【DeepLearning】Exercise:Vectorization

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：

【DeepLearning】Exercise:Vectorization-LMLPHP

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：

【DeepLearning】Exercise:Vectorization-LMLPHP