%---------------------------------------------%
% %
% 工作室提供代做matlab仿真 %
% %
% 详情请访问:http://cn.mikecrm.com/5k6v1DP %
% %
%---------------------------------------------%
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
没有合适的资源?快使用搜索试试~ 我知道了~
温馨提示
【达摩老生出品,必属精品,亲测校正,质量保证】 资源名:卷积神经网络CNN进行图像分类_matlab 资源类型:matlab项目全套源码 源码说明: 全部项目源码都是经过测试校正后百分百成功运行的,如果您下载后不能运行可联系我进行指导或者更换。 适合人群:新手及有一定经验的开发人员
资源推荐
资源详情
资源评论
收起资源包目录
卷积神经网络CNN进行图像分类_matlab.zip (18个子文件)
卷积神经网络CNN进行图像分类_matlab
cnn_start.m 1KB
datalab.mat 20.89MB
cnnnumgradcheck.m 4KB
cnnsetup.m 2KB
expand.m 2KB
cnnff.m 2KB
cnntrain.m 1KB
NewMain.m 738B
cnnapplygrads.m 806B
printConMat.m 2KB
cnnbp.m 2KB
sigm.m 293B
extrc_pca.m 524B
TrainTest.m 2KB
accuracy.m 609B
cnntest.m 451B
flipall.m 325B
datafet.mat 20.89MB
共 18 条
- 1
阿里matlab建模师
- 粉丝: 3214
- 资源: 2782
下载权益
C知道特权
VIP文章
课程特权
开通VIP
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功
- 1
- 2
- 3
- 4
- 5
- 6
前往页