private static Function CreateMLPClassifier(DeviceDescriptor device, int numOutputClasses, int hiddenLayerDim, Function scaledInput, string classifierName)
{
Function dense = CNTKHelper.Dense(scaledInput, hiddenLayerDim, device, Activation.ReLU, "");
dense = CNTKHelper.Dense(dense, (hiddenLayerDim + numOutputClasses) / 2, device, Activation.ReLU, "");
Function classifierOutput = CNTKHelper.Dense(dense, numOutputClasses, device, Activation.Sigmoid, classifierName);
//classifierOutput = CNTKLib.Softmax(classifierOutput);
return(classifierOutput);
}
/// <summary>
/// Create convolution neural network
/// </summary>
/// <param name="features">input feature variable</param>
/// <param name="outDims">number of output classes</param>
/// <param name="device">CPU or GPU device to run the model</param>
/// <param name="classifierName">name of the classifier</param>
/// <returns>the convolution neural network classifier</returns>
static Function CreateConvolutionalNeuralNetwork(Variable features, int inputLayers, int outDims, DeviceDescriptor device, string classifierName)
{
// 28x28x1 -> 14x14x4
int kernelWidth1 = 3, kernelHeight1 = 3, outFeatureMapCount1 = 4;
int hStride1 = 2, vStride1 = 2;
int poolingWindowWidth1 = 3, poolingWindowHeight1 = 3;
Function pooling1 = ConvolutionWithMaxPooling(features, device, kernelWidth1, kernelHeight1,
inputLayers, outFeatureMapCount1, hStride1, vStride1, poolingWindowWidth1, poolingWindowHeight1);
// 14x14x4 -> 7x7x8
int kernelWidth2 = 3, kernelHeight2 = 3, numInputChannels2 = outFeatureMapCount1, outFeatureMapCount2 = 8;
int hStride2 = 2, vStride2 = 2;
int poolingWindowWidth2 = 3, poolingWindowHeight2 = 3;
Function pooling2 = ConvolutionWithMaxPooling(pooling1, device, kernelWidth2, kernelHeight2,
numInputChannels2, outFeatureMapCount2, hStride2, vStride2, poolingWindowWidth2, poolingWindowHeight2);
Function denseLayer = CNTKHelper.Dense(pooling2, outDims, device, Activation.None, classifierName);
return(denseLayer);
}