private static Function FullyConnectedLinearLayer(Variable input, int outputDim, DataType dataType, DeviceDescriptor device,
CNTKDictionary weightIntializer, string name = "")
{
System.Diagnostics.Debug.Assert(input.Shape.Rank == 1);
var inputDim = input.Shape[0];
int[] weightMatrixDimensions = { outputDim, inputDim };
var weights = new Parameter(
weightMatrixDimensions,
dataType,
weightIntializer,
device,
"weights");
var timesFunction = CNTKLib.Times(weights, input, "times");
int[] biasDimension = { outputDim };
var bias = new Parameter(biasDimension, 0.0f, device, "plusParam");
return(CNTKLib.Plus(bias, timesFunction, name));
}
public static Function Dense(Variable operand, int outputDim, DataType dataType, CNTKDictionary weightInitializer, DeviceDescriptor device)
{
return(Dense(operand, outputDim, dataType, weightInitializer, device, ""));
}
public static Function Dense(Variable operand, int outputDim, DataType dataType, CNTKDictionary weightInitializer, DeviceDescriptor device, string name)
{
if (operand.Shape.Rank == 1)
{
return(FullyConnectedLinearLayer(operand, outputDim, dataType, device, weightInitializer, name));
}
//flatten input layer
var newDim = operand.Shape.Dimensions.Aggregate((d1, d2) => d1 * d2);
operand = CNTKLib.Reshape(operand, new[] { newDim });
return(FullyConnectedLinearLayer(operand, outputDim, dataType, device, weightInitializer, name));
}