www.gusucode.com > SAE RBM 程序MATLAB源码代码实现的一个关于sae的例子 > linearDecoderExercise.m
clc; clear all; close all; %% CS294A/CS294W Linear Decoder Exercise % Instructions % ------------ % % This file contains code that helps you get started on the % linear decoder exericse. For this exercise, you will only need to modify % the code in sparseAutoencoderLinearCost.m. You will not need to modify % any code in this file. %%====================================================================== %% STEP 0: Initialization % Here we initialize some parameters used for the exercise. imageChannels = 3; % number of channels (rgb, so 3) patchDim = 8; % patch dimension numPatches = 100000; % number of patches visibleSize = patchDim * patchDim * imageChannels; % number of input units outputSize = visibleSize; % number of output units hiddenSize = 400; % number of hidden units sparsityParam = 0.035; % desired average activation of the hidden units. lambda = 3e-3; % weight decay parameter beta = 5; % weight of sparsity penalty term epsilon = 0.1; % epsilon for ZCA whitening %%====================================================================== %% STEP 1: Create and modify sparseAutoencoderLinearCost.m to use a linear decoder, % and check gradients % You should copy sparseAutoencoderCost.m from your earlier exercise % and rename it to sparseAutoencoderLinearCost.m. % Then you need to rename the function from sparseAutoencoderCost to % sparseAutoencoderLinearCost, and modify it so that the sparse autoencoder % uses a linear decoder instead. Once that is done, you should check % your gradients to verify that they are correct. % NOTE: Modify sparseAutoencoderCost first! % To speed up gradient checking, we will use a reduced network and some % dummy patches % debugHiddenSize = 5; % debugvisibleSize = 8; % patches = rand([8 10]); % theta = initializeParameters(debugHiddenSize, debugvisibleSize); % % [cost, grad] = sparseAutoencoderLinearCost(theta, debugvisibleSize, debugHiddenSize, ... % lambda, sparsityParam, beta, ... % patches); % % % Check gradients % numGrad = computeNumericalGradient( @(x) sparseAutoencoderLinearCost(x, debugvisibleSize, debugHiddenSize, ... % lambda, sparsityParam, beta, ... % patches), theta); % % % Use this to visually compare the gradients side by side % disp([numGrad grad]); % % diff = norm(numGrad-grad)/norm(numGrad+grad); % % Should be small. In our implementation, these values are usually less than 1e-9. % disp(diff); % % assert(diff < 1e-9, 'Difference too large. Check your gradient computation again'); % % % NOTE: Once your gradients check out, you should run step 0 again to % % reinitialize the parameters % %} %%====================================================================== %% STEP 2: Learn features on small patches % In this step, you will use your sparse autoencoder (which now uses a % linear decoder) to learn features on small patches sampled from related % images. %% STEP 2a: Load patches % In this step, we load 100k patches sampled from the STL10 dataset and % visualize them. Note that these patches have been scaled to [0,1] load stlSampledPatches.mat displayColorNetwork(patches(:, 1:100)); %% STEP 2b: Apply preprocessing % In this sub-step, we preprocess the sampled patches, in particular, % ZCA whitening them. % % In a later exercise on convolution and pooling, you will need to replicate % exactly the preprocessing steps you apply to these patches before % using the autoencoder to learn features on them. Hence, we will save the % ZCA whitening and mean image matrices together with the learned features % later on. % Subtract mean patch (hence zeroing the mean of the patches) meanPatch = mean(patches, 2); patches = bsxfun(@minus, patches, meanPatch); % Apply ZCA whitening sigma = patches * patches' / numPatches; [u, s, v] = svd(sigma); ZCAWhite = u * diag(1 ./ sqrt(diag(s) + epsilon)) * u'; patches = ZCAWhite * patches; displayColorNetwork(patches(:, 1:100)); %% STEP 2c: Learn features % You will now use your sparse autoencoder (with linear decoder) to learn % features on the preprocessed patches. This should take around 45 minutes. theta = initializeParameters(hiddenSize, visibleSize); % Use minFunc to minimize the function addpath minFunc/ options = struct; options.Method = 'lbfgs'; options.maxIter = 400; options.display = 'on'; [optTheta, cost] = minFunc( @(p) sparseAutoencoderLinearCost(p, ... visibleSize, hiddenSize, ... lambda, sparsityParam, ... beta, patches), ... theta, options); % Save the learned features and the preprocessing matrices for use in % the later exercise on convolution and pooling fprintf('Saving learned features and preprocessing matrices...\n'); save('STL10Features.mat', 'optTheta', 'ZCAWhite', 'meanPatch'); fprintf('Saved\n'); %% STEP 2d: Visualize learned features W = reshape(optTheta(1:visibleSize * hiddenSize), hiddenSize, visibleSize); b = optTheta(2*hiddenSize*visibleSize+1:2*hiddenSize*visibleSize+hiddenSize); displayColorNetwork( (W*ZCAWhite)');