public override double Forward(Matrix Actual, Matrix Expected, MatrixData data, int layerCount)
{
double error = 0.0;
if (Actual.rows != Expected.rows || Actual.cols != Expected.cols)
{
throw new MatrixException("Actual does not have the same size as Expected");
}
double regularizationValue = 0.0;
for (int i = 0; i < layerCount; i++)
{
regularizationValue = RegularizationFunction.CalculateNorm(data.Data["W" + i.ToString()]);
}
for (int i = 0; i < Actual.rows; i++)
{
for (int j = 0; j < Actual.cols; j++)
{
error += Expected[i, j] * Math.Log(Expected[i, j] / Actual[i, j]);
}
}
error += regularizationValue;
BatchCost += error;
return(error);
}
public override Matrix Forward(Matrix inputs)
{
Params["W"].InMap(MutationFunction.Mutate);
// Calculate Regularization Value On W and B
RegularizationValue = (float)RegularizationFunction.CalculateNorm(Params["W"]);
// Calculate Feed Forward Operation
Params["X"] = inputs;
Params["Z"] = Params["W"] * Params["X"] + Params["B"];
Params["A"] = ActivationFunction.Forward(Params["Z"]);
return(Params["A"]);
}
public override Matrix Backward(Matrix Actual, Matrix Expected, MatrixData data, int layerCount)
{
double error = 0.0;
if (Actual.rows != Expected.rows || Actual.cols != Expected.cols)
{
throw new MatrixException("Actual Matrix does not have the same size as The Expected Matrix");
}
double regularizationValue = 0.0;
for (int i = 0; i < layerCount; i++)
{
regularizationValue = RegularizationFunction.CalculateNorm(data.Data["W" + i.ToString()]);
}
for (int i = 0; i < Actual.rows; i++)
{
for (int j = 0; j < Actual.cols; j++)
{
error += Math.Pow((Actual[i, j] - Expected[i, j]), 2);
}
}
error /= Tao;
error = Math.Exp(error);
error *= Tao;
Matrix gradMatrix = Actual.Duplicate();
for (int i = 0; i < Actual.rows; i++)
{
for (int j = 0; j < Actual.cols; j++)
{
gradMatrix[i, j] = (Actual[i, j] - Expected[i, j]) * error;
}
}
return(gradMatrix);
}