Exercise:Self-Taught Learning

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===Step 3: Extracting features===

-

After the sparse autoencoder is trained, ~~we can~~ 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.

-

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).

+

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 ~~doing so,~~ this step ~~will use your modified function to~~ convert the raw image data to ~~feature~~ unit activations.

+

After completing this step, calling <tt>feedForwardAutoencoder.m</tt> should convert the raw image data to hidden unit activations <math>a^{(2)}</math>.

===Step 4: Training and testing the logistic regression model===

-

~~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>).

+

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===

-

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.

+

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.

+

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]]

+

Exercise:Self-Taught Learning

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@@ Line 43: / Line 43: @@
 ===Step 3: Extracting features===
-After the sparse autoencoder is trained, we can 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.
-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).
+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 doing so, this step will use your modified function to convert the raw image data to feature unit activations.
+After completing this step, calling <tt>feedForwardAutoencoder.m</tt> should convert the raw image data to hidden unit activations <math>a^{(2)}</math>.
 ===Step 4: Training and testing the logistic regression model===
-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>).
+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===
 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.
+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]]
+{{STL}}