public ClassificationLayer(int input_dimension, int output_dimension,
double Gamma, double L1, double L2, double Drop_out)
{
activation_Function = new SoftMaxIVector();
ud = new UniformDistribution();
w = new double[output_dimension, input_dimension];
for (int j = 0; j < w.GetLength(0); j++)
{
for (int k = 0; k < w.GetLength(1); k++)
{
w[j, k] = (ud.NextDouble() - 0.5) * 2.0;
}
}
b = new double[output_dimension, 1];
for (int j = 0; j < b.GetLength(0); j++)
{
b[j, 0] = (ud.NextDouble() - 0.5) * 2.0;
}
gamma = Math.Max(Gamma, 1.0 / 1000 / 1000);
L_1 = Math.Max(L1, 0);
L_2 = Math.Max(L2, 0);
drop_out = Math.Min(Math.Max(Drop_out, 0), 1);
}
public ClassificationLayer(double[,] W, double[,] B,
double Gamma, double L1, double L2, double Drop_out)
{
activation_Function = new SoftMaxIVector();
ud = new UniformDistribution();
w = W;
b = B;
gamma = Math.Max(Gamma, 1.0 / 1000 / 1000);
L_1 = Math.Max(L1, 0);
L_2 = Math.Max(L2, 0);
drop_out = Math.Min(Math.Max(Drop_out, 0), 1);
}