Exercise:Self-Taught Learning
习题链接：Exercise:Self-Taught Learning
feedForwardAutoencoder.m
function [activation] = feedForwardAutoencoder(theta, hiddenSize, visibleSize, data) % theta: trained weights from the autoencoder
% visibleSize: the number of input units (probably 64)
% hiddenSize: the number of hidden units (probably 25)
% data: Our matrix containing the training data as columns. So, data(:,i) is the i-th training example. % We first convert theta to the (W1, W2, b1, b2) matrix/vector format, so that this
% follows the notation convention of the lecture notes. W1 = reshape(theta(1:hiddenSize*visibleSize), hiddenSize, visibleSize);
b1 = theta(2*hiddenSize*visibleSize+1:2*hiddenSize*visibleSize+hiddenSize); %% ---------- YOUR CODE HERE --------------------------------------
% Instructions: Compute the activation of the hidden layer for the Sparse Autoencoder.
activation = sigmoid(W1 * data + repmat(b1, 1, size(data, 2))); %------------------------------------------------------------------- end %-------------------------------------------------------------------
% Here's an implementation of the sigmoid function, which you may find useful
% in your computation of the costs and the gradients. This inputs a (row or
% column) vector (say (z1, z2, z3)) and returns (f(z1), f(z2), f(z3)). function sigm = sigmoid(x)
sigm = 1 ./ (1 + exp(-x));
end
stlExercise.m
%% CS294A/CS294W Self-taught Learning Exercise % Instructions
% ------------
%
% This file contains code that helps you get started on the
% self-taught learning. You will need to complete code in feedForwardAutoencoder.m
% You will also need to have implemented sparseAutoencoderCost.m and
% softmaxCost.m from previous exercises.
%
%% ======================================================================
% STEP : 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. inputSize = * ;
numLabels = ;
hiddenSize = ;
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
maxIter = ; %% ======================================================================
% STEP : Load data from the MNIST database
%
% This loads our training and test data from the MNIST database files.
% We have sorted the data for you in this so that you will not have to
% change it. % Load MNIST database files
mnistData = loadMNISTImages('mnist/train-images-idx3-ubyte');
mnistLabels = loadMNISTLabels('mnist/train-labels-idx1-ubyte'); % Set Unlabeled Set (All Images) % Simulate a Labeled and Unlabeled set
labeledSet = find(mnistLabels >= & mnistLabels <= );
unlabeledSet = find(mnistLabels >= ); numTrain = round(numel(labeledSet)/);
trainSet = labeledSet(:numTrain);
testSet = labeledSet(numTrain+:end); unlabeledData = mnistData(:, unlabeledSet); trainData = mnistData(:, trainSet);
trainLabels = mnistLabels(trainSet)' + 1; % Shift Labels to the Range 1-5 testData = mnistData(:, testSet);
testLabels = mnistLabels(testSet)' + 1; % Shift Labels to the Range 1-5 % Output Some Statistics
fprintf('# examples in unlabeled set: %d\n', size(unlabeledData, ));
fprintf('# examples in supervised training set: %d\n\n', size(trainData, ));
fprintf('# examples in supervised testing set: %d\n\n', size(testData, )); %% ======================================================================
% STEP : Train the sparse autoencoder
% This trains the sparse autoencoder on the unlabeled training
% images. % Randomly initialize the parameters
theta = initializeParameters(hiddenSize, inputSize); %% ----------------- YOUR CODE HERE ----------------------
% Find opttheta by running the sparse autoencoder on
% unlabeledTrainingImages % Use minFunc to minimize the function
addpath minFunc/
options.Method = 'lbfgs'; % Here, we use L-BFGS to optimize our cost
% function. Generally, for minFunc to work, you
% need a function pointer with two outputs: the
% function value and the gradient. In our problem,
% sparseAutoencoderCost.m satisfies this.
options.maxIter = maxIter;% Maximum number of iterations of L-BFGS to run
options.display = 'on'; [opttheta, cost] = minFunc( @(p) sparseAutoencoderCost(p, ...
inputSize, hiddenSize, ...
lambda, sparsityParam, ...
beta, unlabeledData), ...
theta, options); %% ----------------------------------------------------- % Visualize weights
W1 = reshape(opttheta(:hiddenSize * inputSize), hiddenSize, inputSize);
display_network(W1'); %%======================================================================
%% STEP : Extract Features from the Supervised Dataset
%
% You need to complete the code in feedForwardAutoencoder.m so that the
% following command will extract features from the data. trainFeatures = feedForwardAutoencoder(opttheta, hiddenSize, inputSize, ...
trainData); testFeatures = feedForwardAutoencoder(opttheta, hiddenSize, inputSize, ...
testData); %%======================================================================
%% STEP : Train the softmax classifier %% ----------------- YOUR CODE HERE ----------------------
% Use softmaxTrain.m from the previous exercise to train a multi-class
% classifier. % Use lambda = 1e- for the weight regularization for softmax % You need to compute softmaxModel using softmaxTrain on trainFeatures and
% trainLabels lambda = 1e-;
options.maxIter = maxIter;
[softmaxModel] = softmaxTrain(hiddenSize, numLabels, lambda, trainFeatures, trainLabels, options); %% ----------------------------------------------------- %%======================================================================
%% STEP : Testing %% ----------------- YOUR CODE HERE ----------------------
% Compute Predictions on the test set (testFeatures) using softmaxPredict
% and softmaxModel
[pred] = softmaxPredict(softmaxModel, testFeatures); %% ----------------------------------------------------- % Classification Score
fprintf('Test Accuracy: %f%%\n', *mean(pred(:) == testLabels(:))); % (note that we shift the labels by , so that digit now corresponds to
% label )
%
% Accuracy is the proportion of correctly classified images
% The results for our implementation was:
%
% Accuracy: 98.3%
%
%
Test Accuracy: 98.208916%