https://github.com/negrinho/deep_architect_legacy
DeepArchitect: Automatically Designing and Training Deep Architectures
https://github.com/negrinho/deep_architect_legacy
architecture-search auto-ml automatic-machine-learning deep-learning hyperparameter-optimization machine-learning neural-architecture-search
Last synced: 10 months ago
JSON representation
DeepArchitect: Automatically Designing and Training Deep Architectures
- Host: GitHub
- URL: https://github.com/negrinho/deep_architect_legacy
- Owner: negrinho
- License: mit
- Created: 2017-04-28T05:35:51.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2019-10-01T04:08:23.000Z (over 6 years ago)
- Last Synced: 2025-04-09T01:41:23.671Z (about 1 year ago)
- Topics: architecture-search, auto-ml, automatic-machine-learning, deep-learning, hyperparameter-optimization, machine-learning, neural-architecture-search
- Language: Python
- Size: 34.2 KB
- Stars: 145
- Watchers: 10
- Forks: 20
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# DeepArchitect: Automatically Designing and Training Deep Architectures
**IMPORTANT:** This repo is not under active development.
It contains a prototype for the ideas described in this [paper](https://arxiv.org/abs/1704.08792).
See our [NeurIPS 2019 paper](https://arxiv.org/abs/1909.13404) for the latest developments.
The code and documentation for the latest framework can be found [here](https://github.com/negrinho/deep_architect).
This repository contains a Python implementation of the DeepArchitect framework described in
[our paper](https://arxiv.org/abs/1704.08792).
To get familiar with the framework, we recommend starting with
[this tutorial](https://github.com/negrinho/deep_architect_legacy/blob/master/tutorial.ipynb).
A tar file with the logs of the experiments in the paper is available [here](http://www.cs.cmu.edu/~negrinho/assets/papers/deep_architect/logs.tar.gz). You can download it, unzip it in the top folder of the repo, and generate the plots of the paper using `plots.py`. The logs are composed of text and pickle files. It may be informative to inspect them. The experiments reported in the paper can be reproduced using `experiments.py`.
Contributors:
[Renato Negrinho](http://www.cs.cmu.edu/~negrinho/),
[Geoff Gordon](http://www.cs.cmu.edu/~ggordon/),
[Matt Gormley](http://www.cs.cmu.edu/~mgormley/),
[Christoph Dann](http://cdann.net/),
[Matt Barnes](http://www.cs.cmu.edu/~mbarnes1/).
## References
```
@article{negrinho2017deeparchitect,
title={Deeparchitect: Automatically designing and training deep architectures},
author={Negrinho, Renato and Gordon, Geoff},
journal={arXiv preprint arXiv:1704.08792},
year={2017}
}
@article{negrinho2019towards,
title={Towards modular and programmable architecture search},
author={Negrinho, Renato and Patil, Darshan and Le, Nghia and Ferreira, Daniel and Gormley, Matthew and Gordon, Geoffrey},
journal={Neural Information Processing Systems},
year={2019}
}
```