CPU info:
    CPU Model Name: Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz
    Hardware threads: 24
    Total Memory: 268381192 kB
-------------------------------------------------------------------
=== Running /cygdrive/c/jenkins/workspace/CNTK-Test-Windows-W1/x64/release/cntk.exe configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/01_Conv.cntk currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10 OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu DeviceId=0 timestamping=true Train=[SGD=[maxEpochs=10]] Train=[SGD=[epochSize=100]] stderr=-
-------------------------------------------------------------------
Build info: 

		Built time: Aug 16 2016 02:54:53
		Last modified date: Fri Aug 12 05:31:21 2016
		Build type: Release
		Build target: GPU
		Math lib: mkl
		CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
		CUB_PATH: c:\src\cub-1.4.1
		CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
		Build Branch: HEAD
		Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
		Built by svcphil on Philly-Pool3
		Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
-------------------------------------------------------------------
Changed current directory to C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData
08/16/2016 03:02:05: Redirecting stderr to file -_Train_Test.log
08/16/2016 03:02:05: -------------------------------------------------------------------
08/16/2016 03:02:05: Build info: 

08/16/2016 03:02:05: 		Built time: Aug 16 2016 02:54:53
08/16/2016 03:02:05: 		Last modified date: Fri Aug 12 05:31:21 2016
08/16/2016 03:02:05: 		Build type: Release
08/16/2016 03:02:05: 		Build target: GPU
08/16/2016 03:02:05: 		Math lib: mkl
08/16/2016 03:02:05: 		CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5
08/16/2016 03:02:05: 		CUB_PATH: c:\src\cub-1.4.1
08/16/2016 03:02:05: 		CUDNN_PATH: c:\NVIDIA\cudnn-4.0\cuda
08/16/2016 03:02:05: 		Build Branch: HEAD
08/16/2016 03:02:05: 		Build SHA1: 026b1e772b963461e189f8f00aa7ed6951298f84
08/16/2016 03:02:05: 		Built by svcphil on Philly-Pool3
08/16/2016 03:02:05: 		Build Path: c:\Jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
08/16/2016 03:02:05: -------------------------------------------------------------------
08/16/2016 03:02:07: -------------------------------------------------------------------
08/16/2016 03:02:07: GPU info:

08/16/2016 03:02:07: 		Device[0]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
08/16/2016 03:02:07: 		Device[1]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
08/16/2016 03:02:07: 		Device[2]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
08/16/2016 03:02:07: 		Device[3]: cores = 2880; computeCapability = 3.5; type = "GeForce GTX 780 Ti"; memory = 3072 MB
08/16/2016 03:02:07: -------------------------------------------------------------------

08/16/2016 03:02:07: Running on DPHAIM-24 at 2016/08/16 03:02:07
08/16/2016 03:02:07: Command line: 
C:\jenkins\workspace\CNTK-Test-Windows-W1\x64\release\cntk.exe  configFile=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/01_Conv.cntk  currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData  RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu  DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData  ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10  OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu  DeviceId=0  timestamping=true  Train=[SGD=[maxEpochs=10]]  Train=[SGD=[epochSize=100]]  stderr=-



08/16/2016 03:02:07: >>>>>>>>>>>>>>>>>>>> RAW CONFIG (VARIABLES NOT RESOLVED) >>>>>>>>>>>>>>>>>>>>
08/16/2016 03:02:07: RootDir = "."
ConfigDir = "$RootDir$"
DataDir = "$RootDir$"
OutputDir = "$RootDir$/Output"
ModelDir = "$OutputDir$/Models"
ndlMacros = "$ConfigDir$/Macros.ndl"
precision = "float"
deviceId = 0
imageLayout = "cudnn"
initOnCPUOnly=true
command = Train:Test
modelPath = "$ModelDir$/01_Convolution"
stderr = "$OutputDir$/01_Conv"
traceLevel = 1
numMBsToShowResult = 500
Train = [
    action = "train"
     NDLNetworkBuilder = [
        networkDescription = "$ConfigDir$/01_Convolution.ndl"
    ]
    SGD = [
        epochSize = 49984
        minibatchSize = 64
        learningRatesPerMB = 0.01*10:0.003*10:0.001
        momentumPerMB = 0.9*20:0.99
        maxEpochs = 30
        L2RegWeight = 0.03
        dropoutRate = 0*5:0.5
    ]
    reader = [
        readerType = "CNTKTextFormatReader"
        file = "$DataDir$/Train_cntk_text.txt"
        input = [
            features = [
                dim = 3072
                format = "dense"
            ]
            labels = [
                dim = 10
                format = "dense"
            ]
        ]
    ]    
]
Test = [
    action = "test"
    minibatchSize = 16
    reader = [
        readerType = "CNTKTextFormatReader"
        file = "$DataDir$/Test_cntk_text.txt"
        input = [
            features = [
                dim = 3072
                format = "dense"
            ]
            labels = [
                dim = 10
                format = "dense"
            ]
        ]
    ]    
]
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu
DeviceId=0
timestamping=true
Train=[SGD=[maxEpochs=10]]
Train=[SGD=[epochSize=100]]
stderr=-

