keras-team / autokeras
AutoML library for deep learning
AI Architecture Analysis
This repository is indexed by RepoMind. By analyzing keras-team/autokeras in our AI interface, you can instantly generate complete architecture diagrams, visualize control flows, and perform automated security audits across the entire codebase.
Our Agentic Context Augmented Generation (Agentic CAG) engine loads full source files into context, avoiding the fragmentation of traditional RAG systems. Ask questions about the architecture, dependencies, or specific features to see it in action.
Repository Summary (README)
PreviewOfficial Website: autokeras.com
AutoKeras: An AutoML system based on Keras. It is developed by <a href="http://faculty.cs.tamu.edu/xiahu/index.html" target="_blank" rel="nofollow">DATA Lab</a> 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
Ask your questions on our GitHub Discussions.
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!
Cite this work
Haifeng Jin, François Chollet, Qingquan Song, and Xia Hu. "AutoKeras: An AutoML Library for Deep Learning." the Journal of machine Learning research 6 (2023): 1-6. (Download)
Biblatex entry:
@article{JMLR:v24:20-1355,
author = {Haifeng Jin and François Chollet and Qingquan Song and Xia Hu},
title = {AutoKeras: An AutoML Library for Deep Learning},
journal = {Journal of Machine Learning Research},
year = {2023},
volume = {24},
number = {6},
pages = {1--6},
url = {http://jmlr.org/papers/v24/20-1355.html}
}
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.