Exercise:Learning color features with Sparse Autoencoders

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(Step 1: Modify sparseAutoencoderCost.m to use a linear decoder)
(Step 2: Learn features on small patches)
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=== Step 2: Learn features on small patches ===
=== Step 2: Learn features on small patches ===
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You will now use your sparse autoencoder to learn features on a set of 100 000 small 8x8 patches sampled from the larger 96x96 STL10 images (The STL10 dataset comprises 5000 test and 8000 train 96x96 labelled color images belonging to one of ten classes: airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck).  
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You will now use your sparse autoencoder to learn features on a set of 100 000 small 8x8 patches sampled from the larger 96x96 STL10 images (The [http://www.stanford.edu/~acoates//stl10/ STL10 dataset] comprises 5000 test and 8000 train 96x96 labelled color images belonging to one of ten classes: airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck).  
The code provided in this step trains your sparse autoencoder for 400 iterations with the default parameters initialized in step 0. This should take around 45 minutes. Your sparse autoencoder should learn features which when visualized, look like edges and opponent colors, as in the figure below.  
The code provided in this step trains your sparse autoencoder for 400 iterations with the default parameters initialized in step 0. This should take around 45 minutes. Your sparse autoencoder should learn features which when visualized, look like edges and opponent colors, as in the figure below.  

Revision as of 01:23, 23 May 2011

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