08/16/2016 03:02:07: <<<<<<<<<<<<<<<<<<<< RAW CONFIG (VARIABLES NOT RESOLVED)  <<<<<<<<<<<<<<<<<<<<

08/16/2016 03:02:07: >>>>>>>>>>>>>>>>>>>> RAW CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
08/16/2016 03:02:07: RootDir = "."
ConfigDir = "."
DataDir = "."
OutputDir = "./Output"
ModelDir = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models"
ndlMacros = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/Macros.ndl"
precision = "float"
deviceId = 0
imageLayout = "cudnn"
initOnCPUOnly=true
command = Train:Test
modelPath = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution"
stderr = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/01_Conv"
traceLevel = 1
numMBsToShowResult = 500
Train = [
    action = "train"
     NDLNetworkBuilder = [
        networkDescription = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/01_Convolution.ndl"
    ]
    SGD = [
        epochSize = 49984
        minibatchSize = 64
        learningRatesPerMB = 0.01*10:0.003*10:0.001
        momentumPerMB = 0.9*20:0.99
        maxEpochs = 30
        L2RegWeight = 0.03
        dropoutRate = 0*5:0.5
    ]
    reader = [
        readerType = "CNTKTextFormatReader"
        file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData/Train_cntk_text.txt"
        input = [
            features = [
                dim = 3072
                format = "dense"
            ]
            labels = [
                dim = 10
                format = "dense"
            ]
        ]
    ]    
]
Test = [
    action = "test"
    minibatchSize = 16
    reader = [
        readerType = "CNTKTextFormatReader"
        file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData/Test_cntk_text.txt"
        input = [
            features = [
                dim = 3072
                format = "dense"
            ]
            labels = [
                dim = 10
                format = "dense"
            ]
        ]
    ]    
]
currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData
RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu
DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData
ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10
OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu
DeviceId=0
timestamping=true
Train=[SGD=[maxEpochs=10]]
Train=[SGD=[epochSize=100]]
stderr=-

08/16/2016 03:02:07: <<<<<<<<<<<<<<<<<<<< RAW CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<

08/16/2016 03:02:07: >>>>>>>>>>>>>>>>>>>> PROCESSED CONFIG WITH ALL VARIABLES RESOLVED >>>>>>>>>>>>>>>>>>>>
configparameters: 01_Conv.cntk:command=Train:Test
configparameters: 01_Conv.cntk:ConfigDir=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10
configparameters: 01_Conv.cntk:currentDirectory=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData
configparameters: 01_Conv.cntk:DataDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData
configparameters: 01_Conv.cntk:deviceId=0
configparameters: 01_Conv.cntk:imageLayout=cudnn
configparameters: 01_Conv.cntk:initOnCPUOnly=true
configparameters: 01_Conv.cntk:ModelDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models
configparameters: 01_Conv.cntk:modelPath=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution
configparameters: 01_Conv.cntk:ndlMacros=C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/Macros.ndl
configparameters: 01_Conv.cntk:numMBsToShowResult=500
configparameters: 01_Conv.cntk:OutputDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu
configparameters: 01_Conv.cntk:precision=float
configparameters: 01_Conv.cntk:RootDir=.
configparameters: 01_Conv.cntk:RunDir=C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu
configparameters: 01_Conv.cntk:stderr=-
configparameters: 01_Conv.cntk:Test=[
    action = "test"
    minibatchSize = 16
    reader = [
        readerType = "CNTKTextFormatReader"
        file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData/Test_cntk_text.txt"
        input = [
            features = [
                dim = 3072
                format = "dense"
            ]
            labels = [
                dim = 10
                format = "dense"
            ]
        ]
    ]    
]

configparameters: 01_Conv.cntk:timestamping=true
configparameters: 01_Conv.cntk:traceLevel=1
configparameters: 01_Conv.cntk:Train=[
    action = "train"
     NDLNetworkBuilder = [
        networkDescription = "C:\jenkins\workspace\CNTK-Test-Windows-W1\Examples\Image\Miscellaneous\CIFAR-10/01_Convolution.ndl"
    ]
    SGD = [
        epochSize = 49984
        minibatchSize = 64
        learningRatesPerMB = 0.01*10:0.003*10:0.001
        momentumPerMB = 0.9*20:0.99
        maxEpochs = 30
        L2RegWeight = 0.03
        dropoutRate = 0*5:0.5
    ]
    reader = [
        readerType = "CNTKTextFormatReader"
        file = "C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu\TestData/Train_cntk_text.txt"
        input = [
            features = [
                dim = 3072
                format = "dense"
            ]
            labels = [
                dim = 10
                format = "dense"
            ]
        ]
    ]    
] [SGD=[maxEpochs=10]] [SGD=[epochSize=100]]

