【DeepLearning】Exercise: Implement deep networks for digit classification

Exercise: Implement deep networks for digit classification

习题链接：Exercise: Implement deep networks for digit classification

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(:hiddenSize*numClasses), numClasses, hiddenSize);

% Extract out the "stack"
stack = params2stack(theta(hiddenSize*numClasses+:end), netconfig);

%% ---------- YOUR CODE HERE --------------------------------------
%  Instructions: Compute pred using theta assuming that the labels start
%                from .

numCases = size(data, );

% forward
z2 = stack{}.w * data + repmat(stack{}.b, , numCases);
a2 = sigmoid(z2);
z3 = stack{}.w * a2 + repmat(stack{}.b, , numCases);
a3 = sigmoid(z3);
[~, pred] = max(softmaxTheta * a3);

% -----------------------------------------------------------

end

% You might find this useful
function sigm = sigmoid(x)
    sigm =  ./ ( + exp(-x));
end

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(:hiddenSize*numClasses), numClasses, hiddenSize);

% Extract out the "stack"
stack = params2stack(theta(hiddenSize*numClasses+:end), netconfig);

% You will need to compute the following gradients
softmaxThetaGrad = zeros(size(softmaxTheta));
stackgrad = cell(size(stack));
for d = :numel(stack)
    stackgrad{d}.w = zeros(size(stack{d}.w));
    stackgrad{d}.b = zeros(size(stack{d}.b));
end

cost = ; % You need to compute this

% You might find these variables useful
numCases = size(data, );
groundTruth = full(sparse(labels, :numCases, ));

%% --------------------------- 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.
%

z2 = stack{}.w * data + repmat(stack{}.b, , numCases);
a2 = sigmoid(z2);
z3 = stack{}.w * a2 + repmat(stack{}.b, , numCases);
a3 = sigmoid(z3);
M = softmaxTheta * a3;
M = bsxfun(@minus, M, max(M, [], ));
M = exp(M);
M = bsxfun(@rdivide, M, sum(M));
diff = groundTruth - M;

cost = -(/numCases) * sum(sum(groundTruth .* log(M))) + (lambda/) * sum(sum(softmaxTheta .* softmaxTheta));

for i=:numClasses
    softmaxThetaGrad(i, :) = -(/numCases) * (sum(a3 .* repmat(diff(i, :), hiddenSize, ), ))' + lambda * softmaxTheta(i, :);
end

delta3 = - (softmaxTheta' * diff) .* sigmoiddiff(z3);
stackgrad{}.w = delta3 * (a2)' ./ numCases;
stackgrad{}.b = sum(delta3, )./ numCases;
delta2 = (stack{}.w' * delta3) .* sigmoiddiff(z2);
stackgrad{}.w = delta2 * data'./ numCases;
stackgrad{}.b = sum(delta2, )./ numCases;

% -------------------------------------------------------------------------

%% Roll gradient vector
grad = [softmaxThetaGrad(:) ; stack2params(stackgrad)];

end

% You might find this useful
function sigm = sigmoid(x)
    sigm =  ./ ( + exp(-x));
end

function sigmdiff = sigmoiddiff(x)
    sigmdiff = sigmoid(x) .* ( - sigmoid(x));
end

stackedAEExercise.m

%% CS294A/CS294W Stacked Autoencoder Exercise

%  Instructions
%  ------------
%
%  This file contains code that helps you get started on the
%  sstacked autoencoder exercise. You will need to complete code in
%  stackedAECost.m
%  You will also need to have implemented sparseAutoencoderCost.m and
%  softmaxCost.m from previous exercises. You will need the initializeParameters.m
%  loadMNISTImages.m, and loadMNISTLabels.m files from previous exercises.
%
%  For the purpose of completing the assignment, you do not need to
%  change the code in this file.
%
%%======================================================================
%% 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 =  * ;
numClasses = ;
hiddenSizeL1 = ;    % Layer  Hidden Size
hiddenSizeL2 = ;    % Layer  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-;         % weight decay parameter
beta = ;              % weight of sparsity penalty term       

%%======================================================================
%% STEP : 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 == ) = ; % Remap  to  since our labels need to start from 

%%======================================================================
%% STEP : 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

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 = ;    % 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 : 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

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 = ;    % Maximum number of iterations of L-BFGS to run
options.display = 'on';

[sae2OptTheta, cost] = minFunc( @(p) sparseAutoencoderCost(p, ...
                                   hiddenSizeL1, hiddenSizeL2, ...
                                   lambda, sparsityParam, ...
                                   beta, sae1Features), ...
                              sae2Theta, options);

% -------------------------------------------------------------------------

%%======================================================================
%% STEP : 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, );

%% ---------------------- 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.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,
                          % softmaxCost.m satisfies this.
minFuncOptions.display = 'on';

[saeSoftmaxOptTheta, cost] = minFunc( @(p) softmaxCost(p, ...
                                   numClasses, hiddenSizeL2, lambda, ...
                                   sae2Features, trainLabels), ...
                              saeSoftmaxTheta, options);

% -------------------------------------------------------------------------

%%======================================================================
%% STEP : 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
stack = cell(,);
stack{}.w = reshape(sae1OptTheta(:hiddenSizeL1*inputSize), ...
                     hiddenSizeL1, inputSize);
stack{}.b = sae1OptTheta(*hiddenSizeL1*inputSize+:*hiddenSizeL1*inputSize+hiddenSizeL1);
stack{}.w = reshape(sae2OptTheta(:hiddenSizeL2*hiddenSizeL1), ...
                     hiddenSizeL2, hiddenSizeL1);
stack{}.b = sae2OptTheta(*hiddenSizeL2*hiddenSizeL1+:*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".
%
%

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,
                          % softmaxCost.m satisfies this.
minFuncOptions.display = 'on';

[stackedAEOptTheta, cost] = minFunc( @(p) stackedAECost(p, ...
                                   inputSize, hiddenSizeL2, numClasses, ...
                                   netconfig, lambda, trainData, trainLabels), ...
                              stackedAETheta, options);

% -------------------------------------------------------------------------

%%======================================================================
%% STEP : 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 == ) = ; % Remap  to 

[pred] = stackedAEPredict(stackedAETheta, inputSize, hiddenSizeL2, ...
                          numClasses, netconfig, testData);

acc = mean(testLabels(:) == pred(:));
fprintf('Before Finetuning Test Accuracy: %0.3f%%\n', acc * );

[pred] = stackedAEPredict(stackedAEOptTheta, inputSize, hiddenSizeL2, ...
                          numClasses, netconfig, testData);

acc = mean(testLabels(:) == pred(:));
fprintf('After Finetuning Test Accuracy: %0.3f%%\n', acc * );

% 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 %), you should check
% your code for errors, and make sure you are training on the
% entire data set of  28x28 training images
% (unless you modified the loading code, this should be the case)

Before Finetuning Test Accuracy: 87.740%
After Finetuning Test Accuracy: 97.610%