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);
}
}
}
}
}
