public void Train(FeedForwardNeuralNetwork net, double[][] input, double[][] desiredOutput)
{
int inputNeuronCount = net.InputSignal.Length;
int outputNeuronCount = net.OutputSignal.Length;
if (input[0].Length != inputNeuronCount || desiredOutput[0].Length != net.OutputSignal.Length)
throw new Exception("Input or output values number are invalid.");
// maximum allowed epoches
const int maxEpoches = 500000000;
bool go = true;
//number of learning sequences
int sequences = input.Length;
double[][] signal = net.Signal;
double[][] error = net.Error;
double[][][] weight = net.Weight;
double[][][] weightChange = net.WeightChange;
int[] neurons = net.Neurons;
int layerCount = net.Layers;
double previousAverageError = 0;
while (go)
{
// check for infinite loop
if (net.Epoch >= maxEpoches)
throw new Exception("Training takes too long. Try to change network architecture.");
double averageError=0;
for (int curr = 0; curr < sequences; curr++)
{
double[] currInput = input[curr];
double[] currDesiredOutput = desiredOutput[curr];
//setting input signals
net.InputSignal = currInput;
//executing network
net.Pulse();
//calculating errors of output neurons
for (int i = 0; i < outputNeuronCount; i++)
{
error[layerCount - 1][i] =
net.ActivationFunction.Derivative(signal[layerCount - 1][i]) *
(currDesiredOutput[i] - signal[layerCount - 1][i]);
averageError += 0.5f * (currDesiredOutput[i] - signal[layerCount - 1][i]) *
(currDesiredOutput[i] - signal[layerCount - 1][i]);
}
_learningRate = _learningRate * (averageError >= previousAverageError ? 0.2 : 5); //_learningRate * (_adaptationRate * averageError * previousAverageError + 1);
previousAverageError = averageError;
//calculating all errors and wight changes
for (int layer = layerCount - 1; layer > 0; layer--)
{
for (int i = 0; i < neurons[layer - 1]; i++)
{
double temp = 0;
for (int j = 0; j < neurons[layer]; j++)
{
double delta = _learningRate * error[layer][j] * signal[layer - 1][i];
//adding momentum to prevent from detecting a local minima
weight[layer - 1][i][j] += delta + _momentumRate * weightChange[layer - 1][i][j];
weightChange[layer - 1][i][j] = delta;
temp += error[layer][j] * weight[layer - 1][i][j];
}
error[layer - 1][i] = net.ActivationFunction.Derivative(signal[layer - 1][i]) * temp;
}
}
}
if (averageError <= _precision)
go = false;
}
}