Exercise:Self-Taught Learning

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(Step 3: Extracting features)
(Overview)
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You will be building upon your code from the earlier exercises. First, you will train your sparse autoencoder on an "unlabeled" training dataset of handwritten digits. This produces feature that are penstroke-like. We then extract these learned features from a labeled dataset of handwritten digits. These features will then be used as inputs to the softmax classifier that you wrote in the previous exercise.  
You will be building upon your code from the earlier exercises. First, you will train your sparse autoencoder on an "unlabeled" training dataset of handwritten digits. This produces feature that are penstroke-like. We then extract these learned features from a labeled dataset of handwritten digits. These features will then be used as inputs to the softmax classifier that you wrote in the previous exercise.  
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Concretely, for each example in the the labeled training dataset, <math>\textstyle x^{(k)}</math>, we forward propagate the example to obtain the activation of the hidden units <math>\textstyle a^{(2)}</math>. The data is now represented in term of <math>\textstyle a^{(2)}</math> used to train the softmax classifier.  
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Concretely, for each example in the the labeled training dataset <math>\textstyle x^{(k)}</math>, we forward propagate the example to obtain the activation of the hidden units <math>\textstyle a^{(2)}</math>. The data is now represented in term of <math>\textstyle a^{(2)}</math> used to train the softmax classifier.  
Finally, we also extract the same features from the test dataset to obtain predictions.
Finally, we also extract the same features from the test dataset to obtain predictions.

Revision as of 23:34, 10 May 2011

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