Exercise: Implement deep networks for digit classification
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=== Step 2: Train the data on the second stacked autoencoder === | === Step 2: Train the data on the second stacked autoencoder === | ||
| - | We first forward propagate the training set through the first autoencoder (using <tt>feedForwardAutoencoder.m<tt> that you completed in [[Exercise:Self-Taught_Learning]]) to obtain hidden unit activations. These activations are then used to train the second sparse autoencoder. Since this is just an adapted application of a standard autoencoder, it should run similarly with the first. Complete this part of the code so as to learn a first layer of features using your <tt>sparseAutoencoderCost.m</tt> and minFunc. | + | We first forward propagate the training set through the first autoencoder (using <tt>feedForwardAutoencoder.m</tt> that you completed in [[Exercise:Self-Taught_Learning]]) to obtain hidden unit activations. These activations are then used to train the second sparse autoencoder. Since this is just an adapted application of a standard autoencoder, it should run similarly with the first. Complete this part of the code so as to learn a first layer of features using your <tt>sparseAutoencoderCost.m</tt> and minFunc. |
=== Step 3: Implement fine-tuning === | === Step 3: Implement fine-tuning === | ||