08/16/2016 03:02:07: <<<<<<<<<<<<<<<<<<<< PROCESSED CONFIG WITH ALL VARIABLES RESOLVED <<<<<<<<<<<<<<<<<<<<
08/16/2016 03:02:07: Commands: Train Test
08/16/2016 03:02:07: Precision = "float"
08/16/2016 03:02:07: CNTKModelPath: C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution
08/16/2016 03:02:07: CNTKCommandTrainInfo: Train : 10
08/16/2016 03:02:07: CNTKCommandTrainInfo: CNTKNoMoreCommands_Total : 10

08/16/2016 03:02:07: ##############################################################################
08/16/2016 03:02:07: #                                                                            #
08/16/2016 03:02:07: # Action "train"                                                             #
08/16/2016 03:02:07: #                                                                            #
08/16/2016 03:02:07: ##############################################################################

08/16/2016 03:02:07: CNTKCommandTrainBegin: Train
NDLBuilder Using GPU 0

08/16/2016 03:02:08: Creating virgin network.
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 0.000000.
Node 'conv1_act.W' (LearnableParameter operation): Initializing Parameter[32 x 75] <- 0.000000.
Node 'conv1_act.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 32] <- 0.000000.
Node 'conv2_act.W' (LearnableParameter operation): Initializing Parameter[32 x 800] <- 0.000000.
Node 'conv2_act.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 32] <- 0.000000.
Node 'conv3_act.W' (LearnableParameter operation): Initializing Parameter[64 x 800] <- 0.000000.
Node 'conv3_act.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[64 x 3 x 3 x 64] <- 0.000000.
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
Node 'OutputNodes.W' (LearnableParameter operation): Initializing Parameter[10 x 64] <- 0.000000.
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[10] <- 0.000000.
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 128.000000.
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 128.000000.
Node 'featOffs' (LearnableParameter operation): Initializing Parameter[1 x 1] <- 128.000000.
Node 'conv1_act.W' (LearnableParameter operation): Initializing Parameter[32 x 75] <- gaussian(seed=1, range=0.023094*0.004300, onCPU=false).
Microsoft::MSR::CNTK::GPUMatrix<ElemType>::SetGaussianRandomValue (GPU): creating curand object with seed 1, sizeof(ElemType)==4
Node 'conv1_act.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 32] <- 0.000000.
Node 'conv2_act.W' (LearnableParameter operation): Initializing Parameter[32 x 800] <- gaussian(seed=2, range=0.007071*1.414000, onCPU=false).
Node 'conv2_act.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 32] <- 0.000000.
Node 'conv3_act.W' (LearnableParameter operation): Initializing Parameter[64 x 800] <- gaussian(seed=3, range=0.007071*1.414000, onCPU=false).
Node 'conv3_act.b' (LearnableParameter operation): Initializing Parameter[1 x 1 x 64] <- 0.000000.
Node 'h1.W' (LearnableParameter operation): Initializing Parameter[64 x 3 x 3 x 64] <- gaussian(seed=4, range=0.008333*12.000000, onCPU=false).
Node 'h1.b' (LearnableParameter operation): Initializing Parameter[64 x 1] <- 0.000000.
Node 'OutputNodes.W' (LearnableParameter operation): Initializing Parameter[10 x 64] <- gaussian(seed=5, range=0.025000*1.500000, onCPU=false).
Node 'OutputNodes.b' (LearnableParameter operation): Initializing Parameter[10] <- 0.000000.

Post-processing network...

3 roots:
	CE = CrossEntropyWithSoftmax()
	Err = ClassificationError()
	OutputNodes.z = Plus()

Validating network. 34 nodes to process in pass 1.

