Exercise:Self-Taught Learning

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(Overview)
(Step 2: Train the sparse autoencoder)
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===Step 2: Train the sparse autoencoder===
===Step 2: Train the sparse autoencoder===
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Next, we will train the unlabeled dataset on the sparse autoencoder, using the same <tt>sparseAutoencoderCost.m</tt> function from the previous assignments. (Use the frameworks from previous assignments to ensure that your code is working and vectorized.) The training step should take less than 25 minutes (on a reasonably fast computer). When it is completed, a visualization of pen strokes should be displayed.
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Next, use the unlabeled data to train a sparse autoencoder, using the same <tt>sparseAutoencoderCost.m</tt> function as you had written in  the previous exercise. (From the earlier exercise, you should have a working and vectorized implementation of the sparse autoencoder.) For us, the training step took less than 25 minutes on a fast desktop. When training is complete, you should get a visualization of pen strokes like the image shown below:
[[File:selfTaughtFeatures.png]]
[[File:selfTaughtFeatures.png]]
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The features learned by the sparse autoencoder should correspond to penstrokes.
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Informally, the features learned by the sparse autoencoder should correspond to penstrokes.
===Step 3: Extracting features===
===Step 3: Extracting features===

Revision as of 23:42, 10 May 2011

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