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AutoML for deep learning

Project description

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codecov PyPI version Python Tensorflow contributions welcome

Official Website: autokeras.com

AutoKeras: An AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. The goal of AutoKeras is to make machine learning accessible to everyone.

Learning resources

  • A short example.
import autokeras as ak

clf = ak.ImageClassifier()
clf.fit(x_train, y_train)
results = clf.predict(x_test)

drawing &nbsp &nbsp drawing

Installation

To install the package, please use the pip installation as follows:

pip3 install autokeras

Please follow the installation guide for more details.

Note: Currently, AutoKeras is only compatible with Python >= 3.7 and TensorFlow >= 2.8.0.

Community

Stay Up-to-Date

Subscribe to our email list to receive announcements.

Questions and Discussions

GitHub Discussions: Ask your questions on our GitHub Discussions. It is a forum hosted on GitHub. We will monitor and answer the questions there.

Slack: Request an invitation. Use the #autokeras channel for communication.

QQ Group: Join our QQ group 1150366085. Password: akqqgroup

Contributing Code

Here is how we manage our project.

We pick the critical issues to work on from GitHub issues. They will be added to this Project. Some of the issues will then be added to the milestones, which are used to plan for the releases.

Refer to our Contributing Guide to learn the best practices.

Thank all the contributors!

Donation

We accept financial support on Open Collective. Thank every backer for supporting us!

Cite this work

Haifeng Jin, Qingquan Song, and Xia Hu. "Auto-keras: An efficient neural architecture search system." Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2019. (Download)

Biblatex entry:

@inproceedings{jin2019auto,
  title={Auto-Keras: An Efficient Neural Architecture Search System},
  author={Jin, Haifeng and Song, Qingquan and Hu, Xia},
  booktitle={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages={1946--1956},
  year={2019},
  organization={ACM}
}

Acknowledgements

The authors gratefully acknowledge the D3M program of the Defense Advanced Research Projects Agency (DARPA) administered through AFRL contract FA8750-17-2-0116; the Texas A&M College of Engineering, and Texas A&M University.

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