前面记到了深度网络这一章。当时觉得练习应该挺简单的，用不了多少时间，结果训练时间真够长的...途中debug的时候还手贱的clear了一下，又得从头开始运行。不过最终还是调试成功了，sigh~
前一篇博文讲了深度网络的一些基本知识，这次讲义中的练习还是针对MNIST手写库，主要步骤是训练两个自编码器，然后进行softmax回归，最后再整体进行一次微调。
训练自编码器以及softmax回归都是利用前面已经写好的代码。微调部分的代码其实就是一次反向传播。
以下就是代码：
主程序部分：
stackedAEExercise.m
% For the purpose of completing the assignment, you do not need to
% change the code in this file.
%
%%======================================================================
%% 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.
DISPLAY = true;
inputSize = 28 * 28;
numClasses = 10;
hiddenSizeL1 = 200; % Layer 1 Hidden Size
hiddenSizeL2 = 200; % Layer 2 Hidden Size
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
%%======================================================================
%% STEP 1: Load data from the MNIST database
%
% This loads our training data from the MNIST database files.
% Load MNIST database files
trainData = loadMNISTImages('mnist/train-images-idx3-ubyte');
trainLabels = loadMNISTLabels('mnist/train-labels-idx1-ubyte');
trainLabels(trainLabels == 0) = 10; % Remap 0 to 10 since our labels need to start from 1
%%======================================================================
%% STEP 2: Train the first sparse autoencoder
% This trains the first sparse autoencoder on the unlabelled STL training
% images.
% If you've correctly implemented sparseAutoencoderCost.m, you don't need
% to change anything here.
% Randomly initialize the parameters
sae1Theta = initializeParameters(hiddenSizeL1, inputSize);
%% ---------------------- YOUR CODE HERE ---------------------------------
% Instructions: Train the first layer sparse autoencoder, this layer has
% an hidden size of "hiddenSizeL1"
% You should store the optimal parameters in sae1OptTheta
% 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';
[sae1optTheta, cost] = minFunc( @(p) sparseAutoencoderCost(p, ...
inputSize, hiddenSizeL1, ...
lambda, sparsityParam, ...
beta, trainData), ...
sae1Theta, options);
%-------------------------------------------------------------------------
%======================================================================
% STEP 2: Train the second sparse autoencoder
%This trains the second sparse autoencoder on the first autoencoder
%featurse.
%If you've correctly implemented sparseAutoencoderCost.m, you don't need
%to change anything here.
[sae1Features] = feedForwardAutoencoder(sae1optTheta, hiddenSizeL1, ...
inputSize, trainData);
% Randomly initialize the parameters
sae2Theta = initializeParameters(hiddenSizeL2, hiddenSizeL1);
%% ---------------------- YOUR CODE HERE ---------------------------------
% Instructions: Train the second layer sparse autoencoder, this layer has
% an hidden size of "hiddenSizeL2" and an inputsize of
% "hiddenSizeL1"
%
% You should store the optimal parameters in sae2OptTheta
[sae2opttheta, cost] = minFunc( @(p) sparseAutoencoderCost(p, ...
hiddenSizeL1, hiddenSizeL2, ...
lambda, sparsityParam, ...
beta, sae1Features), ...
sae2Theta, options);
%-------------------------------------------------------------------------
%======================================================================
%% STEP 3: Train the softmax classifier
% This trains the sparse autoencoder on the second autoencoder features.
% If you've correctly implemented softmaxCost.m, you don't need
% to change anything here.
[sae2Features] = feedForwardAutoencoder(sae2opttheta, hiddenSizeL2, ...
hiddenSizeL1, sae1Features);
% Randomly initialize the parameters
saeSoftmaxTheta = 0.005 * randn(hiddenSizeL2 * numClasses, 1);
%% ---------------------- YOUR CODE HERE ---------------------------------
% Instructions: Train the softmax classifier, the classifier takes in
% input of dimension "hiddenSizeL2" corresponding to the
% hidden layer size of the 2nd layer.
%
% You should store the optimal parameters in saeSoftmaxOptTheta
%
% NOTE: If you used softmaxTrain to complete this part of the exercise,
% set saeSoftmaxOptTheta = softmaxModel.optTheta(:);
options.maxIter = 100;
softmax_lambda = 1e-4;
numLabels = 10;
softmaxModel = softmaxTrain(hiddenSizeL2, numLabels, softmax_lambda, ...
sae2Features, trainLabels, options);
saeSoftmaxOptTheta = softmaxModel.optTheta(:);
%-------------------------------------------------------------------------
%======================================================================
%% STEP 5: Finetune softmax model
% Implement the stackedAECost to give the combined cost of the whole model
% then run this cell.
