【DeepLearning】Exercise:Vectorization

標籤：

Exercise:Vectorization

習題的連結：Exercise:Vectorization

 

注意點：

MNIST圖片的像素點已經經過歸一化。

如果再使用Exercise:Sparse Autoencoder中的sampleIMAGES.m進行歸一化，

將使得訓練得到的可視化權值如：

 

 

我的實現：

更改train.m的參數設定及訓練樣本選取

%% 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 normalizedimages = loadMNISTImages(‘train-images.idx3-ubyte‘);patches = images(:,1:10000); %display_network(patches(:,randi(size(patches,2),200,1)),8);%  Obtain random parameters thetatheta = initializeParameters(hiddenSize, visibleSize);

 

訓練得到的W1可視化：

【DeepLearning】Exercise:Vectorization