777 строки
53 KiB
Plaintext
777 строки
53 KiB
Plaintext
CPU info:
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CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
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Hardware threads: 24
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Total Memory: 264172964 kB
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-------------------------------------------------------------------
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=== Running /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/Simple/../../../../../../Examples/Other/Simple2d/Config/Simple.cntk currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/Simple/../../../../../../Examples/Other/Simple2d/Config OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu DeviceId=0 timestamping=true Simple_Demo_Train=[SGD=[maxEpochs=3]]
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-------------------------------------------------------------------
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Build info:
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Built time: Aug 16 2016 09:41:56
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Last modified date: Fri Aug 12 07:32:43 2016
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Build type: release
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Build target: GPU
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With 1bit-SGD: no
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Math lib: mkl
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CUDA_PATH: /usr/local/cuda-7.5
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CUB_PATH: /usr/local/cub-1.4.1
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CUDNN_PATH: /usr/local/cudnn-4.0
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Build Branch: HEAD
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Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
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Built by philly on f67b30a647de
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Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
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-------------------------------------------------------------------
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Changed current directory to /home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
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08/16/2016 10:51:39: -------------------------------------------------------------------
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08/16/2016 10:51:39: Build info:
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08/16/2016 10:51:39: Built time: Aug 16 2016 09:41:56
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08/16/2016 10:51:39: Last modified date: Fri Aug 12 07:32:43 2016
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08/16/2016 10:51:39: Build type: release
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08/16/2016 10:51:39: Build target: GPU
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08/16/2016 10:51:39: With 1bit-SGD: no
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08/16/2016 10:51:39: Math lib: mkl
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08/16/2016 10:51:39: CUDA_PATH: /usr/local/cuda-7.5
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08/16/2016 10:51:39: CUB_PATH: /usr/local/cub-1.4.1
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08/16/2016 10:51:39: CUDNN_PATH: /usr/local/cudnn-4.0
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08/16/2016 10:51:39: Build Branch: HEAD
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08/16/2016 10:51:39: Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
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08/16/2016 10:51:39: Built by philly on f67b30a647de
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08/16/2016 10:51:39: Build Path: /home/philly/jenkins/workspace/CNTK-Build-Linux
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08/16/2016 10:51:39: -------------------------------------------------------------------
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08/16/2016 10:51:40: -------------------------------------------------------------------
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08/16/2016 10:51:40: GPU info:
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08/16/2016 10:51:40: Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
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08/16/2016 10:51:40: Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
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08/16/2016 10:51:40: Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
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08/16/2016 10:51:40: Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3071 MB
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08/16/2016 10:51:40: -------------------------------------------------------------------
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08/16/2016 10:51:40: Running on localhost at 2016/08/16 10:51:40
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08/16/2016 10:51:40: Command line:
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/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/build/gpu/release/bin/cntk configFile=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/Simple/../../../../../../Examples/Other/Simple2d/Config/Simple.cntk currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/Simple/../../../../../../Examples/Other/Simple2d/Config OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu DeviceId=0 timestamping=true Simple_Demo_Train=[SGD=[maxEpochs=3]]
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08/16/2016 10:51:40: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
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08/16/2016 10:51:40: RootDir = ".."
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ConfigDir = "$RootDir$/Config"
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DataDir = "$RootDir$/Data"
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OutputDir = "$RootDir$/Output"
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ModelDir = "$OutputDir$/Models"
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deviceId = -1
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command = Simple_Demo_Train:Simple_Demo_Test:Simple_Demo_Output
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precision = "float"
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traceLevel = 1
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modelPath = "$ModelDir$/simple.dnn"
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outputNodeNames = ScaledLogLikelihood
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Simple_Demo_Train = [
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action = "train"
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SimpleNetworkBuilder = [
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layerSizes = 2:50*2:2
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trainingCriterion = "CrossEntropyWithSoftmax"
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evalCriterion = "ClassificationError"
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layerTypes = "Sigmoid"
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initValueScale = 1.0
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applyMeanVarNorm = true
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uniformInit = true
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needPrior = true
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]
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SGD = [
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epochSize = 0
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minibatchSize = 25
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learningRatesPerMB = 0.5:0.2*20:0.1
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momentumPerMB = 0.9
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dropoutRate = 0.0
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maxEpochs = 10
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]
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reader = [
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readerType = "CNTKTextFormatReader"
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file = "$DataDir$/SimpleDataTrain_cntk_text.txt"
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input = [
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features = [
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dim = 2
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format = "dense"
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]
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labels = [
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dim = 2
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format = "dense"
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]
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]
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]
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]
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Simple_Demo_Test = [
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action = "test"
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reader = [
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readerType = "CNTKTextFormatReader"
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file = "$DataDir$/SimpleDataTest_cntk_text.txt"
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input = [
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features = [
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dim = 2
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format = "dense"
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]
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labels = [
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dim = 2
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format = "dense"
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]
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]
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]
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]
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Simple_Demo_Output=[
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action = "write"
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reader = [
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readerType = "CNTKTextFormatReader"
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file = "$DataDir$/SimpleDataTest_cntk_text.txt"
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input = [
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features = [
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dim = 2
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format = "dense"
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]
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labels = [
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dim = 2
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format = "dense"
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]
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]
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]
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outputNodeNames = PosteriorProb : labels
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outputPath = "$OutputDir$/SimpleOutput"
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format = [
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type = "category"
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labelMappingFile = "$DataDir$/SimpleMapping.txt"
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sequenceEpilogue = "\t// %s\n"
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]
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]
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currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
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RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu
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DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
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ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/Simple/../../../../../../Examples/Other/Simple2d/Config
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OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu
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DeviceId=0
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timestamping=true
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Simple_Demo_Train=[SGD=[maxEpochs=3]]
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08/16/2016 10:51:40: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED) <<<<<<<<<<<<<<<<<<<<
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08/16/2016 10:51:40: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
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08/16/2016 10:51:40: RootDir = ".."
