CPU info:
    CPU Model Name: Intel(R) Xeon(R) CPU W3565 @ 3.20GHz
    Hardware threads: 8
    Total Memory: 12580436 kB
-------------------------------------------------------------------
=== Running C:\Program Files\Microsoft MPI\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\HTKDeserializers\DNN\ParallelBMWithAdjustLR/cntk.cntk currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\HTKDeserializers\DNN\ParallelBMWithAdjustLR OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu DeviceId=0 timestamping=true numCPUThreads=4 precision=double speechTrain=[SGD=[ParallelTrain=[parallelizationStartEpoch=2]]] stderr=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu/stderr
CNTK 2.0.beta6.0+ (HEAD 0a2e20, Dec 21 2016 04:21:26) on cntk-muc01 at 2016/12/21 05:28:27

C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe  configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\HTKDeserializers\DNN\ParallelBMWithAdjustLR/cntk.cntk  currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data  RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu  DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data  ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\HTKDeserializers\DNN\ParallelBMWithAdjustLR  OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu  DeviceId=0  timestamping=true  numCPUThreads=4  precision=double  speechTrain=[SGD=[ParallelTrain=[parallelizationStartEpoch=2]]]  stderr=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu/stderr
Changed current directory to C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data
requestnodes [MPIWrapper]: using 2 out of 2 MPI nodes on a single host (2 requested); we (0) are in (participating)
CNTK 2.0.beta6.0+ (HEAD 0a2e20, Dec 21 2016 04:21:26) on cntk-muc01 at 2016/12/21 05:28:27

C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe  configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\HTKDeserializers\DNN\ParallelBMWithAdjustLR/cntk.cntk  currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data  RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu  DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data  ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\HTKDeserializers\DNN\ParallelBMWithAdjustLR  OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu  DeviceId=0  timestamping=true  numCPUThreads=4  precision=double  speechTrain=[SGD=[ParallelTrain=[parallelizationStartEpoch=2]]]  stderr=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu/stderr
Changed current directory to C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data
requestnodes [MPIWrapper]: using 2 out of 2 MPI nodes on a single host (2 requested); we (1) are in (participating)
MPI Rank 0: 12/21/2016 05:28:27: Redirecting stderr to file C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu/stderr_SpeechTrain.logrank0
MPI Rank 0: 12/21/2016 05:28:27: -------------------------------------------------------------------
MPI Rank 0: 12/21/2016 05:28:27: Build info: 
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:27: 		Built time: Dec 21 2016 04:21:26
MPI Rank 0: 12/21/2016 05:28:27: 		Last modified date: Tue Dec 20 18:55:12 2016
MPI Rank 0: 12/21/2016 05:28:27: 		Build type: Release
MPI Rank 0: 12/21/2016 05:28:27: 		Build target: GPU
MPI Rank 0: 12/21/2016 05:28:27: 		With ASGD: yes
MPI Rank 0: 12/21/2016 05:28:27: 		Math lib: mkl
MPI Rank 0: 12/21/2016 05:28:27: 		CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0
MPI Rank 0: 12/21/2016 05:28:27: 		CUB_PATH: c:\src\cub-1.4.1
MPI Rank 0: 12/21/2016 05:28:27: 		CUDNN_PATH: C:\local\cudnn-8.0-windows10-x64-v5.1
MPI Rank 0: 12/21/2016 05:28:27: 		Build Branch: HEAD
MPI Rank 0: 12/21/2016 05:28:27: 		Build SHA1: 0a2e20ddce32ca3cd458ef0358757e1489d9afe3 (modified)
MPI Rank 0: 12/21/2016 05:28:27: 		Built by svcphil on LIANA-09-w
MPI Rank 0: 12/21/2016 05:28:27: 		Build Path: C:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
MPI Rank 0: 12/21/2016 05:28:27: -------------------------------------------------------------------
MPI Rank 0: 12/21/2016 05:28:27: -------------------------------------------------------------------
MPI Rank 0: 12/21/2016 05:28:27: GPU info:
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:27: 		Device[0]: cores = 2496; computeCapability = 5.2; type = "Quadro M4000"; memory = 8192 MB
MPI Rank 0: 12/21/2016 05:28:27: -------------------------------------------------------------------
MPI Rank 0: 
MPI Rank 0: Configuration After Processing and Variable Resolution:
MPI Rank 0: 
MPI Rank 0: configparameters: cntk.cntk:command=SpeechTrain
MPI Rank 0: configparameters: cntk.cntk:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\HTKDeserializers\DNN\ParallelBMWithAdjustLR
MPI Rank 0: configparameters: cntk.cntk:currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data
MPI Rank 0: configparameters: cntk.cntk:DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data
MPI Rank 0: configparameters: cntk.cntk:deviceId=0
MPI Rank 0: configparameters: cntk.cntk:framemode=true
MPI Rank 0: configparameters: cntk.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu/models/cntkSpeech.dnn
MPI Rank 0: configparameters: cntk.cntk:numCPUThreads=4
MPI Rank 0: configparameters: cntk.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu
MPI Rank 0: configparameters: cntk.cntk:parallelTrain=true
MPI Rank 0: configparameters: cntk.cntk:precision=double
MPI Rank 0: configparameters: cntk.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu
MPI Rank 0: configparameters: cntk.cntk:SpeechTrain={
MPI Rank 0:     action = "train"
MPI Rank 0:     BrainScriptNetworkBuilder = {
MPI Rank 0:         layerSizes = 363:512:512:132
MPI Rank 0:         trainingCriterion = 'CE'
MPI Rank 0:         evalCriterion = 'Err'
MPI Rank 0:         applyMeanVarNorm = true
MPI Rank 0:         L = Length(layerSizes)-1    // number of model layers
MPI Rank 0:         features = Input { layerSizes[0] } // 1, tag='feature' 
MPI Rank 0:         labels = Input { layerSizes[L] } // 1, tag='label' 
MPI Rank 0:         featNorm = if applyMeanVarNorm
MPI Rank 0:                    then MeanVarNorm(features)
MPI Rank 0:                    else features
MPI Rank 0:         layers[layer:1..L-1] = if layer > 1
MPI Rank 0:                                then SBFF(layers[layer-1].Eh, layerSizes[layer], layerSizes[layer-1])
MPI Rank 0:                                else SBFF(featNorm, layerSizes[layer], layerSizes[layer-1])
MPI Rank 0:         outLayer = BFF(layers[L-1].Eh, layerSizes[L], layerSizes[L-1])
MPI Rank 0:         outZ = outLayer.z        // + PastValue(layerSizes[L], 1, outLayer.z)
MPI Rank 0:         CE = if trainingCriterion == 'CE'
MPI Rank 0:              then CrossEntropyWithSoftmax(labels, outZ, tag='criterion')
MPI Rank 0:              else Fail('unknown trainingCriterion ' + trainingCriterion)
MPI Rank 0:         Err = if evalCriterion == 'Err' then
MPI Rank 0:               ClassificationError(labels, outZ, tag='evaluation')
MPI Rank 0:               else Fail('unknown evalCriterion ' + evalCriterion)
MPI Rank 0:         logPrior = LogPrior(labels)
MPI Rank 0:         // TODO: how to add a tag to an infix operation?
MPI Rank 0:         ScaledLogLikelihood = Minus (outZ, logPrior, tag='output')
MPI Rank 0:     }
MPI Rank 0:     SGD = {
MPI Rank 0:         epochSize = 20480
MPI Rank 0:         minibatchSize = 64:256:1024
MPI Rank 0:         learningRatesPerMB = 1.0:0.5:0.1
MPI Rank 0:         numMBsToShowResult = 1
MPI Rank 0:         momentumPerMB = 0.9:0.656119
MPI Rank 0:         dropoutRate = 0.0
MPI Rank 0:         maxEpochs = 5
MPI Rank 0:         keepCheckPointFiles = true
MPI Rank 0:         clippingThresholdPerSample = 1#INF
MPI Rank 0:         ParallelTrain = {
MPI Rank 0:             parallelizationMethod = "BlockMomentumSGD"
MPI Rank 0:             distributedMBReading = true
MPI Rank 0:             syncPerfStats=1
MPI Rank 0:             BlockMomentumSGD = {
MPI Rank 0:                 blockSizePerWorker=2048
MPI Rank 0:                 resetSGDMomentum=true
MPI Rank 0:                 useNesterovMomentum=true
MPI Rank 0:             }
MPI Rank 0:         }
MPI Rank 0:         AutoAdjust = {
MPI Rank 0:             reduceLearnRateIfImproveLessThan = 0
MPI Rank 0:             loadBestModel = true
MPI Rank 0:             increaseLearnRateIfImproveMoreThan = 1000000000
MPI Rank 0:             learnRateDecreaseFactor = 0.5
MPI Rank 0:             learnRateIncreaseFactor = 1.382
MPI Rank 0:             autoAdjustLR = "searchBeforeEpoch"
MPI Rank 0:             numMiniBatch4LRSearch = 20
MPI Rank 0:             numPrevLearnRates = 3            
MPI Rank 0:         }
MPI Rank 0:     }
MPI Rank 0:     reader = {
MPI Rank 0:         verbosity = 0 ; randomize = true
MPI Rank 0:         deserializers = ({
MPI Rank 0:             type = "HTKFeatureDeserializer"
MPI Rank 0:             module = "HTKDeserializers"                
MPI Rank 0:             input = { features = { dim = 363 ; scpFile = "glob_0000.scp" } }
MPI Rank 0:         }:{
MPI Rank 0:             type = "HTKMLFDeserializer"
MPI Rank 0:             module = "HTKDeserializers"
MPI Rank 0:             input = { labels = { mlfFile = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/glob_0000.mlf" ; labelMappingFile = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/state.list" ; labelDim = 132 } }
MPI Rank 0:         })
MPI Rank 0:     }
MPI Rank 0: } [SGD=[ParallelTrain=[parallelizationStartEpoch=2]]]
MPI Rank 0: 
MPI Rank 0: configparameters: cntk.cntk:stderr=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu/stderr
MPI Rank 0: configparameters: cntk.cntk:timestamping=true
MPI Rank 0: configparameters: cntk.cntk:traceLevel=1
MPI Rank 0: 12/21/2016 05:28:27: Commands: SpeechTrain
MPI Rank 0: 12/21/2016 05:28:27: precision = "double"
MPI Rank 0: 12/21/2016 05:28:27: Using 4 CPU threads.
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:27: ##############################################################################
MPI Rank 0: 12/21/2016 05:28:27: #                                                                            #
MPI Rank 0: 12/21/2016 05:28:27: # SpeechTrain command (train action)                                         #
MPI Rank 0: 12/21/2016 05:28:27: #                                                                            #
MPI Rank 0: 12/21/2016 05:28:27: ##############################################################################
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:27: WARNING: 'numMiniBatch4LRSearch' is deprecated, please remove it and use 'numSamples4Search' instead.
MPI Rank 0: 12/21/2016 05:28:27: 
MPI Rank 0: Creating virgin network.
MPI Rank 0: 
MPI Rank 0: Post-processing network...
MPI Rank 0: 
MPI Rank 0: 6 roots:
MPI Rank 0: 	CE = CrossEntropyWithSoftmax()
MPI Rank 0: 	Err = ClassificationError()
MPI Rank 0: 	ScaledLogLikelihood = Minus()
MPI Rank 0: 	featNorm.invStdDev = InvStdDev()
MPI Rank 0: 	featNorm.mean = Mean()
MPI Rank 0: 	logPrior._ = Mean()
MPI Rank 0: 
MPI Rank 0: Validating network. 25 nodes to process in pass 1.
MPI Rank 0: 
MPI Rank 0: Validating --> labels = InputValue() :  -> [132 x *]
MPI Rank 0: Validating --> outLayer.W = LearnableParameter() :  -> [132 x 512]
MPI Rank 0: Validating --> layers[2].Eh._.W = LearnableParameter() :  -> [512 x 512]
MPI Rank 0: Validating --> layers[1].Eh._.W = LearnableParameter() :  -> [512 x 363]
MPI Rank 0: Validating --> features = InputValue() :  -> [363 x *]
MPI Rank 0: Validating --> featNorm.mean = Mean (features) : [363 x *] -> [363]
MPI Rank 0: Validating --> featNorm.ElementTimesArgs[0] = Minus (features, featNorm.mean) : [363 x *], [363] -> [363 x *]
MPI Rank 0: Validating --> featNorm.invStdDev = InvStdDev (features) : [363 x *] -> [363]
MPI Rank 0: Validating --> featNorm = ElementTimes (featNorm.ElementTimesArgs[0], featNorm.invStdDev) : [363 x *], [363] -> [363 x *]
MPI Rank 0: Validating --> layers[1].Eh._.z.PlusArgs[0] = Times (layers[1].Eh._.W, featNorm) : [512 x 363], [363 x *] -> [512 x *]
MPI Rank 0: Validating --> layers[1].Eh._.B = LearnableParameter() :  -> [512 x 1]
MPI Rank 0: Validating --> layers[1].Eh._.z = Plus (layers[1].Eh._.z.PlusArgs[0], layers[1].Eh._.B) : [512 x *], [512 x 1] -> [512 x 1 x *]
MPI Rank 0: Validating --> layers[1].Eh = Sigmoid (layers[1].Eh._.z) : [512 x 1 x *] -> [512 x 1 x *]
MPI Rank 0: Validating --> layers[2].Eh._.z.PlusArgs[0] = Times (layers[2].Eh._.W, layers[1].Eh) : [512 x 512], [512 x 1 x *] -> [512 x 1 x *]
MPI Rank 0: Validating --> layers[2].Eh._.B = LearnableParameter() :  -> [512 x 1]
MPI Rank 0: Validating --> layers[2].Eh._.z = Plus (layers[2].Eh._.z.PlusArgs[0], layers[2].Eh._.B) : [512 x 1 x *], [512 x 1] -> [512 x 1 x *]
MPI Rank 0: Validating --> layers[2].Eh = Sigmoid (layers[2].Eh._.z) : [512 x 1 x *] -> [512 x 1 x *]
MPI Rank 0: Validating --> outLayer.z.PlusArgs[0] = Times (outLayer.W, layers[2].Eh) : [132 x 512], [512 x 1 x *] -> [132 x 1 x *]
MPI Rank 0: Validating --> outLayer.B = LearnableParameter() :  -> [132 x 1]
MPI Rank 0: Validating --> outZ = Plus (outLayer.z.PlusArgs[0], outLayer.B) : [132 x 1 x *], [132 x 1] -> [132 x 1 x *]
MPI Rank 0: Validating --> CE = CrossEntropyWithSoftmax (labels, outZ) : [132 x *], [132 x 1 x *] -> [1]
MPI Rank 0: Validating --> Err = ClassificationError (labels, outZ) : [132 x *], [132 x 1 x *] -> [1]
MPI Rank 0: Validating --> logPrior._ = Mean (labels) : [132 x *] -> [132]
MPI Rank 0: Validating --> logPrior = Log (logPrior._) : [132] -> [132]
MPI Rank 0: Validating --> ScaledLogLikelihood = Minus (outZ, logPrior) : [132 x 1 x *], [132] -> [132 x 1 x *]
MPI Rank 0: 
MPI Rank 0: Validating network. 17 nodes to process in pass 2.
MPI Rank 0: 
MPI Rank 0: 
MPI Rank 0: Validating network, final pass.
MPI Rank 0: 
MPI Rank 0: 
MPI Rank 0: 
MPI Rank 0: 
MPI Rank 0: Post-processing network complete.
MPI Rank 0: 
MPI Rank 0: Reading script file glob_0000.scp ... 948 entries
MPI Rank 0: HTKDataDeserializer::HTKDataDeserializer: 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: HTKDataDeserializer::HTKDataDeserializer: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms
MPI Rank 0: total 132 state names in state list C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/state.list
MPI Rank 0: htkmlfreader: reading MLF file C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/glob_0000.mlf ... total 948 entries
MPI Rank 0: MLFDataDeserializer::MLFDataDeserializer: 948 utterances with 252734 frames in 129 classes
MPI Rank 0: 12/21/2016 05:28:28: 
MPI Rank 0: Model has 25 nodes. Using GPU 0.
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:28: Training criterion:   CE = CrossEntropyWithSoftmax
MPI Rank 0: 12/21/2016 05:28:28: Evaluation criterion: Err = ClassificationError
MPI Rank 0: 
MPI Rank 0: 
MPI Rank 0: Allocating matrices for forward and/or backward propagation.
MPI Rank 0: 
MPI Rank 0: Memory Sharing: Out of 40 matrices, 19 are shared as 8, and 21 are not shared.
MPI Rank 0: 
MPI Rank 0: 	{ layers[1].Eh : [512 x 1 x *]
MPI Rank 0: 	  layers[1].Eh._.z.PlusArgs[0] : [512 x *] (gradient) }
MPI Rank 0: 	{ layers[1].Eh._.z : [512 x 1 x *] (gradient)
MPI Rank 0: 	  layers[2].Eh._.z.PlusArgs[0] : [512 x 1 x *] }
MPI Rank 0: 	{ layers[1].Eh : [512 x 1 x *] (gradient)
MPI Rank 0: 	  layers[1].Eh._.B : [512 x 1] (gradient)
MPI Rank 0: 	  layers[2].Eh._.z : [512 x 1 x *] (gradient)
MPI Rank 0: 	  outLayer.z.PlusArgs[0] : [132 x 1 x *] }
MPI Rank 0: 	{ layers[2].Eh._.W : [512 x 512] (gradient)
MPI Rank 0: 	  layers[2].Eh._.z : [512 x 1 x *] }
MPI Rank 0: 	{ layers[1].Eh._.W : [512 x 363] (gradient)
MPI Rank 0: 	  layers[1].Eh._.z : [512 x 1 x *] }
MPI Rank 0: 	{ layers[2].Eh : [512 x 1 x *]
MPI Rank 0: 	  layers[2].Eh._.z.PlusArgs[0] : [512 x 1 x *] (gradient) }
MPI Rank 0: 	{ outLayer.W : [132 x 512] (gradient)
MPI Rank 0: 	  outZ : [132 x 1 x *] }
MPI Rank 0: 	{ layers[2].Eh : [512 x 1 x *] (gradient)
MPI Rank 0: 	  layers[2].Eh._.B : [512 x 1] (gradient)
MPI Rank 0: 	  outZ : [132 x 1 x *] (gradient) }
MPI Rank 0: 
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:28: Training 516740 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:28: 	Node 'layers[1].Eh._.B' (LearnableParameter operation) : [512 x 1]
MPI Rank 0: 12/21/2016 05:28:28: 	Node 'layers[1].Eh._.W' (LearnableParameter operation) : [512 x 363]
MPI Rank 0: 12/21/2016 05:28:28: 	Node 'layers[2].Eh._.B' (LearnableParameter operation) : [512 x 1]
MPI Rank 0: 12/21/2016 05:28:28: 	Node 'layers[2].Eh._.W' (LearnableParameter operation) : [512 x 512]
MPI Rank 0: 12/21/2016 05:28:28: 	Node 'outLayer.B' (LearnableParameter operation) : [132 x 1]
MPI Rank 0: 12/21/2016 05:28:28: 	Node 'outLayer.W' (LearnableParameter operation) : [132 x 512]
MPI Rank 0: 
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:28: Precomputing --> 3 PreCompute nodes found.
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:28: 	featNorm.mean = Mean()
MPI Rank 0: 12/21/2016 05:28:28: 	featNorm.invStdDev = InvStdDev()
MPI Rank 0: 12/21/2016 05:28:28: 	logPrior._ = Mean()
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:33: Precomputing --> Completed.
MPI Rank 0: 
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:33: 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: 12/21/2016 05:28:33: Starting minibatch loop.
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[   1-   1, 0.31%]: CE = 4.91295596 * 64; Err = 0.96875000 * 64; time = 0.0148s; samplesPerSecond = 4311.2
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[   2-   2, 0.63%]: CE = 4.78498529 * 64; Err = 1.00000000 * 64; time = 0.0102s; samplesPerSecond = 6267.1
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[   3-   3, 0.94%]: CE = 4.19018696 * 64; Err = 0.81250000 * 64; time = 0.0097s; samplesPerSecond = 6623.2
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[   4-   4, 1.25%]: CE = 4.46135476 * 64; Err = 0.82812500 * 64; time = 0.0084s; samplesPerSecond = 7642.7
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[   5-   5, 1.56%]: CE = 4.72788003 * 64; Err = 0.92187500 * 64; time = 0.0102s; samplesPerSecond = 6255.5
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[   6-   6, 1.88%]: CE = 4.07654096 * 64; Err = 0.89062500 * 64; time = 0.0083s; samplesPerSecond = 7733.2
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[   7-   7, 2.19%]: CE = 4.50165607 * 64; Err = 0.96875000 * 64; time = 0.0104s; samplesPerSecond = 6133.2
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[   8-   8, 2.50%]: CE = 4.93153999 * 64; Err = 0.89062500 * 64; time = 0.0099s; samplesPerSecond = 6464.6
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[   9-   9, 2.81%]: CE = 4.79817443 * 64; Err = 0.93750000 * 64; time = 0.0100s; samplesPerSecond = 6386.6
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  10-  10, 3.13%]: CE = 4.46089875 * 64; Err = 0.96875000 * 64; time = 0.0088s; samplesPerSecond = 7243.1
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  11-  11, 3.44%]: CE = 4.34462020 * 64; Err = 0.90625000 * 64; time = 0.0097s; samplesPerSecond = 6583.0
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  12-  12, 3.75%]: CE = 3.91243070 * 64; Err = 0.87500000 * 64; time = 0.0085s; samplesPerSecond = 7552.5
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  13-  13, 4.06%]: CE = 4.73715179 * 64; Err = 0.92187500 * 64; time = 0.0101s; samplesPerSecond = 6349.8
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  14-  14, 4.38%]: CE = 4.42160986 * 64; Err = 0.93750000 * 64; time = 0.0091s; samplesPerSecond = 6995.3
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  15-  15, 4.69%]: CE = 4.14675744 * 64; Err = 0.85937500 * 64; time = 0.0092s; samplesPerSecond = 6994.5
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  16-  16, 5.00%]: CE = 4.50951186 * 64; Err = 0.95312500 * 64; time = 0.0099s; samplesPerSecond = 6490.2
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  17-  17, 5.31%]: CE = 4.30758210 * 64; Err = 0.85937500 * 64; time = 0.0107s; samplesPerSecond = 5963.5
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  18-  18, 5.63%]: CE = 4.34534841 * 64; Err = 1.00000000 * 64; time = 0.0097s; samplesPerSecond = 6595.9
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  19-  19, 5.94%]: CE = 4.19517128 * 64; Err = 0.96875000 * 64; time = 0.0093s; samplesPerSecond = 6895.1
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  20-  20, 6.25%]: CE = 4.41248710 * 64; Err = 0.98437500 * 64; time = 0.0113s; samplesPerSecond = 5670.2
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  21-  21, 6.56%]: CE = 4.10891079 * 64; Err = 0.92187500 * 64; time = 0.0101s; samplesPerSecond = 6331.0
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  22-  22, 6.88%]: CE = 4.16379766 * 64; Err = 0.85937500 * 64; time = 0.0106s; samplesPerSecond = 6060.6
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  23-  23, 7.19%]: CE = 4.09455579 * 64; Err = 0.92187500 * 64; time = 0.0095s; samplesPerSecond = 6718.5
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  24-  24, 7.50%]: CE = 3.95980469 * 64; Err = 0.89062500 * 64; time = 0.0085s; samplesPerSecond = 7508.2
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  25-  25, 7.81%]: CE = 4.05428109 * 64; Err = 0.87500000 * 64; time = 0.0113s; samplesPerSecond = 5675.3
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  26-  26, 8.13%]: CE = 4.16245451 * 64; Err = 0.84375000 * 64; time = 0.0089s; samplesPerSecond = 7221.8
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  27-  27, 8.44%]: CE = 3.71756327 * 64; Err = 0.84375000 * 64; time = 0.0091s; samplesPerSecond = 7006.0
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  28-  28, 8.75%]: CE = 3.80779138 * 64; Err = 0.87500000 * 64; time = 0.0100s; samplesPerSecond = 6418.6
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  29-  29, 9.06%]: CE = 3.72564857 * 64; Err = 0.81250000 * 64; time = 0.0098s; samplesPerSecond = 6557.4
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  30-  30, 9.38%]: CE = 4.01963243 * 64; Err = 0.87500000 * 64; time = 0.0096s; samplesPerSecond = 6656.3
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  31-  31, 9.69%]: CE = 3.68590709 * 64; Err = 0.89062500 * 64; time = 0.0099s; samplesPerSecond = 6438.0
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  32-  32, 10.00%]: CE = 3.81516754 * 64; Err = 0.78125000 * 64; time = 0.0098s; samplesPerSecond = 6516.0
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  33-  33, 10.31%]: CE = 3.93685037 * 64; Err = 0.87500000 * 64; time = 0.0085s; samplesPerSecond = 7564.1
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  34-  34, 10.63%]: CE = 3.96481462 * 64; Err = 0.90625000 * 64; time = 0.0097s; samplesPerSecond = 6619.1
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  35-  35, 10.94%]: CE = 3.57865409 * 64; Err = 0.84375000 * 64; time = 0.0090s; samplesPerSecond = 7126.2
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  36-  36, 11.25%]: CE = 3.72265528 * 64; Err = 0.85937500 * 64; time = 0.0103s; samplesPerSecond = 6201.6
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  37-  37, 11.56%]: CE = 3.71485627 * 64; Err = 0.84375000 * 64; time = 0.0087s; samplesPerSecond = 7377.5
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  38-  38, 11.88%]: CE = 4.04042687 * 64; Err = 0.87500000 * 64; time = 0.0105s; samplesPerSecond = 6068.7
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  39-  39, 12.19%]: CE = 3.48663283 * 64; Err = 0.76562500 * 64; time = 0.0088s; samplesPerSecond = 7247.2
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  40-  40, 12.50%]: CE = 3.48828968 * 64; Err = 0.81250000 * 64; time = 0.0103s; samplesPerSecond = 6194.9
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  41-  41, 12.81%]: CE = 3.46883616 * 64; Err = 0.78125000 * 64; time = 0.0096s; samplesPerSecond = 6660.4
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  42-  42, 13.13%]: CE = 4.12832965 * 64; Err = 0.90625000 * 64; time = 0.0095s; samplesPerSecond = 6705.8
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  43-  43, 13.44%]: CE = 3.82286476 * 64; Err = 0.90625000 * 64; time = 0.0093s; samplesPerSecond = 6910.0
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  44-  44, 13.75%]: CE = 3.99396471 * 64; Err = 0.90625000 * 64; time = 0.0097s; samplesPerSecond = 6576.9
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  45-  45, 14.06%]: CE = 3.84953256 * 64; Err = 0.89062500 * 64; time = 0.0083s; samplesPerSecond = 7702.5
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  46-  46, 14.37%]: CE = 3.57917953 * 64; Err = 0.79687500 * 64; time = 0.0101s; samplesPerSecond = 6331.6
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  47-  47, 14.69%]: CE = 3.86079148 * 64; Err = 0.84375000 * 64; time = 0.0093s; samplesPerSecond = 6901.0
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  48-  48, 15.00%]: CE = 3.88891763 * 64; Err = 0.85937500 * 64; time = 0.0102s; samplesPerSecond = 6288.7
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  49-  49, 15.31%]: CE = 3.94662742 * 64; Err = 0.89062500 * 64; time = 0.0085s; samplesPerSecond = 7525.9
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  50-  50, 15.63%]: CE = 3.83644301 * 64; Err = 0.87500000 * 64; time = 0.0104s; samplesPerSecond = 6159.2
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  51-  51, 15.94%]: CE = 3.66716866 * 64; Err = 0.89062500 * 64; time = 0.0086s; samplesPerSecond = 7453.1
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  52-  52, 16.25%]: CE = 4.00651571 * 64; Err = 0.90625000 * 64; time = 0.0101s; samplesPerSecond = 6332.2
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  53-  53, 16.56%]: CE = 3.80511656 * 64; Err = 0.81250000 * 64; time = 0.0089s; samplesPerSecond = 7201.5
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  54-  54, 16.88%]: CE = 3.93380989 * 64; Err = 0.85937500 * 64; time = 0.0104s; samplesPerSecond = 6149.1
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  55-  55, 17.19%]: CE = 3.49394937 * 64; Err = 0.84375000 * 64; time = 0.0093s; samplesPerSecond = 6847.1
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  56-  56, 17.50%]: CE = 3.42224075 * 64; Err = 0.84375000 * 64; time = 0.0102s; samplesPerSecond = 6261.0
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  57-  57, 17.81%]: CE = 3.76078536 * 64; Err = 0.85937500 * 64; time = 0.0099s; samplesPerSecond = 6454.2
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  58-  58, 18.13%]: CE = 3.80639497 * 64; Err = 0.87500000 * 64; time = 0.0100s; samplesPerSecond = 6395.5
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  59-  59, 18.44%]: CE = 3.55543971 * 64; Err = 0.89062500 * 64; time = 0.0095s; samplesPerSecond = 6765.3
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  60-  60, 18.75%]: CE = 3.55947249 * 64; Err = 0.82812500 * 64; time = 0.0083s; samplesPerSecond = 7699.7
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  61-  61, 19.06%]: CE = 3.21133907 * 64; Err = 0.79687500 * 64; time = 0.0105s; samplesPerSecond = 6105.7
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  62-  62, 19.38%]: CE = 3.30807309 * 64; Err = 0.68750000 * 64; time = 0.0084s; samplesPerSecond = 7657.3
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  63-  63, 19.69%]: CE = 3.54643060 * 64; Err = 0.78125000 * 64; time = 0.0100s; samplesPerSecond = 6394.2
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  64-  64, 20.00%]: CE = 3.48819921 * 64; Err = 0.85937500 * 64; time = 0.0083s; samplesPerSecond = 7683.1
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  65-  65, 20.31%]: CE = 3.53098379 * 64; Err = 0.81250000 * 64; time = 0.0103s; samplesPerSecond = 6236.0
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  66-  66, 20.63%]: CE = 3.18218574 * 64; Err = 0.70312500 * 64; time = 0.0083s; samplesPerSecond = 7708.1
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  67-  67, 20.94%]: CE = 3.62919777 * 64; Err = 0.79687500 * 64; time = 0.0105s; samplesPerSecond = 6075.0
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  68-  68, 21.25%]: CE = 3.30344749 * 64; Err = 0.76562500 * 64; time = 0.0097s; samplesPerSecond = 6604.7
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  69-  69, 21.56%]: CE = 3.11192070 * 64; Err = 0.75000000 * 64; time = 0.0094s; samplesPerSecond = 6822.3
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  70-  70, 21.88%]: CE = 3.70063691 * 64; Err = 0.79687500 * 64; time = 0.0091s; samplesPerSecond = 7027.6
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  71-  71, 22.19%]: CE = 3.76244503 * 64; Err = 0.84375000 * 64; time = 0.0092s; samplesPerSecond = 6990.0
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  72-  72, 22.50%]: CE = 3.52103388 * 64; Err = 0.81250000 * 64; time = 0.0092s; samplesPerSecond = 6947.5
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  73-  73, 22.81%]: CE = 3.73227550 * 64; Err = 0.87500000 * 64; time = 0.0108s; samplesPerSecond = 5945.7
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  74-  74, 23.13%]: CE = 3.28056294 * 64; Err = 0.75000000 * 64; time = 0.0092s; samplesPerSecond = 6952.0
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  75-  75, 23.44%]: CE = 3.88497398 * 64; Err = 0.89062500 * 64; time = 0.0112s; samplesPerSecond = 5721.9
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  76-  76, 23.75%]: CE = 3.62146548 * 64; Err = 0.85937500 * 64; time = 0.0107s; samplesPerSecond = 5983.5
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  77-  77, 24.06%]: CE = 3.11930348 * 64; Err = 0.73437500 * 64; time = 0.0099s; samplesPerSecond = 6437.3
