Exercise:Self-Taught Learning
From Ufldl
(→Step 2: Train the sparse autoencoder) |
(→Step 3: Extracting features) |
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===Step 3: Extracting features=== | ===Step 3: Extracting features=== | ||
| - | After the sparse autoencoder is trained, | + | 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 ( | + | 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). |
| - | After | + | 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=== | ===Step 4: Training and testing the logistic regression model=== | ||
