static void sinTrain()
{
Network network = new Network(ActivationType.TanH, 1, 7, 1);
double[][] inputs = new double[64][];
double[][] outputs = new double[64][];
for (int i = 0; i < 64; i++)
{
inputs[i] = new double[] { (double)i / 64 * Math.PI * 2 };
}
for (int i = 0; i < 64; i++)
{
outputs[i] = new double[] { Math.Sin(inputs[i][0]) };
}
while (true)
{
network.Randomize(new Random());
GradientDescentTeacher teacher = new GradientDescentTeacher(network);
int numOfGens = teacher.TrainNetwork(inputs, outputs, 0.03, 8);
Console.WriteLine($"Num of generations: {numOfGens}");
desmosPrint1N1(network, 0, Math.PI * 2);
Console.ReadKey();
Console.Clear();
}
}
static void xorTrain()
{
Network network = new Network(ActivationType.Sigmoid, 2, 2, 1);
double[][] inputs =
{
new double[] { 0, 0 },
new double[] { 0, 1 },
new double[] { 1, 0 },
new double[] { 1, 1 }
};
double[][] xorOutputs =
{
new double[] { 0 },
new double[] { 1 },
new double[] { 1 },
new double[] { 0 }
};
while (true)
{
network.Randomize(new Random());
GradientDescentTeacher teacher = new GradientDescentTeacher(network);
int numOfGens = teacher.TrainNetwork(inputs, xorOutputs, 0.01);
Console.WriteLine($"Num of generations: {numOfGens}");
network.PrintWeights();
if (CheckNetwork(network, inputs, xorOutputs))
{
Console.WriteLine("XOR pass");
}
else
{
Console.WriteLine("XOR fail");
}
Console.ReadKey();
Console.Clear();
}
}