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  78-  78, 24.38%]: CE = 3.34530218 * 64; Err = 0.87500000 * 64; time = 0.0106s; samplesPerSecond = 6046.9
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  79-  79, 24.69%]: CE = 3.51426589 * 64; Err = 0.84375000 * 64; time = 0.0104s; samplesPerSecond = 6166.3
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  80-  80, 25.00%]: CE = 3.40713594 * 64; Err = 0.81250000 * 64; time = 0.0104s; samplesPerSecond = 6126.2
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  81-  81, 25.31%]: CE = 3.59134827 * 64; Err = 0.82812500 * 64; time = 0.0089s; samplesPerSecond = 7222.7
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  82-  82, 25.62%]: CE = 3.52703040 * 64; Err = 0.82812500 * 64; time = 0.0110s; samplesPerSecond = 5841.0
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  83-  83, 25.94%]: CE = 3.22259624 * 64; Err = 0.79687500 * 64; time = 0.0096s; samplesPerSecond = 6685.5
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  84-  84, 26.25%]: CE = 3.64961943 * 64; Err = 0.82812500 * 64; time = 0.0095s; samplesPerSecond = 6718.5
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  85-  85, 26.56%]: CE = 3.70782192 * 64; Err = 0.76562500 * 64; time = 0.0070s; samplesPerSecond = 9145.5
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  86-  86, 26.88%]: CE = 3.53921564 * 64; Err = 0.89062500 * 64; time = 0.0114s; samplesPerSecond = 5626.9
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  87-  87, 27.19%]: CE = 3.38712792 * 64; Err = 0.81250000 * 64; time = 0.0086s; samplesPerSecond = 7457.5
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  88-  88, 27.50%]: CE = 3.66470493 * 64; Err = 0.78125000 * 64; time = 0.0101s; samplesPerSecond = 6366.9
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  89-  89, 27.81%]: CE = 3.12758734 * 64; Err = 0.84375000 * 64; time = 0.0097s; samplesPerSecond = 6602.7
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  90-  90, 28.13%]: CE = 3.52072988 * 64; Err = 0.82812500 * 64; time = 0.0099s; samplesPerSecond = 6475.1
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  91-  91, 28.44%]: CE = 3.45630741 * 64; Err = 0.76562500 * 64; time = 0.0098s; samplesPerSecond = 6559.4
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  92-  92, 28.75%]: CE = 3.19535282 * 64; Err = 0.78125000 * 64; time = 0.0100s; samplesPerSecond = 6375.8
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  93-  93, 29.06%]: CE = 3.40545723 * 64; Err = 0.81250000 * 64; time = 0.0084s; samplesPerSecond = 7625.4
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  94-  94, 29.38%]: CE = 3.47518793 * 64; Err = 0.70312500 * 64; time = 0.0104s; samplesPerSecond = 6125.6
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  95-  95, 29.69%]: CE = 3.32919398 * 64; Err = 0.78125000 * 64; time = 0.0095s; samplesPerSecond = 6715.6
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  96-  96, 30.00%]: CE = 3.86499937 * 64; Err = 0.93750000 * 64; time = 0.0098s; samplesPerSecond = 6520.0
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  97-  97, 30.31%]: CE = 3.42288014 * 64; Err = 0.84375000 * 64; time = 0.0099s; samplesPerSecond = 6441.2
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  98-  98, 30.63%]: CE = 3.31506114 * 64; Err = 0.82812500 * 64; time = 0.0099s; samplesPerSecond = 6492.2
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  99-  99, 30.94%]: CE = 3.28863365 * 64; Err = 0.76562500 * 64; time = 0.0098s; samplesPerSecond = 6502.7
MPI Rank 0: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[ 100- 100, 31.25%]: CE = 3.20182099 * 64; Err = 0.76562500 * 64; time = 0.0100s; samplesPerSecond = 6425.1
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 101- 101, 31.56%]: CE = 3.75128437 * 64; Err = 0.89062500 * 64; time = 0.0098s; samplesPerSecond = 6556.0
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 102- 102, 31.87%]: CE = 3.57333316 * 64; Err = 0.84375000 * 64; time = 0.0099s; samplesPerSecond = 6439.3
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 103- 103, 32.19%]: CE = 3.65041879 * 64; Err = 0.81250000 * 64; time = 0.0093s; samplesPerSecond = 6854.5
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 104- 104, 32.50%]: CE = 3.45052191 * 64; Err = 0.82812500 * 64; time = 0.0101s; samplesPerSecond = 6320.4
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 105- 105, 32.81%]: CE = 3.57278549 * 64; Err = 0.85937500 * 64; time = 0.0097s; samplesPerSecond = 6608.2
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 106- 106, 33.13%]: CE = 3.35244169 * 64; Err = 0.87500000 * 64; time = 0.0100s; samplesPerSecond = 6426.3
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 107- 107, 33.44%]: CE = 3.29949185 * 64; Err = 0.76562500 * 64; time = 0.0099s; samplesPerSecond = 6435.4
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 108- 108, 33.75%]: CE = 3.78609758 * 64; Err = 0.82812500 * 64; time = 0.0102s; samplesPerSecond = 6263.5
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 109- 109, 34.06%]: CE = 3.22622650 * 64; Err = 0.78125000 * 64; time = 0.0089s; samplesPerSecond = 7185.4
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 110- 110, 34.38%]: CE = 3.29821989 * 64; Err = 0.79687500 * 64; time = 0.0101s; samplesPerSecond = 6318.5
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 111- 111, 34.69%]: CE = 3.44143907 * 64; Err = 0.82812500 * 64; time = 0.0092s; samplesPerSecond = 6923.4
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 112- 112, 35.00%]: CE = 3.44276929 * 64; Err = 0.85937500 * 64; time = 0.0103s; samplesPerSecond = 6198.5
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 113- 113, 35.31%]: CE = 3.18216790 * 64; Err = 0.76562500 * 64; time = 0.0081s; samplesPerSecond = 7895.4
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 114- 114, 35.63%]: CE = 3.18609709 * 64; Err = 0.78125000 * 64; time = 0.0102s; samplesPerSecond = 6268.4
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 115- 115, 35.94%]: CE = 3.06550821 * 64; Err = 0.73437500 * 64; time = 0.0096s; samplesPerSecond = 6646.6
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 116- 116, 36.25%]: CE = 3.43583629 * 64; Err = 0.79687500 * 64; time = 0.0100s; samplesPerSecond = 6394.2
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 117- 117, 36.56%]: CE = 3.10193105 * 64; Err = 0.78125000 * 64; time = 0.0084s; samplesPerSecond = 7635.4
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 118- 118, 36.88%]: CE = 3.42968418 * 64; Err = 0.81250000 * 64; time = 0.0103s; samplesPerSecond = 6186.0
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 119- 119, 37.19%]: CE = 2.85043824 * 64; Err = 0.60937500 * 64; time = 0.0102s; samplesPerSecond = 6299.2
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 120- 120, 37.50%]: CE = 3.50428373 * 64; Err = 0.85937500 * 64; time = 0.0100s; samplesPerSecond = 6398.7
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 121- 121, 37.81%]: CE = 3.28751701 * 64; Err = 0.82812500 * 64; time = 0.0097s; samplesPerSecond = 6600.7
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 122- 122, 38.13%]: CE = 3.79916343 * 64; Err = 0.89062500 * 64; time = 0.0099s; samplesPerSecond = 6433.5
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 123- 123, 38.44%]: CE = 3.55702537 * 64; Err = 0.82812500 * 64; time = 0.0097s; samplesPerSecond = 6599.3
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 124- 124, 38.75%]: CE = 3.00217445 * 64; Err = 0.71875000 * 64; time = 0.0101s; samplesPerSecond = 6332.2
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 125- 125, 39.06%]: CE = 3.07327108 * 64; Err = 0.75000000 * 64; time = 0.0093s; samplesPerSecond = 6878.8
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 126- 126, 39.38%]: CE = 2.88353063 * 64; Err = 0.59375000 * 64; time = 0.0099s; samplesPerSecond = 6439.3
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 127- 127, 39.69%]: CE = 3.13059468 * 64; Err = 0.79687500 * 64; time = 0.0105s; samplesPerSecond = 6070.4
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 128- 128, 40.00%]: CE = 3.21732650 * 64; Err = 0.85937500 * 64; time = 0.0094s; samplesPerSecond = 6831.8
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 129- 129, 40.31%]: CE = 2.97299345 * 64; Err = 0.71875000 * 64; time = 0.0107s; samplesPerSecond = 5991.4
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 130- 130, 40.63%]: CE = 2.93691495 * 64; Err = 0.79687500 * 64; time = 0.0108s; samplesPerSecond = 5900.2
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 131- 131, 40.94%]: CE = 3.31837783 * 64; Err = 0.68750000 * 64; time = 0.0105s; samplesPerSecond = 6093.5
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 132- 132, 41.25%]: CE = 2.91929775 * 64; Err = 0.78125000 * 64; time = 0.0105s; samplesPerSecond = 6119.7
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 133- 133, 41.56%]: CE = 3.07940161 * 64; Err = 0.68750000 * 64; time = 0.0106s; samplesPerSecond = 6042.9
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 134- 134, 41.88%]: CE = 3.28344492 * 64; Err = 0.75000000 * 64; time = 0.0100s; samplesPerSecond = 6402.6
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 135- 135, 42.19%]: CE = 3.18447176 * 64; Err = 0.78125000 * 64; time = 0.0087s; samplesPerSecond = 7390.3
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 136- 136, 42.50%]: CE = 2.79093256 * 64; Err = 0.71875000 * 64; time = 0.0116s; samplesPerSecond = 5501.1
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 137- 137, 42.81%]: CE = 2.87937588 * 64; Err = 0.70312500 * 64; time = 0.0107s; samplesPerSecond = 5960.7
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 138- 138, 43.13%]: CE = 2.64594163 * 64; Err = 0.68750000 * 64; time = 0.0090s; samplesPerSecond = 7134.9
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 139- 139, 43.44%]: CE = 2.94206439 * 64; Err = 0.84375000 * 64; time = 0.0102s; samplesPerSecond = 6270.2
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 140- 140, 43.75%]: CE = 3.51285987 * 64; Err = 0.82812500 * 64; time = 0.0102s; samplesPerSecond = 6293.0
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 141- 141, 44.06%]: CE = 3.04888687 * 64; Err = 0.81250000 * 64; time = 0.0095s; samplesPerSecond = 6755.3
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 142- 142, 44.38%]: CE = 3.13123367 * 64; Err = 0.76562500 * 64; time = 0.0098s; samplesPerSecond = 6500.8
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 143- 143, 44.69%]: CE = 2.92926400 * 64; Err = 0.71875000 * 64; time = 0.0101s; samplesPerSecond = 6361.2
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 144- 144, 45.00%]: CE = 3.00144780 * 64; Err = 0.71875000 * 64; time = 0.0100s; samplesPerSecond = 6417.3
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 145- 145, 45.31%]: CE = 2.90962694 * 64; Err = 0.67187500 * 64; time = 0.0091s; samplesPerSecond = 7035.3
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 146- 146, 45.63%]: CE = 3.03283171 * 64; Err = 0.79687500 * 64; time = 0.0102s; samplesPerSecond = 6280.1
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 147- 147, 45.94%]: CE = 3.06942741 * 64; Err = 0.73437500 * 64; time = 0.0090s; samplesPerSecond = 7146.0
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 148- 148, 46.25%]: CE = 2.86661978 * 64; Err = 0.65625000 * 64; time = 0.0100s; samplesPerSecond = 6429.6
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 149- 149, 46.56%]: CE = 2.76894440 * 64; Err = 0.68750000 * 64; time = 0.0099s; samplesPerSecond = 6493.5
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 150- 150, 46.88%]: CE = 2.71313692 * 64; Err = 0.59375000 * 64; time = 0.0101s; samplesPerSecond = 6328.5
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 151- 151, 47.19%]: CE = 2.74131048 * 64; Err = 0.65625000 * 64; time = 0.0084s; samplesPerSecond = 7613.6
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 152- 152, 47.50%]: CE = 3.28257238 * 64; Err = 0.71875000 * 64; time = 0.0101s; samplesPerSecond = 6368.2
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 153- 153, 47.81%]: CE = 3.08491448 * 64; Err = 0.76562500 * 64; time = 0.0099s; samplesPerSecond = 6477.1
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 154- 154, 48.13%]: CE = 2.98917665 * 64; Err = 0.71875000 * 64; time = 0.0100s; samplesPerSecond = 6400.6
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 155- 155, 48.44%]: CE = 2.90881148 * 64; Err = 0.81250000 * 64; time = 0.0092s; samplesPerSecond = 6946.0
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 156- 156, 48.75%]: CE = 3.58531995 * 64; Err = 0.78125000 * 64; time = 0.0099s; samplesPerSecond = 6456.2
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 157- 157, 49.06%]: CE = 3.28706069 * 64; Err = 0.75000000 * 64; time = 0.0089s; samplesPerSecond = 7216.1
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 158- 158, 49.38%]: CE = 3.06029676 * 64; Err = 0.81250000 * 64; time = 0.0101s; samplesPerSecond = 6307.9
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 159- 159, 49.69%]: CE = 2.95483403 * 64; Err = 0.68750000 * 64; time = 0.0092s; samplesPerSecond = 6966.4
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 160- 160, 50.00%]: CE = 3.07409648 * 64; Err = 0.76562500 * 64; time = 0.0091s; samplesPerSecond = 7016.8
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 161- 161, 50.31%]: CE = 2.69786051 * 64; Err = 0.67187500 * 64; time = 0.0103s; samplesPerSecond = 6204.6
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 162- 162, 50.63%]: CE = 2.80402381 * 64; Err = 0.70312500 * 64; time = 0.0093s; samplesPerSecond = 6855.2
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 163- 163, 50.94%]: CE = 2.62768914 * 64; Err = 0.62500000 * 64; time = 0.0092s; samplesPerSecond = 6936.2
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 164- 164, 51.25%]: CE = 2.64449167 * 64; Err = 0.67187500 * 64; time = 0.0101s; samplesPerSecond = 6319.7
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 165- 165, 51.56%]: CE = 3.08919011 * 64; Err = 0.79687500 * 64; time = 0.0090s; samplesPerSecond = 7138.1
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 166- 166, 51.88%]: CE = 3.07122141 * 64; Err = 0.70312500 * 64; time = 0.0104s; samplesPerSecond = 6149.7
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 167- 167, 52.19%]: CE = 3.05111668 * 64; Err = 0.73437500 * 64; time = 0.0098s; samplesPerSecond = 6523.3
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 168- 168, 52.50%]: CE = 2.90345804 * 64; Err = 0.73437500 * 64; time = 0.0101s; samplesPerSecond = 6336.6
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 169- 169, 52.81%]: CE = 2.58801822 * 64; Err = 0.62500000 * 64; time = 0.0080s; samplesPerSecond = 8022.1
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 170- 170, 53.13%]: CE = 2.68278033 * 64; Err = 0.68750000 * 64; time = 0.0108s; samplesPerSecond = 5935.8
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 171- 171, 53.44%]: CE = 2.89664835 * 64; Err = 0.70312500 * 64; time = 0.0085s; samplesPerSecond = 7566.8
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 172- 172, 53.75%]: CE = 2.61913736 * 64; Err = 0.64062500 * 64; time = 0.0103s; samplesPerSecond = 6219.0
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 173- 173, 54.06%]: CE = 2.68386883 * 64; Err = 0.65625000 * 64; time = 0.0091s; samplesPerSecond = 7070.3
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 174- 174, 54.37%]: CE = 2.63044619 * 64; Err = 0.65625000 * 64; time = 0.0103s; samplesPerSecond = 6239.0
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 175- 175, 54.69%]: CE = 2.39899721 * 64; Err = 0.60937500 * 64; time = 0.0076s; samplesPerSecond = 8384.6
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 176- 176, 55.00%]: CE = 2.88430255 * 64; Err = 0.67187500 * 64; time = 0.0108s; samplesPerSecond = 5949.6
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 177- 177, 55.31%]: CE = 2.83595866 * 64; Err = 0.70312500 * 64; time = 0.0086s; samplesPerSecond = 7413.4
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 178- 178, 55.63%]: CE = 2.79519571 * 64; Err = 0.64062500 * 64; time = 0.0102s; samplesPerSecond = 6249.4
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 179- 179, 55.94%]: CE = 2.76600024 * 64; Err = 0.67187500 * 64; time = 0.0083s; samplesPerSecond = 7688.6
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 180- 180, 56.25%]: CE = 2.59895511 * 64; Err = 0.54687500 * 64; time = 0.0103s; samplesPerSecond = 6190.2
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 181- 181, 56.56%]: CE = 2.93763654 * 64; Err = 0.75000000 * 64; time = 0.0083s; samplesPerSecond = 7684.9
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 182- 182, 56.88%]: CE = 2.93634742 * 64; Err = 0.73437500 * 64; time = 0.0102s; samplesPerSecond = 6301.1
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 183- 183, 57.19%]: CE = 2.59901571 * 64; Err = 0.68750000 * 64; time = 0.0091s; samplesPerSecond = 7002.2
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 184- 184, 57.50%]: CE = 2.81753002 * 64; Err = 0.73437500 * 64; time = 0.0103s; samplesPerSecond = 6214.2
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 185- 185, 57.81%]: CE = 3.04424260 * 64; Err = 0.73437500 * 64; time = 0.0098s; samplesPerSecond = 6561.4
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 186- 186, 58.13%]: CE = 2.49622625 * 64; Err = 0.64062500 * 64; time = 0.0101s; samplesPerSecond = 6311.0
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 187- 187, 58.44%]: CE = 2.94745408 * 64; Err = 0.71875000 * 64; time = 0.0102s; samplesPerSecond = 6286.8
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 188- 188, 58.75%]: CE = 2.80802583 * 64; Err = 0.71875000 * 64; time = 0.0082s; samplesPerSecond = 7758.5
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 189- 189, 59.06%]: CE = 2.54977638 * 64; Err = 0.67187500 * 64; time = 0.0096s; samplesPerSecond = 6659.0
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 190- 190, 59.38%]: CE = 2.90849909 * 64; Err = 0.68750000 * 64; time = 0.0116s; samplesPerSecond = 5539.7
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 191- 191, 59.69%]: CE = 2.89470021 * 64; Err = 0.71875000 * 64; time = 0.0093s; samplesPerSecond = 6888.4
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 192- 192, 60.00%]: CE = 2.55056761 * 64; Err = 0.64062500 * 64; time = 0.0104s; samplesPerSecond = 6172.8
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 193- 193, 60.31%]: CE = 2.39014720 * 64; Err = 0.59375000 * 64; time = 0.0096s; samplesPerSecond = 6673.6
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 194- 194, 60.62%]: CE = 2.61720826 * 64; Err = 0.65625000 * 64; time = 0.0106s; samplesPerSecond = 6047.4
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 195- 195, 60.94%]: CE = 2.59802571 * 64; Err = 0.65625000 * 64; time = 0.0098s; samplesPerSecond = 6499.4
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 196- 196, 61.25%]: CE = 2.94597696 * 64; Err = 0.75000000 * 64; time = 0.0079s; samplesPerSecond = 8065.5
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 197- 197, 61.56%]: CE = 2.79771307 * 64; Err = 0.75000000 * 64; time = 0.0094s; samplesPerSecond = 6815.0
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 198- 198, 61.88%]: CE = 3.20417932 * 64; Err = 0.71875000 * 64; time = 0.0115s; samplesPerSecond = 5570.1
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 199- 199, 62.19%]: CE = 2.27155558 * 64; Err = 0.53125000 * 64; time = 0.0099s; samplesPerSecond = 6475.1
MPI Rank 0: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 200- 200, 62.50%]: CE = 2.87449908 * 64; Err = 0.68750000 * 64; time = 0.0093s; samplesPerSecond = 6913.7
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 201- 201, 62.81%]: CE = 2.71210245 * 64; Err = 0.65625000 * 64; time = 0.0080s; samplesPerSecond = 7960.2
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 202- 202, 63.13%]: CE = 2.44766371 * 64; Err = 0.57812500 * 64; time = 0.0103s; samplesPerSecond = 6219.0
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 203- 203, 63.44%]: CE = 2.68243088 * 64; Err = 0.70312500 * 64; time = 0.0082s; samplesPerSecond = 7799.2
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 204- 204, 63.75%]: CE = 2.40962202 * 64; Err = 0.54687500 * 64; time = 0.0102s; samplesPerSecond = 6253.1
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 205- 205, 64.06%]: CE = 2.48400547 * 64; Err = 0.59375000 * 64; time = 0.0082s; samplesPerSecond = 7762.3
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 206- 206, 64.38%]: CE = 2.49121254 * 64; Err = 0.60937500 * 64; time = 0.0101s; samplesPerSecond = 6363.1
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 207- 207, 64.69%]: CE = 2.84691899 * 64; Err = 0.75000000 * 64; time = 0.0082s; samplesPerSecond = 7790.6
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 208- 208, 65.00%]: CE = 2.45273493 * 64; Err = 0.59375000 * 64; time = 0.0101s; samplesPerSecond = 6333.5
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 209- 209, 65.31%]: CE = 2.75036440 * 64; Err = 0.68750000 * 64; time = 0.0077s; samplesPerSecond = 8319.3
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 210- 210, 65.63%]: CE = 2.49555051 * 64; Err = 0.71875000 * 64; time = 0.0109s; samplesPerSecond = 5866.7
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 211- 211, 65.94%]: CE = 2.71109113 * 64; Err = 0.68750000 * 64; time = 0.0091s; samplesPerSecond = 7005.3
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 212- 212, 66.25%]: CE = 2.38218216 * 64; Err = 0.59375000 * 64; time = 0.0089s; samplesPerSecond = 7164.4
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 213- 213, 66.56%]: CE = 2.60308722 * 64; Err = 0.57812500 * 64; time = 0.0101s; samplesPerSecond = 6330.4
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 214- 214, 66.88%]: CE = 2.65611547 * 64; Err = 0.68750000 * 64; time = 0.0099s; samplesPerSecond = 6452.3
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 215- 215, 67.19%]: CE = 2.49633370 * 64; Err = 0.57812500 * 64; time = 0.0098s; samplesPerSecond = 6500.8
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 216- 216, 67.50%]: CE = 2.23315412 * 64; Err = 0.60937500 * 64; time = 0.0099s; samplesPerSecond = 6496.1
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 217- 217, 67.81%]: CE = 2.94093183 * 64; Err = 0.73437500 * 64; time = 0.0098s; samplesPerSecond = 6503.4
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 218- 218, 68.13%]: CE = 2.69840742 * 64; Err = 0.65625000 * 64; time = 0.0094s; samplesPerSecond = 6808.5
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 219- 219, 68.44%]: CE = 2.57215231 * 64; Err = 0.60937500 * 64; time = 0.0099s; samplesPerSecond = 6457.5
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 220- 220, 68.75%]: CE = 2.57160696 * 64; Err = 0.68750000 * 64; time = 0.0094s; samplesPerSecond = 6779.7
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 221- 221, 69.06%]: CE = 2.57776681 * 64; Err = 0.65625000 * 64; time = 0.0099s; samplesPerSecond = 6455.5
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 222- 222, 69.38%]: CE = 2.32289644 * 64; Err = 0.57812500 * 64; time = 0.0096s; samplesPerSecond = 6648.7
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 223- 223, 69.69%]: CE = 2.66432343 * 64; Err = 0.70312500 * 64; time = 0.0099s; samplesPerSecond = 6474.5
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 224- 224, 70.00%]: CE = 2.20387606 * 64; Err = 0.65625000 * 64; time = 0.0089s; samplesPerSecond = 7212.1
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 225- 225, 70.31%]: CE = 2.39888933 * 64; Err = 0.59375000 * 64; time = 0.0092s; samplesPerSecond = 6938.4
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 226- 226, 70.63%]: CE = 2.80393339 * 64; Err = 0.70312500 * 64; time = 0.0102s; samplesPerSecond = 6288.1
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 227- 227, 70.94%]: CE = 2.71082242 * 64; Err = 0.68750000 * 64; time = 0.0099s; samplesPerSecond = 6481.7
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 228- 228, 71.25%]: CE = 2.62244612 * 64; Err = 0.70312500 * 64; time = 0.0082s; samplesPerSecond = 7777.4
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 229- 229, 71.56%]: CE = 2.29777087 * 64; Err = 0.62500000 * 64; time = 0.0101s; samplesPerSecond = 6358.7
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 230- 230, 71.88%]: CE = 2.51121239 * 64; Err = 0.65625000 * 64; time = 0.0089s; samplesPerSecond = 7208.0
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 231- 231, 72.19%]: CE = 2.76103008 * 64; Err = 0.64062500 * 64; time = 0.0092s; samplesPerSecond = 6964.8
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 232- 232, 72.50%]: CE = 3.01432561 * 64; Err = 0.76562500 * 64; time = 0.0101s; samplesPerSecond = 6314.8
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 233- 233, 72.81%]: CE = 2.99024474 * 64; Err = 0.76562500 * 64; time = 0.0093s; samplesPerSecond = 6853.7
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 234- 234, 73.13%]: CE = 2.86664042 * 64; Err = 0.81250000 * 64; time = 0.0099s; samplesPerSecond = 6492.8
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 235- 235, 73.44%]: CE = 2.60998588 * 64; Err = 0.67187500 * 64; time = 0.0097s; samplesPerSecond = 6623.9
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 236- 236, 73.75%]: CE = 2.18201917 * 64; Err = 0.53125000 * 64; time = 0.0098s; samplesPerSecond = 6556.7
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 237- 237, 74.06%]: CE = 2.17418609 * 64; Err = 0.57812500 * 64; time = 0.0098s; samplesPerSecond = 6529.9
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 238- 238, 74.38%]: CE = 2.25759717 * 64; Err = 0.64062500 * 64; time = 0.0098s; samplesPerSecond = 6553.3
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 239- 239, 74.69%]: CE = 2.17788677 * 64; Err = 0.60937500 * 64; time = 0.0100s; samplesPerSecond = 6384.0
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 240- 240, 75.00%]: CE = 2.20328249 * 64; Err = 0.54687500 * 64; time = 0.0095s; samplesPerSecond = 6712.1
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 241- 241, 75.31%]: CE = 2.60590014 * 64; Err = 0.60937500 * 64; time = 0.0099s; samplesPerSecond = 6477.1
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 242- 242, 75.63%]: CE = 2.09884739 * 64; Err = 0.56250000 * 64; time = 0.0097s; samplesPerSecond = 6631.4
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 243- 243, 75.94%]: CE = 2.10587746 * 64; Err = 0.54687500 * 64; time = 0.0098s; samplesPerSecond = 6550.7
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 244- 244, 76.25%]: CE = 2.64457627 * 64; Err = 0.73437500 * 64; time = 0.0097s; samplesPerSecond = 6613.6
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 245- 245, 76.56%]: CE = 2.47600990 * 64; Err = 0.64062500 * 64; time = 0.0097s; samplesPerSecond = 6586.4
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 246- 246, 76.88%]: CE = 2.88789135 * 64; Err = 0.68750000 * 64; time = 0.0094s; samplesPerSecond = 6788.3
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 247- 247, 77.19%]: CE = 2.51823068 * 64; Err = 0.56250000 * 64; time = 0.0075s; samplesPerSecond = 8493.7
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 248- 248, 77.50%]: CE = 2.24877264 * 64; Err = 0.62500000 * 64; time = 0.0120s; samplesPerSecond = 5354.8
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 249- 249, 77.81%]: CE = 2.51043156 * 64; Err = 0.71875000 * 64; time = 0.0098s; samplesPerSecond = 6543.3
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 250- 250, 78.13%]: CE = 2.54234511 * 64; Err = 0.70312500 * 64; time = 0.0101s; samplesPerSecond = 6358.7
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 251- 251, 78.44%]: CE = 2.68548933 * 64; Err = 0.70312500 * 64; time = 0.0107s; samplesPerSecond = 5989.7
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 252- 252, 78.75%]: CE = 2.23175466 * 64; Err = 0.57812500 * 64; time = 0.0100s; samplesPerSecond = 6421.2
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 253- 253, 79.06%]: CE = 2.24553589 * 64; Err = 0.60937500 * 64; time = 0.0104s; samplesPerSecond = 6157.4
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 254- 254, 79.38%]: CE = 2.28765068 * 64; Err = 0.62500000 * 64; time = 0.0105s; samplesPerSecond = 6110.4
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 255- 255, 79.69%]: CE = 2.54161451 * 64; Err = 0.62500000 * 64; time = 0.0102s; samplesPerSecond = 6275.7
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 256- 256, 80.00%]: CE = 2.35401834 * 64; Err = 0.59375000 * 64; time = 0.0081s; samplesPerSecond = 7864.3
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 257- 257, 80.31%]: CE = 2.18137731 * 64; Err = 0.56250000 * 64; time = 0.0112s; samplesPerSecond = 5729.6
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 258- 258, 80.63%]: CE = 2.51499174 * 64; Err = 0.59375000 * 64; time = 0.0102s; samplesPerSecond = 6276.4
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 259- 259, 80.94%]: CE = 2.12242410 * 64; Err = 0.65625000 * 64; time = 0.0084s; samplesPerSecond = 7588.3
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 260- 260, 81.25%]: CE = 2.57230724 * 64; Err = 0.68750000 * 64; time = 0.0101s; samplesPerSecond = 6314.8
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 261- 261, 81.56%]: CE = 2.24717210 * 64; Err = 0.57812500 * 64; time = 0.0088s; samplesPerSecond = 7232.5
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 262- 262, 81.88%]: CE = 2.46805084 * 64; Err = 0.60937500 * 64; time = 0.0103s; samplesPerSecond = 6192.0
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 263- 263, 82.19%]: CE = 1.94672270 * 64; Err = 0.48437500 * 64; time = 0.0089s; samplesPerSecond = 7182.9
