public NeuralNet(NeuralNetParameters parameters)
{
wagesBetweenInputAndFirstHiddenLayer = ArrayAllocatorUtils.Allocate <double>(parameters.InputLayerSize, parameters.HiddenLayerSize);
wagesBetweenHiddenLayers = ArrayAllocatorUtils.Allocate <double>(parameters.NumberOfHiddenLayers - 1, parameters.HiddenLayerSize, parameters.HiddenLayerSize);
wagesBetweenLastHiddenAndOutputLayer = ArrayAllocatorUtils.Allocate <double>(parameters.HiddenLayerSize, parameters.OutputLayerSize);
biasesInHiddenLayers = ArrayAllocatorUtils.Allocate <double>(parameters.NumberOfHiddenLayers, parameters.HiddenLayerSize);
biasesInOutputLayer = ArrayAllocatorUtils.Allocate <double>(parameters.OutputLayerSize);
this.activationFunctionType = parameters.ActivationFunctionType;
activationFunction = ActivationFunctionFactory.Get(activationFunctionType);
}
public static IActivationFunction GetActivationFunction(EActivationFunctionType type)
{
if (type == EActivationFunctionType.Sum)
{
return((IActivationFunction) new CSum());
}
else if (type == EActivationFunctionType.Tanh)
{
return((IActivationFunction) new CTanh());
}
else if (type == EActivationFunctionType.Step)
{
return((IActivationFunction) new CStep());
}
throw new ApplicationException(type.ToString() + " is not supported.");
}
public static IActivationFunction Get(EActivationFunctionType type)
{
switch (type)
{
case EActivationFunctionType.Sum:
return(new Sum());
case EActivationFunctionType.Tanh:
return(new Tanh());
case EActivationFunctionType.Step:
return(new Step());
default:
throw new ArgumentException(type.ToString() + " is not supported.");
}
}
public MultiLayerNeuralNet(int inputLayerSize, int outputLayerSize, int hiddenLayerSize, int numberOfHiddenLayers, EActivationFunctionType activationFunctionType = EActivationFunctionType.Tanh)
{
this.wagesBetweenInputAndFirstHiddenLayer = new float[inputLayerSize, hiddenLayerSize];
this.wagesBetweenHiddenLayers = new float[numberOfHiddenLayers - 1][, ];
this.biasesInHiddenLayers = new float[numberOfHiddenLayers][];
for (int i = 0; i < this.wagesBetweenHiddenLayers.Length; i++)
{
this.wagesBetweenHiddenLayers[i] = new float[hiddenLayerSize, hiddenLayerSize];
}
for (int i = 0; i < this.biasesInHiddenLayers.Length; i++)
{
this.biasesInHiddenLayers[i] = new float[hiddenLayerSize];
}
this.wagesBetweenLastHiddenAndOutputLayer = new float[hiddenLayerSize, outputLayerSize];
this.biasesInOutputLayer = new float[outputLayerSize];
this.activationFunction = CActivationFunctionProvider.GetActivationFunction(activationFunctionType);
}
