *] (gradient)
MPI Rank 0: 	  W0*features+B0 : [512 x 1 x *] (gradient)
MPI Rank 0: 	  W1*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: 	  W2*H1 : [132 x 1 x *] (gradient) }
MPI Rank 0: 	{ H2 : [512 x 1 x *]
MPI Rank 0: 	  W0*features+B0 : [512 x 1 x *]
MPI Rank 0: 	  W1 : [512 x 512] (gradient)
MPI Rank 0: 	  W1*H1 : [512 x 1 x *] }
MPI Rank 0: 
MPI Rank 0: Here are the ones that don't share memory:
MPI Rank 0: 	{MeanOfFeatures : [363]}
MPI Rank 0: 	{InvStdOfFeatures : [363]}
MPI Rank 0: 	{features : [363 x *]}
MPI Rank 0: 	{W2 : [132 x 512] (gradient)}
MPI Rank 0: 	{W0 : [512 x 363]}
MPI Rank 0: 	{W2 : [132 x 512]}
MPI Rank 0: 	{LogOfPrior : [132]}
MPI Rank 0: 	{B0 : [512 x 1]}
MPI Rank 0: 	{B2 : [132 x 1] (gradient)}
MPI Rank 0: 	{B1 : [512 x 1] (gradient)}
MPI Rank 0: 	{B1 : [512 x 1]}
MPI Rank 0: 	{labels : [132 x *]}
MPI Rank 0: 	{Prior : [132]}
MPI Rank 0: 	{B0 : [512 x 1] (gradient)}
MPI Rank 0: 	{EvalClassificationError : [1]}
MPI Rank 0: 	{CrossEntropyWithSoftmax : [1]}
MPI Rank 0: 	{CrossEntropyWithSoftmax : [1] (gradient)}
MPI Rank 0: 	{MVNormalizedFeatures : [363 x *]}
MPI Rank 0: 	{W1 : [512 x 512]}
MPI Rank 0: 	{B2 : [132 x 1]}
MPI Rank 0: 
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:13: Training 516740 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:13: 	Node 'B0' (LearnableParameter operation) : [512 x 1]
MPI Rank 0: 01/11/2018 08:55:13: 	Node 'B1' (LearnableParameter operation) : [512 x 1]
MPI Rank 0: 01/11/2018 08:55:13: 	Node 'B2' (LearnableParameter operation) : [132 x 1]
MPI Rank 0: 01/11/2018 08:55:13: 	Node 'W0' (LearnableParameter operation) : [512 x 363]
MPI Rank 0: 01/11/2018 08:55:13: 	Node 'W1' (LearnableParameter operation) : [512 x 512]
MPI Rank 0: 01/11/2018 08:55:13: 	Node 'W2' (LearnableParameter operation) : [132 x 512]
MPI Rank 0: 
MPI Rank 0: Initializing dataParallelSGD with FP64 aggregation.
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:13: Precomputing --> 3 PreCompute nodes found.
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:13: 	MeanOfFeatures = Mean()
MPI Rank 0: 01/11/2018 08:55:13: 	InvStdOfFeatures = InvStdDev()
MPI Rank 0: 01/11/2018 08:55:13: 	Prior = Mean()
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:15: Precomputing --> Completed.
MPI Rank 0: 
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:16: Starting Epoch 1: learning rate per sample = 0.015625  effective momentum = 0.900000  momentum as time constant = 607.4 samples
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:16: Starting minibatch loop, DataParallelSGD training (myRank = 0, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 0: 01/11/2018 08:55:16:  Epoch[ 1 of 15]-Minibatch[   1-  10, 3.13%]: CrossEntropyWithSoftmax = 4.59755198 * 640; EvalClassificationError = 0.93125000 * 640; time = 0.2791s; samplesPerSecond = 2293.0
MPI Rank 0: 01/11/2018 08:55:17:  Epoch[ 1 of 15]-Minibatch[  11-  20, 6.25%]: CrossEntropyWithSoftmax = 4.34610349 * 640; EvalClassificationError = 0.92031250 * 640; time = 0.3111s; samplesPerSecond = 2057.2
MPI Rank 0: 01/11/2018 08:55:17:  Epoch[ 1 of 15]-Minibatch[  21-  30, 9.38%]: CrossEntropyWithSoftmax = 3.98222516 * 640; EvalClassificationError = 0.89062500 * 640; time = 0.3266s; samplesPerSecond = 1959.8
MPI Rank 0: 01/11/2018 08:55:17:  Epoch[ 1 of 15]-Minibatch[  31-  40, 12.50%]: CrossEntropyWithSoftmax = 3.74152814 * 640; EvalClassificationError = 0.84531250 * 640; time = 0.2985s; samplesPerSecond = 2144.0
MPI Rank 0: 01/11/2018 08:55:18:  Epoch[ 1 of 15]-Minibatch[  41-  50, 15.63%]: CrossEntropyWithSoftmax = 3.83818572 * 640; EvalClassificationError = 0.86718750 * 640; time = 0.2654s; samplesPerSecond = 2411.5
MPI Rank 0: 01/11/2018 08:55:18:  Epoch[ 1 of 15]-Minibatch[  51-  60, 18.75%]: CrossEntropyWithSoftmax = 3.71641238 * 640; EvalClassificationError = 0.87500000 * 640; time = 0.2891s; samplesPerSecond = 2214.1
MPI Rank 0: 01/11/2018 08:55:18:  Epoch[ 1 of 15]-Minibatch[  61-  70, 21.88%]: CrossEntropyWithSoftmax = 3.41802791 * 640; EvalClassificationError = 0.79687500 * 640; time = 0.3182s; samplesPerSecond = 2011.5
MPI Rank 0: 01/11/2018 08:55:18:  Epoch[ 1 of 15]-Minibatch[  71-  80, 25.00%]: CrossEntropyWithSoftmax = 3.53832947 * 640; EvalClassificationError = 0.82812500 * 640; time = 0.3138s; samplesPerSecond = 2039.6
MPI Rank 0: 01/11/2018 08:55:19:  Epoch[ 1 of 15]-Minibatch[  81-  90, 28.13%]: CrossEntropyWithSoftmax = 3.50628076 * 640; EvalClassificationError = 0.81718750 * 640; time = 0.2879s; samplesPerSecond = 2223.1
MPI Rank 0: 01/11/2018 08:55:19:  Epoch[ 1 of 15]-Minibatch[  91- 100, 31.25%]: CrossEntropyWithSoftmax = 3.41478252 * 640; EvalClassificationError = 0.80781250 * 640; time = 0.2735s; samplesPerSecond = 2340.4
MPI Rank 0: 01/11/2018 08:55:19:  Epoch[ 1 of 15]-Minibatch[ 101- 110, 34.38%]: CrossEntropyWithSoftmax = 3.51031210 * 640; EvalClassificationError = 0.82812500 * 640; time = 0.3017s; samplesPerSecond = 2121.0
MPI Rank 0: 01/11/2018 08:55:20:  Epoch[ 1 of 15]-Minibatch[ 111- 120, 37.50%]: CrossEntropyWithSoftmax = 3.28365485 * 640; EvalClassificationError = 0.79375000 * 640; time = 0.3307s; samplesPerSecond = 1935.3
MPI Rank 0: 01/11/2018 08:55:20:  Epoch[ 1 of 15]-Minibatch[ 121- 130, 40.63%]: CrossEntropyWithSoftmax = 3.20932117 * 640; EvalClassificationError = 0.79531250 * 640; time = 0.3075s; samplesPerSecond = 2081.2
MPI Rank 0: 01/11/2018 08:55:20:  Epoch[ 1 of 15]-Minibatch[ 131- 140, 43.75%]: CrossEntropyWithSoftmax = 3.07460535 * 640; EvalClassificationError = 0.75468750 * 640; time = 0.2778s; samplesPerSecond = 2304.1
MPI Rank 0: 01/11/2018 08:55:21:  Epoch[ 1 of 15]-Minibatch[ 141- 150, 46.88%]: CrossEntropyWithSoftmax = 2.97529104 * 640; EvalClassificationError = 0.72031250 * 640; time = 0.2799s; samplesPerSecond = 2286.4
MPI Rank 0: 01/11/2018 08:55:21:  Epoch[ 1 of 15]-Minibatch[ 151- 160, 50.00%]: CrossEntropyWithSoftmax = 3.11968883 * 640; EvalClassificationError = 0.74531250 * 640; time = 0.3128s; samplesPerSecond = 2046.2
MPI Rank 0: 01/11/2018 08:55:21:  Epoch[ 1 of 15]-Minibatch[ 161- 170, 53.13%]: CrossEntropyWithSoftmax = 2.84172140 * 640; EvalClassificationError = 0.71093750 * 640; time = 0.3279s; samplesPerSecond = 1951.6
MPI Rank 0: 01/11/2018 08:55:21:  Epoch[ 1 of 15]-Minibatch[ 171- 180, 56.25%]: CrossEntropyWithSoftmax = 2.74031745 * 640; EvalClassificationError = 0.66093750 * 640; time = 0.2970s; samplesPerSecond = 2154.6
MPI Rank 0: 01/11/2018 08:55:22:  Epoch[ 1 of 15]-Minibatch[ 181- 190, 59.38%]: CrossEntropyWithSoftmax = 2.83858085 * 640; EvalClassificationError = 0.72656250 * 640; time = 0.3039s; samplesPerSecond = 2106.3
MPI Rank 0: 01/11/2018 08:55:22:  Epoch[ 1 of 15]-Minibatch[ 191- 200, 62.50%]: CrossEntropyWithSoftmax = 2.74632253 * 640; EvalClassificationError = 0.69218750 * 640; time = 0.2963s; samplesPerSecond = 2160.0
MPI Rank 0: 01/11/2018 08:55:22:  Epoch[ 1 of 15]-Minibatch[ 201- 210, 65.63%]: CrossEntropyWithSoftmax = 2.61033254 * 640; EvalClassificationError = 0.66250000 * 640; time = 0.3165s; samplesPerSecond = 2021.8
MPI Rank 0: 01/11/2018 08:55:23:  Epoch[ 1 of 15]-Minibatch[ 211- 220, 68.75%]: CrossEntropyWithSoftmax = 2.61330754 * 640; EvalClassificationError = 0.65000000 * 640; time = 0.3128s; samplesPerSecond = 2046.0
MPI Rank 0: 01/11/2018 08:55:23:  Epoch[ 1 of 15]-Minibatch[ 221- 230, 71.88%]: CrossEntropyWithSoftmax = 2.54591810 * 640; EvalClassificationError = 0.66406250 * 640; time = 0.2879s; samplesPerSecond = 2223.4
MPI Rank 0: 01/11/2018 08:55:23:  Epoch[ 1 of 15]-Minibatch[ 231- 240, 75.00%]: CrossEntropyWithSoftmax = 2.57566512 * 640; EvalClassificationError = 0.66093750 * 640; time = 0.2712s; samplesPerSecond = 2360.2
MPI Rank 0: 01/11/2018 08:55:24:  Epoch[ 1 of 15]-Minibatch[ 241- 250, 78.13%]: CrossEntropyWithSoftmax = 2.49164945 * 640; EvalClassificationError = 0.63281250 * 640; time = 0.2998s; samplesPerSecond = 2134.7
MPI Rank 0: 01/11/2018 08:55:24:  Epoch[ 1 of 15]-Minibatch[ 251- 260, 81.25%]: CrossEntropyWithSoftmax = 2.39954797 * 640; EvalClassificationError = 0.62812500 * 640; time = 0.3377s; samplesPerSecond = 1895.2
MPI Rank 0: 01/11/2018 08:55:24:  Epoch[ 1 of 15]-Minibatch[ 261- 270, 84.38%]: CrossEntropyWithSoftmax = 2.27034227 * 640; EvalClassificationError = 0.59375000 * 640; time = 0.2996s; samplesPerSecond = 2136.2
MPI Rank 0: 01/11/2018 08:55:24:  Epoch[ 1 of 15]-Minibatch[ 271- 280, 87.50%]: CrossEntropyWithSoftmax = 2.52112387 * 640; EvalClassificationError = 0.66093750 * 640; time = 0.2752s; samplesPerSecond = 2325.8
MPI Rank 0: 01/11/2018 08:55:25:  Epoch[ 1 of 15]-Minibatch[ 281- 290, 90.63%]: CrossEntropyWithSoftmax = 2.27800991 * 640; EvalClassificationError = 0.59062500 * 640; time = 0.2837s; samplesPerSecond = 2255.9
MPI Rank 0: 01/11/2018 08:55:25:  Epoch[ 1 of 15]-Minibatch[ 291- 300, 93.75%]: CrossEntropyWithSoftmax = 2.26783634 * 640; EvalClassificationError = 0.61093750 * 640; time = 0.3078s; samplesPerSecond = 2079.5
MPI Rank 0: 01/11/2018 08:55:25:  Epoch[ 1 of 15]-Minibatch[ 301- 310, 96.88%]: CrossEntropyWithSoftmax = 2.24590355 * 640; EvalClassificationError = 0.58593750 * 640; time = 0.3253s; samplesPerSecond = 1967.4
MPI Rank 0: 01/11/2018 08:55:26:  Epoch[ 1 of 15]-Minibatch[ 311- 320, 100.00%]: CrossEntropyWithSoftmax = 2.24415615 * 640; EvalClassificationError = 0.59843750 * 640; time = 0.2998s; samplesPerSecond = 2134.4
MPI Rank 0: 01/11/2018 08:55:26: Finished Epoch[ 1 of 15]: [Training] CrossEntropyWithSoftmax = 3.04696987 * 20480; EvalClassificationError = 0.73583984 * 20480; totalSamplesSeen = 20480; learningRatePerSample = 0.015625; epochTime=9.65457s
MPI Rank 0: 01/11/2018 08:55:29: Final Results: Minibatch[1-1299]: CrossEntropyWithSoftmax = 2.24737799 * 83050; perplexity = 9.46289145; EvalClassificationError = 0.61431668 * 83050
MPI Rank 0: 01/11/2018 08:55:29: Finished Epoch[ 1 of 15]: [Validate] CrossEntropyWithSoftmax = 2.24737799 * 83050; EvalClassificationError = 0.61431668 * 83050
MPI Rank 0: 01/11/2018 08:55:29: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 2.247378 (Epoch 1); EvalClassificationError = 0.614317 (Epoch 1)
MPI Rank 0: 01/11/2018 08:55:29: SGD: Saving checkpoint model 'C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn.1'
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:29: Starting Epoch 2: learning rate per sample = 0.001953  effective momentum = 0.656119  momentum as time constant = 607.5 samples
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:29: Starting minibatch loop, DataParallelSGD training (myRank = 0, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 0: 01/11/2018 08:55:30:  Epoch[ 2 of 15]-Minibatch[   1-  10, 12.50%]: CrossEntropyWithSoftmax = 2.14624416 * 2560; EvalClassificationError = 0.56953125 * 2560; time = 0.4082s; samplesPerSecond = 6271.6
MPI Rank 0: 01/11/2018 08:55:30:  Epoch[ 2 of 15]-Minibatch[  11-  20, 25.00%]: CrossEntropyWithSoftmax = 2.06174352 * 2560; EvalClassificationError = 0.55742187 * 2560; time = 0.3816s; samplesPerSecond = 6708.1
