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autokeras

by keras-team

keras-team /autokeras

AutoML library for deep learning

7.2K Stars 1.2K Forks Last release: 23 days ago (1.0.3) MIT License 1.1K Commits 37 Releases

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Official Website: autokeras.com

Let's Chat!

To make AutoKeras better, I would like to hear your thoughts. I am happy to answer any questions you have about our project.Join our Slack and send me (Haifeng Jin) a message. I will schedule a meeting with you.

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 for everyone.

Example

Here is a short example of using the package.

import autokeras as ak clf = ak.ImageClassifier() clf.fit(x\_train, y\_train) results = clf.predict(x\_test)

For detailed tutorial, please check here.

Installation

To install the package, please use the

pip

installation as follows:

pip3 install git+https://github.com/keras-team/[email protected] pip3 install autokeras==1.0.3

Please follow the installation guide for more details.

Note: Currently, AutoKeras is only compatible with Python >= 3.5 and TensorFlow >= 2.2.0.

Community

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

Twitter: You can also follow us on Twitter @autokeras for the latest news.

Emails: Subscribe our email list to receive announcements.

Online Meetings: Join the Google group and our online meetings will appear on your Google Calendar.

Contributing

You can follow the Contributing Guide to become a contributor.

If you don't know where to start, please join our community on Slack and ask us. We will help you get started!

Thank all the contributors!

Backers

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

Organizations:

Individuals:

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} }

DISCLAIMER

Please note that this is a pre-release version of the AutoKeras which is still undergoing final testing before its official release. The website, its software and all content found on it are provided on an "as is" and "as available" basis. AutoKeras does not give any warranties, whether express or implied, as to the suitability or usability of the website, its software or any of its content. AutoKeras will not be liable for any loss, whether such loss is direct, indirect, special or consequential, suffered by any party as a result of their use of the libraries or content. Any usage of the libraries is done at the user's own risk and the user will be solely responsible for any damage to any computer system or loss of data that results from such activities. Should you encounter any bugs, glitches, lack of functionality or other problems on the website, please let us know immediately so we can rectify these accordingly. Your help in this regard is greatly appreciated.

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.

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