// creating API to handle matrix multiplication, maybe switch over to GPU subroutines
public static void NeuralNetowrkWithAnnMath()
{
//Initialize example matrices to teach neural net.
Matrix xArray = new Matrix(
new double[4, 3] {
// columns then rows.... ugh stupid c#
{ 0, 0, 1 },
{ 0, 1, 1 },
{ 1, 0, 1 },
{ 1, 1, 1 }
});
VectorN yArray = new VectorN(new double[4] {
0, 0, 1, 1
});
// fill wieghted array with random weights,
// and teach it to conform
// with the approximate statistics
Random r = new Random(1);
VectorN syn0 = new VectorN(new double[3] {
2 * r.NextDouble() - 1,
2 * r.NextDouble() - 1,
2 * r.NextDouble() - 1
});
VectorN l1 = new VectorN(4);
// Begin learning iteration loop.
for (int i = 0; i < 100000; i++)
{
Matrix l0 = xArray;
l1 = VectorN.Product(l0, syn0);
l1 = VectorN.ApplyCustomOperation(AMath.Sigmoid, l1);
VectorN l1_error = VectorN.Subtract(yArray, l1);
VectorN l1_delta = new VectorN(l1.Size);
l1_delta = VectorN.Product(l1_error,
VectorN.ApplyCustomOperation(AMath.SigmoidLinear, l1));
VectorN weights = VectorN.Product(l0.Transpose(), l1_delta);
syn0 = VectorN.Add(syn0, weights);
}
Console.WriteLine("layer 1: \n" + l1);
}
/// <summary>
/// Runs the Three layer test neural network.
/// </summary>
public static void RunNet()
{
//Initialize example matrices to teach neural net.
Matrix xArray = new Matrix(
new double[4, 3] {
// columns then rows....
{ 0, 0, 1 },
{ 0, 1, 1 },
{ 1, 0, 1 },
{ 1, 1, 1 }
});
VectorN yArray = new VectorN(new double[4] {
0, 1, 1, 0
});
// fill wieghted array with random weights,
// and teach it to conform
// with the approximate statistics
Random r = new Random(1);
Matrix l0 = new Matrix(3, 4);
Matrix l1 = new Matrix(3, 4);
VectorN l2 = new VectorN(4);
Matrix syn0 = new Matrix(3, 4); // random array of a 3X4 matrix
VectorN syn1 = new VectorN(4); // random array of a 4X1 vector
// Seeding the neurons with random weights.
for (int j = 0; j < syn0.Columns; j++)
{
for (int i = 0; i < syn0.Rows; i++)
{
syn0[i, j] = 2 * r.NextDouble() - 1;
}
syn1[j] = 2 * r.NextDouble() - 1;
}
// begin firing neurons.
for (int i = 0; i < 60001; i++)
{
l0 = xArray;
l1 = Matrix.ApplyCustomOperation(AMath.Sigmoid, Matrix.Dot(l0, syn0));
l2 = VectorN.ApplyCustomOperation(AMath.Sigmoid, VectorN.Product(l1, syn1));
VectorN l2_error = VectorN.Subtract(yArray, l2);
// Print out the average error.
if (i % 10000 == 0)
{
Console.WriteLine("-------- test " + i + " --------");
Console.WriteLine("Error: " + l2_error.Mean(true));
}
// inner product
VectorN l2_delta = VectorN.Product(l2_error, VectorN.ApplyCustomOperation(AMath.SigmoidLinear, l2));
// scalar product
double l1_error = l2_delta.Dot(syn1);
// Apply that scalar to l1 to get l1_delta.
Matrix l1_delta = Matrix.ApplyCustomOperation(AMath.SigmoidLinear, l1).Scalar(l1_error);
syn1 = VectorN.Add(syn1, VectorN.Product(l1.Transpose(), l2_delta));
Matrix weights = Matrix.Dot(l0.Transpose(), l1_delta);
syn0.Add(weights);
if (i % 10000 == 0)
{
Console.WriteLine();
Console.ForegroundColor = ConsoleColor.Yellow;
Console.WriteLine("l1 : \n" + l1);
Console.ForegroundColor = ConsoleColor.Gray;
Console.WriteLine("l2 delta: " + l2_delta);
Console.WriteLine("syn1: " + syn1);
}
}
}