tensorlayer 1.8.6rc4

Reinforcement Learning and Deep Learning Library for Researcher and Engineer.

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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, 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 <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.