Sparse Autoendcoder Excercise

This is an exercise on implementing a sparse autoencoder. The excerise comes from Prof. Andrew Ng's UFLDL tutorial

The description and tutorial of the excerise can be also found in the PDF files in this folder.

Note that I am implementing it in Python instead of MATLAB. The basic algorithm is the same.

The code requires numpy and scipy. matplotlib is needed for visualization.

Example

• Gradient checking: gradient_test.py

• Training and visualize hidden units: visualize.py

You may also play with the parameters in the code to see the difference on the results.

The main codes are in sparse_autoencoder.py. Here the SparseAutoencoder class is designed to be quite general, so you may use it for other types of data.