MPI Rank 0: 01/11/2018 08:55:31:  Epoch[ 2 of 15]-Minibatch[  21-  30, 37.50%]: CrossEntropyWithSoftmax = 2.04994567 * 2560; EvalClassificationError = 0.55351562 * 2560; time = 0.4067s; samplesPerSecond = 6294.1
MPI Rank 0: 01/11/2018 08:55:31:  Epoch[ 2 of 15]-Minibatch[  31-  40, 50.00%]: CrossEntropyWithSoftmax = 2.03695762 * 2560; EvalClassificationError = 0.56132812 * 2560; time = 0.4494s; samplesPerSecond = 5697.0
MPI Rank 0: 01/11/2018 08:55:31:  Epoch[ 2 of 15]-Minibatch[  41-  50, 62.50%]: CrossEntropyWithSoftmax = 2.03086449 * 2560; EvalClassificationError = 0.55664063 * 2560; time = 0.4317s; samplesPerSecond = 5930.4
MPI Rank 0: 01/11/2018 08:55:32:  Epoch[ 2 of 15]-Minibatch[  51-  60, 75.00%]: CrossEntropyWithSoftmax = 1.97306418 * 2560; EvalClassificationError = 0.53671875 * 2560; time = 0.3929s; samplesPerSecond = 6515.6
MPI Rank 0: 01/11/2018 08:55:32:  Epoch[ 2 of 15]-Minibatch[  61-  70, 87.50%]: CrossEntropyWithSoftmax = 1.96746291 * 2560; EvalClassificationError = 0.53164062 * 2560; time = 0.3911s; samplesPerSecond = 6545.9
MPI Rank 0: 01/11/2018 08:55:33:  Epoch[ 2 of 15]-Minibatch[  71-  80, 100.00%]: CrossEntropyWithSoftmax = 1.95498387 * 2560; EvalClassificationError = 0.53750000 * 2560; time = 0.4787s; samplesPerSecond = 5348.4
MPI Rank 0: 01/11/2018 08:55:33: Finished Epoch[ 2 of 15]: [Training] CrossEntropyWithSoftmax = 2.02765830 * 20480; EvalClassificationError = 0.55053711 * 20480; totalSamplesSeen = 40960; learningRatePerSample = 0.001953125; epochTime=3.36904s
MPI Rank 0: 01/11/2018 08:55:35: Final Results: Minibatch[1-326]: CrossEntropyWithSoftmax = 1.93559232 * 83050; perplexity = 6.92814655; EvalClassificationError = 0.53506321 * 83050
MPI Rank 0: 01/11/2018 08:55:35: Finished Epoch[ 2 of 15]: [Validate] CrossEntropyWithSoftmax = 1.93559232 * 83050; EvalClassificationError = 0.53506321 * 83050
MPI Rank 0: 01/11/2018 08:55:35: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.935592 (Epoch 2); EvalClassificationError = 0.535063 (Epoch 2)
MPI Rank 0: 01/11/2018 08:55:35: SGD: Saving checkpoint model 'C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn.2'
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:35: Starting Epoch 3: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:35: Starting minibatch loop, DataParallelSGD training (myRank = 0, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 0: 01/11/2018 08:55:36:  Epoch[ 3 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.95358670 * 10240; EvalClassificationError = 0.53603516 * 10240; time = 0.6611s; samplesPerSecond = 15490.4
MPI Rank 0: 01/11/2018 08:55:36:  Epoch[ 3 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.97541130 * 10240; EvalClassificationError = 0.55253906 * 10240; time = 0.7178s; samplesPerSecond = 14266.1
MPI Rank 0: 01/11/2018 08:55:36: Finished Epoch[ 3 of 15]: [Training] CrossEntropyWithSoftmax = 1.96449900 * 20480; EvalClassificationError = 0.54428711 * 20480; totalSamplesSeen = 61440; learningRatePerSample = 9.7656251e-05; epochTime=1.38487s
MPI Rank 0: 01/11/2018 08:55:39: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.91503561 * 83050; perplexity = 6.78718045; EvalClassificationError = 0.52745334 * 83050
MPI Rank 0: 01/11/2018 08:55:39: Finished Epoch[ 3 of 15]: [Validate] CrossEntropyWithSoftmax = 1.91503561 * 83050; EvalClassificationError = 0.52745334 * 83050
MPI Rank 0: 01/11/2018 08:55:39: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.915036 (Epoch 3); EvalClassificationError = 0.527453 (Epoch 3)
MPI Rank 0: 01/11/2018 08:55:39: SGD: Saving checkpoint model 'C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn.3'
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:39: Starting Epoch 4: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:39: Starting minibatch loop, DataParallelSGD training (myRank = 0, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 0: 01/11/2018 08:55:39:  Epoch[ 4 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.94063438 * 10240; EvalClassificationError = 0.53203125 * 10240; time = 0.6953s; samplesPerSecond = 14728.1
MPI Rank 0: 01/11/2018 08:55:40:  Epoch[ 4 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.92737921 * 10240; EvalClassificationError = 0.53046875 * 10240; time = 0.7299s; samplesPerSecond = 14030.0
MPI Rank 0: 01/11/2018 08:55:40: Finished Epoch[ 4 of 15]: [Training] CrossEntropyWithSoftmax = 1.93400680 * 20480; EvalClassificationError = 0.53125000 * 20480; totalSamplesSeen = 81920; learningRatePerSample = 9.7656251e-05; epochTime=1.4571s
MPI Rank 0: 01/11/2018 08:55:42: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.90598730 * 83050; perplexity = 6.72604500; EvalClassificationError = 0.52635762 * 83050
MPI Rank 0: 01/11/2018 08:55:42: Finished Epoch[ 4 of 15]: [Validate] CrossEntropyWithSoftmax = 1.90598730 * 83050; EvalClassificationError = 0.52635762 * 83050
MPI Rank 0: 01/11/2018 08:55:42: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.905987 (Epoch 4); EvalClassificationError = 0.526358 (Epoch 4)
MPI Rank 0: 01/11/2018 08:55:42: SGD: Saving checkpoint model 'C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn.4'
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:42: Starting Epoch 5: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:42: Starting minibatch loop, DataParallelSGD training (myRank = 0, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 0: 01/11/2018 08:55:43:  Epoch[ 5 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.93993557 * 10240; EvalClassificationError = 0.53144531 * 10240; time = 0.7435s; samplesPerSecond = 13773.3
MPI Rank 0: 01/11/2018 08:55:44:  Epoch[ 5 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.91667918 * 10240; EvalClassificationError = 0.52070313 * 10240; time = 0.7478s; samplesPerSecond = 13693.9
MPI Rank 0: 01/11/2018 08:55:44: Finished Epoch[ 5 of 15]: [Training] CrossEntropyWithSoftmax = 1.92830738 * 20480; EvalClassificationError = 0.52607422 * 20480; totalSamplesSeen = 102400; learningRatePerSample = 9.7656251e-05; epochTime=1.52979s
MPI Rank 0: 01/11/2018 08:55:46: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.89836333 * 83050; perplexity = 6.67496079; EvalClassificationError = 0.52422637 * 83050
MPI Rank 0: 01/11/2018 08:55:46: Finished Epoch[ 5 of 15]: [Validate] CrossEntropyWithSoftmax = 1.89836333 * 83050; EvalClassificationError = 0.52422637 * 83050
MPI Rank 0: 01/11/2018 08:55:46: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.898363 (Epoch 5); EvalClassificationError = 0.524226 (Epoch 5)
MPI Rank 0: 01/11/2018 08:55:46: SGD: Saving checkpoint model 'C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn.5'
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:46: Starting Epoch 6: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:46: Starting minibatch loop, DataParallelSGD training (myRank = 0, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 0: 01/11/2018 08:55:47:  Epoch[ 6 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.93109689 * 10240; EvalClassificationError = 0.53535156 * 10240; time = 0.7208s; samplesPerSecond = 14205.6
MPI Rank 0: 01/11/2018 08:55:47:  Epoch[ 6 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.91132106 * 10240; EvalClassificationError = 0.52353516 * 10240; time = 0.6887s; samplesPerSecond = 14868.4
MPI Rank 0: 01/11/2018 08:55:47: Finished Epoch[ 6 of 15]: [Training] CrossEntropyWithSoftmax = 1.92120897 * 20480; EvalClassificationError = 0.52944336 * 20480; totalSamplesSeen = 122880; learningRatePerSample = 9.7656251e-05; epochTime=1.43266s
MPI Rank 0: 01/11/2018 08:55:50: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.89149689 * 83050; perplexity = 6.62928457; EvalClassificationError = 0.52290187 * 83050
MPI Rank 0: 01/11/2018 08:55:50: Finished Epoch[ 6 of 15]: [Validate] CrossEntropyWithSoftmax = 1.89149689 * 83050; EvalClassificationError = 0.52290187 * 83050
MPI Rank 0: 01/11/2018 08:55:50: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.891497 (Epoch 6); EvalClassificationError = 0.522902 (Epoch 6)
MPI Rank 0: 01/11/2018 08:55:50: SGD: Saving checkpoint model 'C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn.6'
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:50: Starting Epoch 7: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:50: Starting minibatch loop, DataParallelSGD training (myRank = 0, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 0: 01/11/2018 08:55:51:  Epoch[ 7 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.88512539 * 10240; EvalClassificationError = 0.51572266 * 10240; time = 0.9209s; samplesPerSecond = 11119.7
MPI Rank 0: 01/11/2018 08:55:51:  Epoch[ 7 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.91798861 * 10240; EvalClassificationError = 0.53535156 * 10240; time = 0.9500s; samplesPerSecond = 10778.8
MPI Rank 0: 01/11/2018 08:55:52: Finished Epoch[ 7 of 15]: [Training] CrossEntropyWithSoftmax = 1.90155700 * 20480; EvalClassificationError = 0.52553711 * 20480; totalSamplesSeen = 143360; learningRatePerSample = 9.7656251e-05; epochTime=1.90937s
MPI Rank 0: 01/11/2018 08:55:54: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.88459874 * 83050; perplexity = 6.58371212; EvalClassificationError = 0.52246839 * 83050
MPI Rank 0: 01/11/2018 08:55:54: Finished Epoch[ 7 of 15]: [Validate] CrossEntropyWithSoftmax = 1.88459874 * 83050; EvalClassificationError = 0.52246839 * 83050
MPI Rank 0: 01/11/2018 08:55:54: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.884599 (Epoch 7); EvalClassificationError = 0.522468 (Epoch 7)
MPI Rank 0: 01/11/2018 08:55:54: SGD: Saving checkpoint model 'C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn.7'
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:54: Starting Epoch 8: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:54: Starting minibatch loop, DataParallelSGD training (myRank = 0, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 0: 01/11/2018 08:55:54:  Epoch[ 8 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.89139877 * 10240; EvalClassificationError = 0.52099609 * 10240; time = 0.7247s; samplesPerSecond = 14130.5
MPI Rank 0: 01/11/2018 08:55:55:  Epoch[ 8 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.87492662 * 10240; EvalClassificationError = 0.51923828 * 10240; time = 0.6993s; samplesPerSecond = 14642.7
MPI Rank 0: 01/11/2018 08:55:55: Finished Epoch[ 8 of 15]: [Training] CrossEntropyWithSoftmax = 1.88316269 * 20480; EvalClassificationError = 0.52011719 * 20480; totalSamplesSeen = 163840; learningRatePerSample = 9.7656251e-05; epochTime=1.429s
MPI Rank 0: 01/11/2018 08:55:57: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.87932390 * 83050; perplexity = 6.54907556; EvalClassificationError = 0.52132450 * 83050
MPI Rank 0: 01/11/2018 08:55:57: Finished Epoch[ 8 of 15]: [Validate] CrossEntropyWithSoftmax = 1.87932390 * 83050; EvalClassificationError = 0.52132450 * 83050
MPI Rank 0: 01/11/2018 08:55:57: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.879324 (Epoch 8); EvalClassificationError = 0.521325 (Epoch 8)
MPI Rank 0: 01/11/2018 08:55:57: SGD: Saving checkpoint model 'C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn.8'