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 264- 264, 82.50%]: CE = 2.34898229 * 64; Err = 0.67187500 * 64; time = 0.0098s; samplesPerSecond = 6511.3
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 265- 265, 82.81%]: CE = 2.19361248 * 64; Err = 0.57812500 * 64; time = 0.0087s; samplesPerSecond = 7339.4
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 266- 266, 83.13%]: CE = 1.96058399 * 64; Err = 0.46875000 * 64; time = 0.0101s; samplesPerSecond = 6308.5
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 267- 267, 83.44%]: CE = 2.02827934 * 64; Err = 0.53125000 * 64; time = 0.0091s; samplesPerSecond = 7061.7
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 268- 268, 83.75%]: CE = 2.16395773 * 64; Err = 0.56250000 * 64; time = 0.0103s; samplesPerSecond = 6199.7
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 269- 269, 84.06%]: CE = 2.42837196 * 64; Err = 0.64062500 * 64; time = 0.0092s; samplesPerSecond = 6974.0
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 270- 270, 84.38%]: CE = 2.56277231 * 64; Err = 0.75000000 * 64; time = 0.0102s; samplesPerSecond = 6283.1
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 271- 271, 84.69%]: CE = 2.35831855 * 64; Err = 0.59375000 * 64; time = 0.0084s; samplesPerSecond = 7597.3
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 272- 272, 85.00%]: CE = 2.48323539 * 64; Err = 0.70312500 * 64; time = 0.0105s; samplesPerSecond = 6066.9
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 273- 273, 85.31%]: CE = 2.66412354 * 64; Err = 0.67187500 * 64; time = 0.0093s; samplesPerSecond = 6858.9
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 274- 274, 85.63%]: CE = 2.35827343 * 64; Err = 0.65625000 * 64; time = 0.0099s; samplesPerSecond = 6451.0
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 275- 275, 85.94%]: CE = 2.35993611 * 64; Err = 0.59375000 * 64; time = 0.0091s; samplesPerSecond = 7058.6
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 276- 276, 86.25%]: CE = 2.27682017 * 64; Err = 0.59375000 * 64; time = 0.0100s; samplesPerSecond = 6387.9
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 277- 277, 86.56%]: CE = 2.58742110 * 64; Err = 0.70312500 * 64; time = 0.0086s; samplesPerSecond = 7467.9
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 278- 278, 86.88%]: CE = 2.59364573 * 64; Err = 0.70312500 * 64; time = 0.0104s; samplesPerSecond = 6140.3
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 279- 279, 87.19%]: CE = 2.58154982 * 64; Err = 0.67187500 * 64; time = 0.0099s; samplesPerSecond = 6473.1
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 280- 280, 87.50%]: CE = 2.65251947 * 64; Err = 0.71875000 * 64; time = 0.0101s; samplesPerSecond = 6339.8
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 281- 281, 87.81%]: CE = 2.42794113 * 64; Err = 0.56250000 * 64; time = 0.0088s; samplesPerSecond = 7286.0
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 282- 282, 88.13%]: CE = 2.31306675 * 64; Err = 0.56250000 * 64; time = 0.0102s; samplesPerSecond = 6301.1
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 283- 283, 88.44%]: CE = 2.30780317 * 64; Err = 0.57812500 * 64; time = 0.0099s; samplesPerSecond = 6466.0
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 284- 284, 88.75%]: CE = 2.20092907 * 64; Err = 0.71875000 * 64; time = 0.0099s; samplesPerSecond = 6483.6
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 285- 285, 89.06%]: CE = 2.37127008 * 64; Err = 0.60937500 * 64; time = 0.0099s; samplesPerSecond = 6451.0
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 286- 286, 89.38%]: CE = 1.96581596 * 64; Err = 0.51562500 * 64; time = 0.0101s; samplesPerSecond = 6359.9
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 287- 287, 89.69%]: CE = 2.38139796 * 64; Err = 0.68750000 * 64; time = 0.0093s; samplesPerSecond = 6882.5
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 288- 288, 90.00%]: CE = 2.17378766 * 64; Err = 0.56250000 * 64; time = 0.0098s; samplesPerSecond = 6524.0
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 289- 289, 90.31%]: CE = 2.43769870 * 64; Err = 0.62500000 * 64; time = 0.0083s; samplesPerSecond = 7735.1
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 290- 290, 90.63%]: CE = 1.92877315 * 64; Err = 0.48437500 * 64; time = 0.0102s; samplesPerSecond = 6245.7
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 291- 291, 90.94%]: CE = 2.40592700 * 64; Err = 0.62500000 * 64; time = 0.0092s; samplesPerSecond = 6986.9
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 292- 292, 91.25%]: CE = 2.08578061 * 64; Err = 0.59375000 * 64; time = 0.0094s; samplesPerSecond = 6838.3
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 293- 293, 91.56%]: CE = 2.00803832 * 64; Err = 0.51562500 * 64; time = 0.0082s; samplesPerSecond = 7797.3
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 294- 294, 91.88%]: CE = 2.17692353 * 64; Err = 0.57812500 * 64; time = 0.0096s; samplesPerSecond = 6638.3
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 295- 295, 92.19%]: CE = 2.50142509 * 64; Err = 0.70312500 * 64; time = 0.0091s; samplesPerSecond = 7003.0
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 296- 296, 92.50%]: CE = 2.23106504 * 64; Err = 0.60937500 * 64; time = 0.0092s; samplesPerSecond = 6969.4
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 297- 297, 92.81%]: CE = 2.15600594 * 64; Err = 0.59375000 * 64; time = 0.0093s; samplesPerSecond = 6858.1
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 298- 298, 93.13%]: CE = 2.57861376 * 64; Err = 0.68750000 * 64; time = 0.0097s; samplesPerSecond = 6625.9
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 299- 299, 93.44%]: CE = 2.07193617 * 64; Err = 0.56250000 * 64; time = 0.0101s; samplesPerSecond = 6358.0
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 300- 300, 93.75%]: CE = 2.16370481 * 64; Err = 0.60937500 * 64; time = 0.0101s; samplesPerSecond = 6351.1
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 301- 301, 94.06%]: CE = 2.24899831 * 64; Err = 0.56250000 * 64; time = 0.0096s; samplesPerSecond = 6637.6
MPI Rank 0: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 302- 302, 94.38%]: CE = 1.87617314 * 64; Err = 0.54687500 * 64; time = 0.0100s; samplesPerSecond = 6370.7
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 303- 303, 94.69%]: CE = 2.22035878 * 64; Err = 0.56250000 * 64; time = 0.0106s; samplesPerSecond = 6048.6
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 304- 304, 95.00%]: CE = 2.23859583 * 64; Err = 0.65625000 * 64; time = 0.0099s; samplesPerSecond = 6494.8
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 305- 305, 95.31%]: CE = 2.36221656 * 64; Err = 0.59375000 * 64; time = 0.0082s; samplesPerSecond = 7762.3
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 306- 306, 95.63%]: CE = 2.11637634 * 64; Err = 0.54687500 * 64; time = 0.0119s; samplesPerSecond = 5390.4
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 307- 307, 95.94%]: CE = 2.32528810 * 64; Err = 0.57812500 * 64; time = 0.0102s; samplesPerSecond = 6245.7
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 308- 308, 96.25%]: CE = 2.06869602 * 64; Err = 0.50000000 * 64; time = 0.0074s; samplesPerSecond = 8676.8
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 309- 309, 96.56%]: CE = 2.10471025 * 64; Err = 0.56250000 * 64; time = 0.0118s; samplesPerSecond = 5435.2
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 310- 310, 96.88%]: CE = 2.69881704 * 64; Err = 0.71875000 * 64; time = 0.0105s; samplesPerSecond = 6081.3
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 311- 311, 97.19%]: CE = 2.21301732 * 64; Err = 0.65625000 * 64; time = 0.0103s; samplesPerSecond = 6191.4
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 312- 312, 97.50%]: CE = 2.34597297 * 64; Err = 0.60937500 * 64; time = 0.0096s; samplesPerSecond = 6698.1
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 313- 313, 97.81%]: CE = 2.08858265 * 64; Err = 0.57812500 * 64; time = 0.0102s; samplesPerSecond = 6261.0
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 314- 314, 98.13%]: CE = 2.10805385 * 64; Err = 0.54687500 * 64; time = 0.0089s; samplesPerSecond = 7202.3
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 315- 315, 98.44%]: CE = 2.29975623 * 64; Err = 0.60937500 * 64; time = 0.0100s; samplesPerSecond = 6419.9
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 316- 316, 98.75%]: CE = 2.29188916 * 64; Err = 0.60937500 * 64; time = 0.0083s; samplesPerSecond = 7691.4
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 317- 317, 99.06%]: CE = 2.03062764 * 64; Err = 0.50000000 * 64; time = 0.0107s; samplesPerSecond = 5971.8
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 318- 318, 99.38%]: CE = 2.29874982 * 64; Err = 0.59375000 * 64; time = 0.0083s; samplesPerSecond = 7730.4
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 319- 319, 99.69%]: CE = 2.22342700 * 64; Err = 0.68750000 * 64; time = 0.0102s; samplesPerSecond = 6280.7
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 320- 320, 100.00%]: CE = 2.32233814 * 64; Err = 0.59375000 * 64; time = 0.0079s; samplesPerSecond = 8054.4
MPI Rank 0: 12/21/2016 05:28:37: Finished Epoch[ 1 of 5]: [Training] CE = 3.02444900 * 20480; Err = 0.72885742 * 20480; totalSamplesSeen = 20480; learningRatePerSample = 0.015625; epochTime=3.18526s
MPI Rank 0: 12/21/2016 05:28:37: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu/models/cntkSpeech.dnn.1'
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:37: Starting Epoch 2: learning rate per sample = 0.001953  effective momentum = 0.656119  momentum as time constant = 607.5 samples
MPI Rank 0: Parallel training (2 workers) using BlockMomentumSGD with block momentum = 0.5000, block momentum time constant (per worker) = 2954.6394, block learning rate = 1.0000, block size per worker = 2048 samples, using Nesterov-style block momentum, resetting SGD momentum after sync.
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:37: Starting minibatch loop, distributed reading is ENABLED.
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[   1-   1, 1.25%]: CE = 2.04621069 * 128; Err = 0.56250000 * 128; time = 0.0214s; samplesPerSecond = 5977.4
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[   2-   2, 2.50%]: CE = 2.17708778 * 128; Err = 0.58593750 * 128; time = 0.0169s; samplesPerSecond = 7587.9
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[   3-   3, 3.75%]: CE = 2.34813693 * 128; Err = 0.65625000 * 128; time = 0.0173s; samplesPerSecond = 7379.6
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[   4-   4, 5.00%]: CE = 2.32449083 * 128; Err = 0.64062500 * 128; time = 0.0175s; samplesPerSecond = 7333.6
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[   5-   5, 6.25%]: CE = 1.88510886 * 128; Err = 0.50781250 * 128; time = 0.0180s; samplesPerSecond = 7109.9
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[   6-   6, 7.50%]: CE = 2.37002367 * 128; Err = 0.60156250 * 128; time = 0.0161s; samplesPerSecond = 7970.1
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[   7-   7, 8.75%]: CE = 2.26305040 * 128; Err = 0.61718750 * 128; time = 0.0172s; samplesPerSecond = 7457.0
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[   8-   8, 10.00%]: CE = 2.29855550 * 128; Err = 0.64062500 * 128; time = 0.0176s; samplesPerSecond = 7260.4
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[   9-   9, 11.25%]: CE = 2.46680236 * 128; Err = 0.68750000 * 128; time = 0.0175s; samplesPerSecond = 7308.4
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  10-  10, 12.50%]: CE = 1.99182081 * 128; Err = 0.52343750 * 128; time = 0.0171s; samplesPerSecond = 7476.6
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  11-  11, 13.75%]: CE = 1.94202161 * 128; Err = 0.57031250 * 128; time = 0.0170s; samplesPerSecond = 7514.4
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  12-  12, 15.00%]: CE = 2.20437533 * 128; Err = 0.58593750 * 128; time = 0.0169s; samplesPerSecond = 7577.1
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  13-  13, 16.25%]: CE = 2.31128223 * 128; Err = 0.60937500 * 128; time = 0.0171s; samplesPerSecond = 7472.7
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  14-  14, 17.50%]: CE = 2.29976865 * 128; Err = 0.62500000 * 128; time = 0.0172s; samplesPerSecond = 7425.0
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  15-  15, 18.75%]: CE = 2.01466608 * 128; Err = 0.52343750 * 128; time = 0.0169s; samplesPerSecond = 7558.8
MPI Rank 0: 		(model aggregation stats): 1-th sync point was hit, introducing a 0.01-seconds latency this time; accumulated time on sync point = 0.01 seconds , average latency = 0.01 seconds
MPI Rank 0: 		(model aggregation stats) 1-th sync:     0.31 seconds since last report (0.01 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 0: 		(model aggregation stats) 1-th sync: totalThroughput = 13.10k samplesPerSecond , throughputPerWorker = 6.55k samplesPerSecond
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  16-  16, 20.00%]: CE = 2.24454636 * 128; Err = 0.59375000 * 128; time = 0.0407s; samplesPerSecond = 3145.9
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  17-  17, 21.25%]: CE = 2.25498615 * 128; Err = 0.58593750 * 128; time = 0.0179s; samplesPerSecond = 7158.4
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  18-  18, 22.50%]: CE = 2.07039205 * 128; Err = 0.57812500 * 128; time = 0.0184s; samplesPerSecond = 6954.3
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  19-  19, 23.75%]: CE = 1.54332140 * 128; Err = 0.39843750 * 128; time = 0.0178s; samplesPerSecond = 7177.3
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  20-  20, 25.00%]: CE = 2.28595867 * 128; Err = 0.61718750 * 128; time = 0.0182s; samplesPerSecond = 7049.6
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  21-  21, 26.25%]: CE = 2.00097462 * 128; Err = 0.55468750 * 128; time = 0.0182s; samplesPerSecond = 7050.8
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  22-  22, 27.50%]: CE = 1.89541624 * 128; Err = 0.49218750 * 128; time = 0.0181s; samplesPerSecond = 7089.4
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  23-  23, 28.75%]: CE = 2.17076816 * 128; Err = 0.60156250 * 128; time = 0.0164s; samplesPerSecond = 7788.7
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  24-  24, 30.00%]: CE = 2.14484872 * 128; Err = 0.52343750 * 128; time = 0.0172s; samplesPerSecond = 7445.3
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  25-  25, 31.25%]: CE = 1.98272942 * 128; Err = 0.51562500 * 128; time = 0.0174s; samplesPerSecond = 7344.9
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  26-  26, 32.50%]: CE = 2.15380788 * 128; Err = 0.53906250 * 128; time = 0.0178s; samplesPerSecond = 7196.7
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  27-  27, 33.75%]: CE = 2.00961418 * 128; Err = 0.57031250 * 128; time = 0.0174s; samplesPerSecond = 7346.6
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  28-  28, 35.00%]: CE = 1.99086509 * 128; Err = 0.48437500 * 128; time = 0.0174s; samplesPerSecond = 7361.0
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  29-  29, 36.25%]: CE = 2.30500580 * 128; Err = 0.60156250 * 128; time = 0.0176s; samplesPerSecond = 7285.6
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  30-  30, 37.50%]: CE = 2.22538862 * 128; Err = 0.58593750 * 128; time = 0.0174s; samplesPerSecond = 7339.9
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  31-  31, 38.75%]: CE = 2.02304991 * 128; Err = 0.51562500 * 128; time = 0.0175s; samplesPerSecond = 7319.7
MPI Rank 0: 		(model aggregation stats): 2-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.01 seconds , average latency = 0.00 seconds
MPI Rank 0: 		(model aggregation stats) 2-th sync:     0.30 seconds since last report (0.00 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 0: 		(model aggregation stats) 2-th sync: totalThroughput = 13.57k samplesPerSecond , throughputPerWorker = 6.78k samplesPerSecond
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  32-  32, 40.00%]: CE = 2.08122418 * 128; Err = 0.54687500 * 128; time = 0.0332s; samplesPerSecond = 3860.0
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  33-  33, 41.25%]: CE = 2.11152507 * 128; Err = 0.53906250 * 128; time = 0.0175s; samplesPerSecond = 7328.9
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  34-  34, 42.50%]: CE = 1.95276514 * 128; Err = 0.57031250 * 128; time = 0.0175s; samplesPerSecond = 7334.4
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  35-  35, 43.75%]: CE = 1.97726051 * 128; Err = 0.56250000 * 128; time = 0.0171s; samplesPerSecond = 7493.3
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  36-  36, 45.00%]: CE = 2.17844485 * 128; Err = 0.61718750 * 128; time = 0.0158s; samplesPerSecond = 8082.8
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  37-  37, 46.25%]: CE = 1.98699662 * 128; Err = 0.53906250 * 128; time = 0.0174s; samplesPerSecond = 7373.7
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  38-  38, 47.50%]: CE = 1.99237188 * 128; Err = 0.53906250 * 128; time = 0.0176s; samplesPerSecond = 7255.0
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  39-  39, 48.75%]: CE = 2.01392744 * 128; Err = 0.52343750 * 128; time = 0.0175s; samplesPerSecond = 7299.7
MPI Rank 0: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  40-  40, 50.00%]: CE = 1.95863719 * 128; Err = 0.53906250 * 128; time = 0.0178s; samplesPerSecond = 7201.1
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  41-  41, 51.25%]: CE = 2.04755340 * 128; Err = 0.53906250 * 128; time = 0.0172s; samplesPerSecond = 7461.8
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  42-  42, 52.50%]: CE = 2.17596474 * 128; Err = 0.64843750 * 128; time = 0.0174s; samplesPerSecond = 7336.1
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  43-  43, 53.75%]: CE = 2.22751313 * 128; Err = 0.63281250 * 128; time = 0.0175s; samplesPerSecond = 7328.9
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  44-  44, 55.00%]: CE = 2.01731189 * 128; Err = 0.52343750 * 128; time = 0.0177s; samplesPerSecond = 7221.4
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  45-  45, 56.25%]: CE = 1.96079757 * 128; Err = 0.58593750 * 128; time = 0.0173s; samplesPerSecond = 7399.3
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  46-  46, 57.50%]: CE = 2.18325683 * 128; Err = 0.56250000 * 128; time = 0.0177s; samplesPerSecond = 7236.5
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  47-  47, 58.75%]: CE = 2.08112152 * 128; Err = 0.57812500 * 128; time = 0.0182s; samplesPerSecond = 7039.5
MPI Rank 0: 		(model aggregation stats): 3-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.01 seconds , average latency = 0.00 seconds
MPI Rank 0: 		(model aggregation stats) 3-th sync:     0.30 seconds since last report (0.00 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 0: 		(model aggregation stats) 3-th sync: totalThroughput = 13.56k samplesPerSecond , throughputPerWorker = 6.78k samplesPerSecond
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  48-  48, 60.00%]: CE = 1.89594937 * 128; Err = 0.52343750 * 128; time = 0.0369s; samplesPerSecond = 3472.9
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  49-  49, 61.25%]: CE = 1.93867044 * 128; Err = 0.51562500 * 128; time = 0.0182s; samplesPerSecond = 7044.6
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  50-  50, 62.50%]: CE = 1.94696868 * 128; Err = 0.54687500 * 128; time = 0.0170s; samplesPerSecond = 7537.8
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  51-  51, 63.75%]: CE = 1.89376884 * 128; Err = 0.55468750 * 128; time = 0.0181s; samplesPerSecond = 7053.9
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  52-  52, 65.00%]: CE = 1.97393916 * 128; Err = 0.57031250 * 128; time = 0.0167s; samplesPerSecond = 7647.7
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  53-  53, 66.25%]: CE = 2.16872511 * 128; Err = 0.53906250 * 128; time = 0.0173s; samplesPerSecond = 7414.3
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  54-  54, 67.50%]: CE = 2.15993970 * 128; Err = 0.60937500 * 128; time = 0.0176s; samplesPerSecond = 7287.6
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  55-  55, 68.75%]: CE = 2.00906572 * 128; Err = 0.53125000 * 128; time = 0.0174s; samplesPerSecond = 7369.0
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  56-  56, 70.00%]: CE = 1.89000808 * 128; Err = 0.54687500 * 128; time = 0.0131s; samplesPerSecond = 9749.4
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  57-  57, 71.25%]: CE = 1.90423684 * 128; Err = 0.55468750 * 128; time = 0.0156s; samplesPerSecond = 8212.0
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  58-  58, 72.50%]: CE = 1.91510403 * 128; Err = 0.54687500 * 128; time = 0.0095s; samplesPerSecond = 13487.9
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  59-  59, 73.75%]: CE = 2.00206690 * 128; Err = 0.53125000 * 128; time = 0.0094s; samplesPerSecond = 13606.9
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  60-  60, 75.00%]: CE = 2.04667281 * 128; Err = 0.56250000 * 128; time = 0.0095s; samplesPerSecond = 13439.7
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  61-  61, 76.25%]: CE = 1.97803721 * 128; Err = 0.51562500 * 128; time = 0.0093s; samplesPerSecond = 13805.0
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  62-  62, 77.50%]: CE = 1.83661828 * 128; Err = 0.52343750 * 128; time = 0.0094s; samplesPerSecond = 13669.4
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  63-  63, 78.75%]: CE = 1.88327931 * 128; Err = 0.54687500 * 128; time = 0.0091s; samplesPerSecond = 14052.0
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  64-  64, 80.00%]: CE = 2.11534140 * 128; Err = 0.60937500 * 128; time = 0.0093s; samplesPerSecond = 13769.4
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  65-  65, 81.25%]: CE = 2.16230390 * 128; Err = 0.58593750 * 128; time = 0.0092s; samplesPerSecond = 13987.5
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  66-  66, 82.50%]: CE = 2.13342620 * 128; Err = 0.61718750 * 128; time = 0.0092s; samplesPerSecond = 13899.4
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  67-  67, 83.75%]: CE = 1.80170369 * 128; Err = 0.49218750 * 128; time = 0.0093s; samplesPerSecond = 13794.6
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  68-  68, 85.00%]: CE = 2.13547951 * 128; Err = 0.59375000 * 128; time = 0.0091s; samplesPerSecond = 14075.2
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  69-  69, 86.25%]: CE = 1.87771364 * 128; Err = 0.53906250 * 128; time = 0.0094s; samplesPerSecond = 13679.6
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  70-  70, 87.50%]: CE = 1.80406584 * 128; Err = 0.50000000 * 128; time = 0.0092s; samplesPerSecond = 13855.8
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  71-  71, 88.75%]: CE = 2.02216089 * 128; Err = 0.51562500 * 128; time = 0.0091s; samplesPerSecond = 13999.8
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  72-  72, 90.00%]: CE = 2.05838722 * 128; Err = 0.55468750 * 128; time = 0.0092s; samplesPerSecond = 13858.8
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  73-  73, 91.25%]: CE = 1.83577660 * 128; Err = 0.50000000 * 128; time = 0.0091s; samplesPerSecond = 14024.3
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  74-  74, 92.50%]: CE = 1.95166728 * 128; Err = 0.53906250 * 128; time = 0.0094s; samplesPerSecond = 13666.5
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  75-  75, 93.75%]: CE = 1.92254694 * 128; Err = 0.55468750 * 128; time = 0.0093s; samplesPerSecond = 13773.8
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  76-  76, 95.00%]: CE = 1.91965482 * 128; Err = 0.52343750 * 128; time = 0.0091s; samplesPerSecond = 13990.6
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  77-  77, 96.25%]: CE = 1.95992305 * 128; Err = 0.50781250 * 128; time = 0.0092s; samplesPerSecond = 13864.8
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  78-  78, 97.50%]: CE = 2.11772801 * 128; Err = 0.58593750 * 128; time = 0.0092s; samplesPerSecond = 13842.3
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  79-  79, 98.75%]: CE = 1.87594063 * 128; Err = 0.48437500 * 128; time = 0.0092s; samplesPerSecond = 13935.8
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  80-  80, 100.00%]: CE = 1.93475436 * 128; Err = 0.51562500 * 128; time = 0.0093s; samplesPerSecond = 13793.1
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  81-  81, 101.25%]: CE = 1.98965998 * 128; Err = 0.52343750 * 128; time = 0.0092s; samplesPerSecond = 13846.8
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  82-  82, 102.50%]: CE = 1.96250616 * 128; Err = 0.58593750 * 128; time = 0.0093s; samplesPerSecond = 13770.8
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  83-  83, 103.75%]: CE = 1.89156264 * 128; Err = 0.55468750 * 128; time = 0.0093s; samplesPerSecond = 13742.8
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  84-  84, 105.00%]: CE = 1.98132112 * 128; Err = 0.55468750 * 128; time = 0.0094s; samplesPerSecond = 13676.7
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  85-  85, 106.25%]: CE = 2.04288157 * 128; Err = 0.53125000 * 128; time = 0.0093s; samplesPerSecond = 13797.6
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  86-  86, 107.50%]: CE = 2.01657890 * 128; Err = 0.58593750 * 128; time = 0.0094s; samplesPerSecond = 13686.9
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  87-  87, 108.75%]: CE = 2.05797887 * 128; Err = 0.52343750 * 128; time = 0.0095s; samplesPerSecond = 13496.4
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  88-  88, 110.00%]: CE = 1.87100099 * 128; Err = 0.52343750 * 128; time = 0.0097s; samplesPerSecond = 13157.9
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  89-  89, 111.25%]: CE = 2.03788831 * 128; Err = 0.56250000 * 128; time = 0.0096s; samplesPerSecond = 13352.8
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  90-  90, 112.50%]: CE = 1.98743413 * 128; Err = 0.57031250 * 128; time = 0.0092s; samplesPerSecond = 13966.2
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  91-  91, 113.75%]: CE = 2.02666813 * 128; Err = 0.49218750 * 128; time = 0.0096s; samplesPerSecond = 13334.7
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  92-  92, 115.00%]: CE = 1.96605680 * 128; Err = 0.53125000 * 128; time = 0.0096s; samplesPerSecond = 13336.1
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  93-  93, 116.25%]: CE = 2.07576441 * 128; Err = 0.59375000 * 128; time = 0.0102s; samplesPerSecond = 12567.5
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  94-  94, 117.50%]: CE = 1.90720948 * 128; Err = 0.58593750 * 128; time = 0.0105s; samplesPerSecond = 12225.4
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  95-  95, 118.75%]: CE = 2.00432920 * 128; Err = 0.57031250 * 128; time = 0.0102s; samplesPerSecond = 12530.6
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  96-  96, 120.00%]: CE = 1.88575775 * 128; Err = 0.52343750 * 128; time = 0.0103s; samplesPerSecond = 12376.7
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  97-  97, 121.25%]: CE = 1.88803903 * 128; Err = 0.58593750 * 128; time = 0.0102s; samplesPerSecond = 12579.9
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  98-  98, 122.50%]: CE = 1.72565000 * 128; Err = 0.49218750 * 128; time = 0.0103s; samplesPerSecond = 12435.6
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  99-  99, 123.75%]: CE = 2.05912371 * 128; Err = 0.53125000 * 128; time = 0.0100s; samplesPerSecond = 12787.2
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[ 100- 100, 125.00%]: CE = 2.00262941 * 128; Err = 0.50000000 * 128; time = 0.0104s; samplesPerSecond = 12329.0
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[ 101- 101, 126.25%]: CE = 2.01561997 * 128; Err = 0.53906250 * 128; time = 0.0106s; samplesPerSecond = 12125.8
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[ 102- 102, 127.50%]: CE = 1.99238381 * 128; Err = 0.54687500 * 128; time = 0.0103s; samplesPerSecond = 12400.7
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[ 103- 103, 128.75%]: CE = 1.88524397 * 128; Err = 0.49218750 * 128; time = 0.0097s; samplesPerSecond = 13261.5
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[ 104- 104, 130.00%]: CE = 2.03128662 * 91; Err = 0.51648352 * 91; time = 0.0064s; samplesPerSecond = 14163.4
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[ 105- 105, 131.25%]: CE = 0.00000000 * 0; Err = 0.00000000 * 0; time = 1.5000e-005s; samplesPerSecond = 0.0
MPI Rank 0: 		(model aggregation stats): 4-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.01 seconds , average latency = 0.00 seconds
MPI Rank 0: 		(model aggregation stats) 4-th sync:     0.63 seconds since last report (0.00 seconds on comm.); 8192 samples processed by 2 workers (7131 by me);
MPI Rank 0: 		(model aggregation stats) 4-th sync: totalThroughput = 13.06k samplesPerSecond , throughputPerWorker = 6.53k samplesPerSecond
MPI Rank 0: 12/21/2016 05:28:38: Finished Epoch[ 2 of 5]: [Training] CE = 2.03791679 * 20480; Err = 0.55712891 * 20480; totalSamplesSeen = 40960; learningRatePerSample = 0.001953125; epochTime=1.54554s
MPI Rank 0: 12/21/2016 05:28:38: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu/models/cntkSpeech.dnn.2'
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:38: Starting Epoch 3: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 0: Parallel training (2 workers) using BlockMomentumSGD with block momentum = 0.5000, block momentum time constant (per worker) = 2954.6394, block learning rate = 1.0000, block size per worker = 2048 samples, using Nesterov-style block momentum, resetting SGD momentum after sync.
