Exercise: Implement deep networks for digit classification

From Ufldl

Jump to: navigation, search
(→Step 1: Train the data on the first stacked autoencoder)
Line 32: Line 32:
=== Step 2: Train the data on the second stacked autoencoder ===
=== Step 2: Train the data on the second stacked autoencoder ===
-
Run the training set through the first autoencoder to obtain hidden unit activation, then train this data on the second autoencoder. Since this is just an adapted application of a standard autoencoder, it should run identically with the first.
+
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.
-
 
+
-
Note: This step assumes that you have changed the method signature of sparseAutoencoderCost from
+
-
<tt>function [cost, grad] = sparseAutoencoderCost(...)</tt> to <tt>function [cost, grad, activation] = sparseAutoencoderCost(...)</tt> in the [[Exercise:Self-Taught_Learning|previous assignment]].
+
=== Step 3: Implement fine-tuning ===
=== Step 3: Implement fine-tuning ===

Revision as of 05:05, 10 May 2011

Retrieved from "http://ufldl.stanford.edu/wiki/index.php/Exercise:_Implement_deep_networks_for_digit_classification"
Personal tools