Validating --> labels = InputValue() :  -> [10 x *]
Validating --> OutputNodes.W = LearnableParameter() :  -> [10 x 64]
Validating --> h1.W = LearnableParameter() :  -> [64 x 3 x 3 x 64]
Validating --> conv3_act.W = LearnableParameter() :  -> [64 x 800]
Validating --> conv2_act.W = LearnableParameter() :  -> [32 x 800]
Validating --> conv1_act.W = LearnableParameter() :  -> [32 x 75]
Validating --> features = InputValue() :  -> [32 x 32 x 3 x *]
Validating --> featOffs = LearnableParameter() :  -> [1 x 1]
Validating --> featScaled = Minus (features, featOffs) : [32 x 32 x 3 x *], [1 x 1] -> [32 x 32 x 3 x *]
Validating --> conv1_act.c = Convolution (conv1_act.W, featScaled) : [32 x 75], [32 x 32 x 3 x *] -> [32 x 32 x 32 x *]
Validating --> conv1_act.b = LearnableParameter() :  -> [1 x 1 x 32]
Validating --> conv1_act.p = Plus (conv1_act.c, conv1_act.b) : [32 x 32 x 32 x *], [1 x 1 x 32] -> [32 x 32 x 32 x *]
Validating --> conv1_act.y = RectifiedLinear (conv1_act.p) : [32 x 32 x 32 x *] -> [32 x 32 x 32 x *]
Validating --> pool1 = MaxPooling (conv1_act.y) : [32 x 32 x 32 x *] -> [15 x 15 x 32 x *]
Validating --> conv2_act.c = Convolution (conv2_act.W, pool1) : [32 x 800], [15 x 15 x 32 x *] -> [15 x 15 x 32 x *]
Validating --> conv2_act.b = LearnableParameter() :  -> [1 x 1 x 32]
Validating --> conv2_act.p = Plus (conv2_act.c, conv2_act.b) : [15 x 15 x 32 x *], [1 x 1 x 32] -> [15 x 15 x 32 x *]
Validating --> conv2_act.y = RectifiedLinear (conv2_act.p) : [15 x 15 x 32 x *] -> [15 x 15 x 32 x *]
Validating --> pool2 = MaxPooling (conv2_act.y) : [15 x 15 x 32 x *] -> [7 x 7 x 32 x *]
Validating --> conv3_act.c = Convolution (conv3_act.W, pool2) : [64 x 800], [7 x 7 x 32 x *] -> [7 x 7 x 64 x *]
Validating --> conv3_act.b = LearnableParameter() :  -> [1 x 1 x 64]
Validating --> conv3_act.p = Plus (conv3_act.c, conv3_act.b) : [7 x 7 x 64 x *], [1 x 1 x 64] -> [7 x 7 x 64 x *]
Validating --> conv3_act.y = RectifiedLinear (conv3_act.p) : [7 x 7 x 64 x *] -> [7 x 7 x 64 x *]
Validating --> pool3 = MaxPooling (conv3_act.y) : [7 x 7 x 64 x *] -> [3 x 3 x 64 x *]
Validating --> h1.t = Times (h1.W, pool3) : [64 x 3 x 3 x 64], [3 x 3 x 64 x *] -> [64 x *]
Validating --> h1.b = LearnableParameter() :  -> [64 x 1]
Validating --> h1.z = Plus (h1.t, h1.b) : [64 x *], [64 x 1] -> [64 x 1 x *]
Validating --> h1.y = RectifiedLinear (h1.z) : [64 x 1 x *] -> [64 x 1 x *]
Validating --> h1_d = Dropout (h1.y) : [64 x 1 x *] -> [64 x 1 x *]
Validating --> OutputNodes.t = Times (OutputNodes.W, h1_d) : [10 x 64], [64 x 1 x *] -> [10 x 1 x *]
Validating --> OutputNodes.b = LearnableParameter() :  -> [10]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [10 x 1 x *], [10] -> [10 x 1 x *]
Validating --> CE = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [10 x *], [10 x 1 x *] -> [1]
Validating --> Err = ClassificationError (labels, OutputNodes.z) : [10 x *], [10 x 1 x *] -> [1]

Validating network. 21 nodes to process in pass 2.


Validating network, final pass.


conv1_act.c: using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 32, Kernel: 5 x 5 x 3, Map: 1 x 1 x 32, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.

pool1: using cuDNN convolution engine for geometry: Input: 32 x 32 x 32, Output: 15 x 15 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.

conv2_act.c: using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 15 x 15 x 32, Kernel: 5 x 5 x 32, Map: 1 x 1 x 32, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.

pool2: using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 7 x 7 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.

conv3_act.c: using cuDNN convolution engine for geometry: Input: 7 x 7 x 32, Output: 7 x 7 x 64, Kernel: 5 x 5 x 32, Map: 1 x 1 x 64, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.

pool3: using cuDNN convolution engine for geometry: Input: 7 x 7 x 64, Output: 3 x 3 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.


13 out of 34 nodes do not share the minibatch layout with the input data.

Post-processing network complete.

08/16/2016 03:02:09: Created model with 34 nodes on GPU 0.

