Metadata-Version: 2.1
Name: tensorlayer
Version: 1.8.6rc0
Summary: Reinforcement Learning and Deep Learning Library for Researcher and Engineer.
Home-page: https://github.com/tensorlayer/tensorlayer
Author: TensorLayer Contributors
Author-email: hao.dong11@imperial.ac.uk
Maintainer: TensorLayer Contributors
Maintainer-email: hao.dong11@imperial.ac.uk
License: apache
Download-URL: https://github.com/tensorlayer/tensorlayer
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        TensorLayer is a deep learning and reinforcement learning library on top
        of `TensorFlow`_. It provides rich neural layers and utility functions
        to help researchers and engineers build real-world AI applications.
        TensorLayer is awarded the 2017 Best Open Source Software by the
        prestigious `ACM Multimedia Society`_.
        
        Why another deep learning library: TensorLayer
        ==============================================
        
        Features
        --------
        
        As TensorFlow users, we have been looking for a library that can serve
        for various development phases. This library is easy for beginners by
        providing rich neural network implementations, examples and tutorials.
        Later, its APIs shall naturally allow users to leverage the powerful
        features of TensorFlow, exhibiting best performance in addressing
        real-world problems. In the end, the extra abstraction shall not
        compromise TensorFlow performance, and thus suit for production
        deployment. TensorLayer is a novel library that aims to satisfy these
        requirements.
        
        It has three key features:
        
        -  **Simplicity** : TensorLayer lifts the low-level dataflow abstraction
           of TensorFlow to *high-level* layers. It also provides users with
           `rich examples`_ to minimize learning barrier.
        -  **Flexibility** : TensorLayer APIs are transparent: it does not mask
           TensorFlow from users; but leaving massive hooks that support diverse
           *low-level tuning*.
        -  **Zero-cost Abstraction** : TensorLayer has negligible overheads and
           can thus achieve the *full performance* of TensorFlow.
        
        Negligible overhead
        -------------------
        
        To show the overhead, we train classic deep learning models using
        TensorLayer and native TensorFlow on a Titan X Pascal GPU.
        
        +-------------+---------------+---------------+---------------+
        |             | CIFAR-10      | PTB LSTM      | Word2Vec      |
        +=============+===============+===============+===============+
        | TensorLayer | 2528 images/s | 18063 words/s | 58167 words/s |
        +-------------+---------------+---------------+---------------+
        | TensorFlow  | 2530 images/s | 18075 words/s | 58181 words/s |
        +-------------+---------------+---------------+---------------+
        
        Why using TensorLayer instead of Keras or TFLearn
        -------------------------------------------------
        
        Similar to TensorLayer, Keras and TFLearn are also popular TensorFlow
        wrapper libraries. These libraries are comfortable to start with. They
        provide high-level abstractions; but mask the underlying engine from
        users. It is thus hard to customize model behaviors and touch the
        essential features of TensorFlow.
        
        Without compromise in simplicity, TensorLayer APIs are generally more
        flexible and transparent. Users often find it easy to start with the
        examples and tutorials of TensorLayer, and then dive into the TensorFlow
        low-level APIs only if need. TensorLayer does not create library
        lock-in. Users can easily import models from Keras, TFSlim and TFLearn
        into a TensorLayer environment.
        
        TensorLayer has a fast growing usage in academic and industry
        organizations. It is used by researchers from Imperial College London,
        Carnegie Mellon University, Stanford University, University of
        Technology of Compiegne (UTC), Tsinghua University, UCLA, and etc., as
        well as engineers from Google, Microsoft, Alibaba, Tencent, Xiaomi,
        Penguins Innovate, Bloomberg and many others.
        
        Installation
        ============
        
        TensorLayer has pre-requisites including TensorFlow, numpy, matplotlib
        and nltk (optional). For GPU support, CUDA and cuDNN are required.
        
        The simplest way to install TensorLayer is to use the Python Package
        Index (PyPI):
        
        .. code:: bash
        
           # for last stable version
           pip install tensorlayer
        
           # for latest release candidate
           pip install --pre tensorlayer
        
        Alternatively, you can install the development version by directly
        pulling from github:
        
        .. code:: bash
        
           pip install git+https://github.com/tensorlayer/tensorlayer.git
        
        Using Docker - a ready-to-use environment
        -----------------------------------------
        
        The `TensorLayer containers`_ are built on top of the official
        `TensorFlow containers`_:
        
        Containers with CPU support
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        .. code:: bash
        
           # for CPU version and Python 2
           docker pull tensorlayer/tensorlayer:latest
           docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest
        
           # for CPU version and Python 3
           docker pull tensorlayer/tensorlayer:latest-py3
           docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest-py3
        
        Containers with GPU support
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        NVIDIA-Docker is required for these containers to work: `Project Link`_
        
        .. code:: bash
        
           # for GPU version and Python 2
           docker pull tensorlayer/tensorlayer:latest-gpu
           nvidia-docker run -it --rm -p 8888:88888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest-gpu
        
           # for GPU version and Python 3
           docker pull tensorlayer/tensorlayer:latest-gpu-py3
           nvidia-docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest-gpu-py3
        
        Contribute to TensorLayer
        =========================
        
        Please read the `Contributor Guideline`_ before submitting your PRs.
        
        Citation
        ========
        
        If you find this project useful, we would be grateful if you cite the
        TensorLayer paper：
        
        ::
        
           @article{tensorlayer2017,
               author  = {Dong, Hao and Supratak, Akara and Mai, Luo and Liu, Fangde and Oehmichen, Axel and Yu, Simiao and Guo, Yike},
               journal = {ACM Multimedia},
               title   = {{TensorLayer: A Versatile Library for Efficient Deep Learning Development}},
               url     = {http://tensorlayer.org},
               year    = {2017}
           }
        
        License
        =======
        
        TensorLayer is released under the Apache 2.0 license.
        
        .. _Contributor Guideline: https://github.com/tensorlayer/tensorlayer/blob/rearrange-readme/CONTRIBUTING.md
        
        .. _TensorLayer containers: https://hub.docker.com/r/tensorlayer/tensorlayer/
        .. _TensorFlow containers: https://hub.docker.com/r/tensorflow/tensorflow/
        .. _Project Link: https://github.com/NVIDIA/nvidia-docker
        
        .. _rich examples: https://github.com/tensorlayer/awesome-tensorlayer
        
        .. _Mentioned in Awesome TensorLayer: https://github.com/tensorlayer/awesome-tensorlayer
        .. _English Documentation: https://tensorlayer.readthedocs.io/
        .. _Chinese Documentation: https://tensorlayercn.readthedocs.io/
        .. _Chinese Book: http://www.broadview.com.cn/book/5059/
        
        .. _TensorFlow: https://www.tensorflow.org
        .. _ACM Multimedia Society: http://www.acmmm.org/2017/mm-2017-awardees/
        
Keywords: deep learning,machine learning,computer vision,nlp,supervised learning,unsupervised learning,reinforcement learning,tensorflow
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Information Technology
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Utilities
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Environment :: Console
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Provides-Extra: doc
Provides-Extra: tf_gpu
Provides-Extra: tf_cpu
Provides-Extra: all
Provides-Extra: test
Provides-Extra: all_gpu
Provides-Extra: dev
Provides-Extra: all_cpu
Provides-Extra: extra
Provides-Extra: db
