public double Train(ref double[] input, ref double[] desired, double TrainingRate, double Momentum)
{
// Parameter Validation
if (input.Length != inputSize)
{
throw new ArgumentException("Invalid input parameter");
}
if (desired.Length != layerSize[layerCount - 1])
{
throw new ArgumentException("Invalid input parameter");
}
// local variables
double error = 0.0, sum = 0.0, weightDelta = 0.0, biasDelta = 0.0;
double[] output = new double[layerSize[layerCount - 1]];
// Run the Network
Run(ref input, out output);
// Back propagate the error
for (int l = layerCount - 1; l >= 0; l--)
{
// Output layer
if (l == layerCount - 1)
{
for (int k = 0; k < layerSize[l]; k++)
{
delta[l][k] = output[k] - desired[k];
error += Math.Pow(delta[l][k], 2);
delta[l][k] *= TransferFunctions.EvaluateDerivative(transferFunction[l], layerInput[l][k]);
}
}
else // Hidden Layer
{
for (int i = 0; i < layerSize[l]; i++)
{
sum = 0.0;
for (int j = 0; j < layerSize[l + 1]; j++)
{
sum += weight[l + 1][i][j] * delta[l + 1][j];
}
sum *= TransferFunctions.EvaluateDerivative(transferFunction[l], layerInput[l][i]);
delta[l][i] = sum;
}
}
}
// Update the weights and biases
for (int l = 0; l < layerCount; l++)
{
for (int i = 0; i < (l == 0 ? inputSize : layerSize[l - 1]); i++)
{
for (int j = 0; j < layerSize[l]; j++)
{
weightDelta = TrainingRate * delta[l][j] * (l == 0 ? input[i] : layerOutput[l - 1][i]);
weight[l][i][j] -= weightDelta + Momentum * previousWeight[l][i][j];
previousWeight[l][i][j] = weightDelta;
}
}
}
for (int l = 0; l < layerCount; l++)
{
for (int i = 0; i < layerSize[l]; i++)
{
biasDelta = TrainingRate * delta[l][i];
biases[l][i] -= biasDelta + Momentum * previousDelta[l][i];
previousDelta[l][i] = biasDelta;
}
}
return(error);
}
//ağın eğitilmesi backpropagation
public double Train(ref double[] input, ref double[] desired, double TrainingRate, double Momentum)
{
// Local variable
double error = 0.0, sum = 0.0, weightDelta = 0.0, biasDelta = 0.0;
double[] output = new double[layerSize[layerCount - 1]];
// Ağın çalıştırılması
Run(ref input, out output);
// Hatayı geri yaymak
for (int l = layerCount - 1; l >= 0; l--)
{
// Çıktı katmanı
if (l == layerCount - 1)
{
for (int k = 0; k < layerSize[l]; k++)
{
delta[l][k] = output[k] - desired[k];
error += Math.Pow(delta[l][k], 2);
delta[l][k] *= TransferFunctions.EvaluateDerivative(transferFunction[l],
layerInput[l][k]);
}
}
else // Gizli katman
{
for (int i = 0; i < layerSize[l]; i++)
{
sum = 0.0;
for (int j = 0; j < layerSize[l + 1]; j++)
{
sum += weight[l + 1][i][j] * delta[l + 1][j];
}
sum *= TransferFunctions.EvaluateDerivative(transferFunction[l], layerInput[l][i]);
delta[l][i] = sum;
}
}
}
// Ağırlıklar ve bias değerlerinin güncellenmesi
for (int l = 0; l < layerCount; l++)
{
for (int i = 0; i < (l == 0 ? inputSize : layerSize[l - 1]); i++)
{
for (int j = 0; j < layerSize[l]; j++)
{
weightDelta = TrainingRate * delta[l][j] * (l == 0 ? input[i] : layerOutput[l - 1][i])
+ Momentum * previousWeightDelta[l][i][j];
weight[l][i][j] -= weightDelta;
previousWeightDelta[l][i][j] = weightDelta;
}
}
}
for (int l = 0; l < layerCount; l++)
{
for (int i = 0; i < layerSize[l]; i++)
{
biasDelta = TrainingRate * delta[l][i];
bias[l][i] -= biasDelta + Momentum * previousBiasDelta[l][i];
previousBiasDelta[l][i] = biasDelta;
}
}
return(error);
}