UFLDL教程Exercise答案(6)：Implement deep networks for digit classification

教程地址：http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial

Exercise地址：http://deeplearning.stanford.edu/wiki/index.php/Exercise:_Implement_deep_networks_for_digit_classification

代码

将sparseAutoencoderCost.m   softmaxCost.m feedForwardAutoencoder.m   initializeParameters.m  loadMNISTImages.m   loadMNISTLabels.m文件以及minFunc文件夹从以前的文件中拷贝到本节练习所在的工作路径中。

Step 0: Initialize constants and parameters ——代码已给
Step 00: Load data from the MNIST database——代码已给

根据自己的训练集所在的路径修改相应路径。

我的路径设置为：'F:/DeepLearning/UFLDL/mnist/train-images.idx3-ubyte' ；'F:/DeepLearning/UFLDL/mnist/train-labels.idx1-ubyte'

Step 1: Train the data on the first stacked autoencoder ——stackedAEExercise.m

%% ---------------------- YOUR CODE HERE  ---------------------------------
%  Instructions: Train the first layer sparse autoencoder, this layer has
%                an hidden size of "hiddenSizeL1"
%                You should store the optimal parameters in sae1OptTheta
sae1OptTheta = sae1Theta;
%  Use minFunc to minimize the function  
addpath minFunc/  
options.Method = 'lbfgs'; % Here, we use L-BFGS to optimize our cost function.  
options.maxIter = 400;    % Maximum number of iterations of L-BFGS to run   
options.display = 'on';  
[sae1OptTheta, cost] = minFunc( @(p) sparseAutoencoderCost(p, ...  
                                   inputSize, hiddenSizeL1, ...  
                                   lambda, sparsityParam, ...  
                                   beta, trainData), ...