jens
7b34529b24
git-svn-id: http://moon:8086/svn/matlab/trunk@91 801c6759-fa7c-4059-a304-17956f83a07c
51 lines
1.8 KiB
Matlab
51 lines
1.8 KiB
Matlab
%% We will use minFunc for this exercise, but you can use your
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% own optimizer of choice
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clear all;
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addpath(genpath('../common/')) % path to minfunc
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%% These parameters should give you sane results. We recommend experimenting
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% with these values after you have a working solution.
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global params;
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params.m=10000; % num patches
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params.patchWidth=9; % width of a patch
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params.n=params.patchWidth^2; % dimensionality of input to RICA
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params.lambda = 0.0005; % sparsity cost
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params.numFeatures = 50; % number of filter banks to learn
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params.epsilon = 1e-2; % epsilon to use in square-sqrt nonlinearity
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% Load MNIST data set
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data = loadMNISTImages('../common/train-images-idx3-ubyte');
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%% Preprocessing
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% Our strategy is as follows:
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% 1) Sample random patches in the images
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% 2) Apply standard ZCA transformation to the data
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% 3) Normalize each patch to be between 0 and 1 with l2 normalization
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% Step 1) Sample patches
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patches = samplePatches(data,params.patchWidth,params.m);
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% Step 2) Apply ZCA
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patches = zca2(patches);
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% Step 3) Normalize each patch. Each patch should be normalized as
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% x / ||x||_2 where x is the vector representation of the patch
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m = sqrt(sum(patches.^2) + (1e-8));
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x = bsxfunwrap(@rdivide,patches,m);
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%% Run the optimization
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options.Method = 'lbfgs';
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options.MaxFunEvals = Inf;
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options.MaxIter = 500;
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%options.display = 'off';
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options.outputFcn = @showBases;
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% initialize with random weights
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randTheta = randn(params.numFeatures,params.n)*0.01; % 1/sqrt(params.n);
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randTheta = randTheta ./ repmat(sqrt(sum(randTheta.^2,2)), 1, size(randTheta,2));
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randTheta = randTheta(:);
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% optimize
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[opttheta, cost, exitflag] = minFunc( @(theta) softICACost(theta, x, params), randTheta, options); % Use x or xw
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% display result
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W = reshape(opttheta, params.numFeatures, params.n);
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display_network(W');
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