The autoencoder is trained with a dataset of 10000 images (IMAGES.mat) composed by 64 input units. It has a single layer with 25 units and an output layer with 64 units.
Code developed by Marcos Canales Mayo, based on the Unsupervised Feature Learning and Deep Learning tutorial from the Stanford University