private static double Score(SimpleMatrix features, IList <SimpleMatrix> weights)
{
for (int i = 0; i < weights.Count; i += 2)
{
features = weights[i].Mult(features).Plus(weights[i + 1]);
if (weights[i].NumRows() > 1)
{
features = NeuralUtils.ElementwiseApplyReLU(features);
}
}
return(features.ElementSum());
}
/// <summary>Applies softmax to all of the elements of the matrix.</summary>
/// <remarks>
/// Applies softmax to all of the elements of the matrix. The return
/// matrix will have all of its elements sum to 1. If your matrix is
/// not already a vector, be sure this is what you actually want.
/// </remarks>
public static SimpleMatrix Softmax(SimpleMatrix input)
{
SimpleMatrix output = new SimpleMatrix(input);
for (int i = 0; i < output.NumRows(); ++i)
{
for (int j = 0; j < output.NumCols(); ++j)
{
output.Set(i, j, Math.Exp(output.Get(i, j)));
}
}
double sum = output.ElementSum();
// will be safe, since exp should never return 0
return(output.Scale(1.0 / sum));
}