08/16/2016 03:02:09: Training criterion node(s):
08/16/2016 03:02:09: 	CE = CrossEntropyWithSoftmax

08/16/2016 03:02:09: Evaluation criterion node(s):
08/16/2016 03:02:09: 	Err = ClassificationError


Allocating matrices for forward and/or backward propagation.

Memory Sharing: Out of 63 matrices, 38 are shared as 17, and 25 are not shared.

	{ conv2_act.c : [15 x 15 x 32 x *] (gradient)
	  conv2_act.y : [15 x 15 x 32 x *] }
	{ h1.t : [64 x *] (gradient)
	  h1.y : [64 x 1 x *] }
	{ conv2_act.W : [32 x 800] (gradient)
	  conv2_act.p : [15 x 15 x 32 x *] }
	{ conv2_act.b : [1 x 1 x 32] (gradient)
	  conv2_act.y : [15 x 15 x 32 x *] (gradient) }
	{ h1.z : [64 x 1 x *] (gradient)
	  pool3 : [3 x 3 x 64 x *] (gradient) }
	{ conv3_act.c : [7 x 7 x 64 x *] (gradient)
	  conv3_act.y : [7 x 7 x 64 x *] }
	{ conv3_act.W : [64 x 800] (gradient)
	  conv3_act.p : [7 x 7 x 64 x *] }
	{ conv3_act.p : [7 x 7 x 64 x *] (gradient)
	  pool2 : [7 x 7 x 32 x *] (gradient)
	  pool3 : [3 x 3 x 64 x *] }
	{ conv3_act.b : [1 x 1 x 64] (gradient)
	  conv3_act.y : [7 x 7 x 64 x *] (gradient)
	  h1.t : [64 x *] }
	{ conv1_act.c : [32 x 32 x 32 x *] (gradient)
	  conv1_act.y : [32 x 32 x 32 x *] }
	{ h1.W : [64 x 3 x 3 x 64] (gradient)
	  h1.z : [64 x 1 x *] }
	{ OutputNodes.t : [10 x 1 x *]
	  h1.b : [64 x 1] (gradient)
	  h1.y : [64 x 1 x *] (gradient) }
	{ conv2_act.p : [15 x 15 x 32 x *] (gradient)
	  pool1 : [15 x 15 x 32 x *] (gradient)
	  pool2 : [7 x 7 x 32 x *] }
	{ conv1_act.W : [32 x 75] (gradient)
	  conv1_act.p : [32 x 32 x 32 x *] }
	{ conv1_act.b : [1 x 1 x 32] (gradient)
	  conv1_act.y : [32 x 32 x 32 x *] (gradient) }
	{ OutputNodes.W : [10 x 64] (gradient)
	  OutputNodes.z : [10 x 1 x *] (gradient) }
	{ conv1_act.p : [32 x 32 x 32 x *] (gradient)
	  pool1 : [15 x 15 x 32 x *] }


08/16/2016 03:02:09: Training 116906 parameters in 10 out of 10 parameter tensors and 29 nodes with gradient:

08/16/2016 03:02:09: 	Node 'OutputNodes.W' (LearnableParameter operation) : [10 x 64]
08/16/2016 03:02:09: 	Node 'OutputNodes.b' (LearnableParameter operation) : [10]
08/16/2016 03:02:09: 	Node 'conv1_act.W' (LearnableParameter operation) : [32 x 75]
08/16/2016 03:02:09: 	Node 'conv1_act.b' (LearnableParameter operation) : [1 x 1 x 32]
08/16/2016 03:02:09: 	Node 'conv2_act.W' (LearnableParameter operation) : [32 x 800]
08/16/2016 03:02:09: 	Node 'conv2_act.b' (LearnableParameter operation) : [1 x 1 x 32]
08/16/2016 03:02:09: 	Node 'conv3_act.W' (LearnableParameter operation) : [64 x 800]
08/16/2016 03:02:09: 	Node 'conv3_act.b' (LearnableParameter operation) : [1 x 1 x 64]
08/16/2016 03:02:09: 	Node 'h1.W' (LearnableParameter operation) : [64 x 3 x 3 x 64]
08/16/2016 03:02:09: 	Node 'h1.b' (LearnableParameter operation) : [64 x 1]

08/16/2016 03:02:09: No PreCompute nodes found, or all already computed. Skipping pre-computation step.