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:57: Starting Epoch 9: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:55:57: Starting minibatch loop, DataParallelSGD training (myRank = 0, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 0: 01/11/2018 08:55:58:  Epoch[ 9 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.86868159 * 10240; EvalClassificationError = 0.51103516 * 10240; time = 0.7620s; samplesPerSecond = 13438.3
MPI Rank 0: 01/11/2018 08:55:59:  Epoch[ 9 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.86708001 * 10240; EvalClassificationError = 0.52001953 * 10240; time = 0.7483s; samplesPerSecond = 13684.7
MPI Rank 0: 01/11/2018 08:55:59: Finished Epoch[ 9 of 15]: [Training] CrossEntropyWithSoftmax = 1.86788080 * 20480; EvalClassificationError = 0.51552734 * 20480; totalSamplesSeen = 184320; learningRatePerSample = 9.7656251e-05; epochTime=1.53524s
MPI Rank 0: 01/11/2018 08:56:01: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.87278473 * 83050; perplexity = 6.50638972; EvalClassificationError = 0.52086695 * 83050
MPI Rank 0: 01/11/2018 08:56:01: Finished Epoch[ 9 of 15]: [Validate] CrossEntropyWithSoftmax = 1.87278473 * 83050; EvalClassificationError = 0.52086695 * 83050
MPI Rank 0: 01/11/2018 08:56:01: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.872785 (Epoch 9); EvalClassificationError = 0.520867 (Epoch 9)
MPI Rank 0: 01/11/2018 08:56:01: SGD: Saving checkpoint model 'C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn.9'
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:01: Starting Epoch 10: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:01: Starting minibatch loop, DataParallelSGD training (myRank = 0, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 0: 01/11/2018 08:56:02:  Epoch[10 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.90473850 * 10240; EvalClassificationError = 0.52822266 * 10240; time = 0.7539s; samplesPerSecond = 13583.3
MPI Rank 0: 01/11/2018 08:56:03:  Epoch[10 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.85821242 * 10240; EvalClassificationError = 0.51484375 * 10240; time = 0.7371s; samplesPerSecond = 13893.2
MPI Rank 0: 01/11/2018 08:56:03: Finished Epoch[10 of 15]: [Training] CrossEntropyWithSoftmax = 1.88147546 * 20480; EvalClassificationError = 0.52153320 * 20480; totalSamplesSeen = 204800; learningRatePerSample = 9.7656251e-05; epochTime=1.52367s
MPI Rank 0: 01/11/2018 08:56:05: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.86655806 * 83050; perplexity = 6.46600244; EvalClassificationError = 0.51947020 * 83050
MPI Rank 0: 01/11/2018 08:56:05: Finished Epoch[10 of 15]: [Validate] CrossEntropyWithSoftmax = 1.86655806 * 83050; EvalClassificationError = 0.51947020 * 83050
MPI Rank 0: 01/11/2018 08:56:05: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.866558 (Epoch 10); EvalClassificationError = 0.519470 (Epoch 10)
MPI Rank 0: 01/11/2018 08:56:05: SGD: Saving checkpoint model 'C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn.10'
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:05: Starting Epoch 11: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:05: Starting minibatch loop, DataParallelSGD training (myRank = 0, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 0: 01/11/2018 08:56:06:  Epoch[11 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.87411367 * 10240; EvalClassificationError = 0.51142578 * 10240; time = 0.7290s; samplesPerSecond = 14046.2
MPI Rank 0: 01/11/2018 08:56:06:  Epoch[11 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.87067306 * 10240; EvalClassificationError = 0.52158203 * 10240; time = 0.7060s; samplesPerSecond = 14503.8
MPI Rank 0: 01/11/2018 08:56:06: Finished Epoch[11 of 15]: [Training] CrossEntropyWithSoftmax = 1.87239337 * 20480; EvalClassificationError = 0.51650391 * 20480; totalSamplesSeen = 225280; learningRatePerSample = 9.7656251e-05; epochTime=1.46908s
MPI Rank 0: 01/11/2018 08:56:08: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.85980825 * 83050; perplexity = 6.42250511; EvalClassificationError = 0.51703793 * 83050
MPI Rank 0: 01/11/2018 08:56:08: Finished Epoch[11 of 15]: [Validate] CrossEntropyWithSoftmax = 1.85980825 * 83050; EvalClassificationError = 0.51703793 * 83050
MPI Rank 0: 01/11/2018 08:56:08: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.859808 (Epoch 11); EvalClassificationError = 0.517038 (Epoch 11)
MPI Rank 0: 01/11/2018 08:56:08: SGD: Saving checkpoint model 'C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn.11'
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:09: Starting Epoch 12: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:09: Starting minibatch loop, DataParallelSGD training (myRank = 0, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 0: 01/11/2018 08:56:09:  Epoch[12 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.87570276 * 10240; EvalClassificationError = 0.52138672 * 10240; time = 0.7610s; samplesPerSecond = 13455.3
MPI Rank 0: 01/11/2018 08:56:10:  Epoch[12 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.84544781 * 10240; EvalClassificationError = 0.50859375 * 10240; time = 0.7629s; samplesPerSecond = 13423.0
MPI Rank 0: 01/11/2018 08:56:10: Finished Epoch[12 of 15]: [Training] CrossEntropyWithSoftmax = 1.86057528 * 20480; EvalClassificationError = 0.51499023 * 20480; totalSamplesSeen = 245760; learningRatePerSample = 9.7656251e-05; epochTime=1.54962s
MPI Rank 0: 01/11/2018 08:56:12: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.85346328 * 83050; perplexity = 6.38188356; EvalClassificationError = 0.51517158 * 83050
MPI Rank 0: 01/11/2018 08:56:12: Finished Epoch[12 of 15]: [Validate] CrossEntropyWithSoftmax = 1.85346328 * 83050; EvalClassificationError = 0.51517158 * 83050
MPI Rank 0: 01/11/2018 08:56:12: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.853463 (Epoch 12); EvalClassificationError = 0.515172 (Epoch 12)
MPI Rank 0: 01/11/2018 08:56:12: SGD: Saving checkpoint model 'C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn.12'
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:12: Starting Epoch 13: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:12: Starting minibatch loop, DataParallelSGD training (myRank = 0, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 0: 01/11/2018 08:56:13:  Epoch[13 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.85293961 * 10046; EvalClassificationError = 0.52020705 * 10046; time = 0.9919s; samplesPerSecond = 10128.3
MPI Rank 0: 01/11/2018 08:56:14:  Epoch[13 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.88311779 * 10240; EvalClassificationError = 0.52031250 * 10240; time = 0.9144s; samplesPerSecond = 11199.1
MPI Rank 0: 01/11/2018 08:56:14: Finished Epoch[13 of 15]: [Training] CrossEntropyWithSoftmax = 1.86894139 * 20480; EvalClassificationError = 0.51992187 * 20480; totalSamplesSeen = 266240; learningRatePerSample = 9.7656251e-05; epochTime=1.98317s
MPI Rank 0: 01/11/2018 08:56:16: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.84713665 * 83050; perplexity = 6.34163517; EvalClassificationError = 0.51273931 * 83050
MPI Rank 0: 01/11/2018 08:56:16: Finished Epoch[13 of 15]: [Validate] CrossEntropyWithSoftmax = 1.84713665 * 83050; EvalClassificationError = 0.51273931 * 83050
MPI Rank 0: 01/11/2018 08:56:16: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.847137 (Epoch 13); EvalClassificationError = 0.512739 (Epoch 13)
MPI Rank 0: 01/11/2018 08:56:16: SGD: Saving checkpoint model 'C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn.13'
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:16: Starting Epoch 14: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:16: Starting minibatch loop, DataParallelSGD training (myRank = 0, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 0: 01/11/2018 08:56:17:  Epoch[14 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.86634456 * 10240; EvalClassificationError = 0.50937500 * 10240; time = 0.7696s; samplesPerSecond = 13306.4
MPI Rank 0: 01/11/2018 08:56:18:  Epoch[14 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.85430186 * 10240; EvalClassificationError = 0.51328125 * 10240; time = 0.7187s; samplesPerSecond = 14247.7
MPI Rank 0: 01/11/2018 08:56:18: Finished Epoch[14 of 15]: [Training] CrossEntropyWithSoftmax = 1.86032321 * 20480; EvalClassificationError = 0.51132813 * 20480; totalSamplesSeen = 286720; learningRatePerSample = 9.7656251e-05; epochTime=1.51483s
MPI Rank 0: 01/11/2018 08:56:20: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.84154028 * 83050; perplexity = 6.30624417; EvalClassificationError = 0.51331728 * 83050
MPI Rank 0: 01/11/2018 08:56:20: Finished Epoch[14 of 15]: [Validate] CrossEntropyWithSoftmax = 1.84154028 * 83050; EvalClassificationError = 0.51331728 * 83050
MPI Rank 0: 01/11/2018 08:56:20: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.841540 (Epoch 14); EvalClassificationError = 0.512739 (Epoch 13)
MPI Rank 0: 01/11/2018 08:56:20: SGD: Saving checkpoint model 'C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn.14'
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:20: Starting Epoch 15: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:20: Starting minibatch loop, DataParallelSGD training (myRank = 0, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 0: 01/11/2018 08:56:21:  Epoch[15 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.82826227 * 10240; EvalClassificationError = 0.50820312 * 10240; time = 0.7227s; samplesPerSecond = 14169.8
MPI Rank 0: 01/11/2018 08:56:22:  Epoch[15 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.85323706 * 10240; EvalClassificationError = 0.51826172 * 10240; time = 0.7536s; samplesPerSecond = 13588.9
MPI Rank 0: 01/11/2018 08:56:22: Finished Epoch[15 of 15]: [Training] CrossEntropyWithSoftmax = 1.84074966 * 20480; EvalClassificationError = 0.51323242 * 20480; totalSamplesSeen = 307200; learningRatePerSample = 9.7656251e-05; epochTime=1.51174s
MPI Rank 0: 01/11/2018 08:56:24: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.83558729 * 83050; perplexity = 6.26881470; EvalClassificationError = 0.51134256 * 83050
MPI Rank 0: 01/11/2018 08:56:24: Finished Epoch[15 of 15]: [Validate] CrossEntropyWithSoftmax = 1.83558729 * 83050; EvalClassificationError = 0.51134256 * 83050
MPI Rank 0: 01/11/2018 08:56:24: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.835587 (Epoch 15); EvalClassificationError = 0.511343 (Epoch 15)
MPI Rank 0: 01/11/2018 08:56:24: SGD: Saving checkpoint model 'C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn'
MPI Rank 0: 01/11/2018 08:56:24: Best epoch for criterion 'CrossEntropyWithSoftmax' is 15 and model C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn copied to C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn_CrossEntropyWithSoftmax
MPI Rank 0: 01/11/2018 08:56:24: Best epoch for criterion 'EvalClassificationError' is 15 and model C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn copied to C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn_EvalClassificationError
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:24: Action "train" complete.
