public static void Train()
{
double[] inputs = { 3, 4 };
double result = 1;
Neuron hiddenNeuron1 = new Neuron();
Neuron hiddenNeuron2 = new Neuron();
Neuron outputNeuron = new Neuron();
hiddenNeuron1.weights = new double[] { 0.12, 0.10 };
hiddenNeuron2.weights = new double[] { 0.12, 0.08 };
outputNeuron.weights = new double[] { 0.14, 0.15 };
hiddenNeuron1.inputs = new double[] { inputs[0], inputs[1] };
hiddenNeuron2.inputs = new double[] { inputs[0], inputs[1] };
for (int i = 0; i < 1000; i++)
{
outputNeuron.inputs = new double[] { hiddenNeuron1.Output, hiddenNeuron2.Output };
Console.WriteLine($"Iteration: {i}: Expected output: {1}, network output: {outputNeuron.Output}");
outputNeuron.error = Sigmoid.Derivative(outputNeuron.Output) * (result - outputNeuron.Output);
outputNeuron.AdjustWeights();
hiddenNeuron1.error = Sigmoid.Derivative(hiddenNeuron1.Output) * outputNeuron.error * outputNeuron.weights[0];
hiddenNeuron2.error = Sigmoid.Derivative(hiddenNeuron2.Output) * outputNeuron.error * outputNeuron.weights[1];
hiddenNeuron1.AdjustWeights();
hiddenNeuron2.AdjustWeights();
}
}
private void AdjustWeights(double delta)
{
_output.AdjustWeights(delta);
foreach (Neuron neuron in _hidden)
{
neuron.AdjustWeights(_output.ErrorFeedback(neuron));
}
}
void Train()
{
for (int i = 0; i < 4; i++)
{
H1.inputs = new float[] { dataInput[i, 0], dataInput[i, 1] };
H1.inputs = new float[] { dataInput[i, 0], dataInput[i, 1] };
OutputNeuron.inputs = new float[] { H1.output, H2.output };
Debug.Log(dataInput[i, 0] + " xor " + dataInput[i, 1] + " = " + OutputNeuron.output);
OutputNeuron.error = sigmoid.derivative(OutputNeuron.output) * (ExpectedResults[i] - OutputNeuron.output);
OutputNeuron.AdjustWeights();
H1.error = sigmoid.derivative(H1.output) * OutputNeuron.error * OutputNeuron.weights[0];
H2.error = sigmoid.derivative(H2.output) * OutputNeuron.error * OutputNeuron.weights[1];
}
if (OutputNeuron.error == 0f)
{
Debug.LogWarning("SUCCESS!");
}
}
