jens
7b34529b24
git-svn-id: http://moon:8086/svn/matlab/trunk@91 801c6759-fa7c-4059-a304-17956f83a07c
57 lines
1.9 KiB
Matlab
57 lines
1.9 KiB
Matlab
addpath ../common
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addpath ../common/minFunc_2012/minFunc
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addpath ../common/minFunc_2012/minFunc/compiled
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% Load the MNIST data for this exercise.
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% train.X and test.X will contain the training and testing images.
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% Each matrix has size [n,m] where:
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% m is the number of examples.
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% n is the number of pixels in each image.
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% train.y and test.y will contain the corresponding labels (0 or 1).
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binary_digits = true;
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[train,test] = ex1_load_mnist(binary_digits);
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% Add row of 1s to the dataset to act as an intercept term.
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train.X = [ones(1,size(train.X,2)); train.X];
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test.X = [ones(1,size(test.X,2)); test.X];
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% Training set dimensions
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m=size(train.X,2);
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n=size(train.X,1);
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% Train logistic regression classifier using minFunc
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options = struct('MaxIter', 100);
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% First, we initialize theta to some small random values.
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theta = rand(n,1)*0.001;
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% Call minFunc with the logistic_regression.m file as the objective function.
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%
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% TODO: Implement batch logistic regression in the logistic_regression.m file!
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%
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tic;
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theta=minFunc(@logistic_regression, theta, options, train.X, train.y);
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fprintf('Optimization took %f seconds.\n', toc);
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% Now, call minFunc again with logistic_regression_vec.m as objective.
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%
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% TODO: Implement batch logistic regression in logistic_regression_vec.m using
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% MATLAB's vectorization features to speed up your code. Compare the running
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% time for your logistic_regression.m and logistic_regression_vec.m implementations.
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%
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% Uncomment the lines below to run your vectorized code.
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%theta = rand(n,1)*0.001;
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%tic;
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%theta=minFunc(@logistic_regression_vec, theta, options, train.X, train.y);
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%fprintf('Optimization took %f seconds.\n', toc);
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% Print out training accuracy.
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tic;
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accuracy = binary_classifier_accuracy(theta,train.X,train.y);
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fprintf('Training accuracy: %2.1f%%\n', 100*accuracy);
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% Print out accuracy on the test set.
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accuracy = binary_classifier_accuracy(theta,test.X,test.y);
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fprintf('Test accuracy: %2.1f%%\n', 100*accuracy);
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