// Three possibilities:
// 2D -> 2D
// 2D -> 1D
// 1D -> 1D
public NeuronMatrix(Layer l1, Layer l2)
{
Debug.Assert(!(l1.type == Layer.LAYER_1D && l2.type == Layer.LAYER_2D));
if (l1 is Layer2D && l2 is Layer2D)
{
Layer2D prev = (Layer2D)l1;
Layer2D curr = (Layer2D)l2;
weight = new double[prev.depth, curr.depth, curr.kernelWidth, curr.kernelHeight];
bias = new double[curr.depth];
initializeWeights(prev.depth * curr.kernelWidth * curr.kernelHeight);
}
else if (l1 is Layer2D && l2 is Layer1D)
{
Layer2D prev = (Layer2D)l1;
Layer1D curr = (Layer1D)l2;
weight = new double[prev.depth, prev.width, prev.height, curr.size];
bias = new double[curr.size];
initializeWeights(prev.depth * prev.width * prev.height);
}
else if (l1 is Layer1D && l2 is Layer1D)
{
Layer1D prev = (Layer1D)l1;
Layer1D curr = (Layer1D)l2;
weight = new double[1, 1, prev.size, curr.size];
bias = new double[curr.size];
initializeWeights(prev.size);
}
}
// back propagation ends at the input layer
public override double[,,] backPropagate(double[,,] currActivated, double[,,] nextError, double learningRate)
{
if (nextLayer is Layer1D)
{
Layer1D next = (Layer1D)nextLayer;
for (int i = 0; i < size; i++)
{
for (int j = 0; j < next.size; j++)
{
next.matrix.weight[0, 0, i, j] -= currActivated[0, 0, i] * nextError[0, 0, j] * learningRate;
}
}
for (int i = 0; i < next.size; i++)
{
next.matrix.bias[i] -= nextError[0, 0, i] * learningRate;
}
}
else if (nextLayer is Layer2D)
{
throw new Exception("Invalid Layer");
}
return(null);
}
public override double[,,] forwardPropagate(double[,,] prevActivated)
{
double[,,] ret = new double[1, 1, size];
for (int i = 0; i < size; i++)
{
ret[0, 0, i] = matrix.bias[i];
}
if (prevLayer is Layer1D)
{
Layer1D prev = (Layer1D)prevLayer;
for (int i = 0; i < prev.size; i++)
{
for (int j = 0; j < size; j++)
{
ret[0, 0, j] += matrix.weight[0, 0, i, j] * prevActivated[0, 0, i];
}
}
for (int i = 0; i < size; i++)
{
ret[0, 0, i] = Math.Tanh(ret[0, 0, i]);
}
}
else if (prevLayer is Layer2D)
{
Layer2D prev = (Layer2D)prevLayer;
for (int i = 0; i < prev.depth; i++)
{
for (int j = 0; j < prev.width; j++)
{
for (int k = 0; k < prev.height; k++)
{
for (int l = 0; l < size; l++)
{
ret[0, 0, l] += matrix.weight[i, j, k, l] * prevActivated[i, j, k];
}
}
}
}
for (int i = 0; i < size; i++)
{
ret[0, 0, i] = Math.Tanh(ret[0, 0, i]);
}
}
return(ret);
}
public override double[,,] backPropagate(double[,,] currActivated, double[,,] nextError, double learningRate)
{
double[,,] ret = new double[depth, width, height];
if (nextLayer is Layer1D)
{
Layer1D next = (Layer1D)nextLayer;
for (int d = 0; d < depth; d++)
{
for (int i = 0; i < width; i++)
{
for (int j = 0; j < height; j++)
{
for (int k = 0; k < next.size; k++)
{
ret[d, i, j] += next.matrix.weight[d, i, j, k] * nextError[0, 0, k] * (1 - currActivated[d, i, j] * currActivated[d, i, j]);
}
}
}
}
for (int d = 0; d < depth; d++)
{
for (int i = 0; i < width; i++)
{
for (int j = 0; j < height; j++)
{
for (int k = 0; k < next.size; k++)
{
next.matrix.weight[d, i, j, k] -= nextError[0, 0, k] * currActivated[d, i, j] * learningRate;
}
}
}
}
for (int d = 0; d < depth; d++)
{
for (int i = 0; i < next.size; i++)
{
next.matrix.bias[d] -= nextError[0, 0, i] * learningRate;
}
}
}
else if (nextLayer is ConvolutionalLayer)
{
ConvolutionalLayer next = (ConvolutionalLayer)nextLayer;
for (int d = 0; d < depth; d++)
{
for (int i = 0; i < next.kernelWidth; i++)
{
for (int j = 0; j < next.kernelHeight; j++)
{
for (int nd = 0; nd < next.depth; nd++)
{
for (int m = 0; m < next.width; m++)
{
for (int n = 0; n < next.height; n++)
{
ret[d, i + m, j + n] += next.matrix.weight[d, nd, i, j] * nextError[nd, m, n] * (1 - currActivated[d, i + m, j + n] * currActivated[d, i + m, j + n]);
}
}
}
}
}
}
for (int d = 0; d < depth; d++)
{
for (int i = 0; i < next.kernelWidth; i++)
{
for (int j = 0; j < next.kernelHeight; j++)
{
for (int nd = 0; nd < next.depth; nd++)
{
for (int m = 0; m < next.width; m++)
{
for (int n = 0; n < next.height; n++)
{
next.matrix.weight[d, nd, i, j] -= nextError[nd, m, n] * currActivated[d, i + m, j + n] * learningRate;
}
}
}
}
}
}
for (int d = 0; d < depth; d++)
{
for (int nd = 0; nd < next.depth; nd++)
{
for (int i = 0; i < next.width; i++)
{
for (int j = 0; j < next.height; j++)
{
next.matrix.bias[d] -= nextError[nd, i, j] * learningRate;
}
}
}
}
}
return(ret);
}
