Exercise:Self-Taught Learning

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(Step 3: Extracting features)
(Step 3: Extracting features)
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After the sparse autoencoder is trained, you will use it to extract features from the handwritten digit images.  
After the sparse autoencoder is trained, you will use it to extract features from the handwritten digit images.  
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Complete <tt>feedForwardAutoencoder.m</tt> to produce a matrix whose columns correspond to activation of the hidden layer for each example, i.e., the vector <math>a^{(2)}</math> corresponding to activation of layer 2.  (Recall that we treat the inputs as layer 1).
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Complete <tt>feedForwardAutoencoder.m</tt> to produce a matrix whose columns correspond to activations of the hidden layer for each example, i.e., the vector <math>a^{(2)}</math> corresponding to activation of layer 2.  (Recall that we treat the inputs as layer 1).
After completing this step, calling <tt>feedForwardAutoencoder.m</tt> should convert the raw image data to hidden unit activations <math>a^{(2)}</math>.
After completing this step, calling <tt>feedForwardAutoencoder.m</tt> should convert the raw image data to hidden unit activations <math>a^{(2)}</math>.

Revision as of 23:45, 10 May 2011

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