- mlp.py - MLP classifier on MNIST (in JAX and Haiku).
- vae.py - Bernoulli VAE generative model on MNIST.
See: USING A BERNOULLI VAE ON REAL-VALUED OBSERVATIONS. - bayes.py - Variational Bayes NN classifier on MNIST.
Install dependencies with pip install -r requirements.txt
. It was tested in Python 3.7. As always, use of a virtual environment is recommended.
Each file is an independent implementation that uses HumbleSL library (see below), run with: python <file>
. Run python <file> --help
to see all the configurable parameters.
It's a straightforward supervised learning (SL) Python library. It provides all the boilerplate code needed to do Deep SL: a network definition factory, metrics and losses, a data loader, train loop, etc.
It's backed by the JAX library and the Haiku framework. It uses TensorFlow Datasets for data loading and preprocessing.