//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 get_map_error_from_subsampling(float[,] sigma_next_layer)
{//W transp*sigma_prev*f_derived(ul)
float[,] upsampled_next = ConvFuncs.upsample(sigma_next_layer, outputwidth, outputheight);
for (int j = 0; j < outputheight; j++)
{
for (int i = 0; i < outputwidth; i++)
{
error[i, j] = upsampled_next[i, j] * ActFuncs.f_act_sigma_deriv(non_activated_stage[i, j]);
}
}
}
void convolution(float[,] input)
{
float[,] f = ConvFuncs.fold(input, weights, outputwidth, outputheight, w, h);
for (int j = 0; j < outputheight; j++)
{
for (int i = 0; i < outputwidth; i++)
{
non_activated_stage[i, j] = f[i, j] + b;
output[i, j] = ActFuncs.f_act_sigma(f[i, j] + b);
}
}
}
public void get_output()
{
float[,] temp = ConvFuncs.upsample(input, outputwidth, outputheight);
for (int j = 0; j < outputheight; j++)
{
for (int i = 0; i < outputwidth; i++)
{
non_activated_stage[i, j] = temp[i, j];
output[i, j] = ActFuncs.f_act_linear(non_activated_stage[i, j]);
deriv_non_activated_stage[i, j] = ActFuncs.f_act_linear_deriv(non_activated_stage[i, j]);
}
}
}
public void get_output()
{
float[,] temp = subsample(this.input);
for (int j = 0; j < outputheight; j++)
{
for (int i = 0; i < outputwidth; i++)
{
non_activated_stage[i, j] = a * temp[i, j] + b;
output[i, j] = ActFuncs.f_act_linear(non_activated_stage[i, j]);
deriv_non_activated_stage[i, j] = ActFuncs.f_act_linear_deriv(non_activated_stage[i, j]);
}
}
}
//get error from only connected next convolutional map
public void get_map_error_from_convolution(ConvolutionFeatureMap next_l_fm)
{//W transp*sigma_prev*f_derived(ul)
this.error = new float[outputwidth, outputheight];
//1) get deconvolution (back fold) of next layer's error
float[,] summfold = ConvFuncs.back_fold(next_l_fm.error, next_l_fm.weights, next_l_fm.outputwidth, next_l_fm.outputheight, next_l_fm.w, next_l_fm.h);
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];
}
}
}
//all-to-all connection
public void getmapErrorFromMpl(float[,] sigma_next_layer)
{
//clear error
error = new float[outputwidth, outputheight];
//sigma naxt layer = summ(err_nexl_layer * weight_next_layer)
for (int j = 0; j < outputheight; j++)
{
for (int i = 0; i < outputwidth; i++)
{
error[i, j] += sigma_next_layer[i, j] * ActFuncs.f_act_linear_deriv(non_activated_stage[i, j]);
b += error[i, j];
}
}
}