现在有空整理一下关于深度学习中怎么加入dropout方法来防止测试过程的过拟合现象。
首先了解一下dropout的实现原理:
这些理论的解释在百度上有很多。。。。
这里重点记录一下怎么实现这一技术
参考别人的博客,主要http://www.cnblogs.com/dupuleng/articles/4340293.html
讲解一下用Matlab中的深度学习工具箱怎么实现dropout
首先要载入工具包。DeepLearn Toolbox是一个非常有用的matlab deep learning工具包,下载地址:https://github.com/rasmusbergpalm/DeepLearnToolbox
要使用它首先要将该工具包添加到matlab的搜索路径中,
1、将包复制到matlab 的toolbox中,作者的路径是D:\program Files\matlab\toolbox\
2、在matlab的命令行中输入:
cd D:\program Files\matlab\toolbox\deepLearnToolbox\ addpath(gepath('D:\program Files\matlab\toolbox\deepLearnToolbox-master\') savepath %保存,这样就不需要每次都添加一次
3、验证添加是否成功,在命令行中输入
which saesetup
果成功就会出现,saesetup.m的路径D:\program Files\matlab\toolbox\deepLearnToolbox-master\SAE\saesetup.m
4、使用deepLearnToolbox 工具包,做一个简单的demo,将autoencoder模型使用dropout前后的结果进行比较。
load mnist_uint8; train_x = double(train_x(1:2000,:)) / 255; test_x = double(test_x(1:1000,:)) / 255; train_y = double(train_y(1:2000,:)); test_y = double(test_y(1:1000,:)); %% //实验一without dropout rand('state',0) sae = saesetup([784 100]); sae.ae{1}.activation_function = 'sigm'; sae.ae{1}.learningRate = 1; opts.numepochs = 10; opts.batchsize = 100; sae = saetrain(sae , train_x , opts ); visualize(sae.ae{1}.W{1}(:,2:end)'); nn = nnsetup([784 100 10]);% //初步构造了一个输入-隐含-输出层网络,其中包括了 % //权值的初始化,学习率,momentum,激发函数类型, % //惩罚系数,dropout等 nn.W{1} = sae.ae{1}.W{1}; opts.numepochs = 10; % //Number of full sweeps through data opts.batchsize = 100; % //Take a mean gradient step over this many samples [nn, ~] = nntrain(nn, train_x, train_y, opts); [er, ~] = nntest(nn, test_x, test_y); str = sprintf('testing error rate is: %f',er); fprintf(str); %% //实验二:with dropout rand('state',0) sae = saesetup([784 100]); sae.ae{1}.activation_function = 'sigm'; sae.ae{1}.learningRate = 1; opts.numepochs = 10; opts.bachsize = 100; sae = saetrain(sae , train_x , opts ); figure; visualize(sae.ae{1}.W{1}(:,2:end)'); nn = nnsetup([784 100 10]);% //初步构造了一个输入-隐含-输出层网络,其中包括了 % //权值的初始化,学习率,momentum,激发函数类型, % //惩罚系数,dropout等 nn.dropoutFraction = 0.5; nn.W{1} = sae.ae{1}.W{1}; opts.numepochs = 10; % //Number of full sweeps through data opts.batchsize = 100; % //Take a mean gradient step over this many samples [nn, L] = nntrain(nn, train_x, train_y, opts); [er, bad] = nntest(nn, test_x, test_y); str = sprintf('testing error rate is: %f',er); fprintf(str);
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