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:38: Starting minibatch loop, distributed reading is ENABLED.
MPI Rank 0: 12/21/2016 05:28:38:  Epoch[ 3 of 5]-Minibatch[   1-   1, 5.00%]: CE = 1.92970354 * 512; Err = 0.50585938 * 512; time = 0.0797s; samplesPerSecond = 6425.9
MPI Rank 0: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[   2-   2, 10.00%]: CE = 1.92764867 * 512; Err = 0.52343750 * 512; time = 0.0597s; samplesPerSecond = 8570.9
MPI Rank 0: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[   3-   3, 15.00%]: CE = 2.00687305 * 512; Err = 0.55078125 * 512; time = 0.0612s; samplesPerSecond = 8371.3
MPI Rank 0: 		(model aggregation stats): 1-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 0: 		(model aggregation stats) 1-th sync:     0.28 seconds since last report (0.00 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 0: 		(model aggregation stats) 1-th sync: totalThroughput = 14.46k samplesPerSecond , throughputPerWorker = 7.23k samplesPerSecond
MPI Rank 0: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[   4-   4, 20.00%]: CE = 1.87892688 * 512; Err = 0.50585938 * 512; time = 0.0770s; samplesPerSecond = 6652.0
MPI Rank 0: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[   5-   5, 25.00%]: CE = 1.97706875 * 512; Err = 0.54687500 * 512; time = 0.0627s; samplesPerSecond = 8167.7
MPI Rank 0: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[   6-   6, 30.00%]: CE = 1.98393011 * 512; Err = 0.56640625 * 512; time = 0.0528s; samplesPerSecond = 9694.8
MPI Rank 0: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[   7-   7, 35.00%]: CE = 1.92396216 * 512; Err = 0.54101563 * 512; time = 0.0695s; samplesPerSecond = 7363.9
MPI Rank 0: 		(model aggregation stats): 2-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 0: 		(model aggregation stats) 2-th sync:     0.26 seconds since last report (0.00 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 0: 		(model aggregation stats) 2-th sync: totalThroughput = 15.58k samplesPerSecond , throughputPerWorker = 7.79k samplesPerSecond
MPI Rank 0: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[   8-   8, 40.00%]: CE = 1.90143255 * 512; Err = 0.54296875 * 512; time = 0.0766s; samplesPerSecond = 6683.1
MPI Rank 0: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[   9-   9, 45.00%]: CE = 1.90191854 * 512; Err = 0.53320313 * 512; time = 0.0605s; samplesPerSecond = 8469.8
MPI Rank 0: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[  10-  10, 50.00%]: CE = 2.02959458 * 512; Err = 0.55664063 * 512; time = 0.0609s; samplesPerSecond = 8408.7
MPI Rank 0: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[  11-  11, 55.00%]: CE = 2.02800892 * 512; Err = 0.56445313 * 512; time = 0.0608s; samplesPerSecond = 8424.7
MPI Rank 0: 		(model aggregation stats): 3-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 0: 		(model aggregation stats) 3-th sync:     0.26 seconds since last report (0.00 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 0: 		(model aggregation stats) 3-th sync: totalThroughput = 15.75k samplesPerSecond , throughputPerWorker = 7.88k samplesPerSecond
MPI Rank 0: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[  12-  12, 60.00%]: CE = 1.90570964 * 512; Err = 0.54296875 * 512; time = 0.0769s; samplesPerSecond = 6659.3
MPI Rank 0: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[  13-  13, 65.00%]: CE = 2.09045613 * 512; Err = 0.57031250 * 512; time = 0.0611s; samplesPerSecond = 8385.2
MPI Rank 0: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[  14-  14, 70.00%]: CE = 1.99217449 * 512; Err = 0.55078125 * 512; time = 0.0605s; samplesPerSecond = 8462.4
MPI Rank 0: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[  15-  15, 75.00%]: CE = 1.78680596 * 512; Err = 0.52734375 * 512; time = 0.0628s; samplesPerSecond = 8150.1
MPI Rank 0: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[  16-  16, 80.00%]: CE = 1.93041321 * 512; Err = 0.55664063 * 512; time = 0.0312s; samplesPerSecond = 16398.2
MPI Rank 0: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[  17-  17, 85.00%]: CE = 1.94051124 * 512; Err = 0.56445313 * 512; time = 0.0301s; samplesPerSecond = 16985.1
MPI Rank 0: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[  18-  18, 90.00%]: CE = 1.99870822 * 512; Err = 0.58789063 * 512; time = 0.0305s; samplesPerSecond = 16774.2
MPI Rank 0: 12/21/2016 05:28:40:  Epoch[ 3 of 5]-Minibatch[  19-  19, 95.00%]: CE = 2.00679952 * 512; Err = 0.56835938 * 512; time = 0.0291s; samplesPerSecond = 17590.9
MPI Rank 0: 12/21/2016 05:28:40:  Epoch[ 3 of 5]-Minibatch[  20-  20, 100.00%]: CE = 1.92222693 * 512; Err = 0.54296875 * 512; time = 0.0308s; samplesPerSecond = 16633.6
MPI Rank 0: 12/21/2016 05:28:40:  Epoch[ 3 of 5]-Minibatch[  21-  21, 105.00%]: CE = 1.93006837 * 512; Err = 0.54687500 * 512; time = 0.0301s; samplesPerSecond = 17038.3
MPI Rank 0: 12/21/2016 05:28:40:  Epoch[ 3 of 5]-Minibatch[  22-  22, 110.00%]: CE = 1.95262711 * 512; Err = 0.53125000 * 512; time = 0.0316s; samplesPerSecond = 16214.8
MPI Rank 0: 12/21/2016 05:28:40:  Epoch[ 3 of 5]-Minibatch[  23-  23, 115.00%]: CE = 1.99713868 * 512; Err = 0.54687500 * 512; time = 0.0314s; samplesPerSecond = 16304.2
MPI Rank 0: 12/21/2016 05:28:40:  Epoch[ 3 of 5]-Minibatch[  24-  24, 120.00%]: CE = 2.00598038 * 512; Err = 0.55664063 * 512; time = 0.0307s; samplesPerSecond = 16689.5
MPI Rank 0: 12/21/2016 05:28:40:  Epoch[ 3 of 5]-Minibatch[  25-  25, 125.00%]: CE = 1.91932725 * 501; Err = 0.56686627 * 501; time = 0.0296s; samplesPerSecond = 16941.7
MPI Rank 0: 12/21/2016 05:28:40:  Epoch[ 3 of 5]-Minibatch[  26-  26, 130.00%]: CE = 0.00000000 * 0; Err = 0.00000000 * 0; time = 1.5000e-005s; samplesPerSecond = 0.0
MPI Rank 0: 		(model aggregation stats): 4-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 0: 		(model aggregation stats) 4-th sync:     0.51 seconds since last report (0.00 seconds on comm.); 8192 samples processed by 2 workers (6645 by me);
MPI Rank 0: 		(model aggregation stats) 4-th sync: totalThroughput = 16.07k samplesPerSecond , throughputPerWorker = 8.04k samplesPerSecond
MPI Rank 0: 12/21/2016 05:28:40: Finished Epoch[ 3 of 5]: [Training] CE = 1.94906734 * 20480; Err = 0.54541016 * 20480; totalSamplesSeen = 61440; learningRatePerSample = 9.7656251e-005; epochTime=1.31689s
MPI Rank 0: 12/21/2016 05:28:40: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu/models/cntkSpeech.dnn.3'
MPI Rank 0: ValidateSubNetwork: featNorm.invStdDev InvStdDev operation changed, from [363 x 1] to [363].ValidateSubNetwork: featNorm.mean Mean operation changed, from [363 x 1] to [363].ValidateSubNetwork: logPrior._ Mean operation changed, from [132 x 1] to [132].Parallel training (2 workers) using BlockMomentumSGD with block momentum = 0.5000, block momentum time constant (per worker) = 2954.6394, block learning rate = 1.0000, block size per worker = 2048 samples, using Nesterov-style block momentum, resetting SGD momentum after sync.
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:40: Starting minibatch loop, distributed reading is ENABLED.
MPI Rank 0: 12/21/2016 05:28:40:   BaseAdaptiveLearnRateSearch: Epoch[ 4 of 5]-Minibatch[   1-   1, 5.00%]: CE = 1.83662591 * 512; Err = 0.50390625 * 512; time = 0.0281s; samplesPerSecond = 18212.2
MPI Rank 0: 12/21/2016 05:28:40:   BaseAdaptiveLearnRateSearch: Epoch[ 4 of 5]-Minibatch[   2-   2, 10.00%]: CE = 1.95013576 * 512; Err = 0.53125000 * 512; time = 0.0135s; samplesPerSecond = 37931.5
MPI Rank 0: 12/21/2016 05:28:40:   BaseAdaptiveLearnRateSearch: Epoch[ 4 of 5]-Minibatch[   3-   3, 15.00%]: CE = 0.00000000 * 0; Err = 0.00000000 * 0; time = 0.0008s; samplesPerSecond = 0.0
MPI Rank 0: 		(model aggregation stats): 1-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 0: 		(model aggregation stats) 1-th sync:     0.07 seconds since last report (0.00 seconds on comm.); 1536 samples processed by 2 workers (1024 by me);
MPI Rank 0: 		(model aggregation stats) 1-th sync: totalThroughput = 22.02k samplesPerSecond , throughputPerWorker = 11.01k samplesPerSecond
MPI Rank 0: 12/21/2016 05:28:40:  Finished Mini-Epoch[4]: CE = 1.91381689 * 1536; Err = 0.52148438 * 1536; learningRatePerSample = 0; minibatchSize = 1024
MPI Rank 0: ValidateSubNetwork: featNorm.invStdDev InvStdDev operation changed, from [363 x 1] to [363].ValidateSubNetwork: featNorm.mean Mean operation changed, from [363 x 1] to [363].ValidateSubNetwork: logPrior._ Mean operation changed, from [132 x 1] to [132].Parallel training (2 workers) using BlockMomentumSGD with block momentum = 0.5000, block momentum time constant (per worker) = 2954.6394, block learning rate = 1.0000, block size per worker = 2048 samples, using Nesterov-style block momentum, resetting SGD momentum after sync.
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:40: Starting minibatch loop, distributed reading is ENABLED.
MPI Rank 0: 12/21/2016 05:28:40:   AdaptiveLearnRateSearch: Epoch[ 4 of 5]-Minibatch[   1-   1, 5.00%]: CE = 1.83662591 * 512; Err = 0.50390625 * 512; time = 0.0640s; samplesPerSecond = 8002.6
MPI Rank 0: 12/21/2016 05:28:40:   AdaptiveLearnRateSearch: Epoch[ 4 of 5]-Minibatch[   2-   2, 10.00%]: CE = 1.94792524 * 512; Err = 0.52929688 * 512; time = 0.0322s; samplesPerSecond = 15914.0
MPI Rank 0: 12/21/2016 05:28:40:   AdaptiveLearnRateSearch: Epoch[ 4 of 5]-Minibatch[   3-   3, 15.00%]: CE = 0.00000000 * 0; Err = 0.00000000 * 0; time = 7.5000e-005s; samplesPerSecond = 0.0
MPI Rank 0: 		(model aggregation stats): 1-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 0: 		(model aggregation stats) 1-th sync:     0.12 seconds since last report (0.00 seconds on comm.); 1536 samples processed by 2 workers (1024 by me);
MPI Rank 0: 		(model aggregation stats) 1-th sync: totalThroughput = 12.49k samplesPerSecond , throughputPerWorker = 6.24k samplesPerSecond
MPI Rank 0: 12/21/2016 05:28:40:  Finished Mini-Epoch[4]: CE = 1.91308005 * 1536; Err = 0.52083333 * 1536; learningRatePerSample = 0.0039062502; minibatchSize = 1024
MPI Rank 0: ValidateSubNetwork: featNorm.invStdDev InvStdDev operation changed, from [363 x 1] to [363].ValidateSubNetwork: featNorm.mean Mean operation changed, from [363 x 1] to [363].ValidateSubNetwork: logPrior._ Mean operation changed, from [132 x 1] to [132].12/21/2016 05:28:41:  SearchForBestLearnRate Epoch[4]: Best learningRatePerSample = 0.003906250186, baseCriterion=1.922629502
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:41: Starting Epoch 4: learning rate per sample = 0.003906  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 0: Parallel training (2 workers) using BlockMomentumSGD with block momentum = 0.5000, block momentum time constant (per worker) = 2954.6394, block learning rate = 1.0000, block size per worker = 2048 samples, using Nesterov-style block momentum, resetting SGD momentum after sync.
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:41: Starting minibatch loop, distributed reading is ENABLED.
MPI Rank 0: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[   1-   1, 5.00%]: CE = 1.83662591 * 512; Err = 0.50390625 * 512; time = 0.0654s; samplesPerSecond = 7834.1
MPI Rank 0: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[   2-   2, 10.00%]: CE = 1.94792524 * 512; Err = 0.52929688 * 512; time = 0.0599s; samplesPerSecond = 8545.3
MPI Rank 0: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[   3-   3, 15.00%]: CE = 1.84996779 * 512; Err = 0.50195313 * 512; time = 0.0605s; samplesPerSecond = 8466.4
MPI Rank 0: 		(model aggregation stats): 1-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 0: 		(model aggregation stats) 1-th sync:     0.27 seconds since last report (0.00 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 0: 		(model aggregation stats) 1-th sync: totalThroughput = 14.98k samplesPerSecond , throughputPerWorker = 7.49k samplesPerSecond
MPI Rank 0: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[   4-   4, 20.00%]: CE = 1.91533561 * 512; Err = 0.52929688 * 512; time = 0.0801s; samplesPerSecond = 6394.6
MPI Rank 0: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[   5-   5, 25.00%]: CE = 1.91106814 * 512; Err = 0.55273438 * 512; time = 0.0599s; samplesPerSecond = 8544.0
MPI Rank 0: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[   6-   6, 30.00%]: CE = 1.89016030 * 512; Err = 0.51171875 * 512; time = 0.0630s; samplesPerSecond = 8128.8
MPI Rank 0: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[   7-   7, 35.00%]: CE = 1.85983068 * 512; Err = 0.51953125 * 512; time = 0.0596s; samplesPerSecond = 8591.6
MPI Rank 0: 		(model aggregation stats): 2-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 0: 		(model aggregation stats) 2-th sync:     0.26 seconds since last report (0.00 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 0: 		(model aggregation stats) 2-th sync: totalThroughput = 15.79k samplesPerSecond , throughputPerWorker = 7.89k samplesPerSecond
MPI Rank 0: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[   8-   8, 40.00%]: CE = 1.82250013 * 512; Err = 0.51171875 * 512; time = 0.0756s; samplesPerSecond = 6772.8
MPI Rank 0: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[   9-   9, 45.00%]: CE = 1.92651054 * 512; Err = 0.52929688 * 512; time = 0.0628s; samplesPerSecond = 8148.5
MPI Rank 0: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[  10-  10, 50.00%]: CE = 1.89119121 * 512; Err = 0.55664063 * 512; time = 0.0610s; samplesPerSecond = 8399.9
MPI Rank 0: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[  11-  11, 55.00%]: CE = 1.85766465 * 512; Err = 0.50585938 * 512; time = 0.0607s; samplesPerSecond = 8434.4
MPI Rank 0: 		(model aggregation stats): 3-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 0: 		(model aggregation stats) 3-th sync:     0.26 seconds since last report (0.00 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 0: 		(model aggregation stats) 3-th sync: totalThroughput = 15.57k samplesPerSecond , throughputPerWorker = 7.78k samplesPerSecond
MPI Rank 0: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[  12-  12, 60.00%]: CE = 1.91245983 * 512; Err = 0.53710938 * 512; time = 0.0775s; samplesPerSecond = 6605.5
MPI Rank 0: 12/21/2016 05:28:42:  Epoch[ 4 of 5]-Minibatch[  13-  13, 65.00%]: CE = 1.83122854 * 512; Err = 0.50976563 * 512; time = 0.0628s; samplesPerSecond = 8158.7
MPI Rank 0: 12/21/2016 05:28:42:  Epoch[ 4 of 5]-Minibatch[  14-  14, 70.00%]: CE = 1.82747049 * 512; Err = 0.52539063 * 512; time = 0.0607s; samplesPerSecond = 8432.1
MPI Rank 0: 12/21/2016 05:28:42:  Epoch[ 4 of 5]-Minibatch[  15-  15, 75.00%]: CE = 1.84116676 * 512; Err = 0.50390625 * 512; time = 0.0427s; samplesPerSecond = 11980.5
MPI Rank 0: 12/21/2016 05:28:42:  Epoch[ 4 of 5]-Minibatch[  16-  16, 80.00%]: CE = 1.86439812 * 512; Err = 0.52734375 * 512; time = 0.0608s; samplesPerSecond = 8426.0
MPI Rank 0: 12/21/2016 05:28:42:  Epoch[ 4 of 5]-Minibatch[  17-  17, 85.00%]: CE = 1.87971759 * 512; Err = 0.51757813 * 512; time = 0.0307s; samplesPerSecond = 16683.0
MPI Rank 0: 12/21/2016 05:28:42:  Epoch[ 4 of 5]-Minibatch[  18-  18, 90.00%]: CE = 1.77581930 * 512; Err = 0.53515625 * 512; time = 0.0304s; samplesPerSecond = 16839.3
MPI Rank 0: 12/21/2016 05:28:42:  Epoch[ 4 of 5]-Minibatch[  19-  19, 95.00%]: CE = 1.78889741 * 512; Err = 0.51757813 * 512; time = 0.0305s; samplesPerSecond = 16807.3
MPI Rank 0: 12/21/2016 05:28:42:  Epoch[ 4 of 5]-Minibatch[  20-  20, 100.00%]: CE = 1.83607952 * 512; Err = 0.54101563 * 512; time = 0.0314s; samplesPerSecond = 16330.7
MPI Rank 0: 12/21/2016 05:28:42:  Epoch[ 4 of 5]-Minibatch[  21-  21, 105.00%]: CE = 1.96701587 * 512; Err = 0.53515625 * 512; time = 0.0313s; samplesPerSecond = 16374.6
MPI Rank 0: 12/21/2016 05:28:42:  Epoch[ 4 of 5]-Minibatch[  22-  22, 110.00%]: CE = 1.74090893 * 512; Err = 0.49023438 * 512; time = 0.0300s; samplesPerSecond = 17081.5
MPI Rank 0: 12/21/2016 05:28:42:  Epoch[ 4 of 5]-Minibatch[  23-  23, 115.00%]: CE = 1.79959982 * 512; Err = 0.50390625 * 512; time = 0.0313s; samplesPerSecond = 16332.8
MPI Rank 0: 12/21/2016 05:28:42:  Epoch[ 4 of 5]-Minibatch[  24-  24, 120.00%]: CE = 1.83947195 * 512; Err = 0.49609375 * 512; time = 0.0303s; samplesPerSecond = 16921.7
MPI Rank 0: 12/21/2016 05:28:42:  Epoch[ 4 of 5]-Minibatch[  25-  25, 125.00%]: CE = 1.93199441 * 306; Err = 0.54575163 * 306; time = 0.0175s; samplesPerSecond = 17467.7
MPI Rank 0: 12/21/2016 05:28:42:  Epoch[ 4 of 5]-Minibatch[  26-  26, 130.00%]: CE = 0.00000000 * 0; Err = 0.00000000 * 0; time = 1.4000e-005s; samplesPerSecond = 0.0
MPI Rank 0: 		(model aggregation stats): 4-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 0: 		(model aggregation stats) 4-th sync:     0.51 seconds since last report (0.00 seconds on comm.); 8192 samples processed by 2 workers (6450 by me);
MPI Rank 0: 		(model aggregation stats) 4-th sync: totalThroughput = 16.07k samplesPerSecond , throughputPerWorker = 8.04k samplesPerSecond
MPI Rank 0: 12/21/2016 05:28:42: Finished Epoch[ 4 of 5]: [Training] CE = 1.86802921 * 20480; Err = 0.52246094 * 20480; totalSamplesSeen = 81920; learningRatePerSample = 0.0039062502; epochTime=2.1839s
MPI Rank 0: 12/21/2016 05:28:42: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu/models/cntkSpeech.dnn.4'
MPI Rank 0: ValidateSubNetwork: featNorm.invStdDev InvStdDev operation changed, from [363 x 1] to [363].ValidateSubNetwork: featNorm.mean Mean operation changed, from [363 x 1] to [363].ValidateSubNetwork: logPrior._ Mean operation changed, from [132 x 1] to [132].Parallel training (2 workers) using BlockMomentumSGD with block momentum = 0.5000, block momentum time constant (per worker) = 2954.6394, block learning rate = 1.0000, block size per worker = 2048 samples, using Nesterov-style block momentum, resetting SGD momentum after sync.
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:42: Starting minibatch loop, distributed reading is ENABLED.
MPI Rank 0: 12/21/2016 05:28:42:   BaseAdaptiveLearnRateSearch: Epoch[ 5 of 5]-Minibatch[   1-   1, 5.00%]: CE = 1.95736438 * 512; Err = 0.52343750 * 512; time = 0.0262s; samplesPerSecond = 19558.4
MPI Rank 0: 12/21/2016 05:28:42:   BaseAdaptiveLearnRateSearch: Epoch[ 5 of 5]-Minibatch[   2-   2, 10.00%]: CE = 1.86304613 * 512; Err = 0.55468750 * 512; time = 0.0127s; samplesPerSecond = 40254.7
MPI Rank 0: 12/21/2016 05:28:42:   BaseAdaptiveLearnRateSearch: Epoch[ 5 of 5]-Minibatch[   3-   3, 15.00%]: CE = 0.00000000 * 0; Err = 0.00000000 * 0; time = 0.0008s; samplesPerSecond = 0.0
MPI Rank 0: 		(model aggregation stats): 1-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 0: 		(model aggregation stats) 1-th sync:     0.07 seconds since last report (0.00 seconds on comm.); 1536 samples processed by 2 workers (1024 by me);
MPI Rank 0: 		(model aggregation stats) 1-th sync: totalThroughput = 22.62k samplesPerSecond , throughputPerWorker = 11.31k samplesPerSecond
MPI Rank 0: 12/21/2016 05:28:42:  Finished Mini-Epoch[5]: CE = 1.84766700 * 1536; Err = 0.51627604 * 1536; learningRatePerSample = 0; minibatchSize = 1024
MPI Rank 0: ValidateSubNetwork: featNorm.invStdDev InvStdDev operation changed, from [363 x 1] to [363].ValidateSubNetwork: featNorm.mean Mean operation changed, from [363 x 1] to [363].ValidateSubNetwork: logPrior._ Mean operation changed, from [132 x 1] to [132].Parallel training (2 workers) using BlockMomentumSGD with block momentum = 0.5000, block momentum time constant (per worker) = 2954.6394, block learning rate = 1.0000, block size per worker = 2048 samples, using Nesterov-style block momentum, resetting SGD momentum after sync.
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:43: Starting minibatch loop, distributed reading is ENABLED.
MPI Rank 0: 12/21/2016 05:28:43:   AdaptiveLearnRateSearch: Epoch[ 5 of 5]-Minibatch[   1-   1, 5.00%]: CE = 1.95736438 * 512; Err = 0.52343750 * 512; time = 0.0491s; samplesPerSecond = 10422.8
MPI Rank 0: 12/21/2016 05:28:43:   AdaptiveLearnRateSearch: Epoch[ 5 of 5]-Minibatch[   2-   2, 10.00%]: CE = 1.80265075 * 512; Err = 0.49609375 * 512; time = 0.0454s; samplesPerSecond = 11288.0
MPI Rank 0: 12/21/2016 05:28:43:   AdaptiveLearnRateSearch: Epoch[ 5 of 5]-Minibatch[   3-   3, 15.00%]: CE = 0.00000000 * 0; Err = 0.00000000 * 0; time = 0.0001s; samplesPerSecond = 0.0
MPI Rank 0: 		(model aggregation stats): 1-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 0: 		(model aggregation stats) 1-th sync:     0.13 seconds since last report (0.00 seconds on comm.); 1536 samples processed by 2 workers (1024 by me);
MPI Rank 0: 		(model aggregation stats) 1-th sync: totalThroughput = 12.07k samplesPerSecond , throughputPerWorker = 6.04k samplesPerSecond
MPI Rank 0: 12/21/2016 05:28:43:  Finished Mini-Epoch[5]: CE = 1.82753521 * 1536; Err = 0.49674479 * 1536; learningRatePerSample = 0.0063207938; minibatchSize = 1024
MPI Rank 0: ValidateSubNetwork: featNorm.invStdDev InvStdDev operation changed, from [363 x 1] to [363].ValidateSubNetwork: featNorm.mean Mean operation changed, from [363 x 1] to [363].ValidateSubNetwork: logPrior._ Mean operation changed, from [132 x 1] to [132].12/21/2016 05:28:43:  SearchForBestLearnRate Epoch[5]: Best learningRatePerSample = 0.006320793777, baseCriterion=1.85275755
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:43: Starting Epoch 5: learning rate per sample = 0.006321  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 0: Parallel training (2 workers) using BlockMomentumSGD with block momentum = 0.5000, block momentum time constant (per worker) = 2954.6394, block learning rate = 1.0000, block size per worker = 2048 samples, using Nesterov-style block momentum, resetting SGD momentum after sync.
