cnntrain.m

function net = cnntrain(net, x, y, opts)
    m = size(x, 3);  
    numbatches = m / opts.batchsize;
    
    Feature=[];
    

    if rem(numbatches, 1) ~= 0
        error('numbatches not integer');
    end
    net.rL = [];
    for i = 1 : opts.numepochs
        % disp(X)
        disp(['epoch ' num2str(i) '/' num2str(opts.numepochs)]);
        tic; 
        kk = randperm(m); 
        for l = 1 : numbatches
 
            batch_x = x(:, :, kk((l - 1) * opts.batchsize + 1 : l * opts.batchsize));
            batch_y = y(:,    kk((l - 1) * opts.batchsize + 1 : l * opts.batchsize));

            l

            net = cnnff(net, batch_x);
            

            net = cnnbp(net, batch_y);  % Backpropagation  
            %------------------------

            Feature=[Feature net.fv];
            %-------------------------
            

            net = cnnapplygrads(net, opts);
            if isempty(net.rL)
                net.rL(1) = net.L;  
            end
            net.rL(end + 1) = 0.99 * net.rL(end) + 0.01 * net.L;
        end
        net.Feature=Feature;
        toc;
    end
    
end