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:24: __COMPLETED__
MPI Rank 1: 01/11/2018 08:55:13: Redirecting stderr to file C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/stderr_speechTrain.logrank1
MPI Rank 1: CNTK 2.3.1+ (HEAD db192c, Jan 10 2018 22:59:43) at 2018/01/11 08:55:13
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\SaveBestModelPerCriterion/cntkcv.cntk  currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data  RunDir=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@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\SaveBestModelPerCriterion  OutputDir=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu  DeviceId=-1  timestamping=true  numCPUThreads=3  shareNodeValueMatrices=true  saveBestModelPerCriterion=true  stderr=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/stderr
MPI Rank 1: -------------------------------------------------------------------
MPI Rank 1: Build info: 
MPI Rank 1: 
MPI Rank 1: 		Built time: Jan 10 2018 22:47:38
MPI Rank 1: 		Last modified date: Wed Jan 10 22:18:32 2018
MPI Rank 1: 		Build type: Release
MPI Rank 1: 		Build target: GPU
MPI Rank 1: 		With ASGD: yes
MPI Rank 1: 		Math lib: mkl
MPI Rank 1: 		CUDA version: 9.0.0
MPI Rank 1: 		CUDNN version: 7.0.5
MPI Rank 1: 		Build Branch: HEAD
MPI Rank 1: 		Build SHA1: db192cd3cb9ac688cae719c41e5930a4e3f628ea
MPI Rank 1: 		MPI distribution: Microsoft MPI
MPI Rank 1: 		MPI version: 7.0.12437.6
MPI Rank 1: -------------------------------------------------------------------
MPI Rank 1: -------------------------------------------------------------------
MPI Rank 1: GPU info:
MPI Rank 1: 
MPI Rank 1: 		Device[0]: cores = 3072; computeCapability = 5.2; type = "Tesla M60"; total memory = 8124 MB; free memory = 8001 MB
MPI Rank 1: -------------------------------------------------------------------
MPI Rank 1: 01/11/2018 08:55:13: Using 3 CPU threads.
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:13: ##############################################################################
MPI Rank 1: 01/11/2018 08:55:13: #                                                                            #
MPI Rank 1: 01/11/2018 08:55:13: # speechTrain command (train action)                                         #
MPI Rank 1: 01/11/2018 08:55:13: #                                                                            #
MPI Rank 1: 01/11/2018 08:55:13: ##############################################################################
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:13: 
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: HTKDeserializer: selected '948' utterances grouped into '3' chunks, average chunk size: 316.0 utterances, 84244.7 frames (for I/O: 316.0 utterances, 84244.7 frames)
MPI Rank 1: HTKDeserializer: determined feature kind as '33'-dimensional 'USER' with frame shift 10.0 ms
MPI Rank 1: Total (133) state names in state list 'C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/state.list'
MPI Rank 1: MLFDeserializer: '948' utterances with '252734' frames
MPI Rank 1: Reading script file glob_0000.cv.scp ... 300 entries
MPI Rank 1: HTKDeserializer: selected '300' utterances grouped into '1' chunks, average chunk size: 300.0 utterances, 83050.0 frames (for I/O: 300.0 utterances, 83050.0 frames)
MPI Rank 1: HTKDeserializer: determined feature kind as '33'-dimensional 'USER' with frame shift 10.0 ms
MPI Rank 1: Total (133) state names in state list 'C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/state.list'
MPI Rank 1: MLFDeserializer: '948' utterances with '252734' frames
MPI Rank 1: 01/11/2018 08:55:13: 
MPI Rank 1: Model has 25 nodes. Using CPU.
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:13: Training criterion:   CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
MPI Rank 1: 01/11/2018 08:55:13: 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: Gradient Memory Aliasing: 4 are aliased.
MPI Rank 1: 	W1*H1 (gradient) reuses W1*H1+B1 (gradient)
MPI Rank 1: 	W2*H1 (gradient) reuses HLast (gradient)
MPI Rank 1: 
MPI Rank 1: Memory Sharing: Out of 40 matrices, 20 are shared as 5, and 20 are not shared.
MPI Rank 1: 
MPI Rank 1: Here are the ones that share memory:
MPI Rank 1: 	{ PosteriorProb : [132 x 1 x *]
MPI Rank 1: 	  ScaledLogLikelihood : [132 x 1 x *] }
MPI Rank 1: 	{ HLast : [132 x 1 x *] (gradient)
MPI Rank 1: 	  W0*features+B0 : [512 x 1 x *] (gradient)
MPI Rank 1: 	  W1*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: 	  W2*H1 : [132 x 1 x *] (gradient) }
MPI Rank 1: 	{ H1 : [512 x 1 x *] (gradient)
MPI Rank 1: 	  H2 : [512 x 1 x *] (gradient)
MPI Rank 1: 	  HLast : [132 x 1 x *]
MPI Rank 1: 	  W0*features : [512 x *] (gradient)
MPI Rank 1: 	  W1*H1+B1 : [512 x 1 x *] }
MPI Rank 1: 	{ H2 : [512 x 1 x *]
MPI Rank 1: 	  W0*features+B0 : [512 x 1 x *]
MPI Rank 1: 	  W1 : [512 x 512] (gradient)
MPI Rank 1: 	  W1*H1 : [512 x 1 x *] }
MPI Rank 1: 	{ H1 : [512 x 1 x *]
MPI Rank 1: 	  W0 : [512 x 363] (gradient)
MPI Rank 1: 	  W0*features : [512 x *] }
MPI Rank 1: 
MPI Rank 1: Here are the ones that don't share memory:
MPI Rank 1: 	{features : [363 x *]}
MPI Rank 1: 	{W2 : [132 x 512]}
MPI Rank 1: 	{W2 : [132 x 512] (gradient)}
MPI Rank 1: 	{B2 : [132 x 1]}
MPI Rank 1: 	{InvStdOfFeatures : [363]}
MPI Rank 1: 	{LogOfPrior : [132]}
MPI Rank 1: 	{CrossEntropyWithSoftmax : [1] (gradient)}
MPI Rank 1: 	{B1 : [512 x 1] (gradient)}
MPI Rank 1: 	{W1 : [512 x 512]}
MPI Rank 1: 	{labels : [132 x *]}
MPI Rank 1: 	{B0 : [512 x 1] (gradient)}
MPI Rank 1: 	{CrossEntropyWithSoftmax : [1]}
MPI Rank 1: 	{B2 : [132 x 1] (gradient)}
MPI Rank 1: 	{MVNormalizedFeatures : [363 x *]}
MPI Rank 1: 	{MeanOfFeatures : [363]}
MPI Rank 1: 	{Prior : [132]}
MPI Rank 1: 	{EvalClassificationError : [1]}
MPI Rank 1: 	{B1 : [512 x 1]}
MPI Rank 1: 	{B0 : [512 x 1]}
MPI Rank 1: 	{W0 : [512 x 363]}
MPI Rank 1: 
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:13: Training 516740 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:13: 	Node 'B0' (LearnableParameter operation) : [512 x 1]
MPI Rank 1: 01/11/2018 08:55:13: 	Node 'B1' (LearnableParameter operation) : [512 x 1]
MPI Rank 1: 01/11/2018 08:55:13: 	Node 'B2' (LearnableParameter operation) : [132 x 1]
MPI Rank 1: 01/11/2018 08:55:13: 	Node 'W0' (LearnableParameter operation) : [512 x 363]
MPI Rank 1: 01/11/2018 08:55:13: 	Node 'W1' (LearnableParameter operation) : [512 x 512]
MPI Rank 1: 01/11/2018 08:55:13: 	Node 'W2' (LearnableParameter operation) : [132 x 512]
MPI Rank 1: 
MPI Rank 1: Initializing dataParallelSGD with FP64 aggregation.
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:13: Precomputing --> 3 PreCompute nodes found.
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:13: 	MeanOfFeatures = Mean()
MPI Rank 1: 01/11/2018 08:55:13: 	InvStdOfFeatures = InvStdDev()
MPI Rank 1: 01/11/2018 08:55:13: 	Prior = Mean()
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:16: Precomputing --> Completed.
