Exercise:Self-Taught Learning
From Ufldl
(→Support Code/Data) |
(→Step 3: Extracting features) |
||
Line 43: | Line 43: | ||
After the sparse autoencoder is trained, we can use it to extract features from the handwritten digit images. | After the sparse autoencoder is trained, we can 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 doing so, this step will use your modified function to convert the raw image data to feature unit activations. | + | After doing so, this step will use your modified function to convert the raw image data to feature unit activations. |
===Step 4: Training and testing the logistic regression model=== | ===Step 4: Training and testing the logistic regression model=== |