private void Forward(TensorOld input, int sampleIndex, int filterIndex)
{
var filterOff = Filters.GetRawOffset(filterIndex, 0, 0, 0);
Parallel.For(0, outRows, row =>
{
var startRow = row * RowStride;
Parallel.For(0, outColumns, col =>
{
var startCol = col * ColumnStride;
var sum = 0d;
for (int i = 0; i < FilterRows; i++)
{
var filterStart = i * filterRowLength;
var inputOff = input.GetRawOffset(sampleIndex, startRow + i, startCol, 0);
for (int j = 0; j < filterRowLength; j++)
{
sum += input.values[inputOff + j] * Filters.values[filterStart + j];
}
}
ForwardOutput.SetValueFast(sum + Bias.values[filterIndex], sampleIndex, row, col, filterIndex);
});
});
}
private void SetPaddingInput(TensorOld input)
{
var inputData = input.GetRawValues();
var paddingData = PaddingInput.GetRawValues();
for (int sample = 0; sample < samples; sample++)
{
for (int channel = 0; channel < channels; channel++)
{
for (int i = 0; i < input.shape[2]; i++)
{
var inputStart = input.GetRawOffset(sample, channel, i, 0);
var paddingStart = PaddingInput.GetRawOffset(sample, channel, i + RowPadding, ColumnPadding);
Array.Copy(inputData, inputStart, paddingData, paddingStart, input.shape[3]);
}
}
}
}
//这个方法不会产生多余的临时对象，问题就是不再存储Derivative
//private void ErrorBP(Tensor output, Tensor error, Tensor result, int sampleIndex)
//{
// for (int i = 0; i < categoryNumber; i++)
// {
// var der = 0d;
// for (int j = 0; j < categoryNumber; j++)
// {
// if (i == j)
// der += output[sampleIndex, i] * (1 - output[sampleIndex, j]) * error[sampleIndex, j];
// else
// der += -output[sampleIndex, i] * output[sampleIndex, j] * error[sampleIndex, j];
// }
// result[sampleIndex, i] = der;
// }
//}
private void ErrorBP(TensorOld error)
{
var derData = Derivative.GetRawValues();
var errorData = error.GetRawValues();
var outData = BackwardOutput.GetRawValues();
Parallel.For(0, sampleNumber, sampleIndex =>
{
var errorStart = error.GetRawOffset(sampleIndex, 0);
//这里的两层嵌套执行的并不是严格的矩阵运算，导数应该是：error*jacob，
//因为jacob矩阵是对称的所以使用jacob每行和error相乘的内积，循环写起来方便
Parallel.For(0, categoryNumber, i =>
{
var derStart = Derivative.GetRawOffset(sampleIndex, i, 0);
var sum = 0d;
for (int j = 0; j < categoryNumber; j++)
{
sum += derData[derStart + j] * errorData[errorStart + j];
}
outData[errorStart + i] = sum;
});
});
}