MPI Rank 1: 
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:16: Starting Epoch 1: learning rate per sample = 0.015625  effective momentum = 0.900000  momentum as time constant = 607.4 samples
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:16: Starting minibatch loop, DataParallelSGD training (myRank = 1, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 1: 01/11/2018 08:55:16:  Epoch[ 1 of 15]-Minibatch[   1-  10, 3.13%]: CrossEntropyWithSoftmax = 4.59755198 * 640; EvalClassificationError = 0.93125000 * 640; time = 0.2787s; samplesPerSecond = 2296.7
MPI Rank 1: 01/11/2018 08:55:17:  Epoch[ 1 of 15]-Minibatch[  11-  20, 6.25%]: CrossEntropyWithSoftmax = 4.34610349 * 640; EvalClassificationError = 0.92031250 * 640; time = 0.3111s; samplesPerSecond = 2057.4
MPI Rank 1: 01/11/2018 08:55:17:  Epoch[ 1 of 15]-Minibatch[  21-  30, 9.38%]: CrossEntropyWithSoftmax = 3.98222516 * 640; EvalClassificationError = 0.89062500 * 640; time = 0.3274s; samplesPerSecond = 1954.8
MPI Rank 1: 01/11/2018 08:55:17:  Epoch[ 1 of 15]-Minibatch[  31-  40, 12.50%]: CrossEntropyWithSoftmax = 3.74152814 * 640; EvalClassificationError = 0.84531250 * 640; time = 0.2976s; samplesPerSecond = 2150.2
MPI Rank 1: 01/11/2018 08:55:18:  Epoch[ 1 of 15]-Minibatch[  41-  50, 15.63%]: CrossEntropyWithSoftmax = 3.83818572 * 640; EvalClassificationError = 0.86718750 * 640; time = 0.2654s; samplesPerSecond = 2411.8
MPI Rank 1: 01/11/2018 08:55:18:  Epoch[ 1 of 15]-Minibatch[  51-  60, 18.75%]: CrossEntropyWithSoftmax = 3.71641238 * 640; EvalClassificationError = 0.87500000 * 640; time = 0.2890s; samplesPerSecond = 2214.5
MPI Rank 1: 01/11/2018 08:55:18:  Epoch[ 1 of 15]-Minibatch[  61-  70, 21.88%]: CrossEntropyWithSoftmax = 3.41802791 * 640; EvalClassificationError = 0.79687500 * 640; time = 0.3180s; samplesPerSecond = 2012.5
MPI Rank 1: 01/11/2018 08:55:18:  Epoch[ 1 of 15]-Minibatch[  71-  80, 25.00%]: CrossEntropyWithSoftmax = 3.53832947 * 640; EvalClassificationError = 0.82812500 * 640; time = 0.3138s; samplesPerSecond = 2039.4
MPI Rank 1: 01/11/2018 08:55:19:  Epoch[ 1 of 15]-Minibatch[  81-  90, 28.13%]: CrossEntropyWithSoftmax = 3.50628076 * 640; EvalClassificationError = 0.81718750 * 640; time = 0.2896s; samplesPerSecond = 2209.6
MPI Rank 1: 01/11/2018 08:55:19:  Epoch[ 1 of 15]-Minibatch[  91- 100, 31.25%]: CrossEntropyWithSoftmax = 3.41478252 * 640; EvalClassificationError = 0.80781250 * 640; time = 0.2717s; samplesPerSecond = 2355.4
MPI Rank 1: 01/11/2018 08:55:19:  Epoch[ 1 of 15]-Minibatch[ 101- 110, 34.38%]: CrossEntropyWithSoftmax = 3.51031210 * 640; EvalClassificationError = 0.82812500 * 640; time = 0.3016s; samplesPerSecond = 2121.7
MPI Rank 1: 01/11/2018 08:55:20:  Epoch[ 1 of 15]-Minibatch[ 111- 120, 37.50%]: CrossEntropyWithSoftmax = 3.28365485 * 640; EvalClassificationError = 0.79375000 * 640; time = 0.3306s; samplesPerSecond = 1936.1
MPI Rank 1: 01/11/2018 08:55:20:  Epoch[ 1 of 15]-Minibatch[ 121- 130, 40.63%]: CrossEntropyWithSoftmax = 3.20932117 * 640; EvalClassificationError = 0.79531250 * 640; time = 0.3075s; samplesPerSecond = 2081.4
MPI Rank 1: 01/11/2018 08:55:20:  Epoch[ 1 of 15]-Minibatch[ 131- 140, 43.75%]: CrossEntropyWithSoftmax = 3.07460535 * 640; EvalClassificationError = 0.75468750 * 640; time = 0.2778s; samplesPerSecond = 2304.2
MPI Rank 1: 01/11/2018 08:55:21:  Epoch[ 1 of 15]-Minibatch[ 141- 150, 46.88%]: CrossEntropyWithSoftmax = 2.97529104 * 640; EvalClassificationError = 0.72031250 * 640; time = 0.2799s; samplesPerSecond = 2286.7
MPI Rank 1: 01/11/2018 08:55:21:  Epoch[ 1 of 15]-Minibatch[ 151- 160, 50.00%]: CrossEntropyWithSoftmax = 3.11968883 * 640; EvalClassificationError = 0.74531250 * 640; time = 0.3129s; samplesPerSecond = 2045.6
MPI Rank 1: 01/11/2018 08:55:21:  Epoch[ 1 of 15]-Minibatch[ 161- 170, 53.13%]: CrossEntropyWithSoftmax = 2.84172140 * 640; EvalClassificationError = 0.71093750 * 640; time = 0.3278s; samplesPerSecond = 1952.7
MPI Rank 1: 01/11/2018 08:55:21:  Epoch[ 1 of 15]-Minibatch[ 171- 180, 56.25%]: CrossEntropyWithSoftmax = 2.74031745 * 640; EvalClassificationError = 0.66093750 * 640; time = 0.2971s; samplesPerSecond = 2153.8
MPI Rank 1: 01/11/2018 08:55:22:  Epoch[ 1 of 15]-Minibatch[ 181- 190, 59.38%]: CrossEntropyWithSoftmax = 2.83858085 * 640; EvalClassificationError = 0.72656250 * 640; time = 0.3038s; samplesPerSecond = 2106.6
MPI Rank 1: 01/11/2018 08:55:22:  Epoch[ 1 of 15]-Minibatch[ 191- 200, 62.50%]: CrossEntropyWithSoftmax = 2.74632253 * 640; EvalClassificationError = 0.69218750 * 640; time = 0.2961s; samplesPerSecond = 2161.3
MPI Rank 1: 01/11/2018 08:55:22:  Epoch[ 1 of 15]-Minibatch[ 201- 210, 65.63%]: CrossEntropyWithSoftmax = 2.61033254 * 640; EvalClassificationError = 0.66250000 * 640; time = 0.3167s; samplesPerSecond = 2021.1
MPI Rank 1: 01/11/2018 08:55:23:  Epoch[ 1 of 15]-Minibatch[ 211- 220, 68.75%]: CrossEntropyWithSoftmax = 2.61330754 * 640; EvalClassificationError = 0.65000000 * 640; time = 0.3126s; samplesPerSecond = 2047.2
MPI Rank 1: 01/11/2018 08:55:23:  Epoch[ 1 of 15]-Minibatch[ 221- 230, 71.88%]: CrossEntropyWithSoftmax = 2.54591810 * 640; EvalClassificationError = 0.66406250 * 640; time = 0.2880s; samplesPerSecond = 2222.5
MPI Rank 1: 01/11/2018 08:55:23:  Epoch[ 1 of 15]-Minibatch[ 231- 240, 75.00%]: CrossEntropyWithSoftmax = 2.57566512 * 640; EvalClassificationError = 0.66093750 * 640; time = 0.2711s; samplesPerSecond = 2360.4
MPI Rank 1: 01/11/2018 08:55:24:  Epoch[ 1 of 15]-Minibatch[ 241- 250, 78.13%]: CrossEntropyWithSoftmax = 2.49164945 * 640; EvalClassificationError = 0.63281250 * 640; time = 0.2996s; samplesPerSecond = 2136.2
MPI Rank 1: 01/11/2018 08:55:24:  Epoch[ 1 of 15]-Minibatch[ 251- 260, 81.25%]: CrossEntropyWithSoftmax = 2.39954797 * 640; EvalClassificationError = 0.62812500 * 640; time = 0.3377s; samplesPerSecond = 1895.3
MPI Rank 1: 01/11/2018 08:55:24:  Epoch[ 1 of 15]-Minibatch[ 261- 270, 84.38%]: CrossEntropyWithSoftmax = 2.27034227 * 640; EvalClassificationError = 0.59375000 * 640; time = 0.2997s; samplesPerSecond = 2135.4
MPI Rank 1: 01/11/2018 08:55:24:  Epoch[ 1 of 15]-Minibatch[ 271- 280, 87.50%]: CrossEntropyWithSoftmax = 2.52112387 * 640; EvalClassificationError = 0.66093750 * 640; time = 0.2751s; samplesPerSecond = 2326.1
MPI Rank 1: 01/11/2018 08:55:25:  Epoch[ 1 of 15]-Minibatch[ 281- 290, 90.63%]: CrossEntropyWithSoftmax = 2.27800991 * 640; EvalClassificationError = 0.59062500 * 640; time = 0.2835s; samplesPerSecond = 2257.7
MPI Rank 1: 01/11/2018 08:55:25:  Epoch[ 1 of 15]-Minibatch[ 291- 300, 93.75%]: CrossEntropyWithSoftmax = 2.26783634 * 640; EvalClassificationError = 0.61093750 * 640; time = 0.3079s; samplesPerSecond = 2078.6
MPI Rank 1: 01/11/2018 08:55:25:  Epoch[ 1 of 15]-Minibatch[ 301- 310, 96.88%]: CrossEntropyWithSoftmax = 2.24590355 * 640; EvalClassificationError = 0.58593750 * 640; time = 0.3251s; samplesPerSecond = 1968.6
MPI Rank 1: 01/11/2018 08:55:26:  Epoch[ 1 of 15]-Minibatch[ 311- 320, 100.00%]: CrossEntropyWithSoftmax = 2.24415615 * 640; EvalClassificationError = 0.59843750 * 640; time = 0.2989s; samplesPerSecond = 2140.9
MPI Rank 1: 01/11/2018 08:55:26: Finished Epoch[ 1 of 15]: [Training] CrossEntropyWithSoftmax = 3.04696987 * 20480; EvalClassificationError = 0.73583984 * 20480; totalSamplesSeen = 20480; learningRatePerSample = 0.015625; epochTime=9.64386s
MPI Rank 1: 01/11/2018 08:55:29: Final Results: Minibatch[1-1299]: CrossEntropyWithSoftmax = 2.24737799 * 83050; perplexity = 9.46289145; EvalClassificationError = 0.61431668 * 83050
MPI Rank 1: 01/11/2018 08:55:29: Finished Epoch[ 1 of 15]: [Validate] CrossEntropyWithSoftmax = 2.24737799 * 83050; EvalClassificationError = 0.61431668 * 83050
MPI Rank 1: 01/11/2018 08:55:29: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 2.247378 (Epoch 1); EvalClassificationError = 0.614317 (Epoch 1)
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:29: Starting Epoch 2: learning rate per sample = 0.001953  effective momentum = 0.656119  momentum as time constant = 607.5 samples
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:29: Starting minibatch loop, DataParallelSGD training (myRank = 1, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 1: 01/11/2018 08:55:30:  Epoch[ 2 of 15]-Minibatch[   1-  10, 12.50%]: CrossEntropyWithSoftmax = 2.14624416 * 2560; EvalClassificationError = 0.56953125 * 2560; time = 0.4065s; samplesPerSecond = 6297.9
MPI Rank 1: 01/11/2018 08:55:30:  Epoch[ 2 of 15]-Minibatch[  11-  20, 25.00%]: CrossEntropyWithSoftmax = 2.06174352 * 2560; EvalClassificationError = 0.55742187 * 2560; time = 0.3821s; samplesPerSecond = 6700.6
MPI Rank 1: 01/11/2018 08:55:31:  Epoch[ 2 of 15]-Minibatch[  21-  30, 37.50%]: CrossEntropyWithSoftmax = 2.04994567 * 2560; EvalClassificationError = 0.55351562 * 2560; time = 0.4076s; samplesPerSecond = 6280.2
MPI Rank 1: 01/11/2018 08:55:31:  Epoch[ 2 of 15]-Minibatch[  31-  40, 50.00%]: CrossEntropyWithSoftmax = 2.03695762 * 2560; EvalClassificationError = 0.56132812 * 2560; time = 0.4485s; samplesPerSecond = 5708.5
MPI Rank 1: 01/11/2018 08:55:31:  Epoch[ 2 of 15]-Minibatch[  41-  50, 62.50%]: CrossEntropyWithSoftmax = 2.03086449 * 2560; EvalClassificationError = 0.55664063 * 2560; time = 0.4312s; samplesPerSecond = 5937.1
MPI Rank 1: 01/11/2018 08:55:32:  Epoch[ 2 of 15]-Minibatch[  51-  60, 75.00%]: CrossEntropyWithSoftmax = 1.97306418 * 2560; EvalClassificationError = 0.53671875 * 2560; time = 0.3942s; samplesPerSecond = 6493.4
MPI Rank 1: 01/11/2018 08:55:32:  Epoch[ 2 of 15]-Minibatch[  61-  70, 87.50%]: CrossEntropyWithSoftmax = 1.96746291 * 2560; EvalClassificationError = 0.53164062 * 2560; time = 0.3897s; samplesPerSecond = 6568.7
MPI Rank 1: 01/11/2018 08:55:33:  Epoch[ 2 of 15]-Minibatch[  71-  80, 100.00%]: CrossEntropyWithSoftmax = 1.95498387 * 2560; EvalClassificationError = 0.53750000 * 2560; time = 0.4814s; samplesPerSecond = 5318.2
MPI Rank 1: 01/11/2018 08:55:33: Finished Epoch[ 2 of 15]: [Training] CrossEntropyWithSoftmax = 2.02765830 * 20480; EvalClassificationError = 0.55053711 * 20480; totalSamplesSeen = 40960; learningRatePerSample = 0.001953125; epochTime=3.36896s
MPI Rank 1: 01/11/2018 08:55:35: Final Results: Minibatch[1-326]: CrossEntropyWithSoftmax = 1.93559232 * 83050; perplexity = 6.92814655; EvalClassificationError = 0.53506321 * 83050
MPI Rank 1: 01/11/2018 08:55:35: Finished Epoch[ 2 of 15]: [Validate] CrossEntropyWithSoftmax = 1.93559232 * 83050; EvalClassificationError = 0.53506321 * 83050
MPI Rank 1: 01/11/2018 08:55:35: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.935592 (Epoch 2); EvalClassificationError = 0.535063 (Epoch 2)
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:35: Starting Epoch 3: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:35: Starting minibatch loop, DataParallelSGD training (myRank = 1, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 1: 01/11/2018 08:55:36:  Epoch[ 3 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.95358670 * 10240; EvalClassificationError = 0.53603516 * 10240; time = 0.6593s; samplesPerSecond = 15532.4
