Metadata-Version: 2.1
Name: tensorlayer3
Version: 1.0.0a0
Summary: High Level Tensorflow Deep Learning Library for Researcher and Engineer.
Home-page: https://git.openi.org.cn/TensorLayer/tensorlayer3.0
Author: TensorLayer Contributors
Author-email: tensorlayer@gmail.com
Maintainer: TensorLayer Contributors
Maintainer-email: tensorlayer@gmail.com
License: apache
Download-URL: https://git.openi.org.cn/TensorLayer/tensorlayer3.0
Description: |TENSORLAYER-LOGO|

        

        

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        TensorLayer is a novel TensorFlow-based deep learning and reinforcement

        learning library designed for researchers and engineers. It provides a

        large collection of customizable neural layers / functions that are key

        to build real-world AI applications. TensorLayer is awarded the 2017

        Best Open Source Software by the `ACM Multimedia

        Society <http://www.acmmm.org/2017/mm-2017-awardees/>`__.

        

        Why another deep learning library: TensorLayer

        ==============================================

        

        As deep learning practitioners, we have been looking for a library that

        can address various development purposes. This library is easy to adopt

        by providing diverse examples, tutorials and pre-trained models. Also,

        it allow users to easily fine-tune TensorFlow; while being suitable for

        production deployment. TensorLayer aims to satisfy all these purposes.

        It has three key features:

        

        -  **Simplicity** : TensorLayer lifts the low-level dataflow interface

           of TensorFlow to *high-level* layers / models. It is very easy to

           learn through the rich `example

           codes <https://github.com/tensorlayer/awesome-tensorlayer>`__

           contributed by a wide community.

        -  **Flexibility** : TensorLayer APIs are transparent: it does not

           mask TensorFlow from users; but leaving massive hooks that help

           *low-level tuning* and *deep customization*.

        -  **Zero-cost Abstraction** : TensorLayer can achieve the *full

           power* of TensorFlow. The following table shows the training speeds

           of classic 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   |

           +---------------+-----------------+-----------------+-----------------+

        

        TensorLayer stands at a unique spot in the library landscape. Other

        wrapper libraries like Keras and TFLearn also provide high-level

        abstractions. They, however, often hide the underlying engine from

        users, which make them hard to customize and fine-tune. On the contrary,

        TensorLayer APIs are generally flexible and transparent. Users often

        find it easy to start with the examples and tutorials, and then dive

        into TensorFlow seamlessly. In addition, TensorLayer does not create

        library lock-in through native supports for importing components from

        Keras, TFSlim and TFLearn.

        

        TensorLayer has a fast growing usage among top researchers and

        engineers, from universities like Imperial College London, UC Berkeley,

        Carnegie Mellon University, Stanford University, and University of

        Technology of Compiegne (UTC), and companies like Google, Microsoft,

        Alibaba, Tencent, Xiaomi, and Bloomberg.

        

        Install

        =======

        

        TensorLayer has pre-requisites including TensorFlow, numpy, and others. 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 --upgrade tensorlayer

        

            # for latest release candidate

            pip install --upgrade --pre tensorlayer

        

            # if you want to install the additional dependencies, you can also run

            pip install --upgrade tensorlayer[all]              # all additional dependencies

            pip install --upgrade tensorlayer[extra]            # only the `extra` dependencies

            pip install --upgrade tensorlayer[contrib_loggers]  # only the `contrib_loggers` dependencies

        

        Alternatively, you can install the latest or development version by directly pulling from github:

        

        .. code:: bash

        

            pip install https://github.com/tensorlayer/tensorlayer/archive/master.zip

            # or

            # pip install https://github.com/tensorlayer/tensorlayer/archive/<branch-name>.zip

        

        Using Docker - a ready-to-use environment

        -----------------------------------------

        

        The `TensorLayer

        containers <https://hub.docker.com/r/tensorlayer/tensorlayer/>`__ are

        built on top of the official `TensorFlow

        containers <https://hub.docker.com/r/tensorflow/tensorflow/>`__:

        

        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 <https://github.com/NVIDIA/nvidia-docker>`__

        

        .. 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

        ==========

        

        Please read the `Contributor

        Guideline <https://github.com/tensorlayer/tensorlayer/blob/master/CONTRIBUTING.md>`__

        before submitting your PRs.

        

        Cite

        ====

        

        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.

        

        

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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: tf_cpu
Provides-Extra: tf_gpu
Provides-Extra: extra
Provides-Extra: contrib_loggers
Provides-Extra: test
Provides-Extra: dev
Provides-Extra: doc
Provides-Extra: db
Provides-Extra: all
Provides-Extra: all_cpu
Provides-Extra: all_gpu
Provides-Extra: all_dev
Provides-Extra: all_cpu_dev
Provides-Extra: all_gpu_dev
