void deconvolution(float[,] input)
{
float[,] f = ConvFuncs.back_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];
output[i, j] = ActFuncs.f_act_sigma(f[i, j]);
deriv_non_activated_stage[i, j] = ActFuncs.f_act_sigma_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];
}
}
}
