public SingleLayerNeuralNetwork(long inputsNumber, long outputsNumber, FirstDimensionUnitBuilder <IPerceptron> builder)
{
InputsNumber = inputsNumber;
OutputsNumber = outputsNumber;
Layer = new PerceptronLayer(OutputsNumber, builder);
Layers = new object[] { Layer };
}
public OneHiddenLayerNeuralNetwork(long inputsNumber, long hiddensNumber, long outputsnumber, SecondDimensionUnitBuilder <IPerceptron> builder)
{
InputsNumber = inputsNumber;
HiddensNumber = hiddensNumber;
OutputsNumber = outputsnumber;
HiddenLayer = new PerceptronLayer(HiddensNumber, y => builder.Invoke(0, y));
OutputLayer = new PerceptronLayer(OutputsNumber, y => builder.Invoke(1, y));
Layers = new object[2];
Layers[0] = HiddenLayer;
Layers[1] = OutputLayer;
}
public decimal[] CalculateGradients(PerceptronLayer nextLayer, decimal[] nextLayerGradients, decimal[] currentDerivatives)
{
decimal[] gradients = new decimal[Size];
for (long c = 0; c < Size; c++)
{
decimal gradient = 0;
for (long n = 0; n < nextLayer.Size; n++)
{
gradient += nextLayerGradients[n] * nextLayer.Units[n].Weights[c];
}
gradient *= currentDerivatives[c];
gradients[c] = gradient;
}
return(gradients);
}
