TensorLayer: A Deep Learning and Reinforcement Learning Library for Researchers and Engineers.
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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.



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:

# for master version (Recommended)
$ pip install git+https://github.com/tensorlayer/tensorlayer.git 

# for stable version 
$ pip install tensorlayer

Dockerfile is supplied to build images, build as usual

# for CPU version
$ docker build -t tensorlayer:latest .

# for GPU version
$ docker build -t tensorlayer:latest-gpu -f Dockerfile.gpu . 

Please check documentation for detailed instructions.

Examples and Tutorials

Examples can be found in this folder and Github topic.


Computer Vision

Natural Language Processing

Adversarial Learning

Reinforcement Learning



TensorLayer provides two set of Convolutional layer APIs, see (Advanced) and (Basic) on readthedocs website.


Design Philosophy

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 massive examples and tutorials 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 is able to achieve the full performance of TensorFlow.

Negligible Overhead

TensorLayer has negligible performance overhead. We benchmark classic deep learning models using TensorLayer and native TensorFlow on a Titan X Pascal GPU. Here are the training speeds of respective tasks:

TensorLayer 2528 images/s 18063 words/s 58167 words/s
TensorFlow 2530 images/s 18075 words/s 58181 words/s

Compared with Keras and 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.


The documentation [Online] [PDF] [Epub] [HTML] describes the usages of TensorLayer APIs. It is also a self-contained document that walks through different types of deep neural networks, reinforcement learning and their applications in Natural Language Processing (NLP) problems.

We have included the corresponding modularized implementations of Google TensorFlow Deep Learning tutorial, so you can read the TensorFlow tutorial [en] [cn] along with our document. Chinese documentation is also available.

Academic and Industry Users

TensorLayer has an open and fast growing community. It has been widely used by researchers from Imperial College London, Carnegie Mellon University, Stanford University, Tsinghua University, UCLA, Linköping University and etc., as well as engineers from Google, Microsoft, Alibaba, Tencent, Penguins Innovate, ReFULE4, Bloomberg, GoodAILab and many others.

  • 🇬🇧 If you have any question, we suggest to create an issue to discuss with us.
  • 🇨🇳 我们有中文讨论社区: 如QQ群微信群.

Contribution Guideline

Guideline in 5 lines


If you find this project useful, we would be grateful if you cite the TensorLayer paper:

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}


TensorLayer is released under the Apache 2.0 license.