mlcnn does not train

[masihazad]

"mlcnn.m" has a method named: "updateParams".
Unfortunately in this version, it does not update the output layer's weights, so the whole network is like a car with a working engine but without a mechanism to transfer engine's power to the tires.
I have a suggestion to make it work, to do so I have changed the body of "updateParams" method to:

function self = updateParams(self)
%net = updateParams()
%--------------------------------------------------------------------------
%Update network parameters based on states of netowrk gradient, perform
%regularization such as weight decay and weight rescaling
%--------------------------------------------------------------------------
wPenalty = 0;
for lL = 2:self.nLayers % Changed from: for lL = 2:self.nLayers-1 by Masih Azad
switch self.layers{lL}.type
% CURRENTLY, ONLY UPDATE FILTERS AND FM BIASES
% PERHAPS, IN THE FUTURE, WE'LL BE FANCY, AND DO FANCY UPDATES
case {'conv'} % Changed from: case {'conv','output'} by Masih Azad
lRate = self.layers{lL}.lRate;
for jM = 1:self.layers{lL}.nFM
% UPDATE FEATURE BIASES
self.layers{lL}.b(jM) = self.layers{lL}.b(jM) - ...
lRate*self.layers{lL}.db(jM);

            % UPDATE FILTERS
            for iM = 1:self.layers{lL-1}.nFM
                if self.wPenalty > 0 % L2 REGULARIZATION
                    wPenalty = self.layers{lL}.filter(:,:,iM,jM)*self.wPenalty;
                elseif self.wPenalty < 0 % L1-REGULARIZATION (SUB GRADIENTS)
                    wPenalty = sign(self.layers{lL}.filter(:,:,iM,jM))*abs(self.wPenalty);
                end
                self.layers{lL}.filter(:,:,iM,jM) = ...
                self.layers{lL}.filter(:,:,iM,jM) - ...
                lRate*(self.layers{lL}.dFilter(:,:,iM,jM)+wPenalty);
            end
        end
        %% Added by Masih Azad
        case {'output'}
            lRate = self.layers{lL}.lRate;
            self.layers{lL}.b = self.layers{lL}.b - ...
                                lRate*self.layers{lL}.db;

            if self.wPenalty > 0 % L2 REGULARIZATION
                wPenalty = self.layers{lL}.W*self.wPenalty;
            elseif self.wPenalty < 0 % L1-REGULARIZATION (SUB GRADIENTS)
                wPenalty = sign(self.layers{lL}.W)*abs(self.wPenalty);
            end                
            self.layers{lL}.W = self.layers{lL}.W - lRate*(self.layers{lL}.dW+wPenalty);
        %%
    end
end

end