# autokeras
**Repository Path**: deeplearningrepos/autokeras
## Basic Information
- **Project Name**: autokeras
- **Description**: AutoML library for deep learning
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-03-30
- **Last Updated**: 2021-08-31
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
[](https://github.com/keras-team/autokeras/actions?query=workflow%3ATests+branch%3Amaster)
[](https://codecov.io/gh/keras-team/autokeras)
[](https://badge.fury.io/py/autokeras)


[](https://github.com/keras-team/autokeras/issues)
Official Website: [autokeras.com](https://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.
## Example
Here is a short example of using the package.
```python
import autokeras as ak
clf = ak.ImageClassifier()
clf.fit(x_train, y_train)
results = clf.predict(x_test)
```
For a detailed tutorial, please check [here](https://autokeras.com/tutorial/overview/).
## Installation
To install the package, please use the `pip` installation as follows:
```shell
pip3 install autokeras
```
Please follow the [installation guide](https://autokeras.com/install) for more details.
**Note:** Currently, AutoKeras is only compatible with **Python >= 3.5** and **TensorFlow >= 2.3.0**.
## Community
### Stay Up-to-Date
**Twitter**:
You can also follow us on Twitter [@autokeras](https://twitter.com/autokeras) for the latest news.
**Emails**:
Subscribe to our [email list](https://groups.google.com/forum/#!forum/autokeras-announce/join) to receive announcements.
### Questions and Discussions
**GitHub Discussions**:
Ask your questions on our [GitHub Discussions](https://github.com/keras-team/autokeras/discussions).
It is a forum hosted on GitHub. We will monitor and answer the questions there.
### Instant Communications
**Slack**:
[Request an invitation](https://keras-slack-autojoin.herokuapp.com/).
Use the [#autokeras](https://app.slack.com/client/T0QKJHQRE/CSZ5MKZFU) channel for communication.
**QQ Group**:
Join our QQ group 1150366085. Password: akqqgroup
**Online Meetings**:
Join the [online meeting Google group](https://groups.google.com/forum/#!forum/autokeras/join).
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 critical issues to work on from [GitHub issues](https://github.com/keras-team/autokeras/issues).
They will be added to this [Project](https://github.com/keras-team/autokeras/projects/3).
Some of the issues will then be added to the [milestones](https://github.com/keras-team/autokeras/milestones),
which are used to plan for the releases.
* **Assign the tasks**: 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 meetings.
Please join our [Slack](https://autokeras.com/#community) and send Haifeng Jin a message.
Or drop by our [online meetings](https://autokeras.com/#community) and talk to us.
We will help you get started!
Refer to our [Contributing Guide](https://autokeras.com/contributing/) to learn the best practices.
Thank all the contributors!
## Donation
We accept financial support on [Open Collective](https://opencollective.com/autokeras).
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](https://www.kdd.org/kdd2019/accepted-papers/view/auto-keras-an-efficient-neural-architecture-search-system))
Biblatex entry:
```bibtex
@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.