Exercise:Self-Taught Learning
From Ufldl
(Initial commit) |
(→Step Two: Train the sparse autoencoder: Added instructions on saving weights to disk) |
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Next we will train the input data set on the sparse autoencoder. Copy the <tt>sparseAutoencoderCost.m</tt> function from the previous assignment and run the training step. Use the frameworks from previous assignments to ensure that your code is working and vectorized, as no testing facilities are provided in this assignment. After doing so, running the training step should take about half an hour (on a reasonably fast computer). When it is completed, a visualization of pen strokes should be displayed. | Next we will train the input data set on the sparse autoencoder. Copy the <tt>sparseAutoencoderCost.m</tt> function from the previous assignment and run the training step. Use the frameworks from previous assignments to ensure that your code is working and vectorized, as no testing facilities are provided in this assignment. After doing so, running the training step should take about half an hour (on a reasonably fast computer). When it is completed, a visualization of pen strokes should be displayed. | ||
| - | Hint: This step takes a very long time, so you might want to avoid running it | + | Hint: This step takes a very long time, so you might want to avoid running it on subsequent trials! To do so, after running this step, run <tt>save('theta.mat', 'theta');</tt> from the command line. Then modify one line near the top of the <tt>trainSelfTaught.m</tt> script to set <tt>loadTheta = true;</tt>. This skips the autoencoder training step and loads the saved weights from disk. |
IMAGE HERE | IMAGE HERE | ||