MPI Rank 1: 01/11/2018 08:55:36:  Epoch[ 3 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.97541130 * 10240; EvalClassificationError = 0.55253906 * 10240; time = 0.7188s; samplesPerSecond = 14246.4
MPI Rank 1: 01/11/2018 08:55:36: Finished Epoch[ 3 of 15]: [Training] CrossEntropyWithSoftmax = 1.96449900 * 20480; EvalClassificationError = 0.54428711 * 20480; totalSamplesSeen = 61440; learningRatePerSample = 9.7656251e-05; epochTime=1.38221s
MPI Rank 1: 01/11/2018 08:55:39: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.91503561 * 83050; perplexity = 6.78718045; EvalClassificationError = 0.52745334 * 83050
MPI Rank 1: 01/11/2018 08:55:39: Finished Epoch[ 3 of 15]: [Validate] CrossEntropyWithSoftmax = 1.91503561 * 83050; EvalClassificationError = 0.52745334 * 83050
MPI Rank 1: 01/11/2018 08:55:39: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.915036 (Epoch 3); EvalClassificationError = 0.527453 (Epoch 3)
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:39: Starting Epoch 4: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:39: Starting minibatch loop, DataParallelSGD training (myRank = 1, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 1: 01/11/2018 08:55:39:  Epoch[ 4 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.94063438 * 10240; EvalClassificationError = 0.53203125 * 10240; time = 0.6913s; samplesPerSecond = 14813.1
MPI Rank 1: 01/11/2018 08:55:40:  Epoch[ 4 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.92737921 * 10240; EvalClassificationError = 0.53046875 * 10240; time = 0.7307s; samplesPerSecond = 14014.6
MPI Rank 1: 01/11/2018 08:55:40: Finished Epoch[ 4 of 15]: [Training] CrossEntropyWithSoftmax = 1.93400680 * 20480; EvalClassificationError = 0.53125000 * 20480; totalSamplesSeen = 81920; learningRatePerSample = 9.7656251e-05; epochTime=1.4449s
MPI Rank 1: 01/11/2018 08:55:42: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.90598730 * 83050; perplexity = 6.72604500; EvalClassificationError = 0.52635762 * 83050
MPI Rank 1: 01/11/2018 08:55:42: Finished Epoch[ 4 of 15]: [Validate] CrossEntropyWithSoftmax = 1.90598730 * 83050; EvalClassificationError = 0.52635762 * 83050
MPI Rank 1: 01/11/2018 08:55:42: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.905987 (Epoch 4); EvalClassificationError = 0.526358 (Epoch 4)
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:42: Starting Epoch 5: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:42: Starting minibatch loop, DataParallelSGD training (myRank = 1, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 1: 01/11/2018 08:55:43:  Epoch[ 5 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.93993557 * 10240; EvalClassificationError = 0.53144531 * 10240; time = 0.7413s; samplesPerSecond = 13814.3
MPI Rank 1: 01/11/2018 08:55:44:  Epoch[ 5 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.91667918 * 10240; EvalClassificationError = 0.52070313 * 10240; time = 0.7464s; samplesPerSecond = 13718.8
MPI Rank 1: 01/11/2018 08:55:44: Finished Epoch[ 5 of 15]: [Training] CrossEntropyWithSoftmax = 1.92830738 * 20480; EvalClassificationError = 0.52607422 * 20480; totalSamplesSeen = 102400; learningRatePerSample = 9.7656251e-05; epochTime=1.51756s
MPI Rank 1: 01/11/2018 08:55:46: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.89836333 * 83050; perplexity = 6.67496079; EvalClassificationError = 0.52422637 * 83050
MPI Rank 1: 01/11/2018 08:55:46: Finished Epoch[ 5 of 15]: [Validate] CrossEntropyWithSoftmax = 1.89836333 * 83050; EvalClassificationError = 0.52422637 * 83050
MPI Rank 1: 01/11/2018 08:55:46: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.898363 (Epoch 5); EvalClassificationError = 0.524226 (Epoch 5)
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:46: Starting Epoch 6: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:46: Starting minibatch loop, DataParallelSGD training (myRank = 1, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 1: 01/11/2018 08:55:47:  Epoch[ 6 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.93109689 * 10240; EvalClassificationError = 0.53535156 * 10240; time = 0.7196s; samplesPerSecond = 14229.8
MPI Rank 1: 01/11/2018 08:55:47:  Epoch[ 6 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.91132106 * 10240; EvalClassificationError = 0.52353516 * 10240; time = 0.6908s; samplesPerSecond = 14824.3
MPI Rank 1: 01/11/2018 08:55:47: Finished Epoch[ 6 of 15]: [Training] CrossEntropyWithSoftmax = 1.92120897 * 20480; EvalClassificationError = 0.52944336 * 20480; totalSamplesSeen = 122880; learningRatePerSample = 9.7656251e-05; epochTime=1.43236s
MPI Rank 1: 01/11/2018 08:55:50: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.89149689 * 83050; perplexity = 6.62928457; EvalClassificationError = 0.52290187 * 83050
MPI Rank 1: 01/11/2018 08:55:50: Finished Epoch[ 6 of 15]: [Validate] CrossEntropyWithSoftmax = 1.89149689 * 83050; EvalClassificationError = 0.52290187 * 83050
MPI Rank 1: 01/11/2018 08:55:50: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.891497 (Epoch 6); EvalClassificationError = 0.522902 (Epoch 6)
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:50: Starting Epoch 7: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:50: Starting minibatch loop, DataParallelSGD training (myRank = 1, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 1: 01/11/2018 08:55:51:  Epoch[ 7 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.88512539 * 10240; EvalClassificationError = 0.51572266 * 10240; time = 0.9173s; samplesPerSecond = 11163.3
MPI Rank 1: 01/11/2018 08:55:51:  Epoch[ 7 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.91798861 * 10240; EvalClassificationError = 0.53535156 * 10240; time = 0.9497s; samplesPerSecond = 10782.5
MPI Rank 1: 01/11/2018 08:55:51: Finished Epoch[ 7 of 15]: [Training] CrossEntropyWithSoftmax = 1.90155700 * 20480; EvalClassificationError = 0.52553711 * 20480; totalSamplesSeen = 143360; learningRatePerSample = 9.7656251e-05; epochTime=1.89758s
MPI Rank 1: 01/11/2018 08:55:54: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.88459874 * 83050; perplexity = 6.58371212; EvalClassificationError = 0.52246839 * 83050
MPI Rank 1: 01/11/2018 08:55:54: Finished Epoch[ 7 of 15]: [Validate] CrossEntropyWithSoftmax = 1.88459874 * 83050; EvalClassificationError = 0.52246839 * 83050
MPI Rank 1: 01/11/2018 08:55:54: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.884599 (Epoch 7); EvalClassificationError = 0.522468 (Epoch 7)
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:54: Starting Epoch 8: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:54: Starting minibatch loop, DataParallelSGD training (myRank = 1, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 1: 01/11/2018 08:55:54:  Epoch[ 8 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.89139877 * 10240; EvalClassificationError = 0.52099609 * 10240; time = 0.7230s; samplesPerSecond = 14164.1
MPI Rank 1: 01/11/2018 08:55:55:  Epoch[ 8 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.87492662 * 10240; EvalClassificationError = 0.51923828 * 10240; time = 0.7000s; samplesPerSecond = 14628.6
MPI Rank 1: 01/11/2018 08:55:55: Finished Epoch[ 8 of 15]: [Training] CrossEntropyWithSoftmax = 1.88316269 * 20480; EvalClassificationError = 0.52011719 * 20480; totalSamplesSeen = 163840; learningRatePerSample = 9.7656251e-05; epochTime=1.42732s
MPI Rank 1: 01/11/2018 08:55:57: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.87932390 * 83050; perplexity = 6.54907556; EvalClassificationError = 0.52132450 * 83050
MPI Rank 1: 01/11/2018 08:55:57: Finished Epoch[ 8 of 15]: [Validate] CrossEntropyWithSoftmax = 1.87932390 * 83050; EvalClassificationError = 0.52132450 * 83050
MPI Rank 1: 01/11/2018 08:55:57: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.879324 (Epoch 8); EvalClassificationError = 0.521325 (Epoch 8)
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:57: Starting Epoch 9: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:55:57: Starting minibatch loop, DataParallelSGD training (myRank = 1, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 1: 01/11/2018 08:55:58:  Epoch[ 9 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.86868159 * 10240; EvalClassificationError = 0.51103516 * 10240; time = 0.7610s; samplesPerSecond = 13455.8
MPI Rank 1: 01/11/2018 08:55:59:  Epoch[ 9 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.86708001 * 10240; EvalClassificationError = 0.52001953 * 10240; time = 0.7494s; samplesPerSecond = 13664.8
MPI Rank 1: 01/11/2018 08:55:59: Finished Epoch[ 9 of 15]: [Training] CrossEntropyWithSoftmax = 1.86788080 * 20480; EvalClassificationError = 0.51552734 * 20480; totalSamplesSeen = 184320; learningRatePerSample = 9.7656251e-05; epochTime=1.53405s
MPI Rank 1: 01/11/2018 08:56:01: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.87278473 * 83050; perplexity = 6.50638972; EvalClassificationError = 0.52086695 * 83050
MPI Rank 1: 01/11/2018 08:56:01: Finished Epoch[ 9 of 15]: [Validate] CrossEntropyWithSoftmax = 1.87278473 * 83050; EvalClassificationError = 0.52086695 * 83050
MPI Rank 1: 01/11/2018 08:56:01: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.872785 (Epoch 9); EvalClassificationError = 0.520867 (Epoch 9)
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:01: Starting Epoch 10: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:01: Starting minibatch loop, DataParallelSGD training (myRank = 1, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 1: 01/11/2018 08:56:02:  Epoch[10 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.90473850 * 10240; EvalClassificationError = 0.52822266 * 10240; time = 0.7535s; samplesPerSecond = 13589.8
MPI Rank 1: 01/11/2018 08:56:03:  Epoch[10 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.85821242 * 10240; EvalClassificationError = 0.51484375 * 10240; time = 0.7348s; samplesPerSecond = 13935.3
MPI Rank 1: 01/11/2018 08:56:03: Finished Epoch[10 of 15]: [Training] CrossEntropyWithSoftmax = 1.88147546 * 20480; EvalClassificationError = 0.52153320 * 20480; totalSamplesSeen = 204800; learningRatePerSample = 9.7656251e-05; epochTime=1.5124s
MPI Rank 1: 01/11/2018 08:56:05: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.86655806 * 83050; perplexity = 6.46600244; EvalClassificationError = 0.51947020 * 83050
MPI Rank 1: 01/11/2018 08:56:05: Finished Epoch[10 of 15]: [Validate] CrossEntropyWithSoftmax = 1.86655806 * 83050; EvalClassificationError = 0.51947020 * 83050
MPI Rank 1: 01/11/2018 08:56:05: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.866558 (Epoch 10); EvalClassificationError = 0.519470 (Epoch 10)
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:05: Starting Epoch 11: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:05: Starting minibatch loop, DataParallelSGD training (myRank = 1, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 1: 01/11/2018 08:56:06:  Epoch[11 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.87411367 * 10240; EvalClassificationError = 0.51142578 * 10240; time = 0.7276s; samplesPerSecond = 14073.5
MPI Rank 1: 01/11/2018 08:56:06:  Epoch[11 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.87067306 * 10240; EvalClassificationError = 0.52158203 * 10240; time = 0.7045s; samplesPerSecond = 14535.4