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ConfigDir = "../Config"
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DataDir = "../Data"
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OutputDir = "../Output"
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ModelDir = "/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu/Models"
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deviceId = -1
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command = Simple_Demo_Train:Simple_Demo_Test:Simple_Demo_Output
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precision = "float"
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traceLevel = 1
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modelPath = "/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu/Models/simple.dnn"
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outputNodeNames = ScaledLogLikelihood
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Simple_Demo_Train = [
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action = "train"
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SimpleNetworkBuilder = [
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layerSizes = 2:50*2:2
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trainingCriterion = "CrossEntropyWithSoftmax"
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evalCriterion = "ClassificationError"
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layerTypes = "Sigmoid"
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initValueScale = 1.0
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applyMeanVarNorm = true
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uniformInit = true
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needPrior = true
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]
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SGD = [
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epochSize = 0
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minibatchSize = 25
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learningRatesPerMB = 0.5:0.2*20:0.1
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momentumPerMB = 0.9
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dropoutRate = 0.0
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maxEpochs = 10
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]
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reader = [
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readerType = "CNTKTextFormatReader"
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file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTrain_cntk_text.txt"
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input = [
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features = [
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dim = 2
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format = "dense"
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]
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labels = [
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dim = 2
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format = "dense"
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]
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]
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]
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]
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Simple_Demo_Test = [
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action = "test"
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reader = [
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readerType = "CNTKTextFormatReader"
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file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
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input = [
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features = [
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dim = 2
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format = "dense"
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]
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labels = [
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dim = 2
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format = "dense"
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]
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]
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]
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]
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Simple_Demo_Output=[
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action = "write"
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reader = [
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readerType = "CNTKTextFormatReader"
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file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
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input = [
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features = [
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dim = 2
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format = "dense"
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]
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labels = [
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dim = 2
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format = "dense"
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]
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]
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]
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outputNodeNames = PosteriorProb : labels
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outputPath = "/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu/SimpleOutput"
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format = [
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type = "category"
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labelMappingFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleMapping.txt"
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sequenceEpilogue = "\t// %s\n"
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]
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]
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currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
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RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu
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DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
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ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/Simple/../../../../../../Examples/Other/Simple2d/Config
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OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu
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DeviceId=0
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timestamping=true
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Simple_Demo_Train=[SGD=[maxEpochs=3]]
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08/16/2016 10:51:40: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
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08/16/2016 10:51:40: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
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configparameters: Simple.cntk:command=Simple_Demo_Train:Simple_Demo_Test:Simple_Demo_Output
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configparameters: Simple.cntk:ConfigDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Tests/EndToEndTests/Examples/Other/Simple2d/Simple/../../../../../../Examples/Other/Simple2d/Config
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configparameters: Simple.cntk:currentDirectory=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
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configparameters: Simple.cntk:DataDir=/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data
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configparameters: Simple.cntk:deviceId=0
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configparameters: Simple.cntk:ModelDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu/Models
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configparameters: Simple.cntk:modelPath=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu/Models/simple.dnn
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configparameters: Simple.cntk:OutputDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu
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configparameters: Simple.cntk:outputNodeNames=ScaledLogLikelihood
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configparameters: Simple.cntk:precision=float
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configparameters: Simple.cntk:RootDir=..
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configparameters: Simple.cntk:RunDir=/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu
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configparameters: Simple.cntk:Simple_Demo_Output=[
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action = "write"
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reader = [
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readerType = "CNTKTextFormatReader"
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file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
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input = [
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features = [
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dim = 2
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format = "dense"
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]
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labels = [
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dim = 2
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format = "dense"
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]
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]
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]
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outputNodeNames = PosteriorProb : labels
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outputPath = "/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu/SimpleOutput"
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format = [
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type = "category"
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labelMappingFile = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleMapping.txt"
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sequenceEpilogue = "\t// %s\n"
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]
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]
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configparameters: Simple.cntk:Simple_Demo_Test=[
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action = "test"
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reader = [
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readerType = "CNTKTextFormatReader"
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file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTest_cntk_text.txt"
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input = [
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features = [
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dim = 2
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format = "dense"
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]
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labels = [
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dim = 2
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format = "dense"
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]
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]
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]
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]
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configparameters: Simple.cntk:Simple_Demo_Train=[
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action = "train"
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SimpleNetworkBuilder = [
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layerSizes = 2:50*2:2
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trainingCriterion = "CrossEntropyWithSoftmax"
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|
evalCriterion = "ClassificationError"
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layerTypes = "Sigmoid"
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initValueScale = 1.0
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applyMeanVarNorm = true
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uniformInit = true
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needPrior = true
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]
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SGD = [
|
|
epochSize = 0
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minibatchSize = 25
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learningRatesPerMB = 0.5:0.2*20:0.1
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momentumPerMB = 0.9
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|
dropoutRate = 0.0
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maxEpochs = 10
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]
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|
reader = [
|
|
readerType = "CNTKTextFormatReader"
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|
file = "/home/philly/jenkins/workspace/CNTK-Test-Linux-W1/Examples/Other/Simple2d/Data/SimpleDataTrain_cntk_text.txt"
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|
input = [
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|
features = [
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dim = 2
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format = "dense"
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]
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labels = [
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dim = 2
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format = "dense"
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]
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]
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]
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] [SGD=[maxEpochs=3]]
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configparameters: Simple.cntk:timestamping=true
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configparameters: Simple.cntk:traceLevel=1
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08/16/2016 10:51:40: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
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08/16/2016 10:51:40: Commands: Simple_Demo_Train Simple_Demo_Test Simple_Demo_Output
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08/16/2016 10:51:40: Precision = "float"
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08/16/2016 10:51:40: CNTKModelPath: /tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu/Models/simple.dnn
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08/16/2016 10:51:40: CNTKCommandTrainInfo: Simple_Demo_Train : 3
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08/16/2016 10:51:40: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 3
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08/16/2016 10:51:40: ##############################################################################
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08/16/2016 10:51:40: # #
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08/16/2016 10:51:40: # Action "train" #
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08/16/2016 10:51:40: # #
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08/16/2016 10:51:40: ##############################################################################
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08/16/2016 10:51:40: CNTKCommandTrainBegin: Simple_Demo_Train
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SimpleNetworkBuilder Using GPU 0
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08/16/2016 10:51:40: Creating virgin network.
