//get summary error from connected maps
public void get_map_error_from_convolution()
{//W transp*sigma_prev*f_derived(ul)
this.error = new float[outputwidth, outputheight];
if (conv_maps_next_layer.Count > 0)
{
float[,] summfold = new float[outputwidth, outputheight];
List <float[, ]> part_folds = new List <float[, ]>();
ConvolutionFeatureMap cur_nl_fm;
//1) fold of next layer's maps with their transponed kernels
for (int k = 0; k < conv_maps_next_layer.Count; k++)
{
cur_nl_fm = conv_maps_next_layer[k];
part_folds.Add(ConvFuncs.fold_with_transponed_kernel(cur_nl_fm.error, cur_nl_fm.weights,
cur_nl_fm.outputwidth, cur_nl_fm.outputheight, cur_nl_fm.w, cur_nl_fm.h));
for (int j = 0; j < outputheight; j++)
{
for (int i = 0; i < outputwidth; i++)
{
summfold[i, j] += part_folds[k][i, j];
}
}
}
for (int j = 0; j < outputheight; j++)
{
for (int i = 0; i < outputwidth; i++)
{
error[i, j] = ActFuncs.f_act_linear_deriv(non_activated_stage[i, j]) * summfold[i, j];
b += error[i, j];
}
}
}
}
public void correct_weights()
{
foreach (var input in inputs)
{
float[,] folderr = ConvFuncs.fold_with_transponed_kernel(input, error, w, h, outputwidth, outputheight);
for (int j = 0; j < h; j++)
{
for (int i = 0; i < w; i++)
{
weights[i, j] += folderr[j, i];
b += error[i, j];
}
}
}
}