Exercise:Vectorization
From Ufldl
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=== Step 1: Vectorize your Sparse Autoencoder Implementation === | === Step 1: Vectorize your Sparse Autoencoder Implementation === | ||
- | Using the suggestions from [[Vectorization]] and [[Neural_Network_Vectorization]], vectorize your implementation of <tt>sparseAutoencoderCost.m</tt>. In our implementation, we were able to remove all for-loops with the use of matrix operations, <tt>repmat</tt> (and/or <tt>bsxfun</tt>). A vectorized version of our code ran in under one minute on a fast computer. (Note that you do not need to vectorize the code in the other files.) | + | Using the suggestions from [[Vectorization]] and [[Neural_Network_Vectorization]], vectorize your implementation of <tt>sparseAutoencoderCost.m</tt>. In our implementation, we were able to remove all for-loops with the use of matrix operations, <tt>repmat</tt> (and/or <tt>bsxfun</tt>). A vectorized version of our code ran in under one minute on a fast computer (for learning 25 features from 1000 8x8 image patches). |
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+ | (Note that you do not need to vectorize the code in the other files.) | ||
=== Step 2: Learn features for handwritten digits === | === Step 2: Learn features for handwritten digits === |