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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.
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:
Mode | Lib | Data Format | Max GPU Memory Usage(MB) | Max CPU Memory Usage(MB) | Avg CPU Memory Usage(MB) | Runtime (sec) |
---|---|---|---|---|---|---|
AutoGraph | TensorFlow 2.0 | channel last | 11833 | 2161 | 2136 | 74 |
Tensorlayer 2.0 | channel last | 11833 | 2187 | 2169 | 76 | |
Graph | Keras | channel last | 8677 | 2580 | 2576 | 101 |
Eager | TensorFlow 2.0 | channel last | 8723 | 2052 | 2024 | 97 |
TensorLayer 2.0 | channel last | 8723 | 2010 | 2007 | 95 |
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 lightweight, 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.
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.
You can find a large collection of tutorials, examples and real-world applications using TensorLayer within examples or through the following space:
TensorLayer has extensive documentation for both beginners and professionals. The documentation is available in
both English and Chinese. Please click the following icons to find the documents you need:
If you want to try the experimental features on the the master branch, you can find the latest document
here.
For latest code for TensorLayer 2.0, please build from the source. TensorLayer 2.0 has pre-requisites including TensorFlow 2, numpy, and others. For GPU support, CUDA and cuDNN are required.
Install the stable version:
pip3 install tensorflow-gpu
pip3 install tensorlayer
Install the latest version:
pip3 install https://github.com/tensorlayer/tensorlayer/archive/master.zip
If you want install TensorLayer 1.X, the simplest way to install TensorLayer 1.X is to use the Python Package Index (PyPI):
# for last stable version of TensorLayer 1.X
pip3 install --upgrade tensorlayer==1.X
# for latest release candidate of TensorLayer 1.X
pip3 install --upgrade --pre tensorlayer
# if you want to install the additional dependencies, you can also run
pip3 install --upgrade tensorlayer[all] # all additional dependencies
pip3 install --upgrade tensorlayer[extra] # only the `extra` dependencies
pip3 install --upgrade tensorlayer[contrib_loggers] # only the `contrib_loggers` dependencies
Please read the Contributor Guideline before submitting your PRs.
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}
}
TensorLayer is released under the Apache 2.0 license.
TensorLayerX是一款兼容多深度学习框架后端的深度学习库, 可以使用TensorFlow、MindSpore、PaddlePaddle、PyTorch作为后端计算引擎进行模型训练、推理。
Python other
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