public void Backpropagate(decimal[] Cost, decimal BPrefix = 0)
{
decimal[] NonLinearZs = new decimal[Perceptrons.Length];
for (int i = 0; i < Perceptrons.Length; i++)
{
NonLinearZs[i] = Perceptrons[i].GetZ();
}
for (int i = 0; i < Perceptrons.Length; i++)
{
Perceptron P = Perceptrons[i];
BPrefix = 1m;
// Prepare the prefix being: (dC0/da0) * (da0/dZ0)
BPrefix *= 2 * (P.CurrentActivation - Convert.ToDecimal(LastLabels[i].Strength));
BPrefix *= ActivationFunctions.GetAppropriateDerivativeActivationFunction(LayerActivationFunction)
(NonLinearZs, i)[i];
// Update current weights.
for (int j = 0; j < P.Weights.Length; j++)
{
decimal LR = Convert.ToDecimal(parentNeuralNetwork.LearningRate);
P.Weights[j].Value -= LR * BPrefix * PreviousLayer.GetInput()[j];
}
// Tell last layer to propagate using this perceptron's relative prefix.
PreviousLayer.Backpropagate(Cost, BPrefix);
}
}
