public BackPropagationLearning(ActivationNetwork network)
{
this.network = network;
// create error and deltas arrays
neuronErrors = new double[network.Layers.Length][];
weightsUpdates = new double[network.Layers.Length][][];
thresholdsUpdates = new double[network.Layers.Length][];
// initialize errors and deltas arrays for each layer
for (int i = 0; i < network.Layers.Length; i++)
{
ActivationLayer layer = network.Layers[i];
neuronErrors[i] = new double[layer.Neurons.Length];
weightsUpdates[i] = new double[layer.Neurons.Length][];
thresholdsUpdates[i] = new double[layer.Neurons.Length];
// for each neuron
for (int j = 0; j < weightsUpdates[i].Length; j++)
{
weightsUpdates[i][j] = new double[layer.InputsCount];
}
}
}
/// <summary>
/// Calculate weights updates.
/// </summary>
///
/// <param name="input">Network's input vector.</param>
///
private void CalculateUpdates(double[] input)
{
// 1 - calculate updates for the first layer
ActivationLayer layer = network.Layers[0];
double[] errors = neuronErrors[0];
double[][] layerWeightsUpdates = weightsUpdates[0];
double[] layerThresholdUpdates = thresholdsUpdates[0];
// cache for frequently used values
double cachedMomentum = learningRate * momentum;
double cached1mMomentum = learningRate * (1 - momentum);
double cachedError;
// for each neuron of the layer
for (int i = 0; i < layer.Neurons.Length; i++)
{
cachedError = errors[i] * cached1mMomentum;
double[] neuronWeightUpdates = layerWeightsUpdates[i];
// for each weight of the neuron
for (int j = 0; j < neuronWeightUpdates.Length; j++)
{
// calculate weight update
neuronWeightUpdates[j] = cachedMomentum * neuronWeightUpdates[j] + cachedError * input[j];
}
// calculate threshold update
layerThresholdUpdates[i] = cachedMomentum * layerThresholdUpdates[i] + cachedError;
}
// 2 - for all other layers
for (int k = 1; k < network.Layers.Length; k++)
{
ActivationLayer layerPrev = network.Layers[k - 1];
layer = network.Layers[k];
errors = neuronErrors[k];
layerWeightsUpdates = weightsUpdates[k];
layerThresholdUpdates = thresholdsUpdates[k];
// for each neuron of the layer
for (int i = 0; i < layer.Neurons.Length; i++)
{
cachedError = errors[i] * cached1mMomentum;
double[] neuronWeightUpdates = layerWeightsUpdates[i];
// for each synapse of the neuron
for (int j = 0; j < neuronWeightUpdates.Length; j++)
{
// calculate weight update
neuronWeightUpdates[j] = cachedMomentum * neuronWeightUpdates[j] + cachedError * layerPrev.Neurons[j].Output;
}
// calculate threshold update
layerThresholdUpdates[i] = cachedMomentum * layerThresholdUpdates[i] + cachedError;
}
}
}
public ActivationNetwork(SigmoidFunction function, int inputsCount, params int[] neuronsCount)
{
ActivationFunction = function;
InputsCount = Math.Max(1, inputsCount);
Layers = new ActivationLayer[neuronsCount.Length];
// create each layer
for (int i = 0; i < Layers.Length; i++)
{
Layers[i] = new ActivationLayer(neuronsCount[i], (i == 0) ? inputsCount : neuronsCount[i - 1]);
}
}