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:43: Starting minibatch loop, distributed reading is ENABLED.
MPI Rank 0: 12/21/2016 05:28:43:  Epoch[ 5 of 5]-Minibatch[   1-   1, 5.00%]: CE = 1.95736438 * 512; Err = 0.52343750 * 512; time = 0.0650s; samplesPerSecond = 7876.2
MPI Rank 0: 12/21/2016 05:28:43:  Epoch[ 5 of 5]-Minibatch[   2-   2, 10.00%]: CE = 1.80265075 * 512; Err = 0.49609375 * 512; time = 0.0617s; samplesPerSecond = 8300.8
MPI Rank 0: 12/21/2016 05:28:43:  Epoch[ 5 of 5]-Minibatch[   3-   3, 15.00%]: CE = 1.75549697 * 512; Err = 0.51171875 * 512; time = 0.0613s; samplesPerSecond = 8351.7
MPI Rank 0: 		(model aggregation stats): 1-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 0: 		(model aggregation stats) 1-th sync:     0.27 seconds since last report (0.00 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 0: 		(model aggregation stats) 1-th sync: totalThroughput = 14.92k samplesPerSecond , throughputPerWorker = 7.46k samplesPerSecond
MPI Rank 0: 12/21/2016 05:28:43:  Epoch[ 5 of 5]-Minibatch[   4-   4, 20.00%]: CE = 1.90051737 * 512; Err = 0.54492188 * 512; time = 0.0767s; samplesPerSecond = 6672.5
MPI Rank 0: 12/21/2016 05:28:43:  Epoch[ 5 of 5]-Minibatch[   5-   5, 25.00%]: CE = 1.86454994 * 512; Err = 0.53515625 * 512; time = 0.0616s; samplesPerSecond = 8310.2
MPI Rank 0: 12/21/2016 05:28:43:  Epoch[ 5 of 5]-Minibatch[   6-   6, 30.00%]: CE = 1.81624071 * 512; Err = 0.52343750 * 512; time = 0.0605s; samplesPerSecond = 8461.1
MPI Rank 0: 12/21/2016 05:28:43:  Epoch[ 5 of 5]-Minibatch[   7-   7, 35.00%]: CE = 1.76991108 * 512; Err = 0.52539063 * 512; time = 0.0561s; samplesPerSecond = 9134.2
MPI Rank 0: 		(model aggregation stats): 2-th sync point was hit, introducing a 0.05-seconds latency this time; accumulated time on sync point = 0.05 seconds , average latency = 0.03 seconds
MPI Rank 0: 		(model aggregation stats) 2-th sync:     0.26 seconds since last report (0.00 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 0: 		(model aggregation stats) 2-th sync: totalThroughput = 15.83k samplesPerSecond , throughputPerWorker = 7.92k samplesPerSecond
MPI Rank 0: 12/21/2016 05:28:43:  Epoch[ 5 of 5]-Minibatch[   8-   8, 40.00%]: CE = 1.88699772 * 512; Err = 0.54687500 * 512; time = 0.0794s; samplesPerSecond = 6451.1
MPI Rank 0: 12/21/2016 05:28:43:  Epoch[ 5 of 5]-Minibatch[   9-   9, 45.00%]: CE = 1.74602775 * 512; Err = 0.51171875 * 512; time = 0.0614s; samplesPerSecond = 8341.1
MPI Rank 0: 12/21/2016 05:28:43:  Epoch[ 5 of 5]-Minibatch[  10-  10, 50.00%]: CE = 1.66755622 * 512; Err = 0.49609375 * 512; time = 0.0598s; samplesPerSecond = 8568.0
MPI Rank 0: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  11-  11, 55.00%]: CE = 1.74110886 * 512; Err = 0.50781250 * 512; time = 0.0619s; samplesPerSecond = 8276.6
MPI Rank 0: 		(model aggregation stats): 3-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.05 seconds , average latency = 0.02 seconds
MPI Rank 0: 		(model aggregation stats) 3-th sync:     0.26 seconds since last report (0.00 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 0: 		(model aggregation stats) 3-th sync: totalThroughput = 15.68k samplesPerSecond , throughputPerWorker = 7.84k samplesPerSecond
MPI Rank 0: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  12-  12, 60.00%]: CE = 1.82518528 * 512; Err = 0.51953125 * 512; time = 0.0801s; samplesPerSecond = 6390.9
MPI Rank 0: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  13-  13, 65.00%]: CE = 1.72221058 * 512; Err = 0.48046875 * 512; time = 0.0603s; samplesPerSecond = 8497.8
MPI Rank 0: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  14-  14, 70.00%]: CE = 1.76486948 * 512; Err = 0.53320313 * 512; time = 0.0621s; samplesPerSecond = 8249.3
MPI Rank 0: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  15-  15, 75.00%]: CE = 1.72811109 * 512; Err = 0.50781250 * 512; time = 0.0504s; samplesPerSecond = 10162.0
MPI Rank 0: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  16-  16, 80.00%]: CE = 1.73720486 * 512; Err = 0.48437500 * 512; time = 0.0578s; samplesPerSecond = 8864.6
MPI Rank 0: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  17-  17, 85.00%]: CE = 1.64690249 * 512; Err = 0.47265625 * 512; time = 0.0308s; samplesPerSecond = 16639.0
MPI Rank 0: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  18-  18, 90.00%]: CE = 1.62069217 * 512; Err = 0.45507813 * 512; time = 0.0304s; samplesPerSecond = 16823.8
MPI Rank 0: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  19-  19, 95.00%]: CE = 1.68369876 * 512; Err = 0.46289063 * 512; time = 0.0313s; samplesPerSecond = 16372.0
MPI Rank 0: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  20-  20, 100.00%]: CE = 1.70660558 * 512; Err = 0.51171875 * 512; time = 0.0309s; samplesPerSecond = 16588.4
MPI Rank 0: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  21-  21, 105.00%]: CE = 1.62135182 * 512; Err = 0.50000000 * 512; time = 0.0312s; samplesPerSecond = 16398.2
MPI Rank 0: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  22-  22, 110.00%]: CE = 1.75380156 * 512; Err = 0.50976563 * 512; time = 0.0296s; samplesPerSecond = 17296.1
MPI Rank 0: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  23-  23, 115.00%]: CE = 1.79493194 * 512; Err = 0.50976563 * 512; time = 0.0315s; samplesPerSecond = 16277.2
MPI Rank 0: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  24-  24, 120.00%]: CE = 1.58781091 * 512; Err = 0.47656250 * 512; time = 0.0313s; samplesPerSecond = 16342.2
MPI Rank 0: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  25-  25, 125.00%]: CE = 1.58255106 * 179; Err = 0.46927374 * 179; time = 0.0109s; samplesPerSecond = 16453.7
MPI Rank 0: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  26-  26, 130.00%]: CE = 0.00000000 * 0; Err = 0.00000000 * 0; time = 1.5000e-005s; samplesPerSecond = 0.0
MPI Rank 0: 		(model aggregation stats): 4-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.05 seconds , average latency = 0.01 seconds
MPI Rank 0: 		(model aggregation stats) 4-th sync:     0.51 seconds since last report (0.00 seconds on comm.); 8192 samples processed by 2 workers (6323 by me);
MPI Rank 0: 		(model aggregation stats) 4-th sync: totalThroughput = 16.04k samplesPerSecond , throughputPerWorker = 8.02k samplesPerSecond
MPI Rank 0: 12/21/2016 05:28:44: Finished Epoch[ 5 of 5]: [Training] CE = 1.78255566 * 20480; Err = 0.51186523 * 20480; totalSamplesSeen = 102400; learningRatePerSample = 0.0063207938; epochTime=1.96287s
MPI Rank 0: 12/21/2016 05:28:44: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu/models/cntkSpeech.dnn'
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:44: Action "train" complete.
MPI Rank 0: 
MPI Rank 0: 12/21/2016 05:28:44: __COMPLETED__
MPI Rank 0: ~MPIWrapper
MPI Rank 1: 12/21/2016 05:28:28: Redirecting stderr to file C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu/stderr_SpeechTrain.logrank1
MPI Rank 1: 12/21/2016 05:28:28: -------------------------------------------------------------------
MPI Rank 1: 12/21/2016 05:28:28: Build info: 
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:28: 		Built time: Dec 21 2016 04:21:26
MPI Rank 1: 12/21/2016 05:28:28: 		Last modified date: Tue Dec 20 18:55:12 2016
MPI Rank 1: 12/21/2016 05:28:28: 		Build type: Release
MPI Rank 1: 12/21/2016 05:28:28: 		Build target: GPU
MPI Rank 1: 12/21/2016 05:28:28: 		With ASGD: yes
MPI Rank 1: 12/21/2016 05:28:28: 		Math lib: mkl
MPI Rank 1: 12/21/2016 05:28:28: 		CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0
MPI Rank 1: 12/21/2016 05:28:28: 		CUB_PATH: c:\src\cub-1.4.1
MPI Rank 1: 12/21/2016 05:28:28: 		CUDNN_PATH: C:\local\cudnn-8.0-windows10-x64-v5.1
MPI Rank 1: 12/21/2016 05:28:28: 		Build Branch: HEAD
MPI Rank 1: 12/21/2016 05:28:28: 		Build SHA1: 0a2e20ddce32ca3cd458ef0358757e1489d9afe3 (modified)
MPI Rank 1: 12/21/2016 05:28:28: 		Built by svcphil on LIANA-09-w
MPI Rank 1: 12/21/2016 05:28:28: 		Build Path: C:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
MPI Rank 1: 12/21/2016 05:28:28: -------------------------------------------------------------------
MPI Rank 1: 12/21/2016 05:28:28: -------------------------------------------------------------------
MPI Rank 1: 12/21/2016 05:28:28: GPU info:
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:28: 		Device[0]: cores = 2496; computeCapability = 5.2; type = "Quadro M4000"; memory = 8192 MB
MPI Rank 1: 12/21/2016 05:28:28: -------------------------------------------------------------------
MPI Rank 1: 
MPI Rank 1: Configuration After Processing and Variable Resolution:
MPI Rank 1: 
MPI Rank 1: configparameters: cntk.cntk:command=SpeechTrain
MPI Rank 1: configparameters: cntk.cntk:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\HTKDeserializers\DNN\ParallelBMWithAdjustLR
MPI Rank 1: configparameters: cntk.cntk:currentDirectory=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data
MPI Rank 1: configparameters: cntk.cntk:DataDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data
MPI Rank 1: configparameters: cntk.cntk:deviceId=0
MPI Rank 1: configparameters: cntk.cntk:framemode=true
MPI Rank 1: configparameters: cntk.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu/models/cntkSpeech.dnn
MPI Rank 1: configparameters: cntk.cntk:numCPUThreads=4
MPI Rank 1: configparameters: cntk.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu
MPI Rank 1: configparameters: cntk.cntk:parallelTrain=true
MPI Rank 1: configparameters: cntk.cntk:precision=double
MPI Rank 1: configparameters: cntk.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu
MPI Rank 1: configparameters: cntk.cntk:SpeechTrain={
MPI Rank 1:     action = "train"
MPI Rank 1:     BrainScriptNetworkBuilder = {
MPI Rank 1:         layerSizes = 363:512:512:132
MPI Rank 1:         trainingCriterion = 'CE'
MPI Rank 1:         evalCriterion = 'Err'
MPI Rank 1:         applyMeanVarNorm = true
MPI Rank 1:         L = Length(layerSizes)-1    // number of model layers
MPI Rank 1:         features = Input { layerSizes[0] } // 1, tag='feature' 
MPI Rank 1:         labels = Input { layerSizes[L] } // 1, tag='label' 
MPI Rank 1:         featNorm = if applyMeanVarNorm
MPI Rank 1:                    then MeanVarNorm(features)
MPI Rank 1:                    else features
MPI Rank 1:         layers[layer:1..L-1] = if layer > 1
MPI Rank 1:                                then SBFF(layers[layer-1].Eh, layerSizes[layer], layerSizes[layer-1])
MPI Rank 1:                                else SBFF(featNorm, layerSizes[layer], layerSizes[layer-1])
MPI Rank 1:         outLayer = BFF(layers[L-1].Eh, layerSizes[L], layerSizes[L-1])
MPI Rank 1:         outZ = outLayer.z        // + PastValue(layerSizes[L], 1, outLayer.z)
MPI Rank 1:         CE = if trainingCriterion == 'CE'
MPI Rank 1:              then CrossEntropyWithSoftmax(labels, outZ, tag='criterion')
MPI Rank 1:              else Fail('unknown trainingCriterion ' + trainingCriterion)
MPI Rank 1:         Err = if evalCriterion == 'Err' then
MPI Rank 1:               ClassificationError(labels, outZ, tag='evaluation')
MPI Rank 1:               else Fail('unknown evalCriterion ' + evalCriterion)
MPI Rank 1:         logPrior = LogPrior(labels)
MPI Rank 1:         // TODO: how to add a tag to an infix operation?
MPI Rank 1:         ScaledLogLikelihood = Minus (outZ, logPrior, tag='output')
MPI Rank 1:     }
MPI Rank 1:     SGD = {
MPI Rank 1:         epochSize = 20480
MPI Rank 1:         minibatchSize = 64:256:1024
MPI Rank 1:         learningRatesPerMB = 1.0:0.5:0.1
MPI Rank 1:         numMBsToShowResult = 1
MPI Rank 1:         momentumPerMB = 0.9:0.656119
MPI Rank 1:         dropoutRate = 0.0
MPI Rank 1:         maxEpochs = 5
MPI Rank 1:         keepCheckPointFiles = true
MPI Rank 1:         clippingThresholdPerSample = 1#INF
MPI Rank 1:         ParallelTrain = {
MPI Rank 1:             parallelizationMethod = "BlockMomentumSGD"
MPI Rank 1:             distributedMBReading = true
MPI Rank 1:             syncPerfStats=1
MPI Rank 1:             BlockMomentumSGD = {
MPI Rank 1:                 blockSizePerWorker=2048
MPI Rank 1:                 resetSGDMomentum=true
MPI Rank 1:                 useNesterovMomentum=true
MPI Rank 1:             }
MPI Rank 1:         }
MPI Rank 1:         AutoAdjust = {
MPI Rank 1:             reduceLearnRateIfImproveLessThan = 0
MPI Rank 1:             loadBestModel = true
MPI Rank 1:             increaseLearnRateIfImproveMoreThan = 1000000000
MPI Rank 1:             learnRateDecreaseFactor = 0.5
MPI Rank 1:             learnRateIncreaseFactor = 1.382
MPI Rank 1:             autoAdjustLR = "searchBeforeEpoch"
MPI Rank 1:             numMiniBatch4LRSearch = 20
MPI Rank 1:             numPrevLearnRates = 3            
MPI Rank 1:         }
MPI Rank 1:     }
MPI Rank 1:     reader = {
MPI Rank 1:         verbosity = 0 ; randomize = true
MPI Rank 1:         deserializers = ({
MPI Rank 1:             type = "HTKFeatureDeserializer"
MPI Rank 1:             module = "HTKDeserializers"                
MPI Rank 1:             input = { features = { dim = 363 ; scpFile = "glob_0000.scp" } }
MPI Rank 1:         }:{
MPI Rank 1:             type = "HTKMLFDeserializer"
MPI Rank 1:             module = "HTKDeserializers"
MPI Rank 1:             input = { labels = { mlfFile = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/glob_0000.mlf" ; labelMappingFile = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/state.list" ; labelDim = 132 } }
MPI Rank 1:         })
MPI Rank 1:     }
MPI Rank 1: } [SGD=[ParallelTrain=[parallelizationStartEpoch=2]]]
MPI Rank 1: 
MPI Rank 1: configparameters: cntk.cntk:stderr=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20161221052142.365503\Speech\HTKDeserializers\DNN_ParallelBMWithAdjustLR@release_gpu/stderr
MPI Rank 1: configparameters: cntk.cntk:timestamping=true
MPI Rank 1: configparameters: cntk.cntk:traceLevel=1
MPI Rank 1: 12/21/2016 05:28:28: Commands: SpeechTrain
MPI Rank 1: 12/21/2016 05:28:28: precision = "double"
MPI Rank 1: 12/21/2016 05:28:28: Using 4 CPU threads.
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:28: ##############################################################################
MPI Rank 1: 12/21/2016 05:28:28: #                                                                            #
MPI Rank 1: 12/21/2016 05:28:28: # SpeechTrain command (train action)                                         #
MPI Rank 1: 12/21/2016 05:28:28: #                                                                            #
MPI Rank 1: 12/21/2016 05:28:28: ##############################################################################
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:28: WARNING: 'numMiniBatch4LRSearch' is deprecated, please remove it and use 'numSamples4Search' instead.
MPI Rank 1: 12/21/2016 05:28:28: 
MPI Rank 1: Creating virgin network.
MPI Rank 1: 
MPI Rank 1: Post-processing network...
MPI Rank 1: 
MPI Rank 1: 6 roots:
MPI Rank 1: 	CE = CrossEntropyWithSoftmax()
MPI Rank 1: 	Err = ClassificationError()
MPI Rank 1: 	ScaledLogLikelihood = Minus()
MPI Rank 1: 	featNorm.invStdDev = InvStdDev()
MPI Rank 1: 	featNorm.mean = Mean()
MPI Rank 1: 	logPrior._ = Mean()
MPI Rank 1: 
MPI Rank 1: Validating network. 25 nodes to process in pass 1.
MPI Rank 1: 
MPI Rank 1: Validating --> labels = InputValue() :  -> [132 x *]
MPI Rank 1: Validating --> outLayer.W = LearnableParameter() :  -> [132 x 512]
MPI Rank 1: Validating --> layers[2].Eh._.W = LearnableParameter() :  -> [512 x 512]
MPI Rank 1: Validating --> layers[1].Eh._.W = LearnableParameter() :  -> [512 x 363]
MPI Rank 1: Validating --> features = InputValue() :  -> [363 x *]
MPI Rank 1: Validating --> featNorm.mean = Mean (features) : [363 x *] -> [363]
MPI Rank 1: Validating --> featNorm.ElementTimesArgs[0] = Minus (features, featNorm.mean) : [363 x *], [363] -> [363 x *]
MPI Rank 1: Validating --> featNorm.invStdDev = InvStdDev (features) : [363 x *] -> [363]
MPI Rank 1: Validating --> featNorm = ElementTimes (featNorm.ElementTimesArgs[0], featNorm.invStdDev) : [363 x *], [363] -> [363 x *]
MPI Rank 1: Validating --> layers[1].Eh._.z.PlusArgs[0] = Times (layers[1].Eh._.W, featNorm) : [512 x 363], [363 x *] -> [512 x *]
MPI Rank 1: Validating --> layers[1].Eh._.B = LearnableParameter() :  -> [512 x 1]
MPI Rank 1: Validating --> layers[1].Eh._.z = Plus (layers[1].Eh._.z.PlusArgs[0], layers[1].Eh._.B) : [512 x *], [512 x 1] -> [512 x 1 x *]
MPI Rank 1: Validating --> layers[1].Eh = Sigmoid (layers[1].Eh._.z) : [512 x 1 x *] -> [512 x 1 x *]
MPI Rank 1: Validating --> layers[2].Eh._.z.PlusArgs[0] = Times (layers[2].Eh._.W, layers[1].Eh) : [512 x 512], [512 x 1 x *] -> [512 x 1 x *]
MPI Rank 1: Validating --> layers[2].Eh._.B = LearnableParameter() :  -> [512 x 1]
MPI Rank 1: Validating --> layers[2].Eh._.z = Plus (layers[2].Eh._.z.PlusArgs[0], layers[2].Eh._.B) : [512 x 1 x *], [512 x 1] -> [512 x 1 x *]
MPI Rank 1: Validating --> layers[2].Eh = Sigmoid (layers[2].Eh._.z) : [512 x 1 x *] -> [512 x 1 x *]
MPI Rank 1: Validating --> outLayer.z.PlusArgs[0] = Times (outLayer.W, layers[2].Eh) : [132 x 512], [512 x 1 x *] -> [132 x 1 x *]
MPI Rank 1: Validating --> outLayer.B = LearnableParameter() :  -> [132 x 1]
MPI Rank 1: Validating --> outZ = Plus (outLayer.z.PlusArgs[0], outLayer.B) : [132 x 1 x *], [132 x 1] -> [132 x 1 x *]
MPI Rank 1: Validating --> CE = CrossEntropyWithSoftmax (labels, outZ) : [132 x *], [132 x 1 x *] -> [1]
MPI Rank 1: Validating --> Err = ClassificationError (labels, outZ) : [132 x *], [132 x 1 x *] -> [1]
MPI Rank 1: Validating --> logPrior._ = Mean (labels) : [132 x *] -> [132]
MPI Rank 1: Validating --> logPrior = Log (logPrior._) : [132] -> [132]
MPI Rank 1: Validating --> ScaledLogLikelihood = Minus (outZ, logPrior) : [132 x 1 x *], [132] -> [132 x 1 x *]
MPI Rank 1: 
MPI Rank 1: Validating network. 17 nodes to process in pass 2.
MPI Rank 1: 
MPI Rank 1: 
MPI Rank 1: Validating network, final pass.
MPI Rank 1: 
MPI Rank 1: 
MPI Rank 1: 
MPI Rank 1: 
MPI Rank 1: Post-processing network complete.
MPI Rank 1: 
MPI Rank 1: Reading script file glob_0000.scp ... 948 entries
MPI Rank 1: HTKDataDeserializer::HTKDataDeserializer: 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: HTKDataDeserializer::HTKDataDeserializer: determined feature kind as 33-dimensional 'USER' with frame shift 10.0 ms
MPI Rank 1: total 132 state names in state list C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/state.list
MPI Rank 1: htkmlfreader: reading MLF file C:\jenkins\workspace\CNTK-Test-Windows-W1\Tests\EndToEndTests\Speech\Data/glob_0000.mlf ... total 948 entries
MPI Rank 1: MLFDataDeserializer::MLFDataDeserializer: 948 utterances with 252734 frames in 129 classes
MPI Rank 1: 12/21/2016 05:28:29: 
MPI Rank 1: Model has 25 nodes. Using GPU 0.
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:29: Training criterion:   CE = CrossEntropyWithSoftmax
MPI Rank 1: 12/21/2016 05:28:29: Evaluation criterion: Err = ClassificationError
MPI Rank 1: 
MPI Rank 1: 
MPI Rank 1: Allocating matrices for forward and/or backward propagation.
MPI Rank 1: 
MPI Rank 1: Memory Sharing: Out of 40 matrices, 19 are shared as 8, and 21 are not shared.
MPI Rank 1: 
MPI Rank 1: 	{ layers[2].Eh._.W : [512 x 512] (gradient)
MPI Rank 1: 	  layers[2].Eh._.z : [512 x 1 x *] }
MPI Rank 1: 	{ layers[2].Eh : [512 x 1 x *]
MPI Rank 1: 	  layers[2].Eh._.z.PlusArgs[0] : [512 x 1 x *] (gradient) }
MPI Rank 1: 	{ layers[1].Eh : [512 x 1 x *] (gradient)
MPI Rank 1: 	  layers[1].Eh._.B : [512 x 1] (gradient)
MPI Rank 1: 	  layers[2].Eh._.z : [512 x 1 x *] (gradient)
MPI Rank 1: 	  outLayer.z.PlusArgs[0] : [132 x 1 x *] }
MPI Rank 1: 	{ layers[1].Eh._.z : [512 x 1 x *] (gradient)
MPI Rank 1: 	  layers[2].Eh._.z.PlusArgs[0] : [512 x 1 x *] }
MPI Rank 1: 	{ outLayer.W : [132 x 512] (gradient)
MPI Rank 1: 	  outZ : [132 x 1 x *] }
MPI Rank 1: 	{ layers[1].Eh : [512 x 1 x *]
MPI Rank 1: 	  layers[1].Eh._.z.PlusArgs[0] : [512 x *] (gradient) }
MPI Rank 1: 	{ layers[1].Eh._.W : [512 x 363] (gradient)
MPI Rank 1: 	  layers[1].Eh._.z : [512 x 1 x *] }
MPI Rank 1: 	{ layers[2].Eh : [512 x 1 x *] (gradient)
MPI Rank 1: 	  layers[2].Eh._.B : [512 x 1] (gradient)
MPI Rank 1: 	  outZ : [132 x 1 x *] (gradient) }
MPI Rank 1: 
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:29: Training 516740 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:29: 	Node 'layers[1].Eh._.B' (LearnableParameter operation) : [512 x 1]
MPI Rank 1: 12/21/2016 05:28:29: 	Node 'layers[1].Eh._.W' (LearnableParameter operation) : [512 x 363]
MPI Rank 1: 12/21/2016 05:28:29: 	Node 'layers[2].Eh._.B' (LearnableParameter operation) : [512 x 1]
MPI Rank 1: 12/21/2016 05:28:29: 	Node 'layers[2].Eh._.W' (LearnableParameter operation) : [512 x 512]
MPI Rank 1: 12/21/2016 05:28:29: 	Node 'outLayer.B' (LearnableParameter operation) : [132 x 1]
MPI Rank 1: 12/21/2016 05:28:29: 	Node 'outLayer.W' (LearnableParameter operation) : [132 x 512]
MPI Rank 1: 
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:29: Precomputing --> 3 PreCompute nodes found.
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:29: 	featNorm.mean = Mean()
MPI Rank 1: 12/21/2016 05:28:29: 	featNorm.invStdDev = InvStdDev()
MPI Rank 1: 12/21/2016 05:28:29: 	logPrior._ = Mean()
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:33: Precomputing --> Completed.
MPI Rank 1: 
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:33: 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: 12/21/2016 05:28:33: Starting minibatch loop.
