{作爲CNN學習入門的一部分，筆者在這裏逐步給出UFLDL的各章節Exercise的個人代碼實現，供大家參考指正}
理論部分可以在線參閱(頁面最下方有中文選項)Self-Taught Learning章節的內容。
Notes:
1. 整個過程只是將之前幾章節的Assignment總結了一下，加以運用。
關鍵在於對之前用過的函數的參數的理解，這裏需要根據Input的不同，對函數輸入參數進行調整。詳情請參閱下文代碼。
2. 需要理解無監督學習對特徵的提取過程，然後在有監督學習時也用同樣的方法提取特徵，進而將特徵輸入softmax分類器。
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 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.
inputSize = 28 * 28;
numLabels = 5;
hiddenSize = 200;
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
maxIter = 400;
%% ======================================================================
% STEP 1: 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 >= 0 & mnistLabels <= 4);
unlabeledSet = find(mnistLabels >= 5);
numTrain = round(numel(labeledSet)/2);
trainSet = labeledSet(1:numTrain);
testSet = labeledSet(numTrain+1: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, 2));
fprintf('# examples in supervised training set: %d\n\n', size(trainData, 2));
fprintf('# examples in supervised testing set: %d\n\n', size(testData, 2));
%% ======================================================================
% STEP 2: 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
% opttheta = theta;
patches = unlabeledData;
% 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 = 400; % Maximum number of iterations of L-BFGS to run
options.display = 'on';
[opttheta, cost] = minFunc( @(p) sparseAutoencoderCost(p, ...
inputSize, hiddenSize, ...
lambda, sparsityParam, ...
beta, patches), ...
theta, options);
%% -----------------------------------------------------
% Visualize weights
W1 = reshape(opttheta(1:hiddenSize * inputSize), hiddenSize, inputSize);
display_network(W1');
%%======================================================================
%% STEP 3: 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 4: Train the softmax classifier
softmaxModel = struct;
%% ----------------- YOUR CODE HERE ----------------------
% Use softmaxTrain.m from the previous exercise to train a multi-class
% classifier.
% Use lambda = 1e-4 for the weight regularization for softmax
% You need to compute softmaxModel using softmaxTrain on trainFeatures and
% trainLabels
lambda = 1e-4;
% Randomly initialise theta
theta = 0.005 * randn(numLabels * hiddenSize, 1);
% Implement softmaxCost in softmaxCost.m.
[cost, grad] = softmaxCost(theta, numLabels, hiddenSize, lambda, trainFeatures, trainLabels);
options.maxIter = 100;
softmaxModel = softmaxTrain(hiddenSize, numLabels, lambda, ...
trainFeatures, trainLabels, options);
%% -----------------------------------------------------
%%======================================================================
%% STEP 5: Testing
%% ----------------- YOUR CODE HERE ----------------------
% Compute Predictions on the test set (testFeatures) using softmaxPredict
% and softmaxModel
% You will have to implement softmaxPredict in softmaxPredict.m
[pred] = softmaxPredict(softmaxModel, testFeatures);
%% -----------------------------------------------------
% Classification Score
fprintf('Test Accuracy: %f%%\n', 100*mean(pred(:) == testLabels(:)));
% (note that we shift the labels by 1, so that digit 0 now corresponds to
% label 1)
%
% Accuracy is the proportion of correctly classified images
% The results for our implementation was:
%
% Accuracy: 98.3%
% My Accuracy: 98.221990%
%
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 = 1 ./ (1 + exp(-bsxfun(@plus, W1*data, b1)));
%-------------------------------------------------------------------
end
由於之前的這個.m的代碼在dataSize賦值時沒有考慮之後的代碼複用，所以有如下問題：
dataSize = 10000;
% 此處之前寫死了維度，在做這個練習時，輸入的data維度不同，所以報錯，改爲如下形式：
dataSize = size(data,2);
無監督學習訓練Sparse Autoencoder耗時較多：1189.734/60 = 19.8289 mins
有監督的分類器學習較快，且沒有完成100次迭代(56次)就完成了訓練過程：
不過可能也因爲這個原因，分類準確率比benchmark（98.3%）略低：98.221990%
