void Backpropagation()
{
int outputLayer = layers.Count - 1;
int numberOfHiddenLayers = layers.Count > 2 ? layers.Count - 2 : 0;
//Output layer
for (int i = 0; i < layers[outputLayer].GetNeurons().Count; i++) //Iterate the neurons in the output layer
//Calculate the error for the neuron by subtracting the actual output from the desired output of this output neuron
{
double error = desiredOutputs[i][trainingIndex] - layers[outputLayer].GetNeurons()[i].GetOutput();
//Calculate the errorGradient for the neuron (used for the errorGradientSum in the hidden layer to follow)
layers[outputLayer].GetNeurons()[i].SetErrorGradient(ActivationFunctionHandler.TriggerDerativeFunction(layers[outputLayer].GetNeurons()[i].GetActivationFunction(),
layers[outputLayer].GetNeurons()[i].GetOutput()) * error);
//Update the neuron's weights
for (int j = 0; j < layers[outputLayer].GetNeurons()[i].GetWeights().Count; j++)
{
layers[outputLayer].GetNeurons()[i].GetWeights()[j] += alpha * layers[outputLayer].GetNeurons()[i].GetInputs()[j] * error;
}
//Update the neuron's bias
layers[outputLayer].GetNeurons()[i].SetBias(alpha * -1 * layers[outputLayer].GetNeurons()[i].GetErrorGradient());
}
//Hidden layer
for (int i = numberOfHiddenLayers; i > 1; i--) //Iterate the hidden layers
{
for (int j = 0; j < layers[i].GetNeurons().Count; j++) //Iterate the layer's neurons
//Calculate the errorGradientSum for the previous layer
{
double errorGradientSum = 0;
for (int k = 0; k < layers[i + 1].GetNeurons().Count; k++)
{
errorGradientSum += layers[i + 1].GetNeurons()[k].GetErrorGradient() * layers[i + 1].GetNeurons()[k].GetWeights()[j];
}
//Calculate the errorGradient for the neuron (used for the errorGradientSum in the hidden layer to follow)
layers[i].GetNeurons()[j].SetErrorGradient(ActivationFunctionHandler.TriggerDerativeFunction(layers[i].GetNeurons()[j].GetActivationFunction(),
layers[i].GetNeurons()[j].GetOutput()) * errorGradientSum);
//Update the neuron's weights
for (int k = 0; k < layers[i].GetNeurons()[j].GetWeights().Count; k++)
{
layers[i].GetNeurons()[j].GetWeights()[k] += alpha * layers[i].GetNeurons()[j].GetInputs()[k] * layers[i].GetNeurons()[j].GetErrorGradient();
}
//Update the neuron's bias
layers[i].GetNeurons()[j].SetBias(alpha * -1 * layers[i].GetNeurons()[j].GetErrorGradient());
}
}
}
