public void TrainingLayer()
{
double[][] inputs = new double[][] { new double[] { 0, 1, 0, 1 } };
double[][] outputs = new double[][] { new double[] { 0, 1, 0 } };
var layer = new DenseLayer(inputs[0].Length, outputs[0].Length, new Relu(), new Distance());
for (int iter = 0; iter < 20; iter++)
{
// Generate input data
for (int i = 0; i < inputs[0].Length; i++)
{
inputs[0][i] = rnd.NextDouble();
}
// Train network
int step = 0;
double error = double.MaxValue;
double errorTarget = 0.0001;
while (step < 1000 && error > errorTarget)
{
error = layer.Train(inputs[0], outputs[0], 0.05);
step++;
}
Console.WriteLine($"Iteration: {iter} Step: {step} Error: {error}");
}
}
public void LinearRegressionTest()
{
// y = ax + b
double a = 1, b = -2;
int count = 20;
double[] input = new double[count];
double[] expectedOutput = new double[count];
for (int i = 1; i < count; i++)
{
input[i] = i;
expectedOutput[i] = a * i + b;
}
var layer = new DenseLayer(1, 1, new IdentityActivation(), new Distance());
layer.Initialize();
layer.Biases[0] = 0;
layer.Weights[0, 0] = 2;
int epoc = 0;
double error = 100;
while (++epoc < 10000 && error > 0.01)
{
error = layer.Train(new double[] { input[1] }, new double[] { expectedOutput[1] }, 0.01);
error = layer.Train(new double[] { input[2] }, new double[] { expectedOutput[2] }, 0.01);
error = layer.Train(new double[] { input[3] }, new double[] { expectedOutput[3] }, 0.01);
}
for (int n = 0; n < 20; n++)
{
for (int i = 1; i < 20; i++)
{
}
}
double bias = layer.Biases[0];
double coef = layer.Weights[0, 0];
}
