Deep learning：十五(Self-Taught Learning练习)
Deep learning：十五(Self-Taught Learning练习)
前言：
本次实验主要是练习soft- taught learning的实现。参考的资料为网页：http://deeplearning.stanford.edu/wiki/index.php/Exercise:Self-Taught_Learning。Soft-taught leaning是用的无监督学习来学习到特征提取的参数，然后用有监督学习来训练分类器。这里分别是用的sparse autoencoder和softmax regression。实验的数据依旧是手写数字数据库MNIST Dataset.
实验基础：
从前面的知识可以知道，sparse autoencoder的输出应该是和输入数据尺寸大小一样的，且很相近，那么我们训练出的sparse autoencoder模型该怎样提取出特征向量呢？其实输入样本经过sparse code提取出特征的表达式就是隐含层的输出了，首先来看看前面的经典sparse code模型，如下图所示：
拿掉那个后面的输出层后，隐含层的值就是我们所需要的特征值了，如下图所示：
从教程中可知，在unsupervised learning中有两个观点需要特别注意，一个是self-taught learning，一个是semi-supervised learning。Self-taught learning是完全无监督的。教程中有举了个例子，很好的说明了这个问题，比如说我们需要设计一个系统来分类出轿车和摩托车。如果我们给出的训练样本图片是自然界中随便下载的（也就是说这些图片中可能有轿车和摩托车，有可能都没有，且大多数情况下是没有的），然后使用的是这些样本来特征模型的话，那么此时的方法就叫做self-taught learning。如果我们训练的样本图片都是轿车和摩托车的图片，只是我们不知道哪张图对应哪种车，也就是说没有标注，此时的方法不能叫做是严格的unsupervised feature，只能叫做是semi-supervised learning。
一些matlab函数：
numel:
比如说n = numel(A)表示返回矩阵A中元素的个数。
unique:
unique为找出向量中的非重复元素并进行排序后输出。
实验结果：
采用数字5~9的样本来进行无监督训练，采用的方法是sparse autoencoder，可以提取出这些数据的权值，权值转换成图片显示如下：
但是本次实验主要是进行0~4这5个数字的分类，虽然进行无监督训练用的是数字5~9的训练样本，这依然不会影响后面的结果。只是后面的分类器设计是用的softmax regression，所以是有监督的。最后据官网网页上的结果精度是98%，而直接用原始的像素点进行分类器的设计不仅效果要差（才96%），而且训练的速度也会变慢不少。
实验主要部分代码：
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('train-images.idx3-ubyte');
mnistLabels = loadMNISTLabels('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);
%%增加的一行代码
unlabeledSet = unlabeledSet(1:end/3);
numTest = round(numel(labeledSet)/2);%拿一半的样本来训练%
numTrain = round(numel(labeledSet)/3);
trainSet = labeledSet(1:numTrain);
testSet = labeledSet(numTrain+1:2*numTrain);
unlabeledData = mnistData(:, unlabeledSet);%%为什么这两句连在一起都要出错呢？
% pack;
trainData = mnistData(:, trainSet);
trainLabels = mnistLabels(trainSet)' + 1; % Shift Labels to the Range 1-5
% mnistData2 = mnistData;
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;
addpath minFunc/
options.Method = 'lbfgs';
options.maxIter = 400;
options.display = 'on';
[opttheta, loss] = minFunc( @(p) sparseAutoencoderLoss(p, ...
inputSize, hiddenSize, ...
lambda, sparsityParam, ...
beta, unlabeledData), ...
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
lambda = 1e-4;
inputSize = hiddenSize;
numClasses = numel(unique(trainLabels));%unique为找出向量中的非重复元素并进行排序
% You need to compute softmaxModel using softmaxTrain on trainFeatures and
% trainLabels
% You need to compute softmaxModel using softmaxTrain on trainFeatures and
% trainLabels
options.maxIter = 100;
softmaxModel = softmaxTrain(inputSize, numClasses, lambda, ...
trainFeatures, trainLabels, options);
%% -----------------------------------------------------
%%======================================================================
%% STEP 5: 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', 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%
%
%
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
参考资料:
http://deeplearning.stanford.edu/wiki/index.php/Exercise:Self-Taught_Learning
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