public void UpdateWeights(Matrix targets, double learningRate)
{
if (Next == null)
{
Errors = Matrix.Subtract(targets, Outputs);
Matrix gradient = Matrix.Copy(Outputs);
gradient.Map(x => ActivationFc_derivitiv(x));
gradient.Multiply(Errors);
gradient.Multiply(learningRate);
// Matrix Weight_T = Matrix.Transpose(Inputs);
Matrix delta = Matrix.Multiply(gradient, Matrix.Transpose(Inputs));
Weights.Add(delta);
Bias.Add(gradient);
}
else
{
Errors = Matrix.Multiply(Matrix.Transpose(Next.Weights), Next.Errors);
Matrix gradient = Matrix.Copy(Outputs);
gradient.Map(x => ActivationFc_derivitiv(x));
gradient.Multiply(Errors);
gradient.Multiply(learningRate);
Matrix delta = Matrix.Multiply(gradient, Matrix.Transpose(Inputs));
Weights.Add(delta);
Bias.Add(gradient);
}
if (Prev != null)
{
Prev.UpdateWeights(targets, learningRate);
}
}