08/16/2016 03:02:09: Starting Epoch 1: learning rate per sample = 0.000156  effective momentum = 0.900000  momentum as time constant = 607.4 samples
BlockRandomizer::StartEpoch: epoch 0: frames [0..100] (first sequence at sample 0), data subset 0 of 1

08/16/2016 03:02:09: Starting minibatch loop.
08/16/2016 03:02:14: Finished Epoch[ 1 of 10]: [Training] CE = 2.30223602 * 100; Err = 0.90000000 * 100; totalSamplesSeen = 100; learningRatePerSample = 0.00015625; epochTime=4.93739s
08/16/2016 03:02:14: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.1'

08/16/2016 03:02:14: Starting Epoch 2: learning rate per sample = 0.000156  effective momentum = 0.900000  momentum as time constant = 607.4 samples
BlockRandomizer::StartEpoch: epoch 1: frames [100..200] (first sequence at sample 100), data subset 0 of 1

08/16/2016 03:02:14: Starting minibatch loop.
08/16/2016 03:02:14: Finished Epoch[ 2 of 10]: [Training] CE = 2.30189240 * 100; Err = 0.87000000 * 100; totalSamplesSeen = 200; learningRatePerSample = 0.00015625; epochTime=0.016498s
08/16/2016 03:02:14: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.2'

08/16/2016 03:02:15: Starting Epoch 3: learning rate per sample = 0.000156  effective momentum = 0.900000  momentum as time constant = 607.4 samples
BlockRandomizer::StartEpoch: epoch 2: frames [200..300] (first sequence at sample 200), data subset 0 of 1

08/16/2016 03:02:15: Starting minibatch loop.
08/16/2016 03:02:15: Finished Epoch[ 3 of 10]: [Training] CE = 2.29965256 * 100; Err = 0.86000000 * 100; totalSamplesSeen = 300; learningRatePerSample = 0.00015625; epochTime=0.0146s
08/16/2016 03:02:15: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.3'

08/16/2016 03:02:15: Starting Epoch 4: learning rate per sample = 0.000156  effective momentum = 0.900000  momentum as time constant = 607.4 samples
BlockRandomizer::StartEpoch: epoch 3: frames [300..400] (first sequence at sample 300), data subset 0 of 1

08/16/2016 03:02:15: Starting minibatch loop.
08/16/2016 03:02:15: Finished Epoch[ 4 of 10]: [Training] CE = 2.29966064 * 100; Err = 0.91000000 * 100; totalSamplesSeen = 400; learningRatePerSample = 0.00015625; epochTime=0.01451s
08/16/2016 03:02:15: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.4'

08/16/2016 03:02:15: Starting Epoch 5: learning rate per sample = 0.000156  effective momentum = 0.900000  momentum as time constant = 607.4 samples
BlockRandomizer::StartEpoch: epoch 4: frames [400..500] (first sequence at sample 400), data subset 0 of 1

08/16/2016 03:02:15: Starting minibatch loop.
08/16/2016 03:02:15: Finished Epoch[ 5 of 10]: [Training] CE = 2.30450378 * 100; Err = 0.94000000 * 100; totalSamplesSeen = 500; learningRatePerSample = 0.00015625; epochTime=0.014432s
08/16/2016 03:02:15: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.5'
Setting dropout rate to 0.5.

08/16/2016 03:02:15: Starting Epoch 6: learning rate per sample = 0.000156  effective momentum = 0.900000  momentum as time constant = 607.4 samples
BlockRandomizer::StartEpoch: epoch 5: frames [500..600] (first sequence at sample 500), data subset 0 of 1

08/16/2016 03:02:15: Starting minibatch loop.
(GPU): creating curand object with seed 5
08/16/2016 03:02:15: Finished Epoch[ 6 of 10]: [Training] CE = 2.29013901 * 100; Err = 0.81000000 * 100; totalSamplesSeen = 600; learningRatePerSample = 0.00015625; epochTime=0.023069s
08/16/2016 03:02:15: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.6'

08/16/2016 03:02:15: Starting Epoch 7: learning rate per sample = 0.000156  effective momentum = 0.900000  momentum as time constant = 607.4 samples
BlockRandomizer::StartEpoch: epoch 6: frames [600..700] (first sequence at sample 600), data subset 0 of 1

08/16/2016 03:02:15: Starting minibatch loop.
(GPU): creating curand object with seed 6
08/16/2016 03:02:15: Finished Epoch[ 7 of 10]: [Training] CE = 2.29815735 * 100; Err = 0.93000000 * 100; totalSamplesSeen = 700; learningRatePerSample = 0.00015625; epochTime=0.030436s
08/16/2016 03:02:15: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.7'

08/16/2016 03:02:15: Starting Epoch 8: learning rate per sample = 0.000156  effective momentum = 0.900000  momentum as time constant = 607.4 samples
BlockRandomizer::StartEpoch: epoch 7: frames [700..800] (first sequence at sample 700), data subset 0 of 1