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Node 'W0' (LearnableParameter operation): Initializing Parameter[50 x 2] <- 0.000000.
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Node 'W0' (LearnableParameter operation): Initializing Parameter[50 x 2] <- uniform(seed=1, range=0.050000*1.000000, onCPU=false).
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SetUniformRandomValue (GPU): creating curand object with seed 1, sizeof(ElemType)==4
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Node 'B0' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
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Node 'B0' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
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Node 'W1' (LearnableParameter operation): Initializing Parameter[50 x 50] <- 0.000000.
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Node 'W1' (LearnableParameter operation): Initializing Parameter[50 x 50] <- uniform(seed=2, range=0.050000*1.000000, onCPU=false).
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Node 'B1' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
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Node 'B1' (LearnableParameter operation): Initializing Parameter[50 x 1] <- 0.000000.
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Node 'W2' (LearnableParameter operation): Initializing Parameter[2 x 50] <- 0.000000.
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Node 'W2' (LearnableParameter operation): Initializing Parameter[2 x 50] <- uniform(seed=3, range=0.050000*1.000000, onCPU=false).
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Node 'B2' (LearnableParameter operation): Initializing Parameter[2 x 1] <- 0.000000.
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Node 'B2' (LearnableParameter operation): Initializing Parameter[2 x 1] <- 0.000000.
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Post-processing network...
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7 roots:
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CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
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EvalClassificationError = ClassificationError()
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InvStdOfFeatures = InvStdDev()
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MeanOfFeatures = Mean()
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PosteriorProb = Softmax()
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Prior = Mean()
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ScaledLogLikelihood = Minus()
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Validating network. 25 nodes to process in pass 1.
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Validating --> labels = InputValue() : -> [2 x *]
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Validating --> W2 = LearnableParameter() : -> [2 x 50]
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Validating --> W1 = LearnableParameter() : -> [50 x 50]
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Validating --> W0 = LearnableParameter() : -> [50 x 2]
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Validating --> features = InputValue() : -> [2 x *]
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Validating --> MeanOfFeatures = Mean (features) : [2 x *] -> [2]
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Validating --> InvStdOfFeatures = InvStdDev (features) : [2 x *] -> [2]
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Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization (features, MeanOfFeatures, InvStdOfFeatures) : [2 x *], [2], [2] -> [2 x *]
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Validating --> W0*features = Times (W0, MVNormalizedFeatures) : [50 x 2], [2 x *] -> [50 x *]
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Validating --> B0 = LearnableParameter() : -> [50 x 1]
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Validating --> W0*features+B0 = Plus (W0*features, B0) : [50 x *], [50 x 1] -> [50 x 1 x *]
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Validating --> H1 = Sigmoid (W0*features+B0) : [50 x 1 x *] -> [50 x 1 x *]
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Validating --> W1*H1 = Times (W1, H1) : [50 x 50], [50 x 1 x *] -> [50 x 1 x *]
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Validating --> B1 = LearnableParameter() : -> [50 x 1]
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Validating --> W1*H1+B1 = Plus (W1*H1, B1) : [50 x 1 x *], [50 x 1] -> [50 x 1 x *]
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Validating --> H2 = Sigmoid (W1*H1+B1) : [50 x 1 x *] -> [50 x 1 x *]
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Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *] -> [2 x 1 x *]
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Validating --> B2 = LearnableParameter() : -> [2 x 1]
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Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *], [2 x 1] -> [2 x 1 x *]
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Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
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Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *], [2 x 1 x *] -> [1]
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Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *] -> [2 x 1 x *]
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Validating --> Prior = Mean (labels) : [2 x *] -> [2]
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Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
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Validating --> ScaledLogLikelihood = Minus (HLast, LogOfPrior) : [2 x 1 x *], [2] -> [2 x 1 x *]
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Validating network. 17 nodes to process in pass 2.
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Validating network, final pass.
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12 out of 25 nodes do not share the minibatch layout with the input data.
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Post-processing network complete.
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08/16/2016 10:51:40: Created model with 25 nodes on GPU 0.
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08/16/2016 10:51:40: Training criterion node(s):
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08/16/2016 10:51:40: CrossEntropyWithSoftmax = CrossEntropyWithSoftmax
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08/16/2016 10:51:40: Evaluation criterion node(s):
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08/16/2016 10:51:40: EvalClassificationError = ClassificationError
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Allocating matrices for forward and/or backward propagation.
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Memory Sharing: Out of 40 matrices, 19 are shared as 8, and 21 are not shared.
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{ W0 : [50 x 2] (gradient)
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W0*features+B0 : [50 x 1 x *] }
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{ H1 : [50 x 1 x *]
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W0*features : [50 x *] (gradient) }
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{ W0*features+B0 : [50 x 1 x *] (gradient)
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W1*H1 : [50 x 1 x *] }
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{ W1 : [50 x 50] (gradient)
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W1*H1+B1 : [50 x 1 x *] }
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{ H2 : [50 x 1 x *]
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W1*H1 : [50 x 1 x *] (gradient) }
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{ B0 : [50 x 1] (gradient)
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H1 : [50 x 1 x *] (gradient)
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W1*H1+B1 : [50 x 1 x *] (gradient)
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W2*H1 : [2 x 1 x *] }
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{ HLast : [2 x 1 x *]
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W2 : [2 x 50] (gradient) }
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{ B1 : [50 x 1] (gradient)
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H2 : [50 x 1 x *] (gradient)
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HLast : [2 x 1 x *] (gradient) }
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08/16/2016 10:51:40: Training 2802 parameters in 6 out of 6 parameter tensors and 15 nodes with gradient:
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08/16/2016 10:51:40: Node 'B0' (LearnableParameter operation) : [50 x 1]
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08/16/2016 10:51:40: Node 'B1' (LearnableParameter operation) : [50 x 1]
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08/16/2016 10:51:40: Node 'B2' (LearnableParameter operation) : [2 x 1]
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08/16/2016 10:51:40: Node 'W0' (LearnableParameter operation) : [50 x 2]
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08/16/2016 10:51:40: Node 'W1' (LearnableParameter operation) : [50 x 50]
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08/16/2016 10:51:40: Node 'W2' (LearnableParameter operation) : [2 x 50]
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08/16/2016 10:51:40: Precomputing --> 3 PreCompute nodes found.
