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最近一直在看Deep Learning,各类博客、论文看得不少
但是说实话,这样做有些疏于实现,一来呢自己的电脑也不是很好,二来呢我目前也没能力自己去写一个toolbox
只是跟着Andrew Ng的UFLDL tutorial 写了些已有框架的代码(这部分的代码见github)
后来发现了一个matlab的Deep Learning的toolbox,发现其代码很简单,感觉比较适合用来学习算法
再一个就是matlab的实现可以省略掉很多数据结构的代码,使算法思路非常清晰
所以我想在解读这个toolbox的代码的同时来巩固自己学到的,同时也为下一步的实践打好基础
(本文只是从代码的角度解读算法,具体的算法理论步骤还是需要去看paper的
我会在文中给出一些相关的paper的名字,本文旨在梳理一下算法过程,不会深究算法原理和公式)
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使用的代码:DeepLearnToolbox ,下载地址:点击打开,感谢该toolbox的作者
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今天介绍DBN的内容,其中关键部分都是(Restricted Boltzmann Machines, RBM)的步骤,所以先放一张rbm的结构,帮助理解
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照例,我们首先来看一个完整的DBN的例子程序:
这是\tests\test_example_DBN.m 中的ex2
//train dbn dbn.sizes = [100 100]; opts.numepochs = 1; opts.batchsize = 100; opts.momentum = 0; opts.alpha = 1; dbn =dbnsetup(dbn, train_x, opts); //here!!! dbn = dbntrain(dbn, train_x, opts); //here!!! //unfold dbn to nn nn = dbnunfoldtonn(dbn, 10); //here!!! nn.activation_function = 'sigm'; //train nn opts.numepochs = 1; opts.batchsize = 100; nn = nntrain(nn, train_x, train_y, opts); [er, bad] = nntest(nn, test_x, test_y); assert(er < 0.10, 'Too big error');
最后fine tuning的时候用了(一)里看过的nntrain和nntest,参见(一)
这个实在没什么好说的,
for u = 1 : numel(dbn.sizes) - 1 dbn.rbm{u}.alpha = opts.alpha; dbn.rbm{u}.momentum = opts.momentum; dbn.rbm{u}.W = zeros(dbn.sizes(u + 1), dbn.sizes(u)); dbn.rbm{u}.vW = zeros(dbn.sizes(u + 1), dbn.sizes(u)); dbn.rbm{u}.b = zeros(dbn.sizes(u), 1); dbn.rbm{u}.vb = zeros(dbn.sizes(u), 1); dbn.rbm{u}.c = zeros(dbn.sizes(u + 1), 1); dbn.rbm{u}.vc = zeros(dbn.sizes(u + 1), 1); end
function dbn = dbntrain(dbn, x, opts) n = numel(dbn.rbm); //对每一层的rbm进行训练 dbn.rbm{1} = rbmtrain(dbn.rbm{1}, x, opts); for i = 2 : n x = rbmup(dbn.rbm{i - 1}, x); dbn.rbm{i} = rbmtrain(dbn.rbm{i}, x, opts); end end首先映入眼帘的是对第一层进行rbmtrain(),后面每一层在train之前用了rbmup,
for i = 1 : opts.numepochs //迭代次数 kk = randperm(m); err = 0; for l = 1 : numbatches batch = x(kk((l - 1) * opts.batchsize + 1 : l * opts.batchsize), :); v1 = batch; h1 = sigmrnd(repmat(rbm.c', opts.batchsize, 1) + v1 * rbm.W'); //gibbs sampling的过程 v2 = sigmrnd(repmat(rbm.b', opts.batchsize, 1) + h1 * rbm.W); h2 = sigm(repmat(rbm.c', opts.batchsize, 1) + v2 * rbm.W'); //Contrastive Divergence 的过程 //这和《Learning Deep Architectures for AI》里面写cd-1的那段pseudo code是一样的 c1 = h1' * v1; c2 = h2' * v2; //关于momentum,请参看Hinton的《A Practical Guide to Training Restricted Boltzmann Machines》 //它的作用是记录下以前的更新方向,并与现在的方向结合下,跟有可能加快学习的速度 rbm.vW = rbm.momentum * rbm.vW + rbm.alpha * (c1 - c2) / opts.batchsize; rbm.vb = rbm.momentum * rbm.vb + rbm.alpha * sum(v1 - v2)' / opts.batchsize; rbm.vc = rbm.momentum * rbm.vc + rbm.alpha * sum(h1 - h2)' / opts.batchsize; //更新值 rbm.W = rbm.W + rbm.vW; rbm.b = rbm.b + rbm.vb; rbm.c = rbm.c + rbm.vc; err = err + sum(sum((v1 - v2) .^ 2)) / opts.batchsize; end end
function nn = dbnunfoldtonn(dbn, outputsize) %DBNUNFOLDTONN Unfolds a DBN to a NN % outputsize是你的目标输出label,比如在MINST就是10,DBN只负责学习feature % 或者说初始化Weight,是一个unsupervised learning,最后的supervised还得靠NN if(exist('outputsize','var')) size = [dbn.sizes outputsize]; else size = [dbn.sizes]; end nn = nnsetup(size); %把每一层展开后的Weight拿去初始化NN的Weight %注意dbn.rbm{i}.c拿去初始化了bias项的值 for i = 1 : numel(dbn.rbm) nn.W{i} = [dbn.rbm{i}.c dbn.rbm{i}.W]; end end