dmatrix3 ConvLayer::think(dmatrix3 mat)
{
dmatrix3 slab(mat.size(), dmatrix2(Fshape[1], dvec(Fshape[0])));
ivec step(4);
dvec exc(OutShape[1]*OutShape[2]);
dvec act(OutShape[1]*OutShape[2]);
ivec foldshape(2);
foldshape[0] = OutShape[1];
foldshape[1] = OutShape[2];
Inputs = &mat;
for(int f=0;f<Filters.size();f++) {
dmatrix3 filt = Filters[f];
for(int i=0;i<Steps.size();i++) {
step = Steps[i];
slab = invert<real>(slice<real>(invert<real>(mat), step));
exc[i] = frobenius(slab, filt); // This is the "convolve" step
act[i] = sigmoid(exc[exc.size()-1]);
}
Excitations[f] = fold2<real>(exc, foldshape);
Activations[f] = fold2<real>(act, foldshape);
}
return Activations;
}
// Feedforward
void PoolLayer::think(dmatrix3 &mat)
{
ThoughtBubble bubble;
int index=0;
dvec activationVec(OutShape[0]*OutShape[1]*OutShape[2]);
reset();
for(int z=0;z<mat.size();z++) {
for(int i=0;i<Steps.size();i++, index++) {
imatrix2 exc(Fshape[0], Fshape[1]);
int* step(Steps.atp(i));
bubble = max_pool(slice<real>(mat[z], step));
activationVec[index] = bubble.activation;
exc = fold2<int>(bubble.excitation, Fshape);
apply_exc(exc, step, z);
}
}
*Activations.point() = activationVec;
return Activations;
}
