https://github.com/ma-lab-berkeley/redunet
ReduNet
https://github.com/ma-lab-berkeley/redunet
white-box-architecture
Last synced: 2 months ago
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ReduNet
- Host: GitHub
- URL: https://github.com/ma-lab-berkeley/redunet
- Owner: Ma-Lab-Berkeley
- Created: 2021-05-11T04:35:49.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2022-02-17T07:57:11.000Z (over 3 years ago)
- Last Synced: 2025-03-29T08:11:15.091Z (2 months ago)
- Topics: white-box-architecture
- Language: Python
- Homepage:
- Size: 97.7 KB
- Stars: 539
- Watchers: 10
- Forks: 81
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
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README
# Deep Networks from the Principle of Rate Reduction
This repository is the official PyTorch implementation of the paper [ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction](https://arxiv.org/abs/2105.10446) (2021)
by [Kwan Ho Ryan Chan](https://ryanchankh.github.io)* (UC Berkeley), [Yaodong Yu](https://yaodongyu.github.io/)* (UC Berkeley), [Chong You](https://sites.google.com/view/cyou)* (UC Berkeley), [Haozhi Qi](https://haozhi.io/) (UC Berkeley), [John Wright](http://www.columbia.edu/~jw2966/) (Columbia University), and [Yi Ma](http://people.eecs.berkeley.edu/~yima/) (UC Berkeley).
## What is ReduNet?
ReduNet is a deep neural network construcuted naturally by deriving the gradients of the Maximal Coding Rate Reduction (MCR2) [1] objective. Every layer of this network can be interpreted based on its mathematical operations and the network collectively is trained in a feed-forward manner only. In addition, by imposing shift invariant properties to our network, the convolutional operator can be derived using only the data and MCR2 objective function, hence making our network design principled and interpretable.
Figure: Weights and operations for one layer of ReduNet
[1] Yu, Yaodong, Kwan Ho Ryan Chan, Chong You, Chaobing Song, and Yi Ma. "[Learning diverse and discriminative representations via the principle of maximal coding rate reduction](https://proceedings.neurips.cc/paper/2020/file/6ad4174eba19ecb5fed17411a34ff5e6-Paper.pdf)" Advances in Neural Information Processing Systems 33 (2020).
## Requirements
This codebase is written for `python3`. To install necessary python packages, run `conda create --name redunet_official --file requirements.txt`.
## Demo
For a quick demonstration of ReduNet on Gaussian 2D or 3D cases, please visit the notebook by running one of the two commands:
```
$ jupyter notebook ./examples/gaussian2d.ipynb
$ jupyter notebook ./examples/gaussian3d.ipynb
```
## Core Usage and Design
The design of this repository aims to be easy-to-use and easy-to-intergrate to the current framework of your experiment, as long as it uses PyTorch. The `ReduNet` object inherents from `nn.Sequential`, and layers `ReduLayers`, such as `Vector`, `Fourier1D` and `Fourier2D` inherent from `nn.Module`. Loss functions are implemented in `loss.py`. Architectures and Dataset options are located in `load.py` file. Data objects and pre-set architectures are loaded in folders `dataset` and `architectures`. Feel free to add more based on the experiments you want to run. We have provided basic experiment setups, located in `train_.py` and `evaluate_.py`, where `` is the type of experiment. For utility functions, please check out `functional.py` or `utils.py`. Feel free to email us if there are any issues or suggestions.
## Example: Forward Construction
To train a ReduNet using forward construction, please checkout `train_forward.py`. For evaluating, please checkout `evaluate_forward.py`. For example, to train on 40-layer ReduNet on MNIST using 1000 samples per class, run:
```
$ python3 train_forward.py --data mnistvector --arch layers50 --samples 1000
```
After training, you can evaluate the trained model using `evaluate_forward.py`, by running:
```
$ python3 evaluate_forward.py --model_dir ./saved_models/forward/mnistvector+layers50/samples1000
```
, which will evaluate using all available training samples and testing samples. For more training and testing options, please checkout the file `train_forward.py` and `evaluate_forward.py`.
### Experiments in Paper
For code used to generate experimental empirical results listed in our paper, please visit our other repository: [https://github.com/ryanchankh/redunet_paper](https://github.com/ryanchankh/redunet_paper)
## Reference
For technical details and full experimental results, please check the [paper](https://arxiv.org/abs/2105.10446). Please consider citing our work if you find it helpful to yours:
```
@article{chan2021redunet,
title={ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction},
author={Chan, Kwan Ho Ryan and Yu, Yaodong and You, Chong and Qi, Haozhi and Wright, John and Ma, Yi},
journal={arXiv preprint arXiv:2105.10446},
year={2021}
}
```
## License and Contributing
- This README is formatted based on [paperswithcode](https://github.com/paperswithcode/releasing-research-code).
- Feel free to post issues via Github.
## Contact
Please contact [[email protected]]([email protected]) and [[email protected]]([email protected]) if you have any question on the codes.