MPI Rank 1: 01/11/2018 08:56:06: Finished Epoch[11 of 15]: [Training] CrossEntropyWithSoftmax = 1.87239337 * 20480; EvalClassificationError = 0.51650391 * 20480; totalSamplesSeen = 225280; learningRatePerSample = 9.7656251e-05; epochTime=1.45716s
MPI Rank 1: 01/11/2018 08:56:08: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.85980825 * 83050; perplexity = 6.42250511; EvalClassificationError = 0.51703793 * 83050
MPI Rank 1: 01/11/2018 08:56:08: Finished Epoch[11 of 15]: [Validate] CrossEntropyWithSoftmax = 1.85980825 * 83050; EvalClassificationError = 0.51703793 * 83050
MPI Rank 1: 01/11/2018 08:56:08: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.859808 (Epoch 11); EvalClassificationError = 0.517038 (Epoch 11)
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:09: Starting Epoch 12: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:09: Starting minibatch loop, DataParallelSGD training (myRank = 1, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 1: 01/11/2018 08:56:09:  Epoch[12 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.87570276 * 10240; EvalClassificationError = 0.52138672 * 10240; time = 0.7592s; samplesPerSecond = 13487.9
MPI Rank 1: 01/11/2018 08:56:10:  Epoch[12 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.84544781 * 10240; EvalClassificationError = 0.50859375 * 10240; time = 0.7646s; samplesPerSecond = 13391.8
MPI Rank 1: 01/11/2018 08:56:10: Finished Epoch[12 of 15]: [Training] CrossEntropyWithSoftmax = 1.86057528 * 20480; EvalClassificationError = 0.51499023 * 20480; totalSamplesSeen = 245760; learningRatePerSample = 9.7656251e-05; epochTime=1.54861s
MPI Rank 1: 01/11/2018 08:56:12: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.85346328 * 83050; perplexity = 6.38188356; EvalClassificationError = 0.51517158 * 83050
MPI Rank 1: 01/11/2018 08:56:12: Finished Epoch[12 of 15]: [Validate] CrossEntropyWithSoftmax = 1.85346328 * 83050; EvalClassificationError = 0.51517158 * 83050
MPI Rank 1: 01/11/2018 08:56:12: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.853463 (Epoch 12); EvalClassificationError = 0.515172 (Epoch 12)
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:12: Starting Epoch 13: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:12: Starting minibatch loop, DataParallelSGD training (myRank = 1, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 1: 01/11/2018 08:56:13:  Epoch[13 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.85293961 * 10046; EvalClassificationError = 0.52020705 * 10046; time = 0.9884s; samplesPerSecond = 10163.7
MPI Rank 1: 01/11/2018 08:56:14:  Epoch[13 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.88311779 * 10240; EvalClassificationError = 0.52031250 * 10240; time = 0.9154s; samplesPerSecond = 11186.8
MPI Rank 1: 01/11/2018 08:56:14: Finished Epoch[13 of 15]: [Training] CrossEntropyWithSoftmax = 1.86894139 * 20480; EvalClassificationError = 0.51992187 * 20480; totalSamplesSeen = 266240; learningRatePerSample = 9.7656251e-05; epochTime=1.97077s
MPI Rank 1: 01/11/2018 08:56:16: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.84713665 * 83050; perplexity = 6.34163517; EvalClassificationError = 0.51273931 * 83050
MPI Rank 1: 01/11/2018 08:56:16: Finished Epoch[13 of 15]: [Validate] CrossEntropyWithSoftmax = 1.84713665 * 83050; EvalClassificationError = 0.51273931 * 83050
MPI Rank 1: 01/11/2018 08:56:16: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.847137 (Epoch 13); EvalClassificationError = 0.512739 (Epoch 13)
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:16: Starting Epoch 14: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:16: Starting minibatch loop, DataParallelSGD training (myRank = 1, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 1: 01/11/2018 08:56:17:  Epoch[14 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.86634456 * 10240; EvalClassificationError = 0.50937500 * 10240; time = 0.7663s; samplesPerSecond = 13362.8
MPI Rank 1: 01/11/2018 08:56:18:  Epoch[14 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.85430186 * 10240; EvalClassificationError = 0.51328125 * 10240; time = 0.7228s; samplesPerSecond = 14166.5
MPI Rank 1: 01/11/2018 08:56:18: Finished Epoch[14 of 15]: [Training] CrossEntropyWithSoftmax = 1.86032321 * 20480; EvalClassificationError = 0.51132813 * 20480; totalSamplesSeen = 286720; learningRatePerSample = 9.7656251e-05; epochTime=1.51431s
MPI Rank 1: 01/11/2018 08:56:20: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.84154028 * 83050; perplexity = 6.30624417; EvalClassificationError = 0.51331728 * 83050
MPI Rank 1: 01/11/2018 08:56:20: Finished Epoch[14 of 15]: [Validate] CrossEntropyWithSoftmax = 1.84154028 * 83050; EvalClassificationError = 0.51331728 * 83050
MPI Rank 1: 01/11/2018 08:56:20: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.841540 (Epoch 14); EvalClassificationError = 0.512739 (Epoch 13)
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:20: Starting Epoch 15: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:20: Starting minibatch loop, DataParallelSGD training (myRank = 1, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 1: 01/11/2018 08:56:21:  Epoch[15 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.82826227 * 10240; EvalClassificationError = 0.50820312 * 10240; time = 0.7234s; samplesPerSecond = 14155.4
MPI Rank 1: 01/11/2018 08:56:22:  Epoch[15 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.85323706 * 10240; EvalClassificationError = 0.51826172 * 10240; time = 0.7511s; samplesPerSecond = 13634.2
MPI Rank 1: 01/11/2018 08:56:22: Finished Epoch[15 of 15]: [Training] CrossEntropyWithSoftmax = 1.84074966 * 20480; EvalClassificationError = 0.51323242 * 20480; totalSamplesSeen = 307200; learningRatePerSample = 9.7656251e-05; epochTime=1.50181s
MPI Rank 1: 01/11/2018 08:56:24: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.83558729 * 83050; perplexity = 6.26881470; EvalClassificationError = 0.51134256 * 83050
MPI Rank 1: 01/11/2018 08:56:24: Finished Epoch[15 of 15]: [Validate] CrossEntropyWithSoftmax = 1.83558729 * 83050; EvalClassificationError = 0.51134256 * 83050
MPI Rank 1: 01/11/2018 08:56:24: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.835587 (Epoch 15); EvalClassificationError = 0.511343 (Epoch 15)
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:24: Action "train" complete.
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:24: __COMPLETED__
=== Deleting last epoch data
==== Re-running from checkpoint
=== Running c:\local\msmpi-7.0.12437.6\Bin/mpiexec.exe -n 2 C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\DNN\SaveBestModelPerCriterion/cntkcv.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data RunDir=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@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\SaveBestModelPerCriterion OutputDir=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu DeviceId=-1 timestamping=true makeMode=true numCPUThreads=3 shareNodeValueMatrices=true saveBestModelPerCriterion=true stderr=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/stderr
CNTK 2.3.1+ (HEAD db192c, Jan 10 2018 22:59:43) at 2018/01/11 08:56:24

C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe  configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\DNN\SaveBestModelPerCriterion/cntkcv.cntk  currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data  RunDir=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@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\SaveBestModelPerCriterion  OutputDir=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu  DeviceId=-1  timestamping=true  makeMode=true  numCPUThreads=3  shareNodeValueMatrices=true  saveBestModelPerCriterion=true  stderr=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/stderr
Changed current directory to C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data
ping [requestnodes (before change)]: 2 nodes pinging each other
CNTK 2.3.1+ (HEAD db192c, Jan 10 2018 22:59:43) at 2018/01/11 08:56:24

C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe  configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\DNN\SaveBestModelPerCriterion/cntkcv.cntk  currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data  RunDir=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@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\SaveBestModelPerCriterion  OutputDir=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu  DeviceId=-1  timestamping=true  makeMode=true  numCPUThreads=3  shareNodeValueMatrices=true  saveBestModelPerCriterion=true  stderr=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/stderr
Changed current directory to C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data
ping [requestnodes (before change)]: 2 nodes pinging each other
ping [requestnodes (after change)]: 2 nodes pinging each other
ping [requestnodes (after change)]: 2 nodes pinging each other
requestnodes [MPIWrapperMpi]: using 2 out of 2 MPI nodes on a single host (2 requested); we (0) are in (participating)
requestnodes [MPIWrapperMpi]: using 2 out of 2 MPI nodes on a single host (2 requested); we (1) are in (participating)
ping [mpihelper]: 2 nodes pinging each other
ping [mpihelper]: 2 nodes pinging each other
MPI Rank 0: 01/11/2018 08:56:24: Redirecting stderr to file C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/stderr_speechTrain.logrank0
MPI Rank 0: CNTK 2.3.1+ (HEAD db192c, Jan 10 2018 22:59:43) at 2018/01/11 08:56:24
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\SaveBestModelPerCriterion/cntkcv.cntk  currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data  RunDir=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@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\SaveBestModelPerCriterion  OutputDir=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu  DeviceId=-1  timestamping=true  makeMode=true  numCPUThreads=3  shareNodeValueMatrices=true  saveBestModelPerCriterion=true  stderr=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/stderr
MPI Rank 0: -------------------------------------------------------------------
MPI Rank 0: Build info: 
MPI Rank 0: 
MPI Rank 0: 		Built time: Jan 10 2018 22:47:38
MPI Rank 0: 		Last modified date: Wed Jan 10 22:18:32 2018
MPI Rank 0: 		Build type: Release
MPI Rank 0: 		Build target: GPU
MPI Rank 0: 		With ASGD: yes
MPI Rank 0: 		Math lib: mkl
MPI Rank 0: 		CUDA version: 9.0.0
MPI Rank 0: 		CUDNN version: 7.0.5
MPI Rank 0: 		Build Branch: HEAD
MPI Rank 0: 		Build SHA1: db192cd3cb9ac688cae719c41e5930a4e3f628ea
MPI Rank 0: 		MPI distribution: Microsoft MPI
MPI Rank 0: 		MPI version: 7.0.12437.6
MPI Rank 0: -------------------------------------------------------------------
MPI Rank 0: -------------------------------------------------------------------
MPI Rank 0: GPU info:
MPI Rank 0: 
MPI Rank 0: 		Device[0]: cores = 3072; computeCapability = 5.2; type = "Tesla M60"; total memory = 8124 MB; free memory = 8001 MB
MPI Rank 0: -------------------------------------------------------------------
MPI Rank 0: 01/11/2018 08:56:25: Using 3 CPU threads.
