# 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

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[![](https://github.com/keras-team/autokeras/workflows/Tests/badge.svg?branch=master)](https://github.com/keras-team/autokeras/actions?query=workflow%3ATests+branch%3Amaster) [![codecov](https://codecov.io/gh/keras-team/autokeras/branch/master/graph/badge.svg)](https://codecov.io/gh/keras-team/autokeras) [![PyPI version](https://badge.fury.io/py/autokeras.svg)](https://badge.fury.io/py/autokeras) ![Python](https://img.shields.io/badge/python-v3.5.0+-success.svg) ![Tensorflow](https://img.shields.io/badge/tensorflow-v2.3.0+-success.svg) [![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](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.