08/16/2016 03:02:15: Starting minibatch loop.
(GPU): creating curand object with seed 7
08/16/2016 03:02:15: Finished Epoch[ 8 of 10]: [Training] CE = 2.28805984 * 100; Err = 0.89000000 * 100; totalSamplesSeen = 800; learningRatePerSample = 0.00015625; epochTime=0.022867s
08/16/2016 03:02:15: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.8'

08/16/2016 03:02:15: Starting Epoch 9: learning rate per sample = 0.000156  effective momentum = 0.900000  momentum as time constant = 607.4 samples
BlockRandomizer::StartEpoch: epoch 8: frames [800..900] (first sequence at sample 800), data subset 0 of 1

08/16/2016 03:02:15: Starting minibatch loop.
(GPU): creating curand object with seed 8
08/16/2016 03:02:15: Finished Epoch[ 9 of 10]: [Training] CE = 2.29377136 * 100; Err = 0.88000000 * 100; totalSamplesSeen = 900; learningRatePerSample = 0.00015625; epochTime=0.022876s
08/16/2016 03:02:15: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution.9'

08/16/2016 03:02:15: Starting Epoch 10: learning rate per sample = 0.000156  effective momentum = 0.900000  momentum as time constant = 607.4 samples
BlockRandomizer::StartEpoch: epoch 9: frames [900..1000] (first sequence at sample 900), data subset 0 of 1

08/16/2016 03:02:15: Starting minibatch loop.
(GPU): creating curand object with seed 9
08/16/2016 03:02:15: Finished Epoch[10 of 10]: [Training] CE = 2.27813766 * 100; Err = 0.88000000 * 100; totalSamplesSeen = 1000; learningRatePerSample = 0.00015625; epochTime=0.022892s
08/16/2016 03:02:15: SGD: Saving checkpoint model 'C:\Users\svcphil\AppData\Local\Temp\cntk-test-20160816030038.674053\Examples\Image\Miscellaneous\CIFAR-10_01_Convolution@release_gpu/Models/01_Convolution'
08/16/2016 03:02:15: CNTKCommandTrainEnd: Train

08/16/2016 03:02:15: Action "train" complete.


08/16/2016 03:02:15: ##############################################################################
08/16/2016 03:02:15: #                                                                            #
08/16/2016 03:02:15: # Action "test"                                                              #
08/16/2016 03:02:15: #                                                                            #
08/16/2016 03:02:15: ##############################################################################


Post-processing network...

3 roots:
	CE = CrossEntropyWithSoftmax()
	Err = ClassificationError()
	OutputNodes.z = Plus()

Validating network. 34 nodes to process in pass 1.

