In this part of Homework 2, your will implement several variants of the VAE, and run experiments using the MNIST and Binarized MNIST datasets.
You can download the datasets here (extract them to the data/ directory in the root of this repository). I've provided code for loading and processing this data into minibatches in mnist.py and bmnist.py.
Scaffolding for your VAE models is provided in models.py, and you will implement the loss functions for various types of VAE's in losses.py.
Framework code for training your models is found in gaussian_vae.ipynb, pixelcnn.ipynb, and binaryvae. I recommend using Google Colab to execute these notebooks (with a GPU accelerator attached). For debugging, you may find it helpful to modify the default hyper-parameters to build smaller models that are faster to train.