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08/16/2016 10:51:40: MeanOfFeatures = Mean()
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08/16/2016 10:51:40: InvStdOfFeatures = InvStdDev()
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08/16/2016 10:51:40: Prior = Mean()
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BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
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08/16/2016 10:51:40: Precomputing --> Completed.
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08/16/2016 10:51:40: Starting Epoch 1: learning rate per sample = 0.020000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
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BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
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08/16/2016 10:51:40: Starting minibatch loop.
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 1- 10]: CrossEntropyWithSoftmax = 0.70124231 * 250; EvalClassificationError = 0.52800000 * 250; time = 0.0078s; samplesPerSecond = 32034.9
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 11- 20]: CrossEntropyWithSoftmax = 0.76372424 * 250; EvalClassificationError = 0.46400000 * 250; time = 0.0064s; samplesPerSecond = 38892.3
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 21- 30]: CrossEntropyWithSoftmax = 0.72703027 * 250; EvalClassificationError = 0.54400000 * 250; time = 0.0064s; samplesPerSecond = 39166.5
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 31- 40]: CrossEntropyWithSoftmax = 0.73895923 * 250; EvalClassificationError = 0.56800000 * 250; time = 0.0064s; samplesPerSecond = 38886.3
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 41- 50]: CrossEntropyWithSoftmax = 0.70621924 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0065s; samplesPerSecond = 38759.7
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 51- 60]: CrossEntropyWithSoftmax = 0.74767041 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0065s; samplesPerSecond = 38753.7
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 61- 70]: CrossEntropyWithSoftmax = 0.75094434 * 250; EvalClassificationError = 0.45200000 * 250; time = 0.0064s; samplesPerSecond = 38989.4
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 71- 80]: CrossEntropyWithSoftmax = 0.78058936 * 250; EvalClassificationError = 0.48400000 * 250; time = 0.0064s; samplesPerSecond = 38922.6
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 81- 90]: CrossEntropyWithSoftmax = 0.70407129 * 250; EvalClassificationError = 0.50800000 * 250; time = 0.0064s; samplesPerSecond = 39265.0
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 91- 100]: CrossEntropyWithSoftmax = 0.69555762 * 250; EvalClassificationError = 0.49600000 * 250; time = 0.0064s; samplesPerSecond = 38922.6
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 101- 110]: CrossEntropyWithSoftmax = 0.70626123 * 250; EvalClassificationError = 0.52400000 * 250; time = 0.0064s; samplesPerSecond = 38844.0
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 111- 120]: CrossEntropyWithSoftmax = 0.74540430 * 250; EvalClassificationError = 0.50800000 * 250; time = 0.0064s; samplesPerSecond = 39178.8
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 121- 130]: CrossEntropyWithSoftmax = 0.70824414 * 250; EvalClassificationError = 0.47600000 * 250; time = 0.0064s; samplesPerSecond = 39209.5
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 131- 140]: CrossEntropyWithSoftmax = 0.69895020 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0064s; samplesPerSecond = 38886.3
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 141- 150]: CrossEntropyWithSoftmax = 0.70353223 * 250; EvalClassificationError = 0.52400000 * 250; time = 0.0065s; samplesPerSecond = 38669.8
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 151- 160]: CrossEntropyWithSoftmax = 0.69346387 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0064s; samplesPerSecond = 38989.4
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 161- 170]: CrossEntropyWithSoftmax = 0.74449902 * 250; EvalClassificationError = 0.52800000 * 250; time = 0.0064s; samplesPerSecond = 38886.3
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 171- 180]: CrossEntropyWithSoftmax = 0.73767969 * 250; EvalClassificationError = 0.52000000 * 250; time = 0.0064s; samplesPerSecond = 39025.9
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 181- 190]: CrossEntropyWithSoftmax = 0.71876855 * 250; EvalClassificationError = 0.48400000 * 250; time = 0.0063s; samplesPerSecond = 39594.6
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 191- 200]: CrossEntropyWithSoftmax = 0.71509473 * 250; EvalClassificationError = 0.50400000 * 250; time = 0.0064s; samplesPerSecond = 39271.1
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 201- 210]: CrossEntropyWithSoftmax = 0.69956152 * 250; EvalClassificationError = 0.52000000 * 250; time = 0.0064s; samplesPerSecond = 38886.3
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 211- 220]: CrossEntropyWithSoftmax = 0.69785937 * 250; EvalClassificationError = 0.49600000 * 250; time = 0.0062s; samplesPerSecond = 40303.1
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 221- 230]: CrossEntropyWithSoftmax = 0.70736035 * 250; EvalClassificationError = 0.54400000 * 250; time = 0.0063s; samplesPerSecond = 39563.2
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 231- 240]: CrossEntropyWithSoftmax = 0.69820508 * 250; EvalClassificationError = 0.56800000 * 250; time = 0.0062s; samplesPerSecond = 40512.1
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 241- 250]: CrossEntropyWithSoftmax = 0.69537109 * 250; EvalClassificationError = 0.49600000 * 250; time = 0.0063s; samplesPerSecond = 39432.2
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 251- 260]: CrossEntropyWithSoftmax = 0.69347266 * 250; EvalClassificationError = 0.51200000 * 250; time = 0.0062s; samplesPerSecond = 40492.4
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 261- 270]: CrossEntropyWithSoftmax = 0.70801172 * 250; EvalClassificationError = 0.46000000 * 250; time = 0.0061s; samplesPerSecond = 40909.8
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 271- 280]: CrossEntropyWithSoftmax = 0.69131641 * 250; EvalClassificationError = 0.46800000 * 250; time = 0.0062s; samplesPerSecond = 40257.6
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 281- 290]: CrossEntropyWithSoftmax = 0.70370312 * 250; EvalClassificationError = 0.52800000 * 250; time = 0.0062s; samplesPerSecond = 40270.6
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 291- 300]: CrossEntropyWithSoftmax = 0.71200195 * 250; EvalClassificationError = 0.43600000 * 250; time = 0.0061s; samplesPerSecond = 40909.8
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 301- 310]: CrossEntropyWithSoftmax = 0.69506836 * 250; EvalClassificationError = 0.45200000 * 250; time = 0.0062s; samplesPerSecond = 40577.8
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 311- 320]: CrossEntropyWithSoftmax = 0.69935352 * 250; EvalClassificationError = 0.51600000 * 250; time = 0.0061s; samplesPerSecond = 40889.8
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 321- 330]: CrossEntropyWithSoftmax = 0.69887109 * 250; EvalClassificationError = 0.50000000 * 250; time = 0.0062s; samplesPerSecond = 40440.0
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 331- 340]: CrossEntropyWithSoftmax = 0.69604492 * 250; EvalClassificationError = 0.49200000 * 250; time = 0.0062s; samplesPerSecond = 40512.1
