internal override NNDetailedFeedData FeedForward(NNFeedData input)
{
float[] output = new float[Neurons];
int[] maxIndices = new int[Neurons];
for (int zIdx = 0; zIdx < PreviousLayer.Depth; zIdx++)
{
for (int yiIdx = 0, yoIdx = 0; yoIdx < Height; yiIdx += this.Stride, yoIdx++)
{
for (int xiIdx = 0, xoIdx = 0; xoIdx < Width; xiIdx += this.Stride, xoIdx++)
{
float max = float.MinValue;
int maxIdx = -1;
for (int fyIdx = 0; fyIdx < this.FilterSize; fyIdx++)
{
for (int fxIdx = 0; fxIdx < this.FilterSize; fxIdx++)
{
int idx = PreviousLayer.ConvertToNeuronIndex(xiIdx + fxIdx, yiIdx + fyIdx, zIdx);
if (input[idx] > max)
{
max = input[idx];
maxIdx = idx;
}
}
}
int i = ConvertToNeuronIndex(xoIdx, yoIdx, zIdx);
output[i] = max;
maxIndices[i] = maxIdx;
}
}
}
NNDetailedFeedData feedData = new NNDetailedFeedData(this, output, output);
feedData.CustomData[nameof(maxIndices)] = maxIndices;
return(feedData);
}
internal override void PropagateBackward(NNDetailedBackpropagationData backpropagationData)
{
float[] lastRawOutput = backpropagationData.FeedForwardData[this].RawOutputData;
float[] dE_dz = new float[Neurons];
float[] newWeights = new float[this.weights.Length];
// For each filter
for (int fIdx = 0; fIdx < FilterCount; fIdx++)
{
float dE_db = 0;
float[] dE_dw = new float[FilterSize * FilterSize * PreviousLayer.Depth];
for (int yIdx = 0; yIdx < Height; yIdx++)
{
for (int xIdx = 0; xIdx < Width; xIdx++)
{
int oIdx = ConvertToNeuronIndex(xIdx, yIdx, fIdx);
float do_dz = Activation.Gradient(lastRawOutput[oIdx], lastRawOutput);
float dE_do;
if (NextLayer == null)
{
float o = backpropagationData.FeedForwardData[this].OutputData[oIdx];
dE_do = backpropagationData.ErrorGradient(o, oIdx);
}
else
{
dE_do = NextLayer.CalculateErrorByOutputGradient(backpropagationData, oIdx);
}
float dE_dz_tmp = dE_do * do_dz;
dE_dz[oIdx] = dE_dz_tmp;
for (int fzIdx = 0; fzIdx < PreviousLayer.Depth; fzIdx++)
{
for (int fyIdx = 0; fyIdx < FilterSize; fyIdx++)
{
for (int fxIdx = 0; fxIdx < FilterSize; fxIdx++)
{
float dz_dw = backpropagationData.FeedForwardData[PreviousLayer].OutputData[PreviousLayer.ConvertToNeuronIndex(xIdx * Stride + fxIdx, yIdx * Stride + fyIdx, fzIdx)];
dE_dw[ToWeightIndex(fxIdx, fyIdx, fzIdx, 0)] += dE_dz_tmp * dz_dw;
}
}
}
dE_db += dE_do * do_dz; // dz_dw = 1 for bias
}
}
for (int fzIdx = 0; fzIdx < PreviousLayer.Depth; fzIdx++)
{
for (int fyIdx = 0; fyIdx < FilterSize; fyIdx++)
{
for (int fxIdx = 0; fxIdx < FilterSize; fxIdx++)
{
int weightIndex = ToWeightIndex(fxIdx, fyIdx, fzIdx, fIdx);
newWeights[weightIndex] = backpropagationData.CalculateNewWeight(this.weights[weightIndex], dE_dw[ToWeightIndex(fxIdx, fyIdx, fzIdx, 0)], this, weightIndex);
}
}
}
this.biases[fIdx] += backpropagationData.CalculateNewWeight(this.biases[fIdx], dE_db, this, fIdx);
}
backpropagationData.dE_dz[this] = dE_dz;
backpropagationData.UpdatedWeights[this] = newWeights;
}