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[   1-   1, 0.31%]: CE = 4.91295596 * 64; Err = 0.96875000 * 64; time = 0.0143s; samplesPerSecond = 4466.2
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[   2-   2, 0.63%]: CE = 4.78498529 * 64; Err = 1.00000000 * 64; time = 0.0100s; samplesPerSecond = 6407.7
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[   3-   3, 0.94%]: CE = 4.19018696 * 64; Err = 0.81250000 * 64; time = 0.0090s; samplesPerSecond = 7119.0
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[   4-   4, 1.25%]: CE = 4.46135476 * 64; Err = 0.82812500 * 64; time = 0.0103s; samplesPerSecond = 6243.9
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[   5-   5, 1.56%]: CE = 4.72788003 * 64; Err = 0.92187500 * 64; time = 0.0083s; samplesPerSecond = 7697.9
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[   6-   6, 1.88%]: CE = 4.07654096 * 64; Err = 0.89062500 * 64; time = 0.0102s; samplesPerSecond = 6250.6
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[   7-   7, 2.19%]: CE = 4.50165607 * 64; Err = 0.96875000 * 64; time = 0.0093s; samplesPerSecond = 6873.6
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[   8-   8, 2.50%]: CE = 4.93153999 * 64; Err = 0.89062500 * 64; time = 0.0101s; samplesPerSecond = 6349.8
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[   9-   9, 2.81%]: CE = 4.79817443 * 64; Err = 0.93750000 * 64; time = 0.0089s; samplesPerSecond = 7229.2
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  10-  10, 3.13%]: CE = 4.46089875 * 64; Err = 0.96875000 * 64; time = 0.0104s; samplesPerSecond = 6182.4
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  11-  11, 3.44%]: CE = 4.34462020 * 64; Err = 0.90625000 * 64; time = 0.0078s; samplesPerSecond = 8235.7
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  12-  12, 3.75%]: CE = 3.91243070 * 64; Err = 0.87500000 * 64; time = 0.0108s; samplesPerSecond = 5934.2
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  13-  13, 4.06%]: CE = 4.73715179 * 64; Err = 0.92187500 * 64; time = 0.0082s; samplesPerSecond = 7800.1
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  14-  14, 4.38%]: CE = 4.42160986 * 64; Err = 0.93750000 * 64; time = 0.0092s; samplesPerSecond = 6970.9
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  15-  15, 4.69%]: CE = 4.14675744 * 64; Err = 0.85937500 * 64; time = 0.0100s; samplesPerSecond = 6382.8
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  16-  16, 5.00%]: CE = 4.50951186 * 64; Err = 0.95312500 * 64; time = 0.0099s; samplesPerSecond = 6462.0
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  17-  17, 5.31%]: CE = 4.30758210 * 64; Err = 0.85937500 * 64; time = 0.0097s; samplesPerSecond = 6576.9
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  18-  18, 5.63%]: CE = 4.34534841 * 64; Err = 1.00000000 * 64; time = 0.0108s; samplesPerSecond = 5950.2
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  19-  19, 5.94%]: CE = 4.19517128 * 64; Err = 0.96875000 * 64; time = 0.0108s; samplesPerSecond = 5910.6
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  20-  20, 6.25%]: CE = 4.41248710 * 64; Err = 0.98437500 * 64; time = 0.0096s; samplesPerSecond = 6654.9
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  21-  21, 6.56%]: CE = 4.10891079 * 64; Err = 0.92187500 * 64; time = 0.0103s; samplesPerSecond = 6216.6
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  22-  22, 6.88%]: CE = 4.16379766 * 64; Err = 0.85937500 * 64; time = 0.0084s; samplesPerSecond = 7593.7
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  23-  23, 7.19%]: CE = 4.09455579 * 64; Err = 0.92187500 * 64; time = 0.0118s; samplesPerSecond = 5405.9
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  24-  24, 7.50%]: CE = 3.95980469 * 64; Err = 0.89062500 * 64; time = 0.0107s; samplesPerSecond = 5954.6
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  25-  25, 7.81%]: CE = 4.05428109 * 64; Err = 0.87500000 * 64; time = 0.0064s; samplesPerSecond = 10066.1
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  26-  26, 8.13%]: CE = 4.16245451 * 64; Err = 0.84375000 * 64; time = 0.0093s; samplesPerSecond = 6901.8
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  27-  27, 8.44%]: CE = 3.71756327 * 64; Err = 0.84375000 * 64; time = 0.0117s; samplesPerSecond = 5448.7
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  28-  28, 8.75%]: CE = 3.80779138 * 64; Err = 0.87500000 * 64; time = 0.0101s; samplesPerSecond = 6351.1
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  29-  29, 9.06%]: CE = 3.72564857 * 64; Err = 0.81250000 * 64; time = 0.0097s; samplesPerSecond = 6623.2
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  30-  30, 9.38%]: CE = 4.01963243 * 64; Err = 0.87500000 * 64; time = 0.0098s; samplesPerSecond = 6542.0
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  31-  31, 9.69%]: CE = 3.68590709 * 64; Err = 0.89062500 * 64; time = 0.0098s; samplesPerSecond = 6519.3
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  32-  32, 10.00%]: CE = 3.81516754 * 64; Err = 0.78125000 * 64; time = 0.0083s; samplesPerSecond = 7734.1
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  33-  33, 10.31%]: CE = 3.93685037 * 64; Err = 0.87500000 * 64; time = 0.0109s; samplesPerSecond = 5857.6
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  34-  34, 10.63%]: CE = 3.96481462 * 64; Err = 0.90625000 * 64; time = 0.0090s; samplesPerSecond = 7089.1
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  35-  35, 10.94%]: CE = 3.57865409 * 64; Err = 0.84375000 * 64; time = 0.0098s; samplesPerSecond = 6540.0
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  36-  36, 11.25%]: CE = 3.72265528 * 64; Err = 0.85937500 * 64; time = 0.0089s; samplesPerSecond = 7172.5
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  37-  37, 11.56%]: CE = 3.71485627 * 64; Err = 0.84375000 * 64; time = 0.0101s; samplesPerSecond = 6314.1
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  38-  38, 11.88%]: CE = 4.04042687 * 64; Err = 0.87500000 * 64; time = 0.0085s; samplesPerSecond = 7548.1
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  39-  39, 12.19%]: CE = 3.48663283 * 64; Err = 0.76562500 * 64; time = 0.0104s; samplesPerSecond = 6158.6
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  40-  40, 12.50%]: CE = 3.48828968 * 64; Err = 0.81250000 * 64; time = 0.0093s; samplesPerSecond = 6886.2
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  41-  41, 12.81%]: CE = 3.46883616 * 64; Err = 0.78125000 * 64; time = 0.0091s; samplesPerSecond = 7039.9
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  42-  42, 13.13%]: CE = 4.12832965 * 64; Err = 0.90625000 * 64; time = 0.0095s; samplesPerSecond = 6763.2
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  43-  43, 13.44%]: CE = 3.82286476 * 64; Err = 0.90625000 * 64; time = 0.0099s; samplesPerSecond = 6475.8
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  44-  44, 13.75%]: CE = 3.99396471 * 64; Err = 0.90625000 * 64; time = 0.0091s; samplesPerSecond = 7016.8
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  45-  45, 14.06%]: CE = 3.84953256 * 64; Err = 0.89062500 * 64; time = 0.0104s; samplesPerSecond = 6180.0
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  46-  46, 14.37%]: CE = 3.57917953 * 64; Err = 0.79687500 * 64; time = 0.0083s; samplesPerSecond = 7743.5
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  47-  47, 14.69%]: CE = 3.86079148 * 64; Err = 0.84375000 * 64; time = 0.0102s; samplesPerSecond = 6270.8
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  48-  48, 15.00%]: CE = 3.88891763 * 64; Err = 0.85937500 * 64; time = 0.0096s; samplesPerSecond = 6656.3
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  49-  49, 15.31%]: CE = 3.94662742 * 64; Err = 0.89062500 * 64; time = 0.0101s; samplesPerSecond = 6365.6
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  50-  50, 15.63%]: CE = 3.83644301 * 64; Err = 0.87500000 * 64; time = 0.0088s; samplesPerSecond = 7242.3
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  51-  51, 15.94%]: CE = 3.66716866 * 64; Err = 0.89062500 * 64; time = 0.0102s; samplesPerSecond = 6296.1
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  52-  52, 16.25%]: CE = 4.00651571 * 64; Err = 0.90625000 * 64; time = 0.0090s; samplesPerSecond = 7081.2
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  53-  53, 16.56%]: CE = 3.80511656 * 64; Err = 0.81250000 * 64; time = 0.0098s; samplesPerSecond = 6528.6
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  54-  54, 16.88%]: CE = 3.93380989 * 64; Err = 0.85937500 * 64; time = 0.0085s; samplesPerSecond = 7533.0
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  55-  55, 17.19%]: CE = 3.49394937 * 64; Err = 0.84375000 * 64; time = 0.0106s; samplesPerSecond = 6055.4
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  56-  56, 17.50%]: CE = 3.42224075 * 64; Err = 0.84375000 * 64; time = 0.0100s; samplesPerSecond = 6402.6
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  57-  57, 17.81%]: CE = 3.76078536 * 64; Err = 0.85937500 * 64; time = 0.0099s; samplesPerSecond = 6473.1
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  58-  58, 18.13%]: CE = 3.80639497 * 64; Err = 0.87500000 * 64; time = 0.0100s; samplesPerSecond = 6376.4
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  59-  59, 18.44%]: CE = 3.55543971 * 64; Err = 0.89062500 * 64; time = 0.0073s; samplesPerSecond = 8750.3
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  60-  60, 18.75%]: CE = 3.55947249 * 64; Err = 0.82812500 * 64; time = 0.0115s; samplesPerSecond = 5560.4
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  61-  61, 19.06%]: CE = 3.21133907 * 64; Err = 0.79687500 * 64; time = 0.0084s; samplesPerSecond = 7591.0
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  62-  62, 19.38%]: CE = 3.30807309 * 64; Err = 0.68750000 * 64; time = 0.0102s; samplesPerSecond = 6258.6
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  63-  63, 19.69%]: CE = 3.54643060 * 64; Err = 0.78125000 * 64; time = 0.0083s; samplesPerSecond = 7727.6
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  64-  64, 20.00%]: CE = 3.48819921 * 64; Err = 0.85937500 * 64; time = 0.0102s; samplesPerSecond = 6264.7
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  65-  65, 20.31%]: CE = 3.53098379 * 64; Err = 0.81250000 * 64; time = 0.0082s; samplesPerSecond = 7802.0
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  66-  66, 20.63%]: CE = 3.18218574 * 64; Err = 0.70312500 * 64; time = 0.0104s; samplesPerSecond = 6165.7
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  67-  67, 20.94%]: CE = 3.62919777 * 64; Err = 0.79687500 * 64; time = 0.0093s; samplesPerSecond = 6891.4
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  68-  68, 21.25%]: CE = 3.30344749 * 64; Err = 0.76562500 * 64; time = 0.0089s; samplesPerSecond = 7183.7
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  69-  69, 21.56%]: CE = 3.11192070 * 64; Err = 0.75000000 * 64; time = 0.0099s; samplesPerSecond = 6464.6
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  70-  70, 21.88%]: CE = 3.70063691 * 64; Err = 0.79687500 * 64; time = 0.0101s; samplesPerSecond = 6326.6
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  71-  71, 22.19%]: CE = 3.76244503 * 64; Err = 0.84375000 * 64; time = 0.0094s; samplesPerSecond = 6789.0
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  72-  72, 22.50%]: CE = 3.52103388 * 64; Err = 0.81250000 * 64; time = 0.0093s; samplesPerSecond = 6857.4
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  73-  73, 22.81%]: CE = 3.73227550 * 64; Err = 0.87500000 * 64; time = 0.0100s; samplesPerSecond = 6420.5
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  74-  74, 23.13%]: CE = 3.28056294 * 64; Err = 0.75000000 * 64; time = 0.0107s; samplesPerSecond = 5994.8
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  75-  75, 23.44%]: CE = 3.88497398 * 64; Err = 0.89062500 * 64; time = 0.0096s; samplesPerSecond = 6670.8
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  76-  76, 23.75%]: CE = 3.62146548 * 64; Err = 0.85937500 * 64; time = 0.0108s; samplesPerSecond = 5920.4
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  77-  77, 24.06%]: CE = 3.11930348 * 64; Err = 0.73437500 * 64; time = 0.0101s; samplesPerSecond = 6356.8
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  78-  78, 24.38%]: CE = 3.34530218 * 64; Err = 0.87500000 * 64; time = 0.0104s; samplesPerSecond = 6136.1
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  79-  79, 24.69%]: CE = 3.51426589 * 64; Err = 0.84375000 * 64; time = 0.0106s; samplesPerSecond = 6048.0
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  80-  80, 25.00%]: CE = 3.40713594 * 64; Err = 0.81250000 * 64; time = 0.0080s; samplesPerSecond = 7995.0
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  81-  81, 25.31%]: CE = 3.59134827 * 64; Err = 0.82812500 * 64; time = 0.0116s; samplesPerSecond = 5520.6
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  82-  82, 25.62%]: CE = 3.52703040 * 64; Err = 0.82812500 * 64; time = 0.0104s; samplesPerSecond = 6166.9
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  83-  83, 25.94%]: CE = 3.22259624 * 64; Err = 0.79687500 * 64; time = 0.0090s; samplesPerSecond = 7071.8
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  84-  84, 26.25%]: CE = 3.64961943 * 64; Err = 0.82812500 * 64; time = 0.0097s; samplesPerSecond = 6627.3
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  85-  85, 26.56%]: CE = 3.70782192 * 64; Err = 0.76562500 * 64; time = 0.0099s; samplesPerSecond = 6488.9
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  86-  86, 26.88%]: CE = 3.53921564 * 64; Err = 0.89062500 * 64; time = 0.0088s; samplesPerSecond = 7305.1
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  87-  87, 27.19%]: CE = 3.38712792 * 64; Err = 0.81250000 * 64; time = 0.0100s; samplesPerSecond = 6413.5
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  88-  88, 27.50%]: CE = 3.66470493 * 64; Err = 0.78125000 * 64; time = 0.0090s; samplesPerSecond = 7084.3
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  89-  89, 27.81%]: CE = 3.12758734 * 64; Err = 0.84375000 * 64; time = 0.0098s; samplesPerSecond = 6500.1
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  90-  90, 28.13%]: CE = 3.52072988 * 64; Err = 0.82812500 * 64; time = 0.0097s; samplesPerSecond = 6591.8
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  91-  91, 28.44%]: CE = 3.45630741 * 64; Err = 0.76562500 * 64; time = 0.0097s; samplesPerSecond = 6612.3
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  92-  92, 28.75%]: CE = 3.19535282 * 64; Err = 0.78125000 * 64; time = 0.0092s; samplesPerSecond = 6983.1
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  93-  93, 29.06%]: CE = 3.40545723 * 64; Err = 0.81250000 * 64; time = 0.0104s; samplesPerSecond = 6161.0
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  94-  94, 29.38%]: CE = 3.47518793 * 64; Err = 0.70312500 * 64; time = 0.0089s; samplesPerSecond = 7197.5
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  95-  95, 29.69%]: CE = 3.32919398 * 64; Err = 0.78125000 * 64; time = 0.0101s; samplesPerSecond = 6321.0
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  96-  96, 30.00%]: CE = 3.86499937 * 64; Err = 0.93750000 * 64; time = 0.0097s; samplesPerSecond = 6616.4
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  97-  97, 30.31%]: CE = 3.42288014 * 64; Err = 0.84375000 * 64; time = 0.0100s; samplesPerSecond = 6368.8
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  98-  98, 30.63%]: CE = 3.31506114 * 64; Err = 0.82812500 * 64; time = 0.0098s; samplesPerSecond = 6557.4
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[  99-  99, 30.94%]: CE = 3.28863365 * 64; Err = 0.76562500 * 64; time = 0.0100s; samplesPerSecond = 6379.6
MPI Rank 1: 12/21/2016 05:28:34:  Epoch[ 1 of 5]-Minibatch[ 100- 100, 31.25%]: CE = 3.20182099 * 64; Err = 0.76562500 * 64; time = 0.0098s; samplesPerSecond = 6560.7
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 101- 101, 31.56%]: CE = 3.75128437 * 64; Err = 0.89062500 * 64; time = 0.0100s; samplesPerSecond = 6409.6
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 102- 102, 31.87%]: CE = 3.57333316 * 64; Err = 0.84375000 * 64; time = 0.0085s; samplesPerSecond = 7525.0
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 103- 103, 32.19%]: CE = 3.65041879 * 64; Err = 0.81250000 * 64; time = 0.0106s; samplesPerSecond = 6033.2
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 104- 104, 32.50%]: CE = 3.45052191 * 64; Err = 0.82812500 * 64; time = 0.0101s; samplesPerSecond = 6326.0
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 105- 105, 32.81%]: CE = 3.57278549 * 64; Err = 0.85937500 * 64; time = 0.0098s; samplesPerSecond = 6523.3
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 106- 106, 33.13%]: CE = 3.35244169 * 64; Err = 0.87500000 * 64; time = 0.0099s; samplesPerSecond = 6470.5
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 107- 107, 33.44%]: CE = 3.29949185 * 64; Err = 0.76562500 * 64; time = 0.0100s; samplesPerSecond = 6371.3
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 108- 108, 33.75%]: CE = 3.78609758 * 64; Err = 0.82812500 * 64; time = 0.0091s; samplesPerSecond = 7020.6
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 109- 109, 34.06%]: CE = 3.22622650 * 64; Err = 0.78125000 * 64; time = 0.0100s; samplesPerSecond = 6378.3
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 110- 110, 34.38%]: CE = 3.29821989 * 64; Err = 0.79687500 * 64; time = 0.0100s; samplesPerSecond = 6397.4
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 111- 111, 34.69%]: CE = 3.44143907 * 64; Err = 0.82812500 * 64; time = 0.0104s; samplesPerSecond = 6155.6
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 112- 112, 35.00%]: CE = 3.44276929 * 64; Err = 0.85937500 * 64; time = 0.0083s; samplesPerSecond = 7727.6
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 113- 113, 35.31%]: CE = 3.18216790 * 64; Err = 0.76562500 * 64; time = 0.0101s; samplesPerSecond = 6319.7
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 114- 114, 35.63%]: CE = 3.18609709 * 64; Err = 0.78125000 * 64; time = 0.0092s; samplesPerSecond = 6983.8
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 115- 115, 35.94%]: CE = 3.06550821 * 64; Err = 0.73437500 * 64; time = 0.0100s; samplesPerSecond = 6414.1
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 116- 116, 36.25%]: CE = 3.43583629 * 64; Err = 0.79687500 * 64; time = 0.0090s; samplesPerSecond = 7090.6
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 117- 117, 36.56%]: CE = 3.10193105 * 64; Err = 0.78125000 * 64; time = 0.0104s; samplesPerSecond = 6155.6
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 118- 118, 36.88%]: CE = 3.42968418 * 64; Err = 0.81250000 * 64; time = 0.0093s; samplesPerSecond = 6852.2
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 119- 119, 37.19%]: CE = 2.85043824 * 64; Err = 0.60937500 * 64; time = 0.0101s; samplesPerSecond = 6312.3
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 120- 120, 37.50%]: CE = 3.50428373 * 64; Err = 0.85937500 * 64; time = 0.0099s; samplesPerSecond = 6434.1
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 121- 121, 37.81%]: CE = 3.28751701 * 64; Err = 0.82812500 * 64; time = 0.0099s; samplesPerSecond = 6445.8
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 122- 122, 38.13%]: CE = 3.79916343 * 64; Err = 0.89062500 * 64; time = 0.0098s; samplesPerSecond = 6558.0
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 123- 123, 38.44%]: CE = 3.55702537 * 64; Err = 0.82812500 * 64; time = 0.0098s; samplesPerSecond = 6499.4
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 124- 124, 38.75%]: CE = 3.00217445 * 64; Err = 0.71875000 * 64; time = 0.0100s; samplesPerSecond = 6425.7
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 125- 125, 39.06%]: CE = 3.07327108 * 64; Err = 0.75000000 * 64; time = 0.0103s; samplesPerSecond = 6240.2
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 126- 126, 39.38%]: CE = 2.88353063 * 64; Err = 0.59375000 * 64; time = 0.0093s; samplesPerSecond = 6851.5
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 127- 127, 39.69%]: CE = 3.13059468 * 64; Err = 0.79687500 * 64; time = 0.0081s; samplesPerSecond = 7902.2
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 128- 128, 40.00%]: CE = 3.21732650 * 64; Err = 0.85937500 * 64; time = 0.0116s; samplesPerSecond = 5504.0
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 129- 129, 40.31%]: CE = 2.97299345 * 64; Err = 0.71875000 * 64; time = 0.0106s; samplesPerSecond = 6049.7
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 130- 130, 40.63%]: CE = 2.93691495 * 64; Err = 0.79687500 * 64; time = 0.0109s; samplesPerSecond = 5877.5
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 131- 131, 40.94%]: CE = 3.31837783 * 64; Err = 0.68750000 * 64; time = 0.0103s; samplesPerSecond = 6226.9
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 132- 132, 41.25%]: CE = 2.91929775 * 64; Err = 0.78125000 * 64; time = 0.0108s; samplesPerSecond = 5952.9
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 133- 133, 41.56%]: CE = 3.07940161 * 64; Err = 0.68750000 * 64; time = 0.0092s; samplesPerSecond = 6943.7
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 134- 134, 41.88%]: CE = 3.28344492 * 64; Err = 0.75000000 * 64; time = 0.0106s; samplesPerSecond = 6054.3
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 135- 135, 42.19%]: CE = 3.18447176 * 64; Err = 0.78125000 * 64; time = 0.0108s; samplesPerSecond = 5909.5
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 136- 136, 42.50%]: CE = 2.79093256 * 64; Err = 0.71875000 * 64; time = 0.0094s; samplesPerSecond = 6840.5
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 137- 137, 42.81%]: CE = 2.87937588 * 64; Err = 0.70312500 * 64; time = 0.0109s; samplesPerSecond = 5869.9
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 138- 138, 43.13%]: CE = 2.64594163 * 64; Err = 0.68750000 * 64; time = 0.0097s; samplesPerSecond = 6621.8
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 139- 139, 43.44%]: CE = 2.94206439 * 64; Err = 0.84375000 * 64; time = 0.0094s; samplesPerSecond = 6805.6
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 140- 140, 43.75%]: CE = 3.51285987 * 64; Err = 0.82812500 * 64; time = 0.0102s; samplesPerSecond = 6245.7
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 141- 141, 44.06%]: CE = 3.04888687 * 64; Err = 0.81250000 * 64; time = 0.0099s; samplesPerSecond = 6479.7
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 142- 142, 44.38%]: CE = 3.13123367 * 64; Err = 0.76562500 * 64; time = 0.0094s; samplesPerSecond = 6813.6
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 143- 143, 44.69%]: CE = 2.92926400 * 64; Err = 0.71875000 * 64; time = 0.0101s; samplesPerSecond = 6342.9
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 144- 144, 45.00%]: CE = 3.00144780 * 64; Err = 0.71875000 * 64; time = 0.0099s; samplesPerSecond = 6442.5
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 145- 145, 45.31%]: CE = 2.90962694 * 64; Err = 0.67187500 * 64; time = 0.0101s; samplesPerSecond = 6360.6
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 146- 146, 45.63%]: CE = 3.03283171 * 64; Err = 0.79687500 * 64; time = 0.0071s; samplesPerSecond = 9072.9
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 147- 147, 45.94%]: CE = 3.06942741 * 64; Err = 0.73437500 * 64; time = 0.0116s; samplesPerSecond = 5528.2
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 148- 148, 46.25%]: CE = 2.86661978 * 64; Err = 0.65625000 * 64; time = 0.0094s; samplesPerSecond = 6793.3
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 149- 149, 46.56%]: CE = 2.76894440 * 64; Err = 0.68750000 * 64; time = 0.0098s; samplesPerSecond = 6524.0
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 150- 150, 46.88%]: CE = 2.71313692 * 64; Err = 0.59375000 * 64; time = 0.0092s; samplesPerSecond = 6928.7
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 151- 151, 47.19%]: CE = 2.74131048 * 64; Err = 0.65625000 * 64; time = 0.0104s; samplesPerSecond = 6146.2
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 152- 152, 47.50%]: CE = 3.28257238 * 64; Err = 0.71875000 * 64; time = 0.0089s; samplesPerSecond = 7176.5
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 153- 153, 47.81%]: CE = 3.08491448 * 64; Err = 0.76562500 * 64; time = 0.0101s; samplesPerSecond = 6345.4
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 154- 154, 48.13%]: CE = 2.98917665 * 64; Err = 0.71875000 * 64; time = 0.0090s; samplesPerSecond = 7122.2
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 155- 155, 48.44%]: CE = 2.90881148 * 64; Err = 0.81250000 * 64; time = 0.0100s; samplesPerSecond = 6420.5
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 156- 156, 48.75%]: CE = 3.58531995 * 64; Err = 0.78125000 * 64; time = 0.0100s; samplesPerSecond = 6414.1
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 157- 157, 49.06%]: CE = 3.28706069 * 64; Err = 0.75000000 * 64; time = 0.0099s; samplesPerSecond = 6496.1
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 158- 158, 49.38%]: CE = 3.06029676 * 64; Err = 0.81250000 * 64; time = 0.0083s; samplesPerSecond = 7745.4
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 159- 159, 49.69%]: CE = 2.95483403 * 64; Err = 0.68750000 * 64; time = 0.0102s; samplesPerSecond = 6269.0
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 160- 160, 50.00%]: CE = 3.07409648 * 64; Err = 0.76562500 * 64; time = 0.0099s; samplesPerSecond = 6441.9
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 161- 161, 50.31%]: CE = 2.69786051 * 64; Err = 0.67187500 * 64; time = 0.0084s; samplesPerSecond = 7622.7
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 162- 162, 50.63%]: CE = 2.80402381 * 64; Err = 0.70312500 * 64; time = 0.0094s; samplesPerSecond = 6819.4
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 163- 163, 50.94%]: CE = 2.62768914 * 64; Err = 0.62500000 * 64; time = 0.0102s; samplesPerSecond = 6251.8
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 164- 164, 51.25%]: CE = 2.64449167 * 64; Err = 0.67187500 * 64; time = 0.0100s; samplesPerSecond = 6409.6
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 165- 165, 51.56%]: CE = 3.08919011 * 64; Err = 0.79687500 * 64; time = 0.0101s; samplesPerSecond = 6355.5
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 166- 166, 51.88%]: CE = 3.07122141 * 64; Err = 0.70312500 * 64; time = 0.0093s; samplesPerSecond = 6845.7
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 167- 167, 52.19%]: CE = 3.05111668 * 64; Err = 0.73437500 * 64; time = 0.0100s; samplesPerSecond = 6386.0
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 168- 168, 52.50%]: CE = 2.90345804 * 64; Err = 0.73437500 * 64; time = 0.0095s; samplesPerSecond = 6714.9
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 169- 169, 52.81%]: CE = 2.58801822 * 64; Err = 0.62500000 * 64; time = 0.0100s; samplesPerSecond = 6416.7
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 170- 170, 53.13%]: CE = 2.68278033 * 64; Err = 0.68750000 * 64; time = 0.0076s; samplesPerSecond = 8377.0
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 171- 171, 53.44%]: CE = 2.89664835 * 64; Err = 0.70312500 * 64; time = 0.0111s; samplesPerSecond = 5791.9
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 172- 172, 53.75%]: CE = 2.61913736 * 64; Err = 0.64062500 * 64; time = 0.0092s; samplesPerSecond = 6944.4
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 173- 173, 54.06%]: CE = 2.68386883 * 64; Err = 0.65625000 * 64; time = 0.0101s; samplesPerSecond = 6323.5
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 174- 174, 54.37%]: CE = 2.63044619 * 64; Err = 0.65625000 * 64; time = 0.0083s; samplesPerSecond = 7729.5
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 175- 175, 54.69%]: CE = 2.39899721 * 64; Err = 0.60937500 * 64; time = 0.0102s; samplesPerSecond = 6277.6
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 176- 176, 55.00%]: CE = 2.88430255 * 64; Err = 0.67187500 * 64; time = 0.0087s; samplesPerSecond = 7328.5
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 177- 177, 55.31%]: CE = 2.83595866 * 64; Err = 0.70312500 * 64; time = 0.0101s; samplesPerSecond = 6365.0
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 178- 178, 55.63%]: CE = 2.79519571 * 64; Err = 0.64062500 * 64; time = 0.0084s; samplesPerSecond = 7642.7
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 179- 179, 55.94%]: CE = 2.76600024 * 64; Err = 0.67187500 * 64; time = 0.0104s; samplesPerSecond = 6171.6
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 180- 180, 56.25%]: CE = 2.59895511 * 64; Err = 0.54687500 * 64; time = 0.0083s; samplesPerSecond = 7705.3
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 181- 181, 56.56%]: CE = 2.93763654 * 64; Err = 0.75000000 * 64; time = 0.0103s; samplesPerSecond = 6219.0
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 182- 182, 56.88%]: CE = 2.93634742 * 64; Err = 0.73437500 * 64; time = 0.0091s; samplesPerSecond = 7051.6
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 183- 183, 57.19%]: CE = 2.59901571 * 64; Err = 0.68750000 * 64; time = 0.0102s; samplesPerSecond = 6262.8
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 184- 184, 57.50%]: CE = 2.81753002 * 64; Err = 0.73437500 * 64; time = 0.0092s; samplesPerSecond = 6952.7
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 185- 185, 57.81%]: CE = 3.04424260 * 64; Err = 0.73437500 * 64; time = 0.0097s; samplesPerSecond = 6576.9
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 186- 186, 58.13%]: CE = 2.49622625 * 64; Err = 0.64062500 * 64; time = 0.0101s; samplesPerSecond = 6344.2
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 187- 187, 58.44%]: CE = 2.94745408 * 64; Err = 0.71875000 * 64; time = 0.0103s; samplesPerSecond = 6238.4
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 188- 188, 58.75%]: CE = 2.80802583 * 64; Err = 0.71875000 * 64; time = 0.0103s; samplesPerSecond = 6189.6
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 189- 189, 59.06%]: CE = 2.54977638 * 64; Err = 0.67187500 * 64; time = 0.0095s; samplesPerSecond = 6756.0
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 190- 190, 59.38%]: CE = 2.90849909 * 64; Err = 0.68750000 * 64; time = 0.0094s; samplesPerSecond = 6805.6
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 191- 191, 59.69%]: CE = 2.89470021 * 64; Err = 0.71875000 * 64; time = 0.0102s; samplesPerSecond = 6280.7
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 192- 192, 60.00%]: CE = 2.55056761 * 64; Err = 0.64062500 * 64; time = 0.0094s; samplesPerSecond = 6807.8
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 193- 193, 60.31%]: CE = 2.39014720 * 64; Err = 0.59375000 * 64; time = 0.0106s; samplesPerSecond = 6016.2
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 194- 194, 60.62%]: CE = 2.61720826 * 64; Err = 0.65625000 * 64; time = 0.0087s; samplesPerSecond = 7317.6
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 195- 195, 60.94%]: CE = 2.59802571 * 64; Err = 0.65625000 * 64; time = 0.0107s; samplesPerSecond = 6008.3
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 196- 196, 61.25%]: CE = 2.94597696 * 64; Err = 0.75000000 * 64; time = 0.0101s; samplesPerSecond = 6316.6
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 197- 197, 61.56%]: CE = 2.79771307 * 64; Err = 0.75000000 * 64; time = 0.0092s; samplesPerSecond = 6957.3
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 198- 198, 61.88%]: CE = 3.20417932 * 64; Err = 0.71875000 * 64; time = 0.0091s; samplesPerSecond = 7068.7
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 199- 199, 62.19%]: CE = 2.27155558 * 64; Err = 0.53125000 * 64; time = 0.0091s; samplesPerSecond = 7061.7
MPI Rank 1: 12/21/2016 05:28:35:  Epoch[ 1 of 5]-Minibatch[ 200- 200, 62.50%]: CE = 2.87449908 * 64; Err = 0.68750000 * 64; time = 0.0093s; samplesPerSecond = 6886.9