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 341- 350]: CrossEntropyWithSoftmax = 0.69011719 * 250; EvalClassificationError = 0.48800000 * 250; time = 0.0062s; samplesPerSecond = 40617.4
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 351- 360]: CrossEntropyWithSoftmax = 0.68419531 * 250; EvalClassificationError = 0.46000000 * 250; time = 0.0061s; samplesPerSecond = 40783.0
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 361- 370]: CrossEntropyWithSoftmax = 0.67551367 * 250; EvalClassificationError = 0.32400000 * 250; time = 0.0063s; samplesPerSecond = 39904.2
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 371- 380]: CrossEntropyWithSoftmax = 0.67028516 * 250; EvalClassificationError = 0.40000000 * 250; time = 0.0062s; samplesPerSecond = 40044.9
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 381- 390]: CrossEntropyWithSoftmax = 0.65152734 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0062s; samplesPerSecond = 40630.6
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08/16/2016 10:51:40: Epoch[ 1 of 3]-Minibatch[ 391- 400]: CrossEntropyWithSoftmax = 0.63594727 * 250; EvalClassificationError = 0.22000000 * 250; time = 0.0062s; samplesPerSecond = 40283.6
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08/16/2016 10:51:40: Finished Epoch[ 1 of 3]: [Training] CrossEntropyWithSoftmax = 0.70729233 * 10000; EvalClassificationError = 0.47740000 * 10000; totalSamplesSeen = 10000; learningRatePerSample = 0.02; epochTime=0.256818s
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08/16/2016 10:51:40: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu/Models/simple.dnn.1'
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08/16/2016 10:51:40: Starting Epoch 2: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
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BlockRandomizer::StartEpoch: epoch 1: frames [10000..20000] (first sequence at sample 10000), data subset 0 of 1
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08/16/2016 10:51:40: Starting minibatch loop.
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08/16/2016 10:51:40: Epoch[ 2 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.61492108 * 250; EvalClassificationError = 0.26800000 * 250; time = 0.0064s; samplesPerSecond = 38801.8
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08/16/2016 10:51:40: Epoch[ 2 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.59171271 * 250; EvalClassificationError = 0.28400000 * 250; time = 0.0063s; samplesPerSecond = 39923.3
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08/16/2016 10:51:40: Epoch[ 2 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.53591638 * 250; EvalClassificationError = 0.20000000 * 250; time = 0.0062s; samplesPerSecond = 40122.0
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08/16/2016 10:51:40: Epoch[ 2 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.51872742 * 250; EvalClassificationError = 0.14000000 * 250; time = 0.0062s; samplesPerSecond = 40479.3
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.48384375 * 250; EvalClassificationError = 0.12400000 * 250; time = 0.0062s; samplesPerSecond = 40109.1
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.43163501 * 250; EvalClassificationError = 0.11200000 * 250; time = 0.0062s; samplesPerSecond = 40128.4
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.38970386 * 250; EvalClassificationError = 0.12400000 * 250; time = 0.0063s; samplesPerSecond = 39733.0
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.33681616 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0062s; samplesPerSecond = 40044.9
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.31352393 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0062s; samplesPerSecond = 40525.2
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.26829492 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0062s; samplesPerSecond = 40270.6
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.24240820 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0063s; samplesPerSecond = 39531.9
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.21015820 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0062s; samplesPerSecond = 40012.8
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.22358789 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0061s; samplesPerSecond = 40856.3
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.20496631 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0061s; samplesPerSecond = 40756.4
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.20070508 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0062s; samplesPerSecond = 40643.8
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.19224707 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0061s; samplesPerSecond = 40896.5
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.19326562 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0061s; samplesPerSecond = 40789.7
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.21712451 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0061s; samplesPerSecond = 40883.1
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.17562354 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0061s; samplesPerSecond = 40869.7
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.18186035 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0062s; samplesPerSecond = 40577.8
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.14065234 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0062s; samplesPerSecond = 40212.3
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.17710254 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0065s; samplesPerSecond = 38711.7
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.13001953 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0064s; samplesPerSecond = 38819.9
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.21622949 * 250; EvalClassificationError = 0.10000000 * 250; time = 0.0063s; samplesPerSecond = 39613.4
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.21902246 * 250; EvalClassificationError = 0.11200000 * 250; time = 0.0063s; samplesPerSecond = 39904.2
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.18068799 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0064s; samplesPerSecond = 39332.9
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.16232471 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0064s; samplesPerSecond = 39160.4
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.13792139 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0063s; samplesPerSecond = 39607.1
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.16526709 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0064s; samplesPerSecond = 39080.8
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.14743457 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0063s; samplesPerSecond = 39619.7
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.15089160 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0064s; samplesPerSecond = 39339.1
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.12636230 * 250; EvalClassificationError = 0.04400000 * 250; time = 0.0063s; samplesPerSecond = 39834.3
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.16735547 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0063s; samplesPerSecond = 39382.5
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.14530957 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0064s; samplesPerSecond = 39044.2
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.13859570 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0063s; samplesPerSecond = 39638.5
