TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extensive collection of customizable neural layers to build complex AI models. TensorLayer is awarded the 2017 Best Open Source Software by the [ACM Multimedia Society](https://twitter.com/ImperialDSI/status/923928895325442049).
TensorLayer can also be found at [iHub](https://code.ihub.org.cn/projects/328) and [Gitee](https://gitee.com/organizations/TensorLayer).
# News
🔥 Reinforcement Learning Model Zoo: [Low-level APIs for Research](https://github.com/tensorlayer/tensorlayer/tree/master/examples/reinforcement_learning) and [High-level APIs for Production](https://github.com/tensorlayer/RLzoo)
🔥 [Sipeed Maxi-EMC](https://github.com/sipeed/Maix-EMC): Run TensorLayer models on the **low-cost AI chip** (e.g., K210) (Alpha Version)
<!-- 🔥 [NNoM](https://github.com/majianjia/nnom): Run TensorLayer quantized models on the**MCU** (e.g., STM32) (Coming Soon) -->
🔥 [Free GPU and storage resources](https://github.com/fangde/FreeGPU): TensorLayer users can access to free GPU and storage resources donated by SurgicalAI. Thank you SurgicalAI!
# Design Features
TensorLayer is a new deep learning library designed with simplicity, flexibility and high-performance in mind.
- ***Simplicity*** : TensorLayer has a high-level layer/model abstraction which is effortless to learn. You can learn how deep learning can benefit your AI tasks in minutes through the massive [examples](https://github.com/tensorlayer/awesome-tensorlayer).
- ***Flexibility*** : TensorLayer APIs are transparent and flexible, inspired by the emerging PyTorch library. Compared to the Keras abstraction, TensorLayer makes it much easier to build and train complex AI models.
- ***Zero-cost Abstraction*** : Though simple to use, TensorLayer does not require you to make any compromise in the performance of TensorFlow (Check the following benchmark section for more details).
TensorLayer stands at a unique spot in the TensorFlow wrappers. Other wrappers like Keras and TFLearn
hide many powerful features of TensorFlow and provide little support for writing custom AI models. Inspired by PyTorch, TensorLayer APIs are simple, flexible and Pythonic,
making it easy to learn while being flexible enough to cope with complex AI tasks.
TensorLayer has a fast-growing community. It has been used by researchers and engineers all over the world, including those from Peking University,
Imperial College London, UC Berkeley, Carnegie Mellon University, Stanford University, and companies like Google, Microsoft, Alibaba, Tencent, Xiaomi, and Bloomberg.
# Multilingual Documents
TensorLayer has extensive documentation for both beginners and professionals. The documentation is available in
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/):
The following table shows the training speeds of [VGG16](http://www.robots.ox.ac.uk/~vgg/research/very_deep/) using TensorLayer and native TensorFlow on a TITAN Xp.
| Mode | Lib | Data Format | Max GPU Memory Usage(MB) |Max CPU Memory Usage(MB) | Avg CPU Memory Usage(MB) | Runtime (sec) |