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 201- 201, 62.81%]: CE = 2.71210245 * 64; Err = 0.65625000 * 64; time = 0.0099s; samplesPerSecond = 6483.0
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 202- 202, 63.13%]: CE = 2.44766371 * 64; Err = 0.57812500 * 64; time = 0.0085s; samplesPerSecond = 7560.5
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 203- 203, 63.44%]: CE = 2.68243088 * 64; Err = 0.70312500 * 64; time = 0.0102s; samplesPerSecond = 6281.9
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 204- 204, 63.75%]: CE = 2.40962202 * 64; Err = 0.54687500 * 64; time = 0.0082s; samplesPerSecond = 7769.8
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 205- 205, 64.06%]: CE = 2.48400547 * 64; Err = 0.59375000 * 64; time = 0.0103s; samplesPerSecond = 6242.1
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 206- 206, 64.38%]: CE = 2.49121254 * 64; Err = 0.60937500 * 64; time = 0.0082s; samplesPerSecond = 7803.9
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 207- 207, 64.69%]: CE = 2.84691899 * 64; Err = 0.75000000 * 64; time = 0.0101s; samplesPerSecond = 6350.5
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 208- 208, 65.00%]: CE = 2.45273493 * 64; Err = 0.59375000 * 64; time = 0.0083s; samplesPerSecond = 7743.5
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 209- 209, 65.31%]: CE = 2.75036440 * 64; Err = 0.68750000 * 64; time = 0.0101s; samplesPerSecond = 6348.6
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 210- 210, 65.63%]: CE = 2.49555051 * 64; Err = 0.71875000 * 64; time = 0.0083s; samplesPerSecond = 7711.8
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 211- 211, 65.94%]: CE = 2.71109113 * 64; Err = 0.68750000 * 64; time = 0.0104s; samplesPerSecond = 6161.0
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 212- 212, 66.25%]: CE = 2.38218216 * 64; Err = 0.59375000 * 64; time = 0.0097s; samplesPerSecond = 6600.7
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 213- 213, 66.56%]: CE = 2.60308722 * 64; Err = 0.57812500 * 64; time = 0.0091s; samplesPerSecond = 7068.7
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 214- 214, 66.88%]: CE = 2.65611547 * 64; Err = 0.68750000 * 64; time = 0.0099s; samplesPerSecond = 6486.9
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 215- 215, 67.19%]: CE = 2.49633370 * 64; Err = 0.57812500 * 64; time = 0.0099s; samplesPerSecond = 6472.5
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 216- 216, 67.50%]: CE = 2.23315412 * 64; Err = 0.60937500 * 64; time = 0.0099s; samplesPerSecond = 6471.8
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 217- 217, 67.81%]: CE = 2.94093183 * 64; Err = 0.73437500 * 64; time = 0.0094s; samplesPerSecond = 6793.3
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 218- 218, 68.13%]: CE = 2.69840742 * 64; Err = 0.65625000 * 64; time = 0.0099s; samplesPerSecond = 6489.6
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 219- 219, 68.44%]: CE = 2.57215231 * 64; Err = 0.60937500 * 64; time = 0.0094s; samplesPerSecond = 6779.7
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 220- 220, 68.75%]: CE = 2.57160696 * 64; Err = 0.68750000 * 64; time = 0.0099s; samplesPerSecond = 6475.1
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 221- 221, 69.06%]: CE = 2.57776681 * 64; Err = 0.65625000 * 64; time = 0.0099s; samplesPerSecond = 6458.8
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 222- 222, 69.38%]: CE = 2.32289644 * 64; Err = 0.57812500 * 64; time = 0.0097s; samplesPerSecond = 6593.2
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 223- 223, 69.69%]: CE = 2.66432343 * 64; Err = 0.70312500 * 64; time = 0.0098s; samplesPerSecond = 6544.0
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 224- 224, 70.00%]: CE = 2.20387606 * 64; Err = 0.65625000 * 64; time = 0.0099s; samplesPerSecond = 6458.1
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 225- 225, 70.31%]: CE = 2.39888933 * 64; Err = 0.59375000 * 64; time = 0.0092s; samplesPerSecond = 6946.7
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 226- 226, 70.63%]: CE = 2.80393339 * 64; Err = 0.70312500 * 64; time = 0.0094s; samplesPerSecond = 6823.0
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 227- 227, 70.94%]: CE = 2.71082242 * 64; Err = 0.68750000 * 64; time = 0.0088s; samplesPerSecond = 7233.3
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 228- 228, 71.25%]: CE = 2.62244612 * 64; Err = 0.70312500 * 64; time = 0.0100s; samplesPerSecond = 6380.2
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 229- 229, 71.56%]: CE = 2.29777087 * 64; Err = 0.62500000 * 64; time = 0.0090s; samplesPerSecond = 7085.9
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 230- 230, 71.88%]: CE = 2.51121239 * 64; Err = 0.65625000 * 64; time = 0.0099s; samplesPerSecond = 6446.4
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 231- 231, 72.19%]: CE = 2.76103008 * 64; Err = 0.64062500 * 64; time = 0.0092s; samplesPerSecond = 6976.2
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 232- 232, 72.50%]: CE = 3.01432561 * 64; Err = 0.76562500 * 64; time = 0.0088s; samplesPerSecond = 7253.8
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 233- 233, 72.81%]: CE = 2.99024474 * 64; Err = 0.76562500 * 64; time = 0.0098s; samplesPerSecond = 6537.3
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 234- 234, 73.13%]: CE = 2.86664042 * 64; Err = 0.81250000 * 64; time = 0.0096s; samplesPerSecond = 6634.2
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 235- 235, 73.44%]: CE = 2.60998588 * 64; Err = 0.67187500 * 64; time = 0.0099s; samplesPerSecond = 6439.9
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 236- 236, 73.75%]: CE = 2.18201917 * 64; Err = 0.53125000 * 64; time = 0.0097s; samplesPerSecond = 6579.6
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 237- 237, 74.06%]: CE = 2.17418609 * 64; Err = 0.57812500 * 64; time = 0.0098s; samplesPerSecond = 6521.3
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 238- 238, 74.38%]: CE = 2.25759717 * 64; Err = 0.64062500 * 64; time = 0.0098s; samplesPerSecond = 6559.4
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 239- 239, 74.69%]: CE = 2.17788677 * 64; Err = 0.60937500 * 64; time = 0.0098s; samplesPerSecond = 6512.7
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 240- 240, 75.00%]: CE = 2.20328249 * 64; Err = 0.54687500 * 64; time = 0.0096s; samplesPerSecond = 6698.8
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 241- 241, 75.31%]: CE = 2.60590014 * 64; Err = 0.60937500 * 64; time = 0.0099s; samplesPerSecond = 6497.5
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 242- 242, 75.63%]: CE = 2.09884739 * 64; Err = 0.56250000 * 64; time = 0.0098s; samplesPerSecond = 6544.0
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 243- 243, 75.94%]: CE = 2.10587746 * 64; Err = 0.54687500 * 64; time = 0.0096s; samplesPerSecond = 6659.7
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 244- 244, 76.25%]: CE = 2.64457627 * 64; Err = 0.73437500 * 64; time = 0.0098s; samplesPerSecond = 6516.6
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 245- 245, 76.56%]: CE = 2.47600990 * 64; Err = 0.64062500 * 64; time = 0.0098s; samplesPerSecond = 6553.3
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 246- 246, 76.88%]: CE = 2.88789135 * 64; Err = 0.68750000 * 64; time = 0.0098s; samplesPerSecond = 6522.6
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 247- 247, 77.19%]: CE = 2.51823068 * 64; Err = 0.56250000 * 64; time = 0.0098s; samplesPerSecond = 6539.3
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 248- 248, 77.50%]: CE = 2.24877264 * 64; Err = 0.62500000 * 64; time = 0.0075s; samplesPerSecond = 8485.8
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 249- 249, 77.81%]: CE = 2.51043156 * 64; Err = 0.71875000 * 64; time = 0.0103s; samplesPerSecond = 6187.8
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 250- 250, 78.13%]: CE = 2.54234511 * 64; Err = 0.70312500 * 64; time = 0.0108s; samplesPerSecond = 5939.7
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 251- 251, 78.44%]: CE = 2.68548933 * 64; Err = 0.70312500 * 64; time = 0.0103s; samplesPerSecond = 6234.2
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 252- 252, 78.75%]: CE = 2.23175466 * 64; Err = 0.57812500 * 64; time = 0.0105s; samplesPerSecond = 6116.8
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 253- 253, 79.06%]: CE = 2.24553589 * 64; Err = 0.60937500 * 64; time = 0.0103s; samplesPerSecond = 6202.8
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 254- 254, 79.38%]: CE = 2.28765068 * 64; Err = 0.62500000 * 64; time = 0.0103s; samplesPerSecond = 6231.7
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 255- 255, 79.69%]: CE = 2.54161451 * 64; Err = 0.62500000 * 64; time = 0.0103s; samplesPerSecond = 6184.2
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 256- 256, 80.00%]: CE = 2.35401834 * 64; Err = 0.59375000 * 64; time = 0.0102s; samplesPerSecond = 6246.3
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 257- 257, 80.31%]: CE = 2.18137731 * 64; Err = 0.56250000 * 64; time = 0.0098s; samplesPerSecond = 6540.6
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 258- 258, 80.63%]: CE = 2.51499174 * 64; Err = 0.59375000 * 64; time = 0.0071s; samplesPerSecond = 9021.7
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 259- 259, 80.94%]: CE = 2.12242410 * 64; Err = 0.65625000 * 64; time = 0.0117s; samplesPerSecond = 5481.3
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 260- 260, 81.25%]: CE = 2.57230724 * 64; Err = 0.68750000 * 64; time = 0.0091s; samplesPerSecond = 7050.8
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 261- 261, 81.56%]: CE = 2.24717210 * 64; Err = 0.57812500 * 64; time = 0.0099s; samplesPerSecond = 6470.5
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 262- 262, 81.88%]: CE = 2.46805084 * 64; Err = 0.60937500 * 64; time = 0.0084s; samplesPerSecond = 7629.9
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 263- 263, 82.19%]: CE = 1.94672270 * 64; Err = 0.48437500 * 64; time = 0.0099s; samplesPerSecond = 6460.7
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 264- 264, 82.50%]: CE = 2.34898229 * 64; Err = 0.67187500 * 64; time = 0.0097s; samplesPerSecond = 6589.8
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 265- 265, 82.81%]: CE = 2.19361248 * 64; Err = 0.57812500 * 64; time = 0.0098s; samplesPerSecond = 6500.8
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 266- 266, 83.13%]: CE = 1.96058399 * 64; Err = 0.46875000 * 64; time = 0.0092s; samplesPerSecond = 6991.5
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 267- 267, 83.44%]: CE = 2.02827934 * 64; Err = 0.53125000 * 64; time = 0.0101s; samplesPerSecond = 6332.2
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 268- 268, 83.75%]: CE = 2.16395773 * 64; Err = 0.56250000 * 64; time = 0.0093s; samplesPerSecond = 6900.3
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 269- 269, 84.06%]: CE = 2.42837196 * 64; Err = 0.64062500 * 64; time = 0.0101s; samplesPerSecond = 6317.9
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 270- 270, 84.38%]: CE = 2.56277231 * 64; Err = 0.75000000 * 64; time = 0.0082s; samplesPerSecond = 7771.7
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 271- 271, 84.69%]: CE = 2.35831855 * 64; Err = 0.59375000 * 64; time = 0.0103s; samplesPerSecond = 6225.7
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 272- 272, 85.00%]: CE = 2.48323539 * 64; Err = 0.70312500 * 64; time = 0.0092s; samplesPerSecond = 6930.9
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 273- 273, 85.31%]: CE = 2.66412354 * 64; Err = 0.67187500 * 64; time = 0.0099s; samplesPerSecond = 6472.5
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 274- 274, 85.63%]: CE = 2.35827343 * 64; Err = 0.65625000 * 64; time = 0.0098s; samplesPerSecond = 6514.7
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 275- 275, 85.94%]: CE = 2.35993611 * 64; Err = 0.59375000 * 64; time = 0.0099s; samplesPerSecond = 6441.2
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 276- 276, 86.25%]: CE = 2.27682017 * 64; Err = 0.59375000 * 64; time = 0.0083s; samplesPerSecond = 7667.4
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 277- 277, 86.56%]: CE = 2.58742110 * 64; Err = 0.70312500 * 64; time = 0.0104s; samplesPerSecond = 6127.9
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 278- 278, 86.88%]: CE = 2.59364573 * 64; Err = 0.70312500 * 64; time = 0.0093s; samplesPerSecond = 6858.9
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 279- 279, 87.19%]: CE = 2.58154982 * 64; Err = 0.67187500 * 64; time = 0.0100s; samplesPerSecond = 6394.9
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 280- 280, 87.50%]: CE = 2.65251947 * 64; Err = 0.71875000 * 64; time = 0.0100s; samplesPerSecond = 6419.3
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 281- 281, 87.81%]: CE = 2.42794113 * 64; Err = 0.56250000 * 64; time = 0.0097s; samplesPerSecond = 6600.7
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 282- 282, 88.13%]: CE = 2.31306675 * 64; Err = 0.56250000 * 64; time = 0.0092s; samplesPerSecond = 6944.4
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 283- 283, 88.44%]: CE = 2.30780317 * 64; Err = 0.57812500 * 64; time = 0.0100s; samplesPerSecond = 6374.5
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 284- 284, 88.75%]: CE = 2.20092907 * 64; Err = 0.71875000 * 64; time = 0.0097s; samplesPerSecond = 6570.8
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 285- 285, 89.06%]: CE = 2.37127008 * 64; Err = 0.60937500 * 64; time = 0.0100s; samplesPerSecond = 6371.3
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 286- 286, 89.38%]: CE = 1.96581596 * 64; Err = 0.51562500 * 64; time = 0.0091s; samplesPerSecond = 7046.9
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 287- 287, 89.69%]: CE = 2.38139796 * 64; Err = 0.68750000 * 64; time = 0.0103s; samplesPerSecond = 6184.2
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 288- 288, 90.00%]: CE = 2.17378766 * 64; Err = 0.56250000 * 64; time = 0.0083s; samplesPerSecond = 7711.8
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 289- 289, 90.31%]: CE = 2.43769870 * 64; Err = 0.62500000 * 64; time = 0.0107s; samplesPerSecond = 5967.4
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 290- 290, 90.63%]: CE = 1.92877315 * 64; Err = 0.48437500 * 64; time = 0.0083s; samplesPerSecond = 7709.9
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 291- 291, 90.94%]: CE = 2.40592700 * 64; Err = 0.62500000 * 64; time = 0.0086s; samplesPerSecond = 7424.6
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 292- 292, 91.25%]: CE = 2.08578061 * 64; Err = 0.59375000 * 64; time = 0.0099s; samplesPerSecond = 6490.9
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 293- 293, 91.56%]: CE = 2.00803832 * 64; Err = 0.51562500 * 64; time = 0.0102s; samplesPerSecond = 6300.5
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 294- 294, 91.88%]: CE = 2.17692353 * 64; Err = 0.57812500 * 64; time = 0.0088s; samplesPerSecond = 7263.6
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 295- 295, 92.19%]: CE = 2.50142509 * 64; Err = 0.70312500 * 64; time = 0.0099s; samplesPerSecond = 6470.5
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 296- 296, 92.50%]: CE = 2.23106504 * 64; Err = 0.60937500 * 64; time = 0.0093s; samplesPerSecond = 6898.0
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 297- 297, 92.81%]: CE = 2.15600594 * 64; Err = 0.59375000 * 64; time = 0.0093s; samplesPerSecond = 6848.6
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 298- 298, 93.13%]: CE = 2.57861376 * 64; Err = 0.68750000 * 64; time = 0.0092s; samplesPerSecond = 6927.9
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 299- 299, 93.44%]: CE = 2.07193617 * 64; Err = 0.56250000 * 64; time = 0.0094s; samplesPerSecond = 6792.6
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 300- 300, 93.75%]: CE = 2.16370481 * 64; Err = 0.60937500 * 64; time = 0.0101s; samplesPerSecond = 6363.1
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 301- 301, 94.06%]: CE = 2.24899831 * 64; Err = 0.56250000 * 64; time = 0.0098s; samplesPerSecond = 6524.0
MPI Rank 1: 12/21/2016 05:28:36:  Epoch[ 1 of 5]-Minibatch[ 302- 302, 94.38%]: CE = 1.87617314 * 64; Err = 0.54687500 * 64; time = 0.0100s; samplesPerSecond = 6423.8
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 303- 303, 94.69%]: CE = 2.22035878 * 64; Err = 0.56250000 * 64; time = 0.0102s; samplesPerSecond = 6290.5
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 304- 304, 95.00%]: CE = 2.23859583 * 64; Err = 0.65625000 * 64; time = 0.0104s; samplesPerSecond = 6178.8
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 305- 305, 95.31%]: CE = 2.36221656 * 64; Err = 0.59375000 * 64; time = 0.0105s; samplesPerSecond = 6081.3
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 306- 306, 95.63%]: CE = 2.11637634 * 64; Err = 0.54687500 * 64; time = 0.0095s; samplesPerSecond = 6762.5
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 307- 307, 95.94%]: CE = 2.32528810 * 64; Err = 0.57812500 * 64; time = 0.0094s; samplesPerSecond = 6815.8
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 308- 308, 96.25%]: CE = 2.06869602 * 64; Err = 0.50000000 * 64; time = 0.0105s; samplesPerSecond = 6116.2
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 309- 309, 96.56%]: CE = 2.10471025 * 64; Err = 0.56250000 * 64; time = 0.0096s; samplesPerSecond = 6685.5
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 310- 310, 96.88%]: CE = 2.69881704 * 64; Err = 0.71875000 * 64; time = 0.0107s; samplesPerSecond = 6004.3
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 311- 311, 97.19%]: CE = 2.21301732 * 64; Err = 0.65625000 * 64; time = 0.0105s; samplesPerSecond = 6092.9
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 312- 312, 97.50%]: CE = 2.34597297 * 64; Err = 0.60937500 * 64; time = 0.0104s; samplesPerSecond = 6133.2
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 313- 313, 97.81%]: CE = 2.08858265 * 64; Err = 0.57812500 * 64; time = 0.0092s; samplesPerSecond = 6936.2
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 314- 314, 98.13%]: CE = 2.10805385 * 64; Err = 0.54687500 * 64; time = 0.0099s; samplesPerSecond = 6451.0
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 315- 315, 98.44%]: CE = 2.29975623 * 64; Err = 0.60937500 * 64; time = 0.0091s; samplesPerSecond = 7019.9
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 316- 316, 98.75%]: CE = 2.29188916 * 64; Err = 0.60937500 * 64; time = 0.0098s; samplesPerSecond = 6533.9
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 317- 317, 99.06%]: CE = 2.03062764 * 64; Err = 0.50000000 * 64; time = 0.0082s; samplesPerSecond = 7764.2
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 318- 318, 99.38%]: CE = 2.29874982 * 64; Err = 0.59375000 * 64; time = 0.0101s; samplesPerSecond = 6327.2
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 319- 319, 99.69%]: CE = 2.22342700 * 64; Err = 0.68750000 * 64; time = 0.0084s; samplesPerSecond = 7603.7
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 1 of 5]-Minibatch[ 320- 320, 100.00%]: CE = 2.32233814 * 64; Err = 0.59375000 * 64; time = 0.0049s; samplesPerSecond = 13106.7
MPI Rank 1: 12/21/2016 05:28:37: Finished Epoch[ 1 of 5]: [Training] CE = 3.02444900 * 20480; Err = 0.72885742 * 20480; totalSamplesSeen = 20480; learningRatePerSample = 0.015625; epochTime=3.18124s
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:37: Starting Epoch 2: learning rate per sample = 0.001953  effective momentum = 0.656119  momentum as time constant = 607.5 samples
MPI Rank 1: Parallel training (2 workers) using BlockMomentumSGD with block momentum = 0.5000, block momentum time constant (per worker) = 2954.6394, block learning rate = 1.0000, block size per worker = 2048 samples, using Nesterov-style block momentum, resetting SGD momentum after sync.
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:37: Starting minibatch loop, distributed reading is ENABLED.
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[   1-   1, 1.25%]: CE = 2.09278351 * 128; Err = 0.57812500 * 128; time = 0.0294s; samplesPerSecond = 4357.7
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[   2-   2, 2.50%]: CE = 2.18078929 * 128; Err = 0.55468750 * 128; time = 0.0172s; samplesPerSecond = 7460.5
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[   3-   3, 3.75%]: CE = 2.16895392 * 128; Err = 0.60937500 * 128; time = 0.0176s; samplesPerSecond = 7275.2
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[   4-   4, 5.00%]: CE = 2.24801623 * 128; Err = 0.60937500 * 128; time = 0.0174s; samplesPerSecond = 7338.6
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[   5-   5, 6.25%]: CE = 2.18628039 * 128; Err = 0.67968750 * 128; time = 0.0171s; samplesPerSecond = 7505.1
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[   6-   6, 7.50%]: CE = 2.36950291 * 128; Err = 0.64062500 * 128; time = 0.0171s; samplesPerSecond = 7475.8
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[   7-   7, 8.75%]: CE = 1.98754400 * 128; Err = 0.59375000 * 128; time = 0.0176s; samplesPerSecond = 7285.6
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[   8-   8, 10.00%]: CE = 2.29285147 * 128; Err = 0.66406250 * 128; time = 0.0178s; samplesPerSecond = 7179.7
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[   9-   9, 11.25%]: CE = 1.98005135 * 128; Err = 0.53906250 * 128; time = 0.0172s; samplesPerSecond = 7433.2
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  10-  10, 12.50%]: CE = 1.98169796 * 128; Err = 0.51562500 * 128; time = 0.0169s; samplesPerSecond = 7571.7
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  11-  11, 13.75%]: CE = 2.36498402 * 128; Err = 0.67968750 * 128; time = 0.0170s; samplesPerSecond = 7537.8
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  12-  12, 15.00%]: CE = 2.19219951 * 128; Err = 0.61718750 * 128; time = 0.0169s; samplesPerSecond = 7595.5
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  13-  13, 16.25%]: CE = 2.06452491 * 128; Err = 0.53906250 * 128; time = 0.0174s; samplesPerSecond = 7339.0
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  14-  14, 17.50%]: CE = 1.94180051 * 128; Err = 0.56250000 * 128; time = 0.0172s; samplesPerSecond = 7429.8
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  15-  15, 18.75%]: CE = 2.15015004 * 128; Err = 0.57031250 * 128; time = 0.0170s; samplesPerSecond = 7521.0
MPI Rank 1: 		(model aggregation stats): 1-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 1: 		(model aggregation stats) 1-th sync:     0.31 seconds since last report (0.00 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 1: 		(model aggregation stats) 1-th sync: totalThroughput = 13.09k samplesPerSecond , throughputPerWorker = 6.54k samplesPerSecond
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  16-  16, 20.00%]: CE = 1.99832595 * 128; Err = 0.46875000 * 128; time = 0.0320s; samplesPerSecond = 3999.9
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  17-  17, 21.25%]: CE = 2.07071668 * 128; Err = 0.52343750 * 128; time = 0.0179s; samplesPerSecond = 7146.4
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  18-  18, 22.50%]: CE = 1.96928991 * 128; Err = 0.60937500 * 128; time = 0.0182s; samplesPerSecond = 7022.9
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  19-  19, 23.75%]: CE = 2.34985747 * 128; Err = 0.65625000 * 128; time = 0.0180s; samplesPerSecond = 7102.0
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  20-  20, 25.00%]: CE = 1.92885006 * 128; Err = 0.49218750 * 128; time = 0.0176s; samplesPerSecond = 7261.6
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  21-  21, 26.25%]: CE = 2.03928767 * 128; Err = 0.50781250 * 128; time = 0.0186s; samplesPerSecond = 6869.9
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  22-  22, 27.50%]: CE = 2.02655787 * 128; Err = 0.54687500 * 128; time = 0.0166s; samplesPerSecond = 7729.9
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  23-  23, 28.75%]: CE = 1.87866448 * 128; Err = 0.52343750 * 128; time = 0.0180s; samplesPerSecond = 7125.8
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  24-  24, 30.00%]: CE = 1.91108869 * 128; Err = 0.57031250 * 128; time = 0.0172s; samplesPerSecond = 7420.7
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  25-  25, 31.25%]: CE = 2.11885017 * 128; Err = 0.59375000 * 128; time = 0.0173s; samplesPerSecond = 7404.4
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  26-  26, 32.50%]: CE = 2.12920514 * 128; Err = 0.57812500 * 128; time = 0.0177s; samplesPerSecond = 7246.0
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  27-  27, 33.75%]: CE = 2.00735878 * 128; Err = 0.57812500 * 128; time = 0.0174s; samplesPerSecond = 7353.8
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  28-  28, 35.00%]: CE = 2.34481434 * 128; Err = 0.62500000 * 128; time = 0.0175s; samplesPerSecond = 7328.1
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  29-  29, 36.25%]: CE = 2.20659008 * 128; Err = 0.57812500 * 128; time = 0.0176s; samplesPerSecond = 7284.7
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  30-  30, 37.50%]: CE = 2.13527297 * 128; Err = 0.57031250 * 128; time = 0.0176s; samplesPerSecond = 7289.3
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  31-  31, 38.75%]: CE = 2.13058781 * 128; Err = 0.57812500 * 128; time = 0.0173s; samplesPerSecond = 7378.8
MPI Rank 1: 		(model aggregation stats): 2-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 1: 		(model aggregation stats) 2-th sync:     0.30 seconds since last report (0.00 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 1: 		(model aggregation stats) 2-th sync: totalThroughput = 13.56k samplesPerSecond , throughputPerWorker = 6.78k samplesPerSecond
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  32-  32, 40.00%]: CE = 1.87502810 * 128; Err = 0.56250000 * 128; time = 0.0333s; samplesPerSecond = 3847.7
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  33-  33, 41.25%]: CE = 1.91173305 * 128; Err = 0.52343750 * 128; time = 0.0175s; samplesPerSecond = 7333.6
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  34-  34, 42.50%]: CE = 2.24952705 * 128; Err = 0.61718750 * 128; time = 0.0173s; samplesPerSecond = 7408.3
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  35-  35, 43.75%]: CE = 1.95090577 * 128; Err = 0.53906250 * 128; time = 0.0128s; samplesPerSecond = 10034.5
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  36-  36, 45.00%]: CE = 2.25694794 * 128; Err = 0.61718750 * 128; time = 0.0204s; samplesPerSecond = 6285.6
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  37-  37, 46.25%]: CE = 1.93963435 * 128; Err = 0.52343750 * 128; time = 0.0174s; samplesPerSecond = 7372.0
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  38-  38, 47.50%]: CE = 1.97756156 * 128; Err = 0.47656250 * 128; time = 0.0177s; samplesPerSecond = 7222.3
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  39-  39, 48.75%]: CE = 1.95177783 * 128; Err = 0.51562500 * 128; time = 0.0176s; samplesPerSecond = 7293.4
MPI Rank 1: 12/21/2016 05:28:37:  Epoch[ 2 of 5]-Minibatch[  40-  40, 50.00%]: CE = 1.92424985 * 128; Err = 0.55468750 * 128; time = 0.0178s; samplesPerSecond = 7186.6
MPI Rank 1: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  41-  41, 51.25%]: CE = 1.86182077 * 128; Err = 0.50000000 * 128; time = 0.0171s; samplesPerSecond = 7476.2
MPI Rank 1: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  42-  42, 52.50%]: CE = 1.87138664 * 128; Err = 0.57812500 * 128; time = 0.0175s; samplesPerSecond = 7321.0
MPI Rank 1: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  43-  43, 53.75%]: CE = 2.10051544 * 128; Err = 0.58593750 * 128; time = 0.0175s; samplesPerSecond = 7301.4
MPI Rank 1: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  44-  44, 55.00%]: CE = 1.73668662 * 128; Err = 0.43750000 * 128; time = 0.0175s; samplesPerSecond = 7330.6
MPI Rank 1: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  45-  45, 56.25%]: CE = 2.25252786 * 128; Err = 0.58593750 * 128; time = 0.0173s; samplesPerSecond = 7388.6
MPI Rank 1: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  46-  46, 57.50%]: CE = 1.93265905 * 128; Err = 0.53906250 * 128; time = 0.0179s; samplesPerSecond = 7169.3
MPI Rank 1: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  47-  47, 58.75%]: CE = 1.92026864 * 128; Err = 0.53125000 * 128; time = 0.0181s; samplesPerSecond = 7090.2
MPI Rank 1: 		(model aggregation stats): 3-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 1: 		(model aggregation stats) 3-th sync:     0.30 seconds since last report (0.01 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 1: 		(model aggregation stats) 3-th sync: totalThroughput = 13.56k samplesPerSecond , throughputPerWorker = 6.78k samplesPerSecond
MPI Rank 1: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  48-  48, 60.00%]: CE = 1.96897429 * 128; Err = 0.50781250 * 128; time = 0.0371s; samplesPerSecond = 3448.0
MPI Rank 1: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  49-  49, 61.25%]: CE = 1.62916168 * 128; Err = 0.43750000 * 128; time = 0.0180s; samplesPerSecond = 7114.7
MPI Rank 1: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  50-  50, 62.50%]: CE = 2.08670592 * 128; Err = 0.64843750 * 128; time = 0.0183s; samplesPerSecond = 6980.8
MPI Rank 1: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  51-  51, 63.75%]: CE = 1.92556235 * 128; Err = 0.49218750 * 128; time = 0.0164s; samplesPerSecond = 7805.4
MPI Rank 1: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  52-  52, 65.00%]: CE = 2.00072664 * 128; Err = 0.53125000 * 128; time = 0.0169s; samplesPerSecond = 7595.1
MPI Rank 1: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  53-  53, 66.25%]: CE = 2.06829027 * 128; Err = 0.56250000 * 128; time = 0.0171s; samplesPerSecond = 7482.3
MPI Rank 1: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  54-  54, 67.50%]: CE = 1.83341656 * 128; Err = 0.50000000 * 128; time = 0.0177s; samplesPerSecond = 7212.9
MPI Rank 1: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  55-  55, 68.75%]: CE = 1.89431569 * 128; Err = 0.57812500 * 128; time = 0.0172s; samplesPerSecond = 7427.2
MPI Rank 1: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  56-  56, 70.00%]: CE = 1.98866240 * 128; Err = 0.53125000 * 128; time = 0.0170s; samplesPerSecond = 7509.1
MPI Rank 1: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  57-  57, 71.25%]: CE = 1.83148964 * 37; Err = 0.56756757 * 37; time = 0.0059s; samplesPerSecond = 6268.0
MPI Rank 1: 12/21/2016 05:28:38:  Epoch[ 2 of 5]-Minibatch[  58-  58, 72.50%]: CE = 0.00000000 * 0; Err = 0.00000000 * 0; time = 1.4000e-005s; samplesPerSecond = 0.0
MPI Rank 1: 		(model aggregation stats): 4-th sync point was hit, introducing a 0.07-seconds latency this time; accumulated time on sync point = 0.07 seconds , average latency = 0.02 seconds
MPI Rank 1: 		(model aggregation stats) 4-th sync:     0.63 seconds since last report (0.40 seconds on comm.); 8192 samples processed by 2 workers (1061 by me);
MPI Rank 1: 		(model aggregation stats) 4-th sync: totalThroughput = 13.06k samplesPerSecond , throughputPerWorker = 6.53k samplesPerSecond
MPI Rank 1: 12/21/2016 05:28:38: Finished Epoch[ 2 of 5]: [Training] CE = 2.03791679 * 20480; Err = 0.55712891 * 20480; totalSamplesSeen = 40960; learningRatePerSample = 0.001953125; epochTime=1.54541s
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:38: Starting Epoch 3: learning rate per sample = 0.000098  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 1: Parallel training (2 workers) using BlockMomentumSGD with block momentum = 0.5000, block momentum time constant (per worker) = 2954.6394, block learning rate = 1.0000, block size per worker = 2048 samples, using Nesterov-style block momentum, resetting SGD momentum after sync.
