/// <summary>
/// Creates a fully connected layer with the specified activation function.
/// </summary>
/// <param name="input">Input variable (layer) of a given bit depth</param>
/// <param name="outputDim">Output capacity (number of neurons)</param>
/// <param name="activationFunction">Activation function</param>
/// <param name="device">The device on which the calculation is made</param>
/// <param name="name">Layer name</param>
/// <returns></returns>
private static Function createFullyConnectedLinearLayer(Variable input, int outputDim, ActivationFunction activationFunction, DeviceDescriptor device, string name)
{
var dataType = input.DataType;
var inputDim = input.Shape[0];
var weight = new Parameter(new int[] { outputDim, inputDim }, dataType, CNTKLib.GlorotUniformInitializer(
CNTKLib.DefaultParamInitScale,
CNTKLib.SentinelValueForInferParamInitRank,
CNTKLib.SentinelValueForInferParamInitRank, 1), device);
var bias = new Parameter(new int[] { outputDim }, dataType, 0, device);
var fullyConnected = CNTKLib.Times(weight, input) + bias;
var activatedFullyConnected = activationFunction?.ApplyActivationFunction(fullyConnected, device) ?? fullyConnected;
return(Function.Alias(activatedFullyConnected, name));
}
/// <summary>
/// Adds a convolution layer for a two-dimensional vector. If the previous layer has a non-two-dimensional output, an exception is thrown
/// </summary>
/// <param name="kernelWidth">Convolution core width (columns in a two-dimensional matrix)</param>
/// <param name="kernelHeight">Convolution core height (rows in a two-dimensional matrix)</param>
/// <param name="outFeatureMapCount">Bit depth of the output cell after convolution</param>
/// <param name="activationFunction">Activation function for the output layer. If not required, pass null</param>
/// <param name="hStride">The step of moving down the convolution window horizontally (along the matrix columns)</param>
/// <param name="vStride">Step of shifting the convolution window vertically (along the rows of the matrix)</param>
/// <param name="padding">Fill when using convolution</param>
/// <param name="name"></param>
public static Function Build(Variable input, int kernelWidth, int kernelHeight, DeviceDescriptor device, int outFeatureMapCount = 1, int hStride = 1, int vStride = 1, Padding padding = Padding.Valid, ActivationFunction activationFunction = null, string name = "Conv2D")
{
bool[] paddingVector = null;
if (padding == Padding.Valid)
{
paddingVector = new bool[] { false, false, false };
}
if (padding == Padding.Same)
{
paddingVector = new bool[] { true, true, false };
}
var convMap = new Parameter(new int[] { kernelWidth, kernelHeight, 1, outFeatureMapCount }, input.DataType, CNTKLib.GlorotUniformInitializer(), device);
var convolution = CNTKLib.Convolution(convMap, input, new int[] { hStride, vStride, 1 }, new bool[] { true }, paddingVector);
var activatedConvolution = activationFunction?.ApplyActivationFunction(convolution, device) ?? convolution;
return(Function.Alias(activatedConvolution, name));
}
private static Function createResidualLayer2(Function input, int outputDimension, ActivationFunction activationFunction, DeviceDescriptor device, string name)
{
var dataType = input.Output.DataType;
//forwarding of the entrance past 1 layer
var forwarding = input;
if (outputDimension != input.Output.Shape[0])
{
var scales = new Parameter(new int[] { outputDimension, input.Output.Shape[0] }, dataType, CNTKLib.UniformInitializer(CNTKLib.DefaultParamInitScale), device);
forwarding = CNTKLib.Times(scales, forwarding);
}
//create 1 layer
input = Dense.Build(input, outputDimension, activationFunction, device, "");
//creation of 2 layers without activation function
input = Dense.Build(input, outputDimension, null, device, "");
//connection with forwarded input
input = CNTKLib.Plus(input, forwarding);
input = activationFunction?.ApplyActivationFunction(input, device) ?? input;
return(Function.Alias(input, name));
}
private static Function createResidualLayer2(Function input, int outputDimension, ActivationFunction activationFunction, DeviceDescriptor device, string name)
{
var dataType = input.Output.DataType;
//проброс входа мимо 1 слоя
var forwarding = input;
if (outputDimension != input.Output.Shape[0])
{
var scales = new Parameter(new int[] { outputDimension, input.Output.Shape[0] }, dataType, CNTKLib.UniformInitializer(CNTKLib.DefaultParamInitScale), device);
forwarding = CNTKLib.Times(scales, forwarding);
}
//создание 1 слоя
input = Dense.Build(input, outputDimension, activationFunction, device, "");
//создание 2 слоя без функции активации
input = Dense.Build(input, outputDimension, null, device, "");
//соединение с проброшенным входом
input = CNTKLib.Plus(input, forwarding);
input = activationFunction?.ApplyActivationFunction(input, device) ?? input;
return(Function.Alias(input, name));
}
