tensorlayer 1.8.6rc1

Reinforcement Learning and Deep Learning Library for Researcher and Engineer.

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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/