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:38: Starting minibatch loop, distributed reading is ENABLED.
MPI Rank 1: 12/21/2016 05:28:38:  Epoch[ 3 of 5]-Minibatch[   1-   1, 5.00%]: CE = 1.94395242 * 512; Err = 0.50585938 * 512; time = 0.0649s; samplesPerSecond = 7884.8
MPI Rank 1: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[   2-   2, 10.00%]: CE = 1.91829301 * 512; Err = 0.54101563 * 512; time = 0.0752s; samplesPerSecond = 6809.2
MPI Rank 1: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[   3-   3, 15.00%]: CE = 1.87474368 * 512; Err = 0.53125000 * 512; time = 0.0605s; samplesPerSecond = 8460.2
MPI Rank 1: 		(model aggregation stats): 1-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 1: 		(model aggregation stats) 1-th sync:     0.28 seconds since last report (0.00 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 1: 		(model aggregation stats) 1-th sync: totalThroughput = 14.46k samplesPerSecond , throughputPerWorker = 7.23k samplesPerSecond
MPI Rank 1: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[   4-   4, 20.00%]: CE = 1.86118062 * 512; Err = 0.52148438 * 512; time = 0.0778s; samplesPerSecond = 6582.6
MPI Rank 1: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[   5-   5, 25.00%]: CE = 1.89327895 * 512; Err = 0.51562500 * 512; time = 0.0624s; samplesPerSecond = 8198.8
MPI Rank 1: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[   6-   6, 30.00%]: CE = 1.95084124 * 512; Err = 0.53320313 * 512; time = 0.0628s; samplesPerSecond = 8159.1
MPI Rank 1: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[   7-   7, 35.00%]: CE = 2.01212462 * 512; Err = 0.55859375 * 512; time = 0.0597s; samplesPerSecond = 8578.5
MPI Rank 1: 		(model aggregation stats): 2-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 1: 		(model aggregation stats) 2-th sync:     0.26 seconds since last report (0.00 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 1: 		(model aggregation stats) 2-th sync: totalThroughput = 15.58k samplesPerSecond , throughputPerWorker = 7.79k samplesPerSecond
MPI Rank 1: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[   8-   8, 40.00%]: CE = 1.92556904 * 512; Err = 0.58007813 * 512; time = 0.0766s; samplesPerSecond = 6680.9
MPI Rank 1: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[   9-   9, 45.00%]: CE = 1.91264292 * 512; Err = 0.51171875 * 512; time = 0.0603s; samplesPerSecond = 8484.3
MPI Rank 1: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[  10-  10, 50.00%]: CE = 1.89071222 * 512; Err = 0.52929688 * 512; time = 0.0612s; samplesPerSecond = 8363.1
MPI Rank 1: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[  11-  11, 55.00%]: CE = 2.10267317 * 512; Err = 0.60546875 * 512; time = 0.0605s; samplesPerSecond = 8458.9
MPI Rank 1: 		(model aggregation stats): 3-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 1: 		(model aggregation stats) 3-th sync:     0.26 seconds since last report (0.00 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 1: 		(model aggregation stats) 3-th sync: totalThroughput = 15.76k samplesPerSecond , throughputPerWorker = 7.88k samplesPerSecond
MPI Rank 1: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[  12-  12, 60.00%]: CE = 1.94130255 * 512; Err = 0.53515625 * 512; time = 0.0768s; samplesPerSecond = 6664.0
MPI Rank 1: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[  13-  13, 65.00%]: CE = 1.93636276 * 512; Err = 0.54687500 * 512; time = 0.0616s; samplesPerSecond = 8308.3
MPI Rank 1: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[  14-  14, 70.00%]: CE = 1.94802346 * 512; Err = 0.56250000 * 512; time = 0.0611s; samplesPerSecond = 8378.2
MPI Rank 1: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[  15-  15, 75.00%]: CE = 1.98562867 * 512; Err = 0.54101563 * 512; time = 0.0559s; samplesPerSecond = 9163.0
MPI Rank 1: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[  16-  16, 80.00%]: CE = 1.79595464 * 11; Err = 0.54545455 * 11; time = 0.0069s; samplesPerSecond = 1585.0
MPI Rank 1: 12/21/2016 05:28:39:  Epoch[ 3 of 5]-Minibatch[  17-  17, 85.00%]: CE = 0.00000000 * 0; Err = 0.00000000 * 0; time = 2.0000e-005s; samplesPerSecond = 0.0
MPI Rank 1: 		(model aggregation stats): 4-th sync point was hit, introducing a 0.03-seconds latency this time; accumulated time on sync point = 0.03 seconds , average latency = 0.01 seconds
MPI Rank 1: 		(model aggregation stats) 4-th sync:     0.51 seconds since last report (0.28 seconds on comm.); 8192 samples processed by 2 workers (1547 by me);
MPI Rank 1: 		(model aggregation stats) 4-th sync: totalThroughput = 16.07k samplesPerSecond , throughputPerWorker = 8.03k samplesPerSecond
MPI Rank 1: 12/21/2016 05:28:40: Finished Epoch[ 3 of 5]: [Training] CE = 1.94906734 * 20480; Err = 0.54541016 * 20480; totalSamplesSeen = 61440; learningRatePerSample = 9.7656251e-005; epochTime=1.31704s
MPI Rank 1: ValidateSubNetwork: featNorm.invStdDev InvStdDev operation changed, from [363 x 1] to [363].ValidateSubNetwork: featNorm.mean Mean operation changed, from [363 x 1] to [363].ValidateSubNetwork: logPrior._ Mean operation changed, from [132 x 1] to [132].Parallel training (2 workers) using BlockMomentumSGD with block momentum = 0.5000, block momentum time constant (per worker) = 2954.6394, block learning rate = 1.0000, block size per worker = 2048 samples, using Nesterov-style block momentum, resetting SGD momentum after sync.
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:40: Starting minibatch loop, distributed reading is ENABLED.
MPI Rank 1: 12/21/2016 05:28:40:   BaseAdaptiveLearnRateSearch: Epoch[ 4 of 5]-Minibatch[   1-   1, 5.00%]: CE = 1.95468900 * 512; Err = 0.52929688 * 512; time = 0.0285s; samplesPerSecond = 17964.9
MPI Rank 1: 12/21/2016 05:28:40:   BaseAdaptiveLearnRateSearch: Epoch[ 4 of 5]-Minibatch[   2-   2, 10.00%]: CE = 0.00000000 * 0; Err = 0.00000000 * 0; time = 0.0006s; samplesPerSecond = 0.0
MPI Rank 1: 		(model aggregation stats): 1-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 1: 		(model aggregation stats) 1-th sync:     0.07 seconds since last report (0.00 seconds on comm.); 1536 samples processed by 2 workers (512 by me);
MPI Rank 1: 		(model aggregation stats) 1-th sync: totalThroughput = 22.06k samplesPerSecond , throughputPerWorker = 11.03k samplesPerSecond
MPI Rank 1: 12/21/2016 05:28:40:  Finished Mini-Epoch[4]: CE = 1.91381689 * 1536; Err = 0.52148438 * 1536; learningRatePerSample = 0; minibatchSize = 1024
MPI Rank 1: ValidateSubNetwork: featNorm.invStdDev InvStdDev operation changed, from [363 x 1] to [363].ValidateSubNetwork: featNorm.mean Mean operation changed, from [363 x 1] to [363].ValidateSubNetwork: logPrior._ Mean operation changed, from [132 x 1] to [132].Parallel training (2 workers) using BlockMomentumSGD with block momentum = 0.5000, block momentum time constant (per worker) = 2954.6394, block learning rate = 1.0000, block size per worker = 2048 samples, using Nesterov-style block momentum, resetting SGD momentum after sync.
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:40: Starting minibatch loop, distributed reading is ENABLED.
MPI Rank 1: 12/21/2016 05:28:40:   AdaptiveLearnRateSearch: Epoch[ 4 of 5]-Minibatch[   1-   1, 5.00%]: CE = 1.95468900 * 512; Err = 0.52929688 * 512; time = 0.0643s; samplesPerSecond = 7956.9
MPI Rank 1: 12/21/2016 05:28:40:   AdaptiveLearnRateSearch: Epoch[ 4 of 5]-Minibatch[   2-   2, 10.00%]: CE = 0.00000000 * 0; Err = 0.00000000 * 0; time = 7.5000e-005s; samplesPerSecond = 0.0
MPI Rank 1: 		(model aggregation stats): 1-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 1: 		(model aggregation stats) 1-th sync:     0.12 seconds since last report (0.00 seconds on comm.); 1536 samples processed by 2 workers (512 by me);
MPI Rank 1: 		(model aggregation stats) 1-th sync: totalThroughput = 12.50k samplesPerSecond , throughputPerWorker = 6.25k samplesPerSecond
MPI Rank 1: 12/21/2016 05:28:40:  Finished Mini-Epoch[4]: CE = 1.91308005 * 1536; Err = 0.52083333 * 1536; learningRatePerSample = 0.0039062502; minibatchSize = 1024
MPI Rank 1: ValidateSubNetwork: featNorm.invStdDev InvStdDev operation changed, from [363 x 1] to [363].ValidateSubNetwork: featNorm.mean Mean operation changed, from [363 x 1] to [363].ValidateSubNetwork: logPrior._ Mean operation changed, from [132 x 1] to [132].12/21/2016 05:28:41:  SearchForBestLearnRate Epoch[4]: Best learningRatePerSample = 0.003906250186, baseCriterion=1.922629502
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:41: Starting Epoch 4: learning rate per sample = 0.003906  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 1: Parallel training (2 workers) using BlockMomentumSGD with block momentum = 0.5000, block momentum time constant (per worker) = 2954.6394, block learning rate = 1.0000, block size per worker = 2048 samples, using Nesterov-style block momentum, resetting SGD momentum after sync.
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:41: Starting minibatch loop, distributed reading is ENABLED.
MPI Rank 1: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[   1-   1, 5.00%]: CE = 1.95468900 * 512; Err = 0.52929688 * 512; time = 0.0618s; samplesPerSecond = 8288.5
MPI Rank 1: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[   2-   2, 10.00%]: CE = 1.87167107 * 512; Err = 0.53515625 * 512; time = 0.0634s; samplesPerSecond = 8072.5
MPI Rank 1: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[   3-   3, 15.00%]: CE = 1.88950637 * 512; Err = 0.52734375 * 512; time = 0.0609s; samplesPerSecond = 8411.5
MPI Rank 1: 		(model aggregation stats): 1-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 1: 		(model aggregation stats) 1-th sync:     0.27 seconds since last report (0.00 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 1: 		(model aggregation stats) 1-th sync: totalThroughput = 14.99k samplesPerSecond , throughputPerWorker = 7.49k samplesPerSecond
MPI Rank 1: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[   4-   4, 20.00%]: CE = 1.88732813 * 512; Err = 0.52148438 * 512; time = 0.0801s; samplesPerSecond = 6394.8
MPI Rank 1: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[   5-   5, 25.00%]: CE = 1.97210123 * 512; Err = 0.54296875 * 512; time = 0.0617s; samplesPerSecond = 8303.3
MPI Rank 1: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[   6-   6, 30.00%]: CE = 1.85454102 * 512; Err = 0.50390625 * 512; time = 0.0434s; samplesPerSecond = 11786.4
MPI Rank 1: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[   7-   7, 35.00%]: CE = 1.92811151 * 512; Err = 0.52148438 * 512; time = 0.0776s; samplesPerSecond = 6597.2
MPI Rank 1: 		(model aggregation stats): 2-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 1: 		(model aggregation stats) 2-th sync:     0.26 seconds since last report (0.00 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 1: 		(model aggregation stats) 2-th sync: totalThroughput = 15.77k samplesPerSecond , throughputPerWorker = 7.89k samplesPerSecond
MPI Rank 1: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[   8-   8, 40.00%]: CE = 1.82157074 * 512; Err = 0.50195313 * 512; time = 0.0756s; samplesPerSecond = 6768.3
MPI Rank 1: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[   9-   9, 45.00%]: CE = 1.78976739 * 512; Err = 0.48046875 * 512; time = 0.0628s; samplesPerSecond = 8157.4
MPI Rank 1: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[  10-  10, 50.00%]: CE = 1.88394408 * 512; Err = 0.54296875 * 512; time = 0.0608s; samplesPerSecond = 8420.8
MPI Rank 1: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[  11-  11, 55.00%]: CE = 2.00101366 * 512; Err = 0.54492188 * 512; time = 0.0614s; samplesPerSecond = 8342.0
MPI Rank 1: 		(model aggregation stats): 3-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 1: 		(model aggregation stats) 3-th sync:     0.26 seconds since last report (0.00 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 1: 		(model aggregation stats) 3-th sync: totalThroughput = 15.59k samplesPerSecond , throughputPerWorker = 7.79k samplesPerSecond
MPI Rank 1: 12/21/2016 05:28:41:  Epoch[ 4 of 5]-Minibatch[  12-  12, 60.00%]: CE = 1.78858571 * 512; Err = 0.51953125 * 512; time = 0.0770s; samplesPerSecond = 6652.0
MPI Rank 1: 12/21/2016 05:28:42:  Epoch[ 4 of 5]-Minibatch[  13-  13, 65.00%]: CE = 1.85860025 * 512; Err = 0.53515625 * 512; time = 0.0609s; samplesPerSecond = 8403.8
MPI Rank 1: 12/21/2016 05:28:42:  Epoch[ 4 of 5]-Minibatch[  14-  14, 70.00%]: CE = 1.91177316 * 512; Err = 0.54492188 * 512; time = 0.0627s; samplesPerSecond = 8164.7
MPI Rank 1: 12/21/2016 05:28:42:  Epoch[ 4 of 5]-Minibatch[  15-  15, 75.00%]: CE = 1.74248464 * 512; Err = 0.50000000 * 512; time = 0.0609s; samplesPerSecond = 8404.9
MPI Rank 1: 12/21/2016 05:28:42:  Epoch[ 4 of 5]-Minibatch[  16-  16, 80.00%]: CE = 1.98287530 * 206; Err = 0.56796117 * 206; time = 0.0416s; samplesPerSecond = 4954.4
MPI Rank 1: 12/21/2016 05:28:42:  Epoch[ 4 of 5]-Minibatch[  17-  17, 85.00%]: CE = 0.00000000 * 0; Err = 0.00000000 * 0; time = 1.5000e-005s; samplesPerSecond = 0.0
MPI Rank 1: 		(model aggregation stats): 4-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 1: 		(model aggregation stats) 4-th sync:     0.51 seconds since last report (0.27 seconds on comm.); 8192 samples processed by 2 workers (1742 by me);
MPI Rank 1: 		(model aggregation stats) 4-th sync: totalThroughput = 16.07k samplesPerSecond , throughputPerWorker = 8.04k samplesPerSecond
MPI Rank 1: 12/21/2016 05:28:42: Finished Epoch[ 4 of 5]: [Training] CE = 1.86802921 * 20480; Err = 0.52246094 * 20480; totalSamplesSeen = 81920; learningRatePerSample = 0.0039062502; epochTime=2.18401s
MPI Rank 1: ValidateSubNetwork: featNorm.invStdDev InvStdDev operation changed, from [363 x 1] to [363].ValidateSubNetwork: featNorm.mean Mean operation changed, from [363 x 1] to [363].ValidateSubNetwork: logPrior._ Mean operation changed, from [132 x 1] to [132].Parallel training (2 workers) using BlockMomentumSGD with block momentum = 0.5000, block momentum time constant (per worker) = 2954.6394, block learning rate = 1.0000, block size per worker = 2048 samples, using Nesterov-style block momentum, resetting SGD momentum after sync.
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:42: Starting minibatch loop, distributed reading is ENABLED.
MPI Rank 1: 12/21/2016 05:28:42:   BaseAdaptiveLearnRateSearch: Epoch[ 5 of 5]-Minibatch[   1-   1, 5.00%]: CE = 1.72259049 * 512; Err = 0.47070313 * 512; time = 0.0166s; samplesPerSecond = 30897.4
MPI Rank 1: 12/21/2016 05:28:42:   BaseAdaptiveLearnRateSearch: Epoch[ 5 of 5]-Minibatch[   2-   2, 10.00%]: CE = 0.00000000 * 0; Err = 0.00000000 * 0; time = 0.0110s; samplesPerSecond = 0.0
MPI Rank 1: 		(model aggregation stats): 1-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 1: 		(model aggregation stats) 1-th sync:     0.07 seconds since last report (0.00 seconds on comm.); 1536 samples processed by 2 workers (512 by me);
MPI Rank 1: 		(model aggregation stats) 1-th sync: totalThroughput = 22.67k samplesPerSecond , throughputPerWorker = 11.33k samplesPerSecond
MPI Rank 1: 12/21/2016 05:28:42:  Finished Mini-Epoch[5]: CE = 1.84766700 * 1536; Err = 0.51627604 * 1536; learningRatePerSample = 0; minibatchSize = 1024
MPI Rank 1: ValidateSubNetwork: featNorm.invStdDev InvStdDev operation changed, from [363 x 1] to [363].ValidateSubNetwork: featNorm.mean Mean operation changed, from [363 x 1] to [363].ValidateSubNetwork: logPrior._ Mean operation changed, from [132 x 1] to [132].Parallel training (2 workers) using BlockMomentumSGD with block momentum = 0.5000, block momentum time constant (per worker) = 2954.6394, block learning rate = 1.0000, block size per worker = 2048 samples, using Nesterov-style block momentum, resetting SGD momentum after sync.
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:43: Starting minibatch loop, distributed reading is ENABLED.
MPI Rank 1: 12/21/2016 05:28:43:   AdaptiveLearnRateSearch: Epoch[ 5 of 5]-Minibatch[   1-   1, 5.00%]: CE = 1.72259049 * 512; Err = 0.47070313 * 512; time = 0.0654s; samplesPerSecond = 7826.7
MPI Rank 1: 12/21/2016 05:28:43:   AdaptiveLearnRateSearch: Epoch[ 5 of 5]-Minibatch[   2-   2, 10.00%]: CE = 0.00000000 * 0; Err = 0.00000000 * 0; time = 7.3000e-005s; samplesPerSecond = 0.0
MPI Rank 1: 		(model aggregation stats): 1-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 1: 		(model aggregation stats) 1-th sync:     0.13 seconds since last report (0.00 seconds on comm.); 1536 samples processed by 2 workers (512 by me);
MPI Rank 1: 		(model aggregation stats) 1-th sync: totalThroughput = 12.07k samplesPerSecond , throughputPerWorker = 6.04k samplesPerSecond
MPI Rank 1: 12/21/2016 05:28:43:  Finished Mini-Epoch[5]: CE = 1.82753521 * 1536; Err = 0.49674479 * 1536; learningRatePerSample = 0.0063207938; minibatchSize = 1024
MPI Rank 1: ValidateSubNetwork: featNorm.invStdDev InvStdDev operation changed, from [363 x 1] to [363].ValidateSubNetwork: featNorm.mean Mean operation changed, from [363 x 1] to [363].ValidateSubNetwork: logPrior._ Mean operation changed, from [132 x 1] to [132].12/21/2016 05:28:43:  SearchForBestLearnRate Epoch[5]: Best learningRatePerSample = 0.006320793777, baseCriterion=1.85275755
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:43: Starting Epoch 5: learning rate per sample = 0.006321  effective momentum = 0.656119  momentum as time constant = 2429.9 samples
MPI Rank 1: Parallel training (2 workers) using BlockMomentumSGD with block momentum = 0.5000, block momentum time constant (per worker) = 2954.6394, block learning rate = 1.0000, block size per worker = 2048 samples, using Nesterov-style block momentum, resetting SGD momentum after sync.
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:43: Starting minibatch loop, distributed reading is ENABLED.
MPI Rank 1: 12/21/2016 05:28:43:  Epoch[ 5 of 5]-Minibatch[   1-   1, 5.00%]: CE = 1.72259049 * 512; Err = 0.47070313 * 512; time = 0.0662s; samplesPerSecond = 7735.1
MPI Rank 1: 12/21/2016 05:28:43:  Epoch[ 5 of 5]-Minibatch[   2-   2, 10.00%]: CE = 1.81334940 * 512; Err = 0.51562500 * 512; time = 0.0616s; samplesPerSecond = 8316.8
MPI Rank 1: 12/21/2016 05:28:43:  Epoch[ 5 of 5]-Minibatch[   3-   3, 15.00%]: CE = 1.75469950 * 512; Err = 0.50195313 * 512; time = 0.0615s; samplesPerSecond = 8323.2
MPI Rank 1: 		(model aggregation stats): 1-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 1: 		(model aggregation stats) 1-th sync:     0.27 seconds since last report (0.00 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 1: 		(model aggregation stats) 1-th sync: totalThroughput = 14.92k samplesPerSecond , throughputPerWorker = 7.46k samplesPerSecond
MPI Rank 1: 12/21/2016 05:28:43:  Epoch[ 5 of 5]-Minibatch[   4-   4, 20.00%]: CE = 1.94839844 * 512; Err = 0.56445313 * 512; time = 0.0767s; samplesPerSecond = 6673.4
MPI Rank 1: 12/21/2016 05:28:43:  Epoch[ 5 of 5]-Minibatch[   5-   5, 25.00%]: CE = 1.81340242 * 512; Err = 0.55078125 * 512; time = 0.0611s; samplesPerSecond = 8377.4
MPI Rank 1: 12/21/2016 05:28:43:  Epoch[ 5 of 5]-Minibatch[   6-   6, 30.00%]: CE = 1.76005194 * 512; Err = 0.51171875 * 512; time = 0.0616s; samplesPerSecond = 8317.5
MPI Rank 1: 12/21/2016 05:28:43:  Epoch[ 5 of 5]-Minibatch[   7-   7, 35.00%]: CE = 1.90019478 * 512; Err = 0.52539063 * 512; time = 0.0722s; samplesPerSecond = 7093.5
MPI Rank 1: 		(model aggregation stats): 2-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 1: 		(model aggregation stats) 2-th sync:     0.26 seconds since last report (0.00 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 1: 		(model aggregation stats) 2-th sync: totalThroughput = 15.84k samplesPerSecond , throughputPerWorker = 7.92k samplesPerSecond
MPI Rank 1: 12/21/2016 05:28:43:  Epoch[ 5 of 5]-Minibatch[   8-   8, 40.00%]: CE = 1.84919225 * 512; Err = 0.52148438 * 512; time = 0.0624s; samplesPerSecond = 8207.8
MPI Rank 1: 12/21/2016 05:28:43:  Epoch[ 5 of 5]-Minibatch[   9-   9, 45.00%]: CE = 1.87396402 * 512; Err = 0.50390625 * 512; time = 0.0615s; samplesPerSecond = 8319.9
MPI Rank 1: 12/21/2016 05:28:43:  Epoch[ 5 of 5]-Minibatch[  10-  10, 50.00%]: CE = 1.76656360 * 512; Err = 0.50781250 * 512; time = 0.0599s; samplesPerSecond = 8546.9
MPI Rank 1: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  11-  11, 55.00%]: CE = 1.73350646 * 512; Err = 0.47265625 * 512; time = 0.0620s; samplesPerSecond = 8261.1
MPI Rank 1: 		(model aggregation stats): 3-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 1: 		(model aggregation stats) 3-th sync:     0.26 seconds since last report (0.00 seconds on comm.); 4096 samples processed by 2 workers (2048 by me);
MPI Rank 1: 		(model aggregation stats) 3-th sync: totalThroughput = 15.69k samplesPerSecond , throughputPerWorker = 7.84k samplesPerSecond
MPI Rank 1: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  12-  12, 60.00%]: CE = 1.82274869 * 512; Err = 0.54882813 * 512; time = 0.0807s; samplesPerSecond = 6345.6
MPI Rank 1: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  13-  13, 65.00%]: CE = 2.04859191 * 512; Err = 0.55078125 * 512; time = 0.0597s; samplesPerSecond = 8573.8
MPI Rank 1: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  14-  14, 70.00%]: CE = 1.86725595 * 512; Err = 0.55859375 * 512; time = 0.0617s; samplesPerSecond = 8300.5
MPI Rank 1: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  15-  15, 75.00%]: CE = 1.89531398 * 512; Err = 0.54492188 * 512; time = 0.0610s; samplesPerSecond = 8391.8
MPI Rank 1: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  16-  16, 80.00%]: CE = 1.65643464 * 333; Err = 0.48348348 * 333; time = 0.0428s; samplesPerSecond = 7784.7
MPI Rank 1: 12/21/2016 05:28:44:  Epoch[ 5 of 5]-Minibatch[  17-  17, 85.00%]: CE = 0.00000000 * 0; Err = 0.00000000 * 0; time = 2.5000e-005s; samplesPerSecond = 0.0
MPI Rank 1: 		(model aggregation stats): 4-th sync point was hit, introducing a 0.00-seconds latency this time; accumulated time on sync point = 0.00 seconds , average latency = 0.00 seconds
MPI Rank 1: 		(model aggregation stats) 4-th sync:     0.51 seconds since last report (0.27 seconds on comm.); 8192 samples processed by 2 workers (1869 by me);
MPI Rank 1: 		(model aggregation stats) 4-th sync: totalThroughput = 16.04k samplesPerSecond , throughputPerWorker = 8.02k samplesPerSecond
MPI Rank 1: 12/21/2016 05:28:44: Finished Epoch[ 5 of 5]: [Training] CE = 1.78255566 * 20480; Err = 0.51186523 * 20480; totalSamplesSeen = 102400; learningRatePerSample = 0.0063207938; epochTime=1.96299s
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:44: Action "train" complete.
MPI Rank 1: 
MPI Rank 1: 12/21/2016 05:28:44: __COMPLETED__
MPI Rank 1: ~MPIWrapper
