【DeepLearning】Exercise:Vectorization

Exercise:Vectorization

习题的链接：Exercise:Vectorization

注意点：

MNIST图片的像素点已经经过归一化。

如果再使用Exercise:Sparse Autoencoder中的sampleIMAGES.m进行归一化，

将使得训练得到的可视化权值如下图：

更改train.m的参数设置

visibleSize = *;   % number of input units
hiddenSize = ;     % number of hidden units
sparsityParam = 0.1;   % desired average activation of the hidden units.
                     % (This was denoted by the Greek alphabet rho, which looks like a lower-case "p",
             %  in the lecture notes).
lambda = 3e-;     % weight decay parameter
beta = ;            % weight of sparsity penalty term 

更改sampleIMAGES.m

function patches = sampleIMAGES()
% sampleIMAGES
% Returns  patches for training

load images;    % load images from disk 

patchsize = ;  % we'll use 28x28 patches
numpatches = ;

% Initialize patches with zeros.  Your code will fill in this matrix--one
% column per patch,  columns.
patches = zeros(patchsize*patchsize, numpatches);

%% ---------- YOUR CODE HERE --------------------------------------
%  Instructions: Fill in the variable called "patches" using data
%  from images.  

patches = images(:, :);

训练得到的W1可视化：