/**********************************************************************
* MSE Class
***********************************************************************/
double MSE::cost(cube pred, cube y){
dbg_assert(y.n_cols == 1 && y.n_slices == 1);
pred.reshape(pred.n_elem,1,1);
y.reshape(y.n_elem,1,1);
double retn = 0.0f;
for(int i=0; i < y.n_elem; i++) {
retn += pow(pred(i,0,0) - y(i,0,0),2);
}
retn /= y.n_elem;
retn *= 0.5f;
//return(retn);
return(0.5 * mean(mean(square(pred.slice(0) - y.slice(0)))));
}
cube ConvLayer::backward(cube delta) {
// NOTE: delta may come from a linear layer
delta.reshape(_a.n_rows, _a.n_cols, _a.n_slices);
if(!_prev) {
dbg_print("null pointer to _prev in ConvLayer::backward");
return zeros<cube>(0,0,0);
}
// Compute Weight Updates
cube input = addPadding(_prev->getActivationCube(), _ksize);
for(int i = 0; i < _units; i++){
// dz_dw
for(int j=0; j < input.n_slices; j++) {
_dw(i).slice(j) = conv2d(input.slice(j), delta.slice(i));
}
// dz_db
_db(0,0,i) = accu(delta.slice(i));
}
// Compute next delta
int nr = _prev->getActivationCube().n_rows;
int nc = _prev->getActivationCube().n_cols;
int ns = _prev->getActivationCube().n_slices;
delta = addPadding(delta, _ksize);
cube next_delta = zeros<cube>(nr,nc,ns);
for(int i=0; i < ns; i++){
for(int j=0; j < _units; j++) {
next_delta.slice(i) += conv2d(
delta.slice(j),
fliplr(flipud(_w(j).slice(i)))
);
}
}
if(_prev) {
return(_prev->backward(next_delta));
}
return(next_delta);
}