% Initialize the stack using the parameters learned
inputSize = 28*28;
stack = cell(2,1);
stack{1}.w = reshape(sae1optTheta(1:hiddenSizeL1*inputSize), ...
hiddenSizeL1, inputSize);
stack{1}.b = sae1optTheta(2*hiddenSizeL1*inputSize+1:2*hiddenSizeL1*inputSize+hiddenSizeL1);
stack{2}.w = reshape(sae2opttheta(1:hiddenSizeL2*hiddenSizeL1), ...
hiddenSizeL2, hiddenSizeL1);
stack{2}.b = sae2opttheta(2*hiddenSizeL2*hiddenSizeL1+1:2*hiddenSizeL2*hiddenSizeL1+hiddenSizeL2);
% Initialize the parameters for the deep model
[stackparams, netconfig] = stack2params(stack);
stackedAETheta = [ saeSoftmaxOptTheta ; stackparams ];
%% ---------------------- YOUR CODE HERE ---------------------------------
% Instructions: Train the deep network, hidden size here refers to the '
% dimension of the input to the classifier, which corresponds
% to "hiddenSizeL2".
%
%
[stackedAEOptTheta, cost] = minFunc( @(p) stackedAECost(p, inputSize, hiddenSizeL2, ...
numClasses, netconfig, ...
lambda, trainData, trainLabels), ...
stackedAETheta,options);
% -------------------------------------------------------------------------
%%======================================================================
%% STEP 6: Test
% Instructions: You will need to complete the code in stackedAEPredict.m
% before running this part of the code
%
% Get labelled test images
% Note that we apply the same kind of preprocessing as the training set
testData = loadMNISTImages('mnist/t10k-images-idx3-ubyte');
testLabels = loadMNISTLabels('mnist/t10k-labels-idx1-ubyte');
testLabels(testLabels == 0) = 10; % Remap 0 to 10
[pred] = stackedAEPredict(stackedAETheta, inputSize, hiddenSizeL2, ...
numClasses, netconfig, testData);
acc = mean(testLabels(:) == pred(:));
fprintf('Before Finetuning Test Accuracy: %0.3f%%\n', acc * 100);
[pred] = stackedAEPredict(stackedAEOptTheta, inputSize, hiddenSizeL2, ...
numClasses, netconfig, testData);
acc = mean(testLabels(:) == pred(:));
fprintf('After Finetuning Test Accuracy: %0.3f%%\n', acc * 100);
% Accuracy is the proportion of correctly classified images
% The results for our implementation were:
%
% Before Finetuning Test Accuracy: 87.7%
% After Finetuning Test Accuracy: 97.6%
%
% If your values are too low (accuracy less than 95%), you should check
% your code for errors, and make sure you are training on the
% entire data set of 60000 28x28 training images
% (unless you modified the loading code, this should be the case)
微调部分的代价函数：
stackedAECost.m
function [ cost, grad ] = stackedAECost(theta, inputSize, hiddenSize, ...
numClasses, netconfig, ...
lambda, data, labels)
% stackedAECost: Takes a trained softmaxTheta and a training data set with labels,
% and returns cost and gradient using a stacked autoencoder model. Used for
% finetuning.
% theta: trained weights from the autoencoder
% visibleSize: the number of input units
% hiddenSize: the number of hidden units *at the 2nd layer*
% numClasses: the number of categories
% netconfig: the network configuration of the stack
% lambda: the weight regularization penalty
% data: Our matrix containing the training data as columns. So, data(:,i) is the i-th training example.
% labels: A vector containing labels, where labels(i) is the label for the
% i-th training example
%% Unroll softmaxTheta parameter
% We first extract the part which compute the softmax gradient
softmaxTheta = reshape(theta(1:hiddenSize*numClasses), numClasses, hiddenSize);
% Extract out the "stack"
stack = params2stack(theta(hiddenSize*numClasses+1:end), netconfig);
% You will need to compute the following gradients
softmaxThetaGrad = zeros(size(softmaxTheta));
stackgrad = cell(size(stack));
for d = 1:numel(stack)
stackgrad{d}.w = zeros(size(stack{d}.w));
stackgrad{d}.b = zeros(size(stack{d}.b));
end
cost = 0; % You need to compute this
% You might find these variables useful
M = size(data, 2);
groundTruth = full(sparse(labels, 1:M, 1));
%% --------------------------- YOUR CODE HERE -----------------------------
% Instructions: Compute the cost function and gradient vector for
% the stacked autoencoder.
%
% You are given a stack variable which is a cell-array of
% the weights and biases for every layer. In particular, you
% can refer to the weights of Layer d, using stack{d}.w and
% the biases using stack{d}.b . To get the total number of
% layers, you can use numel(stack).