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.14215234 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0064s; samplesPerSecond = 39351.5
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.15903027 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0064s; samplesPerSecond = 39203.4
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.16232520 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0064s; samplesPerSecond = 39191.1
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.13596484 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0064s; samplesPerSecond = 39099.2
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08/16/2016 10:51:41: Epoch[ 2 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.15469434 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0064s; samplesPerSecond = 38965.1
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08/16/2016 10:51:41: Finished Epoch[ 2 of 3]: [Training] CrossEntropyWithSoftmax = 0.24215964 * 10000; EvalClassificationError = 0.09440000 * 10000; totalSamplesSeen = 20000; learningRatePerSample = 0.0080000004; epochTime=0.253663s
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08/16/2016 10:51:41: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu/Models/simple.dnn.2'
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08/16/2016 10:51:41: Starting Epoch 3: learning rate per sample = 0.008000 effective momentum = 0.900000 momentum as time constant = 237.3 samples
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BlockRandomizer::StartEpoch: epoch 2: frames [20000..30000] (first sequence at sample 20000), data subset 0 of 1
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08/16/2016 10:51:41: Starting minibatch loop.
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 1- 10, 2.50%]: CrossEntropyWithSoftmax = 0.18305315 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0064s; samplesPerSecond = 38880.2
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 11- 20, 5.00%]: CrossEntropyWithSoftmax = 0.12945729 * 250; EvalClassificationError = 0.04800000 * 250; time = 0.0063s; samplesPerSecond = 39980.8
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 21- 30, 7.50%]: CrossEntropyWithSoftmax = 0.17735931 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0065s; samplesPerSecond = 38729.7
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 31- 40, 10.00%]: CrossEntropyWithSoftmax = 0.14128339 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0064s; samplesPerSecond = 39013.7
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 41- 50, 12.50%]: CrossEntropyWithSoftmax = 0.16558209 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0064s; samplesPerSecond = 39080.8
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 51- 60, 15.00%]: CrossEntropyWithSoftmax = 0.19102692 * 250; EvalClassificationError = 0.10800000 * 250; time = 0.0065s; samplesPerSecond = 38627.9
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 61- 70, 17.50%]: CrossEntropyWithSoftmax = 0.12279083 * 250; EvalClassificationError = 0.04400000 * 250; time = 0.0064s; samplesPerSecond = 39001.6
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 71- 80, 20.00%]: CrossEntropyWithSoftmax = 0.16642798 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0065s; samplesPerSecond = 38314.2
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 81- 90, 22.50%]: CrossEntropyWithSoftmax = 0.12386572 * 250; EvalClassificationError = 0.04400000 * 250; time = 0.0064s; samplesPerSecond = 38844.0
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 91- 100, 25.00%]: CrossEntropyWithSoftmax = 0.19928418 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0065s; samplesPerSecond = 38681.7
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 101- 110, 27.50%]: CrossEntropyWithSoftmax = 0.14213635 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0064s; samplesPerSecond = 38898.4
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 111- 120, 30.00%]: CrossEntropyWithSoftmax = 0.12377087 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0062s; samplesPerSecond = 40032.0
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 121- 130, 32.50%]: CrossEntropyWithSoftmax = 0.16361621 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0063s; samplesPerSecond = 39789.9
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 131- 140, 35.00%]: CrossEntropyWithSoftmax = 0.19886914 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0063s; samplesPerSecond = 39821.6
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 141- 150, 37.50%]: CrossEntropyWithSoftmax = 0.17207544 * 250; EvalClassificationError = 0.09200000 * 250; time = 0.0063s; samplesPerSecond = 39968.0
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 151- 160, 40.00%]: CrossEntropyWithSoftmax = 0.13323437 * 250; EvalClassificationError = 0.04400000 * 250; time = 0.0063s; samplesPerSecond = 39663.7
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 161- 170, 42.50%]: CrossEntropyWithSoftmax = 0.14397510 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0063s; samplesPerSecond = 39866.1
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 171- 180, 45.00%]: CrossEntropyWithSoftmax = 0.20777515 * 250; EvalClassificationError = 0.10400000 * 250; time = 0.0063s; samplesPerSecond = 39980.8
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 181- 190, 47.50%]: CrossEntropyWithSoftmax = 0.19094092 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0062s; samplesPerSecond = 40057.7
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 191- 200, 50.00%]: CrossEntropyWithSoftmax = 0.14731372 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0062s; samplesPerSecond = 40038.4
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 201- 210, 52.50%]: CrossEntropyWithSoftmax = 0.15483569 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0064s; samplesPerSecond = 39252.6
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 211- 220, 55.00%]: CrossEntropyWithSoftmax = 0.13625415 * 250; EvalClassificationError = 0.04400000 * 250; time = 0.0065s; samplesPerSecond = 38491.1
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 221- 230, 57.50%]: CrossEntropyWithSoftmax = 0.17354004 * 250; EvalClassificationError = 0.08800000 * 250; time = 0.0063s; samplesPerSecond = 39942.5
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 231- 240, 60.00%]: CrossEntropyWithSoftmax = 0.14408350 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0064s; samplesPerSecond = 39013.7
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 241- 250, 62.50%]: CrossEntropyWithSoftmax = 0.13720044 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0062s; samplesPerSecond = 40025.6
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 251- 260, 65.00%]: CrossEntropyWithSoftmax = 0.14236426 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0062s; samplesPerSecond = 40019.2
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 261- 270, 67.50%]: CrossEntropyWithSoftmax = 0.16857861 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0063s; samplesPerSecond = 39847.0
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 271- 280, 70.00%]: CrossEntropyWithSoftmax = 0.18606982 * 250; EvalClassificationError = 0.08400000 * 250; time = 0.0062s; samplesPerSecond = 40381.2
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 281- 290, 72.50%]: CrossEntropyWithSoftmax = 0.16334619 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0062s; samplesPerSecond = 40199.4
