【DeepLearning】Exercise:Vectorization

%% STEP 0: Here we provide the relevant parameters values that will
%  allow your sparse autoencoder to get good filters; you do not need to 
%  change the parameters below.

visibleSize = 28*28;   % number of input units 
hiddenSize = 196;     % 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-3;     % weight decay parameter       
beta = 3;            % weight of sparsity penalty term       

%%======================================================================
%% STEP 1: Implement sampleIMAGES
%
%  After implementing sampleIMAGES, the display_network command should
%  display a random sample of 200 patches from the dataset

% MNIST images have already been normalized
images = loadMNISTImages(‘train-images.idx3-ubyte‘);
patches = images(:,1:10000); 
%display_network(patches(:,randi(size(patches,2),200,1)),8);

%  Obtain random parameters theta
theta = initializeParameters(hiddenSize, visibleSize);