基于MATLAB实现的手写数字的识别效率，用卷积神经网络算法来实现，能够显现百分之九十以上的识别率+使用说明文档

function cnnnumgradcheck(net, x, y) epsilon = 1e-4; er = 1e-8; n = numel(net.layers); for j = 1 : numel(net.ffb) net_m = net; net_p = net; net_p.ffb(j) = net_m.ffb(j) + epsilon; net_m.ffb(j) = net_m.ffb(j) - epsilon; net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y); net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y); d = (net_p.L - net_m.L) / (2 * epsilon); e = abs(d - net.dffb(j)); if e > er error('numerical gradient checking failed'); end end for i = 1 : size(net.ffW, 1) for u = 1 : size(net.ffW, 2) net_m = net; net_p = net; net_p.ffW(i, u) = net_m.ffW(i, u) + epsilon; net_m.ffW(i, u) = net_m.ffW(i, u) - epsilon; net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y); net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y); d = (net_p.L - net_m.L) / (2 * epsilon); e = abs(d - net.dffW(i, u)); if e > er error('numerical gradient checking failed'); end end end for l = n : -1 : 2 if strcmp(net.layers{l}.type, 'c') for j = 1 : numel(net.layers{l}.a) net_m = net; net_p = net; net_p.layers{l}.b{j} = net_m.layers{l}.b{j} + epsilon; net_m.layers{l}.b{j} = net_m.layers{l}.b{j} - epsilon; net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y); net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y); d = (net_p.L - net_m.L) / (2 * epsilon); e = abs(d - net.layers{l}.db{j}); if e > er error('numerical gradient checking failed'); end for i = 1 : numel(net.layers{l - 1}.a) for u = 1 : size(net.layers{l}.k{i}{j}, 1) for v = 1 : size(net.layers{l}.k{i}{j}, 2) net_m = net; net_p = net; net_p.layers{l}.k{i}{j}(u, v) = net_p.layers{l}.k{i}{j}(u, v) + epsilon; net_m.layers{l}.k{i}{j}(u, v) = net_m.layers{l}.k{i}{j}(u, v) - epsilon; net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y); net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y); d = (net_p.L - net_m.L) / (2 * epsilon); e = abs(d - net.layers{l}.dk{i}{j}(u, v)); if e > er error('numerical gradient checking failed'); end end end end end elseif strcmp(net.layers{l}.type, 's') % for j = 1 : numel(net.layers{l}.a) % net_m = net; net_p = net; % net_p.layers{l}.b{j} = net_m.layers{l}.b{j} + epsilon; % net_m.layers{l}.b{j} = net_m.layers{l}.b{j} - epsilon; % net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y); % net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y); % d = (net_p.L - net_m.L) / (2 * epsilon); % e = abs(d - net.layers{l}.db{j}); % if e > er % error('numerical gradient checking failed'); % end % end end end % keyboard end