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 291- 300, 75.00%]: CrossEntropyWithSoftmax = 0.15598535 * 250; EvalClassificationError = 0.07200000 * 250; time = 0.0063s; samplesPerSecond = 39827.9
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 301- 310, 77.50%]: CrossEntropyWithSoftmax = 0.18848584 * 250; EvalClassificationError = 0.09600000 * 250; time = 0.0062s; samplesPerSecond = 40238.2
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 311- 320, 80.00%]: CrossEntropyWithSoftmax = 0.13281348 * 250; EvalClassificationError = 0.05200000 * 250; time = 0.0063s; samplesPerSecond = 39669.9
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 321- 330, 82.50%]: CrossEntropyWithSoftmax = 0.14679150 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0063s; samplesPerSecond = 39419.7
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 331- 340, 85.00%]: CrossEntropyWithSoftmax = 0.13977344 * 250; EvalClassificationError = 0.06800000 * 250; time = 0.0063s; samplesPerSecond = 39726.7
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 341- 350, 87.50%]: CrossEntropyWithSoftmax = 0.20015137 * 250; EvalClassificationError = 0.11200000 * 250; time = 0.0062s; samplesPerSecond = 40244.7
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 351- 360, 90.00%]: CrossEntropyWithSoftmax = 0.12582129 * 250; EvalClassificationError = 0.06000000 * 250; time = 0.0063s; samplesPerSecond = 39388.7
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 361- 370, 92.50%]: CrossEntropyWithSoftmax = 0.18500098 * 250; EvalClassificationError = 0.07600000 * 250; time = 0.0062s; samplesPerSecond = 40051.3
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 371- 380, 95.00%]: CrossEntropyWithSoftmax = 0.15147754 * 250; EvalClassificationError = 0.08000000 * 250; time = 0.0062s; samplesPerSecond = 40057.7
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 381- 390, 97.50%]: CrossEntropyWithSoftmax = 0.11988379 * 250; EvalClassificationError = 0.05600000 * 250; time = 0.0063s; samplesPerSecond = 39827.9
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08/16/2016 10:51:41: Epoch[ 3 of 3]-Minibatch[ 391- 400, 100.00%]: CrossEntropyWithSoftmax = 0.13059082 * 250; EvalClassificationError = 0.06400000 * 250; time = 0.0064s; samplesPerSecond = 39345.3
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08/16/2016 10:51:41: Finished Epoch[ 3 of 3]: [Training] CrossEntropyWithSoftmax = 0.15767216 * 10000; EvalClassificationError = 0.07330000 * 10000; totalSamplesSeen = 30000; learningRatePerSample = 0.0080000004; epochTime=0.255461s
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08/16/2016 10:51:41: SGD: Saving checkpoint model '/tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu/Models/simple.dnn'
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08/16/2016 10:51:41: CNTKCommandTrainEnd: Simple_Demo_Train
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08/16/2016 10:51:41: Action "train" complete.
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08/16/2016 10:51:41: ##############################################################################
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08/16/2016 10:51:41: # #
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08/16/2016 10:51:41: # Action "test" #
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08/16/2016 10:51:41: # #
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08/16/2016 10:51:41: ##############################################################################
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Post-processing network...
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7 roots:
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CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
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EvalClassificationError = ClassificationError()
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InvStdOfFeatures = InvStdDev()
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MeanOfFeatures = Mean()
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PosteriorProb = Softmax()
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Prior = Mean()
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ScaledLogLikelihood = Minus()
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Validating network. 25 nodes to process in pass 1.
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Validating --> labels = InputValue() : -> [2 x *1]
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Validating --> W2 = LearnableParameter() : -> [2 x 50]
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Validating --> W1 = LearnableParameter() : -> [50 x 50]
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Validating --> W0 = LearnableParameter() : -> [50 x 2]
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Validating --> features = InputValue() : -> [2 x *1]
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Validating --> MeanOfFeatures = Mean (features) : [2 x *1] -> [2]
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Validating --> InvStdOfFeatures = InvStdDev (features) : [2 x *1] -> [2]
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Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization (features, MeanOfFeatures, InvStdOfFeatures) : [2 x *1], [2], [2] -> [2 x *1]
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Validating --> W0*features = Times (W0, MVNormalizedFeatures) : [50 x 2], [2 x *1] -> [50 x *1]
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Validating --> B0 = LearnableParameter() : -> [50 x 1]
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Validating --> W0*features+B0 = Plus (W0*features, B0) : [50 x *1], [50 x 1] -> [50 x 1 x *1]
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Validating --> H1 = Sigmoid (W0*features+B0) : [50 x 1 x *1] -> [50 x 1 x *1]
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Validating --> W1*H1 = Times (W1, H1) : [50 x 50], [50 x 1 x *1] -> [50 x 1 x *1]
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Validating --> B1 = LearnableParameter() : -> [50 x 1]
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Validating --> W1*H1+B1 = Plus (W1*H1, B1) : [50 x 1 x *1], [50 x 1] -> [50 x 1 x *1]
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Validating --> H2 = Sigmoid (W1*H1+B1) : [50 x 1 x *1] -> [50 x 1 x *1]
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Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *1] -> [2 x 1 x *1]
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Validating --> B2 = LearnableParameter() : -> [2 x 1]
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Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *1], [2 x 1] -> [2 x 1 x *1]
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Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
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Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *1], [2 x 1 x *1] -> [1]
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Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *1] -> [2 x 1 x *1]
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Validating --> Prior = Mean (labels) : [2 x *1] -> [2]
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Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
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Validating --> ScaledLogLikelihood = Minus (HLast, LogOfPrior) : [2 x 1 x *1], [2] -> [2 x 1 x *1]
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Validating network. 17 nodes to process in pass 2.
