Metadata-Version: 2.1
Name: autokeras
Version: 1.0.12
Summary: AutoML for deep learning
Home-page: http://autokeras.com
Author: Data Analytics at Texas A&M (DATA) Lab, Keras Team
Author-email: jhfjhfj1@gmail.com
License: MIT
Download-URL: https://github.com/keras-team/autokeras/archive/1.0.12.tar.gz
Description: <img src="https://autokeras.com/img/row_red.svg" alt="drawing" width="400px" style="display: block; margin-left: auto; margin-right: auto"/>
        
        [![](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)
        
        Official Website: [autokeras.com](https://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 for 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 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 git+https://github.com/keras-team/keras-tuner.git
        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 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 important 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 the 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!
        
        <a href="https://github.com/keras-team/autokeras/graphs/contributors"><img src="https://notes.haifengjin.com/img/contributors.svg" /></a>
        
        
        ## Donation
        
        We accept financial support on [Open Collective](https://opencollective.com/autokeras).
        Thank every backer for supporting us!
        
        <a href="https://opencollective.com/autokeras#backers" target="_blank"><img src="https://opencollective.com/autokeras/sponsor.svg?avatarHeight=36&width=890&button=false"></a>
        <a href="https://opencollective.com/autokeras#backers" target="_blank"><img src="https://opencollective.com/autokeras/backer.svg?avatarHeight=36&width=890&button=false"></a>
        
        ## 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.
        
Keywords: AutoML,Keras
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Software Development :: Libraries
Description-Content-Type: text/markdown
Provides-Extra: tests
