/// <summary>
/// Computes changes by backpropagating errors
/// </summary>
/// <param name="errors">Vector of error values</param>
/// <param name="speed">Speed modifier</param>
/// <param name="prevLayer">Previous layer neurons</param>
/// <returns>Errors of previous layer</returns>
public virtual double[] Propagate(double[] errors, double speed, Layer prevLayer)
{
StreamWriter writer = new StreamWriter("E:/log.txt", true);
if (loggingEnabled)
{
writer.WriteLine("---- Layer Error Correction ----");
}
double[] prevErrors = new double[prevLayer.number];
for (int i = 0; i < this.number; i++)
{
for (int j = 0; j < prevLayer.number; j++)
{
prevErrors[i] += errors[i] * weights[j, i];
}
if (loggingEnabled)
{
writer.WriteLine($"Eror {i} : value = {prevErrors[i]}");
}
prevErrors[i] = Sigmoid.ActivationDerivative(prevErrors[i]);
if (loggingEnabled)
{
writer.WriteLine($"Error deactivated {i} : value = {prevErrors[i]}");
}
for (int j = 0; j < prevLayer.number; j++)
{
weightCorrection[j, i] = speed * prevErrors[i] * Sigmoid.Activation(prevLayer.nodes[j]);
if (loggingEnabled)
{
//writer.WriteLine($"Weight correction [{i},{j}] : value = { weightCorrection[j, i]}");
}
}
biasCorrecton[i] = prevErrors[i] * speed;
if (loggingEnabled)
{
writer.WriteLine($"Bias correction {i} : value = {biasCorrecton[i]}");
}
}
writer.Flush();
writer.Close();
return(prevErrors);
}
/// <summary>
/// Computes all neurons in all layers, propagetes error.
/// </summary>
/// <param name="input">Input vector</param>
/// <param name="expected">Vector of expected result probabilities</param>
/// <returns>Vector of probabilities</returns>
public double[] Learn(double[] input, double[] expected)
{
double[] result = this.Compute(input);
double[] errors = new double[outputLayer.number];
for (int i = 0; i < result.Length; i++)
{
errors[i] = (expected[i] - result[i]) * Sigmoid.ActivationDerivative(outputLayer.nodes[i]);
}
double[] prevErrors = outputLayer.Propagate(errors, speed, hiddenLayers.Last());
prevErrors = hiddenLayers.Last().Propagate(errors, speed, outputLayer);
for (int i = hiddenLayers.Count - 1; i >= 0; i--)
{
prevErrors = hiddenLayers[i].Propagate(prevErrors, speed, hiddenLayers[i + 1]);
}
foreach (var item in hiddenLayers)
{
item.Apply();
}
outputLayer.Apply();
return(result);
}