public Neuron(int size, WeightSet weights, int type = Network.TANH) { this.size = size; this.prev = new Neuron[size]; this.weights = weights; this.setActivation(type); }
public override void BindTo(ref Layer layer) { prevLayer = layer; Layer2D prev = (Layer2D)prevLayer; this.width = prev.width - this.kernelWidth + 1 + 2 * padding; this.height = prev.height - this.kernelHeight + 1 + 2 * padding; this.size = depth * width * height; this.neurons = new Neuron[size]; for (int i = 0; i < depth; i++) { sharedWeights[i] = new WeightSet(prev.depth * kernelWidth * kernelHeight); for (int j = 0; j < width; j++) { for (int k = 0; k < height; k++) { if (j < padding || j >= width - padding || k < padding || k >= height - padding) { neurons[i * width * height + j * height + k] = new Neuron(0); } else { Neuron[] currPrev = new Neuron[prev.depth * kernelWidth * kernelHeight]; for (int x = 0; x < prev.depth; x++) { for (int y = 0; y < kernelWidth; y++) { for (int z = 0; z < kernelHeight; z++) { int prevIndex = x * prev.width * prev.height + (j + y - padding) * prev.height + (k + z - padding); currPrev[x * kernelWidth * kernelHeight + y * kernelHeight + z] = prev.neurons[prevIndex]; } } } neurons[i * width * height + j * height + k] = new Neuron(prev.depth * kernelWidth * kernelHeight, sharedWeights[i], this.type); neurons[i * width * height + j * height + k].link(currPrev); } } } } }