%
% The last layer of the network is connected to the softmax
% classification layer, softmaxTheta.
%
% You should compute the gradients for the softmaxTheta,
% storing that in softmaxThetaGrad. Similarly, you should
% compute the gradients for each layer in the stack, storing
% the gradients in stackgrad{d}.w and stackgrad{d}.b
% Note that the size of the matrices in stackgrad should
% match exactly that of the size of the matrices in stack.
%
%----------先计算a和z----------------
d = numel(stack); %stack的深度
n = d+1; %网络层数
a = cell(n,1);
z = cell(n,1);
a{1} = data; %a{1}设成输入数据
for l = 2:n %给a{2,...n}和z{2,,...n}赋值
z{l} = stack{l-1}.w * a{l-1} + repmat(stack{l-1}.b,[1,size(a{l-1},2)]);
a{l} = sigmoid(z{l});
end
%------------------------------------
%-------------计算softmax的代价函数和梯度函数-------------
Ma = softmaxTheta * a{n};
NorM = bsxfun(@minus, Ma, max(Ma, [], 1)); %归一化，每列减去此列的最大值，使得M的每个元素不至于太大。
ExpM = exp(NorM);
P = bsxfun(@rdivide,ExpM,sum(ExpM)); %概率
cost = -1/M*(groundTruth(:)'*log(P(:)))+lambda/2*(softmaxTheta(:)'*softmaxTheta(:)); %代价函数
softmaxThetaGrad = -1/M*((groundTruth-P)*a{n}') + lambda*softmaxTheta; %梯度
%--------------------------------------------------------
%--------------计算每一层的delta---------------------
delta = cell(n);
delta{n} = -softmaxTheta'*(groundTruth-P).*(a{n}).*(1-a{n}); %可以参照前面讲义BP算法的实现
for l = n-1:-1:1
delta{l} = stack{l}.w' * delta{l+1}.*(a{l}).*(1-a{l});
end
%----------------------------------------------------
%--------------计算每一层的w和b的梯度-----------------
for l = n-1:-1:1
stackgrad{l}.w = (1/M)*delta{l+1}*a{l}';
stackgrad{l}.b = (1/M)*sum(delta{l+1},2);
end
%----------------------------------------------------
% -------------------------------------------------------------------------
%% Roll gradient vector
grad = [softmaxThetaGrad(:) ; stack2params(stackgrad)];
end
% You might find this useful
function sigm = sigmoid(x)
sigm = 1 ./ (1 + exp(-x));
end
预测函数：
stackedAEPredict.m
function [pred] = stackedAEPredict(theta, inputSize, hiddenSize, numClasses, netconfig, data)
% stackedAEPredict: Takes a trained theta and a test data set,
% and returns the predicted labels for each example.
% theta: trained weights from the autoencoder
% visibleSize: the number of input units
% hiddenSize: the number of hidden units *at the 2nd layer*
% numClasses: the number of categories
% data: Our matrix containing the training data as columns. So, data(:,i) is the i-th training example.
% Your code should produce the prediction matrix
% pred, where pred(i) is argmax_c P(y(c) | x(i)).
%% Unroll theta parameter
% We first extract the part which compute the softmax gradient
softmaxTheta = reshape(theta(1:hiddenSize*numClasses), numClasses, hiddenSize);
% Extract out the "stack"
stack = params2stack(theta(hiddenSize*numClasses+1:end), netconfig);
%% ---------- YOUR CODE HERE --------------------------------------
% Instructions: Compute pred using theta assuming that the labels start
% from 1.
%
%----------先计算a和z----------------
d = numel(stack); %stack的深度
n = d+1; %网络层数
a = cell(n,1);
z = cell(n,1);
a{1} = data; %a{1}设成输入数据
for l = 2:n %给a{2,...n}和z{2,,...n}赋值
z{l} = stack{l-1}.w * a{l-1} + repmat(stack{l-1}.b,[1,size(a{l-1},2)]);
a{l} = sigmoid(z{l});
end
%-------------------------------------
M = softmaxTheta * a{n};
[Y,pred] = max(M,[],1);
% -----------------------------------------------------------
end
% You might find this useful
function sigm = sigmoid(x)
sigm = 1 ./ (1 + exp(-x));
end
最后结果：
跟讲义以及程序注释中有点差别，特别是没有微调的结果，讲义中提到是不到百分之九十的，这里算出来是百分之九十四左右：
但是微调后的结果基本是一样的。
PS:讲义地址：http://deeplearning.stanford.edu/wiki/index.php/Exercise:_Implement_deep_networks_for_digit_classification