Exercise:Self-Taught Learning

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m (Step One: Generate the input and test data sets: Rephrased data set counts)
(Step Three: Training the logistic regression model)
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<tt>function [cost, grad] = sparseAutoencoderCost(...)</tt> to <tt>function [cost, grad, activation] = sparseAutoencoderCost(...)</tt> where activation should be a matrix with each column corresponding to activation of the hidden layer i.e. the vector <math>a^{(2)}</math> corresponding to activation of layer <math>L_{2}</math>. The remainder of the function should remain unchanged.
<tt>function [cost, grad] = sparseAutoencoderCost(...)</tt> to <tt>function [cost, grad, activation] = sparseAutoencoderCost(...)</tt> where activation should be a matrix with each column corresponding to activation of the hidden layer i.e. the vector <math>a^{(2)}</math> corresponding to activation of layer <math>L_{2}</math>. The remainder of the function should remain unchanged.
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After doing so, running this step should
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After doing so, this step will use your modified function to convert the raw image data to feature unit activations. It will then train the softmax regression model on the hidden unit activation and labels.
===Step Four: Training and testing the logistic regression model===
===Step Four: Training and testing the logistic regression model===

Revision as of 19:06, 28 April 2011

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