6、Self-Taught Learning
- 总结：
1）就是自编码隐藏学习的特征，送入softmax分类器。
2）算是对于之前编写的代码的一个总结。
3）fprintf('Test Accuracy: %f%%\n', 100*mean(pred(:) == testLabels(:))); 这个简单的输出准确率的代码写的好呀。
4）实验其实对于训练数据的label都整体加了1，说明了label只是标注和在测试中检测的作用，咋标无所谓。
5）
- 问题：
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5）
- 想法：
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实验需要下载代码：stl_exercise.zip
stlExercise.m
clear;close all;clc;
disp('当前正在执行的程序是：');
disp([mfilename('fullpath'),'.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;% 28 * 28=784
numLabels = 5;% 0-4有监督、5-9无监督，都是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尺寸为[30596,1]
labeledSet = find(mnistLabels >= 0 & mnistLabels <= 4);
%unlabeledSet尺寸为[29404,1]
unlabeledSet = find(mnistLabels >= 5);
%round:Round to nearest integer
%numTrain=15298,labeledSet=30596,所以训练集和测试集数量一样
numTrain = round(numel(labeledSet)/2);
%把0到4的数据，作为训练集，二等分
%trainSet尺寸为[15298,1]
trainSet = labeledSet(1:numTrain);
testSet = labeledSet(numTrain+1:end);
unlabeledData = mnistData(:, unlabeledSet);
trainData = mnistData(:, trainSet);
%label整体加1，就是移动到1-5，这里平移和之前softmax实验的把0替换为10的意义不一样。
%这样替换label是完全对应不上，label都相对于实际数据大1
trainLabels = mnistLabels(trainSet)' + 1; % Shift Labels to the Range 1-5
display_network(trainData(:,1:100)); % Show the first 100 images
%显示训练集前10个label，发现确实是所有的label都加1了
disp('显示训练集前10个样本的标签');
disp(trainLabels(1:10));
set(gcf,'NumberTitle','off');
set(gcf,'Name','显示训练集前100个数据');
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
tic;
opttheta = theta;
% 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, loss] = minFunc( @(p) sparseAutoencoderCost(p, ...
inputSize, hiddenSize, ...
lambda, sparsityParam, ...
beta, unlabeledData), ...
theta, options);
disp(['400次迭代的lbfgs的自编码，费时：',num2str(toc)]);
%% -----------------------------------------------------
% Visualize weights
%取出第一层的权值显示
W1 = reshape(opttheta(1:hiddenSize * inputSize), hiddenSize, inputSize);
figure;
display_network(W1');
set(gcf,'NumberTitle','off');
set(gcf,'Name','稀疏自编码后的第一层的权系数');
print -djpeg weights.jpg
%%======================================================================
%% 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
tic;
lambda = 1e-4;
options.maxIter = 100;
%注意由于中间层输出的特征，所以这里的输入尺寸为hiddenSize
softmaxModel = softmaxTrain(hiddenSize, numLabels, lambda, ...
trainFeatures, trainLabels, options);
disp(['100次迭代的softmax训练，费时：',num2str(toc)]);
%% -----------------------------------------------------
%%======================================================================
%% 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
%注意这里送入的，也是隐藏层输出的激活值
[pred] = softmaxPredict(softmaxModel, testFeatures);
%% -----------------------------------------------------
% Classification Score
%下面这个准确率的计算的简单的语句
fprintf('Test Accuracy: %f%%\n', 100*mean(pred(:) == testLabels(:)));
%Test Accuracy: 98.254674%
% (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%
%
%
%% -----------------------------------------------------
%UFLDL中说不使用激活的特征，准确率只有96%，所以做一个对比试验。
%由于送入的是原始的像素，所以也要用原始的图像训练
softmaxModel = struct;
tic;
lambda = 1e-4;
options.maxIter = 100;
softmaxModel = softmaxTrain(inputSize, numLabels, lambda, ...
trainData, trainLabels, options);
disp(['100次迭代的softmax训练，费时：',num2str(toc)]);
[pred] = softmaxPredict(softmaxModel, testData);
fprintf('Test Accuracy: %f%%\n', 100*mean(pred(:) == testLabels(:)));
%96.764283%
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(bsxfun(@plus,W1*data,b1));
%-------------------------------------------------------------------
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
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