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Revision e1467a79dc6580ae009d827b5e6f274faff3b339 authored by liqunfu on 27 March 2020, 21:42:04 UTC, committed by GitHub on 27 March 2020, 21:42:04 UTC
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04_OneConvBN.cntk
# Parameters can be overwritten on the command line
# for example: cntk configFile=myConfigFile RootDir=../.. 
# For running from Visual Studio add
# currentDirectory=$(SolutionDir)/<path to corresponding data folder> 

command = trainNetwork:testNetwork

precision = "float"; traceLevel = 1 ; deviceId = "auto"

rootDir = ".." ; dataDir = "$rootDir$/DataSets/MNIST" ;
outputDir = "./Output" ;

modelPath = "$outputDir$/Models/04_OneConvBN"
#stderr = "$outputDir$/04_OneConvBN_bs_out"

# TRAINING CONFIG
trainNetwork = {
    action = "train"
    
    BrainScriptNetworkBuilder = {
        imageShape = 28:28:1                        # image dimensions, 1 channel only
        labelDim = 10                               # number of distinct labels
        featScale = 1/256
        Scale{f} = x => Constant(f) .* x
        
        # define a custom layer with 5x5 convolution, batch norm, relu and 2x2 max pooling
        ConvBnReluPoolLayer {outChannels, filterShape} = Sequential (
            ConvolutionalLayer      {outChannels, filterShape, pad=true, bias=false} :
            BatchNormalizationLayer {spatialRank = 2} :
            ReLU :
            MaxPoolingLayer         {(2:2), stride = (2:2)} 
        )

        DenseBnReluLayer {outDim} = Sequential (
            LinearLayer             {outDim} :   
            BatchNormalizationLayer {spatialRank = 0} : ReLU
        )
		
        model = Sequential (
            Scale {featScale} : 
            ConvBnReluPoolLayer {16, (5:5)} : 
            DenseBnReluLayer {64} : 
            LinearLayer {labelDim}
        )
        
        # inputs
        features = Input {imageShape}
        labels = Input {labelDim}

        # apply model to features
        ol = model (features)

        # loss and error computation
        ce   = CrossEntropyWithSoftmax (labels, ol)
        errs = ClassificationError (labels, ol)

        # declare special nodes
        featureNodes    = (features)
        labelNodes      = (labels)
        criterionNodes  = (ce)
        evaluationNodes = (errs)
        outputNodes     = (ol)
    }

    SGD = {
        epochSize = 60000
        minibatchSize = 64
        maxEpochs = 10
        learningRatesPerSample = 0.01*5:0.001
        momentumAsTimeConstant = 0
        numMBsToShowResult = 500
    }

    reader = {
        readerType = "CNTKTextFormatReader"
        # See ../README.md for details on getting the data (Train-28x28_cntk_text.txt).
        file = "$DataDir$/Train-28x28_cntk_text.txt"
        input = {
            features = { dim = 784 ; format = "dense" }
            labels =   { dim = 10  ; format = "dense" }
        }
    }   
}

# TEST CONFIG
testNetwork = {
    action = "test"
    minibatchSize = 1024    # reduce this if you run out of memory

    reader = {
        readerType = "CNTKTextFormatReader"
        file = "$DataDir$/Test-28x28_cntk_text.txt"
        input = {
            features = { dim = 784 ; format = "dense" }
            labels =   { dim = 10  ; format = "dense" }
        }
    }
}
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