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/keras-tuner.git@1.0.2rc1 pip3 install autokeras

Please follow the installation guide for more details.

Note: Currently, AutoKeras is only compatible with Python >= 3.5 and TensorFlow >= 2.3.0.

Community

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

Emails: Subscribe 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.

Instant Communications

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

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

Online Meetings: Join the online meeting Google group. The calendar event will appear on your Google Calendar.

Contributing Code

We engage in keeping everything about AutoKeras open to the public. Everyone can easily join as a developer. Here is how we manage our project.

Triage the issues : We pick the important 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.

: We pick the important 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. Assign the tasks : We assign the tasks to people during the online meetings.

: We assign the tasks to people during the online meetings. Discuss: We can have discussions in multiple places. The code reviews are on GitHub. Questions can be asked in Slack or during the meetings.

Please join our Slack and send Haifeng Jin a message. Or drop by our online meetings and talk to us. We will help you get started!

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.