public void Descend(double[] input, double momentum, double learningrate, bool output, bool usemomentum)
{
for (int i = 0; i < Length; i++)
{
for (int ii = 0; ii < InputLength; ii++)
{
//Weight gradients
WeightGradient[i, ii] = input[ii] * ActivationFunctions.TanhDerriv(Values[i]) * Errors[i];
if (usemomentum)
{
WeightMomentum[i, ii] = (WeightMomentum[i, ii] * momentum) - (learningrate * WeightGradient[i, ii]);
WeightGradient[i, ii] += WeightMomentum[i, ii];
}
}
if (output)
{
continue;
}
//Bias gradients
BiasGradient[i] = ActivationFunctions.TanhDerriv(Values[i]) * Errors[i];
if (usemomentum)
{
BiasMomentum[i] = (BiasMomentum[i] * momentum) - (learningrate * BiasGradient[i]);
BiasGradient[i] += BiasMomentum[i];
}
}
}
public void Backprop(Layer output)
{
Errors = new double[Length];
for (int k = 0; k < output.Length; k++)
{
for (int j = 0; j < Length; j++)
{
Errors[j] += output.Weights[k, j] * ActivationFunctions.TanhDerriv(output.Values[k]) * output.Errors[k];
}
}
}
