Exercise:Sparse Autoencoder

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In this problem set, you will implement the sparse autoencoder
In this problem set, you will implement the sparse autoencoder
algorithm, and show how it discovers that edges are a good
algorithm, and show how it discovers that edges are a good
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representation for natural images.\footnote{Images provided by
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representation for natural images. (Images provided by
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Bruno Olshausen.The sparse autoencoder algorithm is described in
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Bruno Olshausen.) The sparse autoencoder algorithm is described in
the lecture notes found on the course website.
the lecture notes found on the course website.
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an 8×8 image patch from the selected image, and convert the image patch (either  
an 8×8 image patch from the selected image, and convert the image patch (either  
in row-major order or column-major order; it doesn't matter) into a 64-dimensional  
in row-major order or column-major order; it doesn't matter) into a 64-dimensional  
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vector to get a training example <math>x \in \Re^{64}$.</math>
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vector to get a training example <math>x \in \Re^{64}`.</math>
Complete the code in <tt>sampleIMAGES.m</tt>.  Your code should sample 10000 image  
Complete the code in <tt>sampleIMAGES.m</tt>.  Your code should sample 10000 image  

Revision as of 01:17, 22 April 2011

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