DeepLearnToolbox使用总结

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原创最新推荐文章于 2026-04-30 10:25:10 发布 3.1w 阅读 AI 写同款· GEO 优化 ›

Deep Learning学习之 CNN代码解析（MATLAB） MATLAB实现CNN一般会用到deepLearnToolbox-master。但是根据Git上面的说明，现在已经停止更新了，而且有很多功能也不太能够支持，具体的请大家自习看一看Git中的README。deepLearnToolbox-master是一个深度学习matlab包，里面含有很多机器学习算法，如卷积神经网络CNN，深度信念网络DBN，自动编码AutoEncoder（堆栈SAE，卷积CAE）阅读详情

GitHub链接：DeepLearnToolbox

DeepLearnToolbox

A Matlab toolbox for Deep Learning.

Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data. It is inspired by the human brain's apparent deep (layered, hierarchical) architecture. A good overview of the theory of Deep Learning theory is Learning Deep Architectures for AI

Directories included in the toolbox

NN/ - A library for Feedforward Backpropagation Neural Networks

CNN/ - A library for Convolutional Neural Networks

DBN/ - A library for Deep Belief Networks

SAE/ - A library for Stacked Auto-Encoders

CAE/ - A library for Convolutional Auto-Encoders

util/ - Utility functions used by the libraries

data/ - Data used by the examples

tests/ - unit tests to verify toolbox is working

For references on each library check REFS.md

Setup

Download.
addpath(genpath('DeepLearnToolbox'));

Windows下把文件夹加入 path 即可

%LiFeiteng 

path = pwd;
files = dir(path);

for i = 1:length(files)
    
    if files(i).isdir        
        file = files(i).name;       
        addpath([path '/' file])
        disp(['add ' file ' to path!'])
    end   
    
end

我不打算解析代码，想从代码里面学算法是stupid的；有相应的论文，readlist，talk等可以去学习。

DeepLearnToolbox单隐藏层NN的优化策略：mini-Batch SGD

function [nn, L]  = nntrain(nn, train_x, train_y, opts, val_x, val_y)
%NNTRAIN trains a neural net
% [nn, L] = nnff(nn, x, y, opts) trains the neural network nn with input x and
% output y for opts.numepochs epochs, with minibatches of size
% opts.batchsize. Returns a neural network nn with updated activations,
% errors, weights and biases, (nn.a, nn.e, nn.W, nn.b) and L, the sum
% squared error for each training minibatch.

assert(isfloat(train_x), 'train_x must be a float');
assert(nargin == 4 || nargin == 6,'number ofinput arguments must be 4 or 6')

loss.train.e               = [];
loss.train.e_frac          = [];
loss.val.e                 = [];
loss.val.e_frac            = [];
opts.validation = 0;
if nargin == 6
    opts.validation = 1;
end

fhandle = [];
if isfield(opts,'plot') && opts.plot == 1
    fhandle = figure();
end

m = size(train_x, 1);

batchsize = opts.batchsize;
numepochs = opts.numepochs;

numbatches = m / batchsize;

assert(rem(numbatches, 1) == 0, 'numbatches must be a integer');

L = zeros(numepochs*numbatches,1);
n = 1;
for i = 1 : numepochs
    tic;
    
    kk = randperm(m);
    for l = 1 : numbatches
        batch_x = train_x(kk((l - 1) * batchsize + 1 : l * batchsize), :);
        
        %Add noise to input (for use in denoising autoencoder)
        if(nn.inputZeroMaskedFraction ~= 0)
            batch_x = batch_x.*(rand(size(batch_x))>nn.inputZeroMaskedFraction);
        end
        
        batch_y = train_y(kk((l - 1) * batchsize + 1 : l * batchsize), :);
        
        nn = nnff(nn, batch_x, batch_y);
        nn = nnbp(nn);
        nn = nnapplygrads(nn);
        
        L(n) = nn.L;
        
        n = n + 1;
    end
    
    t = toc;
    
    if ishandle(fhandle)
        if opts.validation == 1
            loss = nneval(nn, loss, train_x, train_y, val_x, val_y);
        else
            loss = nneval(nn, loss, train_x, train_y);
        end
        nnupdatefigures(nn, fhandle, loss, opts, i);
    end
        
    disp(['epoch ' num2str(i) '/' num2str(opts.numepochs) '. Took ' num2str(t) ' seconds' '. Mean squared error on training set is ' num2str(mean(L((n-numbatches):(n-1))))]);
    nn.learningRate = nn.learningRate * nn.scaling_learningRate;
end
end

1.不管是在 nntrain、 nnbp还是nnapplygrads中我都没看到对算法收敛性的判断，

而且在实测的过程中有观察到 epoch过程中 mean-squared-error有下降-上升-下降的走势——微小抖动在SGD中算是正常