private void BackPropagate(params double[] targets)
{
NumCalc++;
int actual = targets.ToList().IndexOf(targets.Max());
double[] outputs = OutputLayer.Select(a => a.Value).ToArray();
int predicted = outputs.ToList().IndexOf(outputs.Max());
if (actual == predicted)
{
Accuracy += 1;
}
int i = 0;
OutputLayer.ForEach(a => a.CalculateGradient(targets[i++]));
HiddenLayers.Reverse();
HiddenLayers.ForEach(a => a.AsParallel().ForAll(b => b.CalculateGradient()));
HiddenLayers.ForEach(a => a.AsParallel().ForAll(b => b.UpdateWeights(LearnRate, Momentum)));
HiddenLayers.Reverse();
OutputLayer.AsParallel().ForAll(a => a.UpdateWeights(LearnRate, Momentum));
i = 0;
double error = OutputLayer.Sum(a => Math.Abs(a.CalculateError(targets[i++])));
Error += error;
if (NumCalc % 1000 == 0)
{
Console.WriteLine($"Error: {Error / 1000 / 10} NumCalc: {NumCalc} Accuracy: {Accuracy / 10.0}");
Error = 0;
Accuracy = 0;
}
}
private void ForwardPropagate(params double[] inputs)
{
int i = 0;
InputLayer.ForEach(a => a.Value = inputs[i++]);
HiddenLayers.ForEach(a => a.AsParallel().ForAll(b => b.CalculateValue()));
OutputLayer.AsParallel().ForAll(a => a.CalculateValue());
}