/// <inheritdoc/>
public double Teach(NeuralNetwork network, Node data)
{
double[] output = network.FeedForward(data.Input);
double cost = data.GetCost(output);
Debug.WriteLine($"Neural network cost: {cost}");
double[][] gamma;
//// Initialize the gamma two-dimensional array from layers and layer sizes.
List <double[]> gammaList = new List <double[]>();
for (int i = 0; i < network.Layers.Length; i++)
{
gammaList.Add(new double[network.Layers[i].Size]);
}
gamma = gammaList.ToArray();
for (int i = 0; i < output.Length; i++)
{
gamma[network.Layers.Length - 1][i] = (output[i] - data.ExpectedOutput[i]) * network.ActivateDer(output[i]);
}
int layer = network.Layers.Length - 2;
//// Calculates w' and b' for the last layer in the network.
for (int i = 0; i < network.Layers[layer + 1].Size; i++)
{
//// Previous: layer
network.Layers[layer + 1][i].Bias -= gamma[layer + 1][i] * LearningRate;
for (int j = 0; j < network.Layers[layer].Size; j++)
{
//// Previous: network.Layers[layer][i]...
network.Layers[layer + 1][i].Synapses[j].Weight -= gamma[layer + 1][i] * network.Layers[layer][j].Activation * LearningRate;
//// Randomness:
if (Randomness != 0)
{
network.Layers[layer][i].Synapses[j].Weight += Rand.Current.NextDouble(-Randomness, Randomness);
}
}
}
//// Calculates derivatives for all hidden layers
for (int i = network.Layers.Length - 2; i > 0; i--)
{
layer = i - 1;
//// Looking at layer after for the outputs of this layer.
for (int j = 0; j < network.Layers[i].Size; j++)
{
gamma[i][j] = 0;
for (int k = 0; k < gamma[i + 1].Length; k++)
{
//// Previous: ...network.Layers[i][k]...
gamma[i][j] = gamma[i + 1][k] * network.Layers[i + 1][k].Synapses[j].Weight;
}
//// Calculate resulting gamma.
gamma[i][j] *= network.ActivateDer(network.Neurons[i][j]);
}
//// Looking at layer after for the outputs of this layer.
for (int j = 0; j < network.Layers[i].Size; j++)
{
//// Change biases by -gamma.
//network.Biases[layer][j] -= gamma[i][j] * LearningRate;
network.Layers[i][j].Bias -= gamma[i][j] * LearningRate;
//// Randomness:
if (Randomness != 0)
{
network.Layers[i][j].Bias += Rand.Current.NextDouble(-Randomness, Randomness);
}
//// Looking at current layer.
for (int k = 0; k < network.Layers[layer].Size; k++)
{
//// Change weights by -gamma.
//// Previous: network.Layers[layer][j]...
network.Layers[layer + 1][j].Synapses[k].Weight -= gamma[i][j] * network.Layers[i - 1][k].Activation * LearningRate;
//// Randomness:
if (Randomness != 0)
{
network.Layers[layer][j].Synapses[k].Weight += Rand.Current.NextDouble(-Randomness, Randomness);
}
}
}
}
return(cost);
}