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Validating network, final pass.
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12 out of 25 nodes do not share the minibatch layout with the input data.
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Post-processing network complete.
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evalNodeNames are not specified, using all the default evalnodes and training criterion nodes.
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Allocating matrices for forward and/or backward propagation.
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Memory Sharing: Out of 25 matrices, 2 are shared as 1, and 23 are not shared.
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{ PosteriorProb : [2 x 1 x *1]
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ScaledLogLikelihood : [2 x 1 x *1] }
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BlockRandomizer::StartEpoch: epoch 0: frames [0..603] (first sequence at sample 0), data subset 0 of 1
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08/16/2016 10:51:41: Minibatch[1-1]: EvalClassificationError = 0.04975124 * 603; CrossEntropyWithSoftmax = 0.10845041 * 603
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08/16/2016 10:51:41: Final Results: Minibatch[1-1]: EvalClassificationError = 0.04975124 * 603; CrossEntropyWithSoftmax = 0.10845041 * 603; perplexity = 1.11454964
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08/16/2016 10:51:41: Action "test" complete.
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08/16/2016 10:51:41: ##############################################################################
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08/16/2016 10:51:41: # #
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08/16/2016 10:51:41: # Action "write" #
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08/16/2016 10:51:41: # #
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08/16/2016 10:51:41: ##############################################################################
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Post-processing network...
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8 roots:
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CrossEntropyWithSoftmax = CrossEntropyWithSoftmax()
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EvalClassificationError = ClassificationError()
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InvStdOfFeatures = InvStdDev()
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MeanOfFeatures = Mean()
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PosteriorProb = Softmax()
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Prior = Mean()
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ScaledLogLikelihood = Minus()
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labels = InputValue()
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Validating network. 25 nodes to process in pass 1.
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Validating --> labels = InputValue() : -> [2 x *2]
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Validating --> W2 = LearnableParameter() : -> [2 x 50]
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Validating --> W1 = LearnableParameter() : -> [50 x 50]
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Validating --> W0 = LearnableParameter() : -> [50 x 2]
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Validating --> features = InputValue() : -> [2 x *2]
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Validating --> MeanOfFeatures = Mean (features) : [2 x *2] -> [2]
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Validating --> InvStdOfFeatures = InvStdDev (features) : [2 x *2] -> [2]
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Validating --> MVNormalizedFeatures = PerDimMeanVarNormalization (features, MeanOfFeatures, InvStdOfFeatures) : [2 x *2], [2], [2] -> [2 x *2]
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Validating --> W0*features = Times (W0, MVNormalizedFeatures) : [50 x 2], [2 x *2] -> [50 x *2]
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Validating --> B0 = LearnableParameter() : -> [50 x 1]
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Validating --> W0*features+B0 = Plus (W0*features, B0) : [50 x *2], [50 x 1] -> [50 x 1 x *2]
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Validating --> H1 = Sigmoid (W0*features+B0) : [50 x 1 x *2] -> [50 x 1 x *2]
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Validating --> W1*H1 = Times (W1, H1) : [50 x 50], [50 x 1 x *2] -> [50 x 1 x *2]
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Validating --> B1 = LearnableParameter() : -> [50 x 1]
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Validating --> W1*H1+B1 = Plus (W1*H1, B1) : [50 x 1 x *2], [50 x 1] -> [50 x 1 x *2]
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Validating --> H2 = Sigmoid (W1*H1+B1) : [50 x 1 x *2] -> [50 x 1 x *2]
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Validating --> W2*H1 = Times (W2, H2) : [2 x 50], [50 x 1 x *2] -> [2 x 1 x *2]
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Validating --> B2 = LearnableParameter() : -> [2 x 1]
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Validating --> HLast = Plus (W2*H1, B2) : [2 x 1 x *2], [2 x 1] -> [2 x 1 x *2]
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Validating --> CrossEntropyWithSoftmax = CrossEntropyWithSoftmax (labels, HLast) : [2 x *2], [2 x 1 x *2] -> [1]
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Validating --> EvalClassificationError = ClassificationError (labels, HLast) : [2 x *2], [2 x 1 x *2] -> [1]
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Validating --> PosteriorProb = Softmax (HLast) : [2 x 1 x *2] -> [2 x 1 x *2]
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Validating --> Prior = Mean (labels) : [2 x *2] -> [2]
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Validating --> LogOfPrior = Log (Prior) : [2] -> [2]
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Validating --> ScaledLogLikelihood = Minus (HLast, LogOfPrior) : [2 x 1 x *2], [2] -> [2 x 1 x *2]
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Validating network. 17 nodes to process in pass 2.
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Validating network, final pass.
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12 out of 25 nodes do not share the minibatch layout with the input data.
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Post-processing network complete.
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Allocating matrices for forward and/or backward propagation.
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Memory Sharing: Out of 25 matrices, 3 are shared as 1, and 22 are not shared.
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{ CrossEntropyWithSoftmax : [1]
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EvalClassificationError : [1]
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ScaledLogLikelihood : [2 x 1 x *2] }
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Minibatch[0]: ActualMBSize = 603
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Written to /tmp/cntk-test-20160816095502.258817/Examples/Other/Simple2d_Simple@release_gpu/SimpleOutput*
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Total Samples Evaluated = 603
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08/16/2016 10:51:41: Action "write" complete.
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08/16/2016 10:51:41: __COMPLETED__ |