jens
7b34529b24
git-svn-id: http://moon:8086/svn/matlab/trunk@91 801c6759-fa7c-4059-a304-17956f83a07c
59 lines
2.0 KiB
Matlab
59 lines
2.0 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 to 9).
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binary_digits = false;
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num_classes = 10;
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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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train.y = train.y+1; % make labels 1-based.
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test.y = test.y+1; % make labels 1-based.
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% Training set info
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m=size(train.X,2);
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n=size(train.X,1);
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% Train softmax classifier using minFunc
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options = struct('MaxIter', 200);
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% Initialize theta. We use a matrix where each column corresponds to a class,
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% and each row is a classifier coefficient for that class.
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% Inside minFunc, theta will be stretched out into a long vector (theta(:)).
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% We only use num_classes-1 columns, since the last column is always assumed 0.
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theta = rand(n,num_classes-1)*0.001;
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% Call minFunc with the softmax_regression_vec.m file as objective.
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%
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% TODO: Implement batch softmax regression in the softmax_regression_vec.m
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% file using a vectorized implementation.
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%
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tic;
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theta(:)=minFunc(@softmax_regression_vec, theta(:), options, train.X, train.y);
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fprintf('Optimization took %f seconds.\n', toc);
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theta=[theta, zeros(n,1)]; % expand theta to include the last class.
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% Print out training accuracy.
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tic;
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accuracy = multi_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 test accuracy.
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accuracy = multi_classifier_accuracy(theta,test.X,test.y);
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fprintf('Test accuracy: %2.1f%%\n', 100*accuracy);
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% % for learning curves
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% global test
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% global train
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% test.err{end+1} = multi_classifier_accuracy(theta,test.X,test.y);
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% train.err{end+1} = multi_classifier_accuracy(theta,train.X,train.y);
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