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:25: ##############################################################################
MPI Rank 0: 01/11/2018 08:56:25: #                                                                            #
MPI Rank 0: 01/11/2018 08:56:25: # speechTrain command (train action)                                         #
MPI Rank 0: 01/11/2018 08:56:25: #                                                                            #
MPI Rank 0: 01/11/2018 08:56:25: ##############################################################################
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:25: 
MPI Rank 0: Starting from checkpoint. Loading network from 'C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn.14'.
MPI Rank 0: SimpleNetworkBuilder Using CPU
MPI Rank 0: Reading script file glob_0000.scp ... 948 entries
MPI Rank 0: HTKDeserializer: selected '948' utterances grouped into '3' chunks, average chunk size: 316.0 utterances, 84244.7 frames (for I/O: 316.0 utterances, 84244.7 frames)
MPI Rank 0: HTKDeserializer: determined feature kind as '33'-dimensional 'USER' with frame shift 10.0 ms
MPI Rank 0: Total (133) state names in state list 'C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/state.list'
MPI Rank 0: MLFDeserializer: '948' utterances with '252734' frames
MPI Rank 0: Reading script file glob_0000.cv.scp ... 300 entries
MPI Rank 0: HTKDeserializer: selected '300' utterances grouped into '1' chunks, average chunk size: 300.0 utterances, 83050.0 frames (for I/O: 300.0 utterances, 83050.0 frames)
MPI Rank 0: HTKDeserializer: determined feature kind as '33'-dimensional 'USER' with frame shift 10.0 ms
MPI Rank 0: Total (133) state names in state list 'C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/state.list'
MPI Rank 0: MLFDeserializer: '948' utterances with '252734' frames
MPI Rank 0: 01/11/2018 08:56:25: 
MPI Rank 0: Model has 25 nodes. Using CPU.
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:25: Training criterion:   CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
MPI Rank 0: 01/11/2018 08:56:25: Evaluation criterion: EvalClassificationError = ClassificationError
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:25: Training 516740 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:25: 	Node 'B0' (LearnableParameter operation) : [512 x 1]
MPI Rank 0: 01/11/2018 08:56:25: 	Node 'B1' (LearnableParameter operation) : [512 x 1]
MPI Rank 0: 01/11/2018 08:56:25: 	Node 'B2' (LearnableParameter operation) : [132 x 1]
MPI Rank 0: 01/11/2018 08:56:25: 	Node 'W0' (LearnableParameter operation) : [512 x 363]
MPI Rank 0: 01/11/2018 08:56:25: 	Node 'W1' (LearnableParameter operation) : [512 x 512]
MPI Rank 0: 01/11/2018 08:56:25: 	Node 'W2' (LearnableParameter operation) : [132 x 512]
MPI Rank 0: 
MPI Rank 0: Initializing dataParallelSGD with FP64 aggregation.
MPI Rank 0: 01/11/2018 08:56:25: No PreCompute nodes found, or all already computed. Skipping pre-computation step.
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:25: Starting Epoch 15: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:25: Starting minibatch loop, DataParallelSGD training (myRank = 0, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 0: 01/11/2018 08:56:26:  Epoch[15 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.82826227 * 10240; EvalClassificationError = 0.50820312 * 10240; time = 0.8827s; samplesPerSecond = 11601.0
MPI Rank 0: 01/11/2018 08:56:27:  Epoch[15 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.85323706 * 10240; EvalClassificationError = 0.51826172 * 10240; time = 0.8146s; samplesPerSecond = 12571.1
MPI Rank 0: 01/11/2018 08:56:27: Finished Epoch[15 of 15]: [Training] CrossEntropyWithSoftmax = 1.84074966 * 20480; EvalClassificationError = 0.51323242 * 20480; totalSamplesSeen = 307200; learningRatePerSample = 9.7656251e-05; epochTime=1.76577s
MPI Rank 0: 01/11/2018 08:56:29: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.83558729 * 83050; perplexity = 6.26881470; EvalClassificationError = 0.51134256 * 83050
MPI Rank 0: 01/11/2018 08:56:29: Finished Epoch[15 of 15]: [Validate] CrossEntropyWithSoftmax = 1.83558729 * 83050; EvalClassificationError = 0.51134256 * 83050
MPI Rank 0: 01/11/2018 08:56:29: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.835587 (Epoch 15); EvalClassificationError = 0.511343 (Epoch 15)
MPI Rank 0: 01/11/2018 08:56:29: SGD: Saving checkpoint model 'C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn'
MPI Rank 0: 01/11/2018 08:56:29: Best epoch for criterion 'CrossEntropyWithSoftmax' is 15 and model C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn copied to C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn_CrossEntropyWithSoftmax
MPI Rank 0: 01/11/2018 08:56:29: Best epoch for criterion 'EvalClassificationError' is 15 and model C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn copied to C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn_EvalClassificationError
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:29: Action "train" complete.
MPI Rank 0: 
MPI Rank 0: 01/11/2018 08:56:29: __COMPLETED__
MPI Rank 1: 01/11/2018 08:56:25: Redirecting stderr to file C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/stderr_speechTrain.logrank1
MPI Rank 1: CNTK 2.3.1+ (HEAD db192c, Jan 10 2018 22:59:43) at 2018/01/11 08:56:24
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\SaveBestModelPerCriterion/cntkcv.cntk  currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data  RunDir=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@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\SaveBestModelPerCriterion  OutputDir=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu  DeviceId=-1  timestamping=true  makeMode=true  numCPUThreads=3  shareNodeValueMatrices=true  saveBestModelPerCriterion=true  stderr=C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/stderr
MPI Rank 1: -------------------------------------------------------------------
MPI Rank 1: Build info: 
MPI Rank 1: 
MPI Rank 1: 		Built time: Jan 10 2018 22:47:38
MPI Rank 1: 		Last modified date: Wed Jan 10 22:18:32 2018
MPI Rank 1: 		Build type: Release
MPI Rank 1: 		Build target: GPU
MPI Rank 1: 		With ASGD: yes
MPI Rank 1: 		Math lib: mkl
MPI Rank 1: 		CUDA version: 9.0.0
MPI Rank 1: 		CUDNN version: 7.0.5
MPI Rank 1: 		Build Branch: HEAD
MPI Rank 1: 		Build SHA1: db192cd3cb9ac688cae719c41e5930a4e3f628ea
MPI Rank 1: 		MPI distribution: Microsoft MPI
MPI Rank 1: 		MPI version: 7.0.12437.6
MPI Rank 1: -------------------------------------------------------------------
MPI Rank 1: -------------------------------------------------------------------
MPI Rank 1: GPU info:
MPI Rank 1: 
MPI Rank 1: 		Device[0]: cores = 3072; computeCapability = 5.2; type = "Tesla M60"; total memory = 8124 MB; free memory = 8001 MB
MPI Rank 1: -------------------------------------------------------------------
MPI Rank 1: 01/11/2018 08:56:25: Using 3 CPU threads.
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:25: ##############################################################################
MPI Rank 1: 01/11/2018 08:56:25: #                                                                            #
MPI Rank 1: 01/11/2018 08:56:25: # speechTrain command (train action)                                         #
MPI Rank 1: 01/11/2018 08:56:25: #                                                                            #
MPI Rank 1: 01/11/2018 08:56:25: ##############################################################################
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:25: 
MPI Rank 1: Starting from checkpoint. Loading network from 'C:\local\cygwin-2.8.2-x64\tmp\cntk-test-20180111085400.505371\Speech\DNN_SaveBestModelPerCriterion@release_cpu/models/cntkSpeech.dnn.14'.
MPI Rank 1: SimpleNetworkBuilder Using CPU
MPI Rank 1: Reading script file glob_0000.scp ... 948 entries
MPI Rank 1: HTKDeserializer: selected '948' utterances grouped into '3' chunks, average chunk size: 316.0 utterances, 84244.7 frames (for I/O: 316.0 utterances, 84244.7 frames)
MPI Rank 1: HTKDeserializer: determined feature kind as '33'-dimensional 'USER' with frame shift 10.0 ms
MPI Rank 1: Total (133) state names in state list 'C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/state.list'
MPI Rank 1: MLFDeserializer: '948' utterances with '252734' frames
MPI Rank 1: Reading script file glob_0000.cv.scp ... 300 entries
MPI Rank 1: HTKDeserializer: selected '300' utterances grouped into '1' chunks, average chunk size: 300.0 utterances, 83050.0 frames (for I/O: 300.0 utterances, 83050.0 frames)
MPI Rank 1: HTKDeserializer: determined feature kind as '33'-dimensional 'USER' with frame shift 10.0 ms
MPI Rank 1: Total (133) state names in state list 'C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/state.list'
MPI Rank 1: MLFDeserializer: '948' utterances with '252734' frames
MPI Rank 1: 01/11/2018 08:56:25: 
MPI Rank 1: Model has 25 nodes. Using CPU.
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:25: Training criterion:   CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
MPI Rank 1: 01/11/2018 08:56:25: Evaluation criterion: EvalClassificationError = ClassificationError
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:25: Training 516740 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:25: 	Node 'B0' (LearnableParameter operation) : [512 x 1]
MPI Rank 1: 01/11/2018 08:56:25: 	Node 'B1' (LearnableParameter operation) : [512 x 1]
MPI Rank 1: 01/11/2018 08:56:25: 	Node 'B2' (LearnableParameter operation) : [132 x 1]
MPI Rank 1: 01/11/2018 08:56:25: 	Node 'W0' (LearnableParameter operation) : [512 x 363]
MPI Rank 1: 01/11/2018 08:56:25: 	Node 'W1' (LearnableParameter operation) : [512 x 512]
MPI Rank 1: 01/11/2018 08:56:25: 	Node 'W2' (LearnableParameter operation) : [132 x 512]
MPI Rank 1: 
MPI Rank 1: Initializing dataParallelSGD with FP64 aggregation.
MPI Rank 1: 01/11/2018 08:56:25: No PreCompute nodes found, or all already computed. Skipping pre-computation step.
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:25: Starting Epoch 15: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:25: Starting minibatch loop, DataParallelSGD training (myRank = 1, numNodes = 2, numGradientBits = 64), distributed reading is ENABLED.
MPI Rank 1: 01/11/2018 08:56:26:  Epoch[15 of 15]-Minibatch[   1-  10, 50.00%]: CrossEntropyWithSoftmax = 1.82826227 * 10240; EvalClassificationError = 0.50820312 * 10240; time = 0.8837s; samplesPerSecond = 11587.2
MPI Rank 1: 01/11/2018 08:56:27:  Epoch[15 of 15]-Minibatch[  11-  20, 100.00%]: CrossEntropyWithSoftmax = 1.85323706 * 10240; EvalClassificationError = 0.51826172 * 10240; time = 0.8131s; samplesPerSecond = 12593.4
MPI Rank 1: 01/11/2018 08:56:27: Finished Epoch[15 of 15]: [Training] CrossEntropyWithSoftmax = 1.84074966 * 20480; EvalClassificationError = 0.51323242 * 20480; totalSamplesSeen = 307200; learningRatePerSample = 9.7656251e-05; epochTime=1.75552s
MPI Rank 1: 01/11/2018 08:56:29: Final Results: Minibatch[1-83]: CrossEntropyWithSoftmax = 1.83558729 * 83050; perplexity = 6.26881470; EvalClassificationError = 0.51134256 * 83050
MPI Rank 1: 01/11/2018 08:56:29: Finished Epoch[15 of 15]: [Validate] CrossEntropyWithSoftmax = 1.83558729 * 83050; EvalClassificationError = 0.51134256 * 83050
MPI Rank 1: 01/11/2018 08:56:29: Best epoch per criterion so far: [Validate] CrossEntropyWithSoftmax = 1.835587 (Epoch 15); EvalClassificationError = 0.511343 (Epoch 15)
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:29: Action "train" complete.
MPI Rank 1: 
MPI Rank 1: 01/11/2018 08:56:29: __COMPLETED__
