https://github.com/thu-ml/zhusuan-jittor
Zhusuan with backend Jittor
https://github.com/thu-ml/zhusuan-jittor
bayesian-inference machine-learning probabilistic-programming
Last synced: 11 months ago
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Zhusuan with backend Jittor
- Host: GitHub
- URL: https://github.com/thu-ml/zhusuan-jittor
- Owner: thu-ml
- License: mit
- Created: 2021-06-17T14:46:11.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2022-03-04T05:01:17.000Z (over 4 years ago)
- Last Synced: 2025-06-09T06:37:00.862Z (about 1 year ago)
- Topics: bayesian-inference, machine-learning, probabilistic-programming
- Language: Python
- Homepage:
- Size: 537 KB
- Stars: 14
- Watchers: 4
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: docs/contributing.rst
- License: LICENSE
Awesome Lists containing this project
README
[](https://zhusuan-jittor.readthedocs.io/en/latest/?badge=latest)
# Zhusuan-Jittor
---
ZhuSuan-Jittor is a python probabilistic programming library for
**Bayesian deep learning**, which conjoins the complimentary advantages of
Bayesian methods and deep learning. ZhuSuan-Jittor is built upon
[Jittor](http://cg.cs.tsinghua.edu.cn/jittor/). Unlike existing deep learning
libraries, which are mainly designed for deterministic neural networks and
supervised tasks, ZhuSuan-Jittor provides deep learning style primitives and
algorithms for building probabilistic models and applying Bayesian inference.
The supported inference algorithms include:
* Variational inference with programmable variational posteriors, various
objectives and advanced gradient estimators (SGVB, SWI, etc.).
* Importance sampling for learning and evaluating models, with programmable
proposals.
* MCMC samplers: Hamiltonian Monte Carlo (HMC) with parallel chains, and
Stochastic Gradient MCMC (sgmcmc).
## Installation
ZhuSuan-Jittor is still under development. Before the first stable release (1.0),
please clone the repository and run
```
pip install .
```
in the main directory. This will install ZhuSuan and its dependencies
automatically.
If you are developing ZhuSuan, you may want to install in an
"editable" or "develop" mode. Please refer to the Contributing section below.
## Documentation
* [Tutorials and API docs](https://zhusuan-jittor.readthedocs.io/en/main/?badge=main)
## Examples
We provide examples on traditional hierarchical Bayesian models and recent
deep generative models.
* Gaussian:
[HMC](examples/toy_models/gaussian.py)
* Bayesian Neural Networks:
[SGVB](examples/bayesian_neural_nets/bnn_vi.py),
[SGMCMC](examples/bayesian_neural_nets/bnn_sgmcmc.py)
* Variational Autoencoder (VAE):
[SGVB](examples/variational_autoencoders/vae_mnist.py),
[IWAE](examples/variational_autoencoders/vae_iw.py)
## Citing ZhuSuan-Jittor
If you find ZhuSuan-Jittor useful, please cite it in your publications.
```
@ARTICLE{zhusuan2017,
title={Zhu{S}uan: A Library for {B}ayesian Deep Learning},
author={Shi, Jiaxin and Chen, Jianfei. and Zhu, Jun and Sun, Shengyang
and Luo, Yucen and Gu, Yihong and Zhou, Yuhao},
journal={arXiv preprint arXiv:1709.05870},
year=2017,
}
```
## Contributing
We always welcome contributions to help make ZhuSuan-Jittor better. If you would like
to contribute, please check out the guidelines [here](CONTRIBUTING.md).