Exercise:Self-Taught Learning

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(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>.
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===Step 4: Training and testing the logistic regression model===
===Step 4: Training and testing the logistic regression model===
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In this step, you should use your code from the softmax exercise (<tt>softmaxTrain.m</tt>) to train the softmax classifier using the training features (<tt>trainFeatures</tt>) and labels (<tt>trainLabels</tt>).
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Use your code from the softmax exercise (<tt>softmaxTrain.m</tt>) to train a softmax classifier using the training set features (<tt>trainFeatures</tt>) and labels (<tt>trainLabels</tt>).
===Step 5: Classifying on the test set===
===Step 5: Classifying on the test set===
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Finally, complete the code to make predictions on the test set (<tt>testFeatures</tt>) and see how your learned features perform! If you've done all the steps correctly, you should get an accuracy of about '''98%''' percent.
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Finally, complete the code to make predictions on the test set (<tt>testFeatures</tt>) and see how your learned features perform! If you've done all the steps correctly, you should get an accuracy of about '''98%''' percent.
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As a comparison, when ''raw pixels'' are used (instead of the learned features), we obtained a test accuracy of only around 96% (for the same train and test sets).
[[Category:Exercises]]
[[Category:Exercises]]
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{{STL}}

Latest revision as of 11:02, 26 May 2011

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