Metadata-Version: 2.1
Name: autokeras
Version: 1.0.2
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/1.0.2.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)
        [![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.
        
        ```
        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 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 >= 2.1.0**.
        
        ## Community
        <a href="https://keras-slack-autojoin.herokuapp.com/"><img src="https://github.com/keras-team/autokeras/blob/master/docs/templates/img/slack.png?raw=true" alt="drawing" width="150"/></a>
        
        [Request an invitation](https://keras-slack-autojoin.herokuapp.com/).
        Use the [#autokeras](https://app.slack.com/client/T0QKJHQRE/CSZ5MKZFU) channel for communication.
        
        You can also follow us on Twitter [@autokeras](https://twitter.com/autokeras) for the latest news.
        
        ## Contributors
        
        You can follow the [Contributing Guide](https://autokeras.com/contributing/) to become a contributor.
        Thank all the contributors!
        
        <a href="https://github.com/keras-team/autokeras/graphs/contributors"><img src="https://opencollective.com/autokeras/contributors.svg?avatarHeight=36&width=890&button=false" /></a>
        
        
        ## Backers
        
        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"></a>
        <a href="https://opencollective.com/autokeras#backers" target="_blank"><img src="https://opencollective.com/autokeras/backer.svg?avatarHeight=36&width=890"></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}
        }
        ```
        
        ## DISCLAIMER
        
        Please note that this is a **pre-release** version of the AutoKeras which is still undergoing final testing before its official release. The website, its software and all content found on it are provided on an
        "as is" and "as available" basis. AutoKeras does **not** give any warranties, whether express or implied, as to the suitability or usability of the website, its software or any of its content. AutoKeras will **not** be liable for any loss, whether such loss is direct, indirect, special or consequential, suffered by any party as a result of their use of the libraries or content. Any usage of the libraries is done at the user's own risk and the user will be solely responsible for any damage to any computer system or loss of data that results from such activities. Should you encounter any bugs, glitches, lack of functionality or
        other problems on the website, please let us know immediately so we
        can rectify these accordingly. Your help in this regard is greatly
        appreciated.
        
        ## 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.
        
Keywords: AutoML,Keras
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
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
