caetrain.m

function cae = caetrain(cae, x, opts)
    n = cae.inputkernel(1);
    cae.rL = [];
    for m = 1 : opts.rounds
        tic;
        disp([num2str(m) '/' num2str(opts.rounds) ' rounds']);
        i1 = randi(numel(x));
        l  = randi(size(x{i1}{1},1) - opts.batchsize - n + 1);
        x1{1} = double(x{i1}{1}(l : l + opts.batchsize - 1, :, :)) / 255;

        if n == 1   %Auto Encoder
            x2{1} = x1{1};
        else        %Predictive Encoder
            x2{1} = double(x{i1}{1}(l + n : l + n + opts.batchsize - 1, :, :)) / 255;
        end
        %  Add noise to input, for denoising stacked autoenoder
        x1{1} = x1{1} .* (rand(size(x1{1})) > cae.noise);

        cae = caeup(cae, x1);
        cae = caedown(cae);
        cae = caebp(cae, x2);
        cae = caesdlm(cae, opts, m);
%         caenumgradcheck(cae,x1,x2);
        cae = caeapplygrads(cae);

        if m == 1
            cae.rL(1) = cae.L;
        end
%         cae.rL(m + 1) = 0.99 * cae.rL(m) + 0.01 * cae.L;
        cae.rL(m + 1) = cae.L;
%         if cae.sv < 1e-10
%             disp('Converged');
%             break;
%         end
        toc;
    end

end