AutoML for deep learning
Project description
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)
- Official website tutorials.
- The book of Automated Machine Learning in Action.
- The LiveProjects of Image Classification with AutoKeras.
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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