pyroved 0.2.0

Variational encoder-decoder models in Pyro probabilistic programming language

pyroVED

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pyroVED is an open-source package built on top of the Pyro probabilistic programming language for applications of variational encoder-decoder models in spectral and image analyses. The currently available models include variational autoencoders with translational, rotational, and scale invariances for unsupervised, class-conditioned, and semi-supervised learning, as well as im2spec-type models for predicting spectra from images and vice versa. More models to come!

If you found a bug in the code or would like a specific feature to be added, please create a report/request here.

Examples

The easiest way to start using pyroVED is via Google Colab, which is a free research tool from Google for machine learning education and research built on top of Jupyter Notebook. The following notebooks can be executed in Google Colab by simply clicking on the "Open in Colab" icon:

• Shift-VAE: Mastering the 1D shifts in spectral data

• r-VAE: Disentangling image content from rotations

• j(r)-VAE: Learning (jointly) discrete and continuous representations of data

• ss(r)-VAE: Semi-supervised learning from data with orientational disorder

• im2spec-VED: Predicting 1D spectra from 2D images

Installation

Requirements

• python >= 3.6
• pyro-ppl >= 1.6

Install pyroVED using pip:

pip install pyroved

Latest (unstable) version

To upgrade to the latest (unstable) version, run

pip install --upgrade git+https://github.com/ziatdinovmax/pyroved.git

Development

To run the unit tests, you'll need to have a pytest framework installed:

python3 -m pip install pytest

Then run tests as:

pytest tests

If this is your first time contributing to an open-source project, we highly recommend starting by familiarizing yourself with these very nice and detailed contribution guidelines.