public void Adjust(Layer nextLayer) {
const double alpha = 0.075;
for (int c = 0; c < Weights.Length; c++) {
DeltaWeights[c] = alpha * DeltaWeights[c] + (1.0 - alpha) * Perceptron.LearningRate * nextLayer.Neurons[c].Delta * Value;
Weights[c] += DeltaWeights[c];
}
}
public Layer(int size, Layer next) {
Neurons = new Neuron[size];
this.next = next;
for (int j = 0; j < Length; j++) {
Neurons[j] = new Neuron();
if (next != null) {
Neurons[j].Weights = new double[next.Length];
Neurons[j].DeltaWeights = new double[next.Length];
}
}
}
public void BackPropagate(Layer nextLayer) {
for (int c = 0; c < Weights.Length; c++)
Delta += nextLayer[c].Delta * Weights[c];
}
public void ForwardPropagate(Layer nextLayer) {
for (int c = 0; c < Weights.Length; c++)
nextLayer[c].Value += Value * Weights[c];
}
public void Load(string filePath) {
Stream stream = File.OpenRead(filePath);
BinaryReader reader = new BinaryReader(stream);
layers = new Layer[reader.ReadInt32()];
for (int i = layers.Length - 1; i >= 0; i--)
layers[i] = new Layer(reader.ReadInt32(), i < layers.Length - 1 ? layers[i + 1] : null);
for (int i = layers.Length - 1; i >= 0; i--)
layers[i].Load(reader);
stream.Close();
}
public Perceptron(int[] layersSizes) {
layers = new Layer[layersSizes.Length];
for (int i = layers.Length - 1; i >= 0; i--)
layers[i] = new Layer(layersSizes[i], i < layers.Length - 1 ? layers[i + 1] : null);
}