Validating --> labels = InputValue() :  -> [10 x *1]
Validating --> OutputNodes.W = LearnableParameter() :  -> [10 x 64]
Validating --> h1.W = LearnableParameter() :  -> [64 x 3 x 3 x 64]
Validating --> conv3_act.W = LearnableParameter() :  -> [64 x 800]
Validating --> conv2_act.W = LearnableParameter() :  -> [32 x 800]
Validating --> conv1_act.W = LearnableParameter() :  -> [32 x 75]
Validating --> features = InputValue() :  -> [32 x 32 x 3 x *1]
Validating --> featOffs = LearnableParameter() :  -> [1 x 1]
Validating --> featScaled = Minus (features, featOffs) : [32 x 32 x 3 x *1], [1 x 1] -> [32 x 32 x 3 x *1]
Validating --> conv1_act.c = Convolution (conv1_act.W, featScaled) : [32 x 75], [32 x 32 x 3 x *1] -> [32 x 32 x 32 x *1]
Validating --> conv1_act.b = LearnableParameter() :  -> [1 x 1 x 32]
Validating --> conv1_act.p = Plus (conv1_act.c, conv1_act.b) : [32 x 32 x 32 x *1], [1 x 1 x 32] -> [32 x 32 x 32 x *1]
Validating --> conv1_act.y = RectifiedLinear (conv1_act.p) : [32 x 32 x 32 x *1] -> [32 x 32 x 32 x *1]
Validating --> pool1 = MaxPooling (conv1_act.y) : [32 x 32 x 32 x *1] -> [15 x 15 x 32 x *1]
Validating --> conv2_act.c = Convolution (conv2_act.W, pool1) : [32 x 800], [15 x 15 x 32 x *1] -> [15 x 15 x 32 x *1]
Validating --> conv2_act.b = LearnableParameter() :  -> [1 x 1 x 32]
Validating --> conv2_act.p = Plus (conv2_act.c, conv2_act.b) : [15 x 15 x 32 x *1], [1 x 1 x 32] -> [15 x 15 x 32 x *1]
Validating --> conv2_act.y = RectifiedLinear (conv2_act.p) : [15 x 15 x 32 x *1] -> [15 x 15 x 32 x *1]
Validating --> pool2 = MaxPooling (conv2_act.y) : [15 x 15 x 32 x *1] -> [7 x 7 x 32 x *1]
Validating --> conv3_act.c = Convolution (conv3_act.W, pool2) : [64 x 800], [7 x 7 x 32 x *1] -> [7 x 7 x 64 x *1]
Validating --> conv3_act.b = LearnableParameter() :  -> [1 x 1 x 64]
Validating --> conv3_act.p = Plus (conv3_act.c, conv3_act.b) : [7 x 7 x 64 x *1], [1 x 1 x 64] -> [7 x 7 x 64 x *1]
Validating --> conv3_act.y = RectifiedLinear (conv3_act.p) : [7 x 7 x 64 x *1] -> [7 x 7 x 64 x *1]
Validating --> pool3 = MaxPooling (conv3_act.y) : [7 x 7 x 64 x *1] -> [3 x 3 x 64 x *1]
Validating --> h1.t = Times (h1.W, pool3) : [64 x 3 x 3 x 64], [3 x 3 x 64 x *1] -> [64 x *1]
Validating --> h1.b = LearnableParameter() :  -> [64 x 1]
Validating --> h1.z = Plus (h1.t, h1.b) : [64 x *1], [64 x 1] -> [64 x 1 x *1]
Validating --> h1.y = RectifiedLinear (h1.z) : [64 x 1 x *1] -> [64 x 1 x *1]
Validating --> h1_d = Dropout (h1.y) : [64 x 1 x *1] -> [64 x 1 x *1]
Validating --> OutputNodes.t = Times (OutputNodes.W, h1_d) : [10 x 64], [64 x 1 x *1] -> [10 x 1 x *1]
Validating --> OutputNodes.b = LearnableParameter() :  -> [10]
Validating --> OutputNodes.z = Plus (OutputNodes.t, OutputNodes.b) : [10 x 1 x *1], [10] -> [10 x 1 x *1]
Validating --> CE = CrossEntropyWithSoftmax (labels, OutputNodes.z) : [10 x *1], [10 x 1 x *1] -> [1]
Validating --> Err = ClassificationError (labels, OutputNodes.z) : [10 x *1], [10 x 1 x *1] -> [1]

Validating network. 21 nodes to process in pass 2.


Validating network, final pass.


conv1_act.c: using cuDNN convolution engine for geometry: Input: 32 x 32 x 3, Output: 32 x 32 x 32, Kernel: 5 x 5 x 3, Map: 1 x 1 x 32, Stride: 1 x 1 x 3, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.

pool1: using cuDNN convolution engine for geometry: Input: 32 x 32 x 32, Output: 15 x 15 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.

conv2_act.c: using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 15 x 15 x 32, Kernel: 5 x 5 x 32, Map: 1 x 1 x 32, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.

pool2: using cuDNN convolution engine for geometry: Input: 15 x 15 x 32, Output: 7 x 7 x 32, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.

conv3_act.c: using cuDNN convolution engine for geometry: Input: 7 x 7 x 32, Output: 7 x 7 x 64, Kernel: 5 x 5 x 32, Map: 1 x 1 x 64, Stride: 1 x 1 x 32, Sharing: (1), AutoPad: (1), LowerPad: 0, UpperPad: 0.

pool3: using cuDNN convolution engine for geometry: Input: 7 x 7 x 64, Output: 3 x 3 x 64, Kernel: 3 x 3 x 1, Map: 1, Stride: 2 x 2 x 1, Sharing: (1), AutoPad: (0), LowerPad: 0, UpperPad: 0.


13 out of 34 nodes do not share the minibatch layout with the input data.

Post-processing network complete.

evalNodeNames are not specified, using all the default evalnodes and training criterion nodes.


Allocating matrices for forward and/or backward propagation.

Memory Sharing: Out of 34 matrices, 0 are shared as 0, and 34 are not shared.


BlockRandomizer::StartEpoch: epoch 0: frames [0..10000] (first sequence at sample 0), data subset 0 of 1
08/16/2016 03:02:17: Minibatch[1-500]: Err = 0.86112500 * 8000; CE = 2.28394067 * 8000
08/16/2016 03:02:18: Minibatch[501-625]: Err = 0.86300000 * 2000; CE = 2.28036680 * 2000
08/16/2016 03:02:18: Final Results: Minibatch[1-625]: Err = 0.86150000 * 10000; CE = 2.28322590 * 10000; perplexity = 9.80826991

08/16/2016 03:02:18: Action "test" complete.

08/16/2016 03:02:18: __COMPLETED__
