[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9756":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":16,"stars30d":17,"stars90d":16,"forks30d":16,"starsTrendScore":16,"compositeScore":18,"rankGlobal":10,"rankLanguage":10,"license":19,"archived":20,"fork":20,"defaultBranch":21,"hasWiki":22,"hasPages":20,"topics":23,"createdAt":10,"pushedAt":10,"updatedAt":39,"readmeContent":40,"aiSummary":41,"trendingCount":16,"starSnapshotCount":16,"syncStatus":42,"lastSyncTime":43,"discoverSource":44},9756,"TensorLayer","tensorlayer\u002FTensorLayer","tensorlayer","Deep Learning and Reinforcement Learning Library for Scientists and Engineers ","http:\u002F\u002Ftensorlayerx.com",null,"Python",7384,1588,448,26,0,1,40.6,"Other",false,"master",true,[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,7],"a3c","artificial-intelligence","chatbot","deep-learning","dqn","gan","google","imagenet","neural-network","object-detection","python","reinforcement-learning","tensorflow","tensorflow-tutorial","tensorflow-tutorials","2026-06-12 02:02:12","\u003Ca href=\"https:\u002F\u002Ftensorlayer.readthedocs.io\u002F\">\n    \u003Cdiv align=\"center\">\n        \u003Cimg src=\"img\u002Ftl_transparent_logo.png\" width=\"50%\" height=\"30%\"\u002F>\n    \u003C\u002Fdiv>\n\u003C\u002Fa>\n\n\u003C!--- [![PyPI Version](https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Ftensorlayer.svg)](https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Ftensorlayer) --->\n\u003C!--- ![PyPI - Python Version](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fpyversions\u002Ftensorlayer.svg)) --->\n\n![GitHub last commit (branch)](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flast-commit\u002Ftensorlayer\u002Ftensorlayer\u002Fmaster.svg)\n[![Supported TF Version](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FTensorFlow-2.0.0%2B-brightgreen.svg)](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensorflow\u002Freleases)\n[![Documentation Status](https:\u002F\u002Freadthedocs.org\u002Fprojects\u002Ftensorlayer\u002Fbadge\u002F)](https:\u002F\u002Ftensorlayer.readthedocs.io\u002F)\n[![Build Status](https:\u002F\u002Ftravis-ci.org\u002Ftensorlayer\u002Ftensorlayer.svg?branch=master)](https:\u002F\u002Ftravis-ci.org\u002Ftensorlayer\u002Ftensorlayer)\n[![Downloads](http:\u002F\u002Fpepy.tech\u002Fbadge\u002Ftensorlayer)](http:\u002F\u002Fpepy.tech\u002Fproject\u002Ftensorlayer)\n[![Downloads](https:\u002F\u002Fpepy.tech\u002Fbadge\u002Ftensorlayer\u002Fweek)](https:\u002F\u002Fpepy.tech\u002Fproject\u002Ftensorlayer\u002Fweek)\n[![Docker Pulls](https:\u002F\u002Fimg.shields.io\u002Fdocker\u002Fpulls\u002Ftensorlayer\u002Ftensorlayer.svg)](https:\u002F\u002Fhub.docker.com\u002Fr\u002Ftensorlayer\u002Ftensorlayer\u002F)\n[![Codacy Badge](https:\u002F\u002Fapi.codacy.com\u002Fproject\u002Fbadge\u002FGrade\u002Fd6b118784e25435498e7310745adb848)](https:\u002F\u002Fwww.codacy.com\u002Fapp\u002Ftensorlayer\u002Ftensorlayer)\n\n\u003C!---  [![CircleCI](https:\u002F\u002Fcircleci.com\u002Fgh\u002Ftensorlayer\u002Ftensorlayer\u002Ftree\u002Fmaster.svg?style=svg)](https:\u002F\u002Fcircleci.com\u002Fgh\u002Ftensorlayer\u002Ftensorlayer\u002Ftree\u002Fmaster) --->\n\n\u003C!---  [![Documentation Status](https:\u002F\u002Freadthedocs.org\u002Fprojects\u002Ftensorlayercn\u002Fbadge\u002F)](https:\u002F\u002Ftensorlayercn.readthedocs.io\u002F)\n\u003C!---  [![PyUP Updates](https:\u002F\u002Fpyup.io\u002Frepos\u002Fgithub\u002Ftensorlayer\u002Ftensorlayer\u002Fshield.svg)](https:\u002F\u002Fpyup.io\u002Frepos\u002Fgithub\u002Ftensorlayer\u002Ftensorlayer\u002F) --->\n\n# Please click [TensorLayerX](https:\u002F\u002Fgithub.com\u002Ftensorlayer\u002Ftensorlayerx) 🔥🔥🔥\n\n[TensorLayer](https:\u002F\u002Ftensorlayer.readthedocs.io) 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 advanced AI models quickly, based on this, the community open-sourced mass [tutorials](https:\u002F\u002Fgithub.com\u002Ftensorlayer\u002Ftensorlayer\u002Fblob\u002Fmaster\u002Fexamples\u002Freinforcement_learning\u002FREADME.md) and [applications](https:\u002F\u002Fgithub.com\u002Ftensorlayer). TensorLayer is awarded the 2017 Best Open Source Software by the [ACM Multimedia Society](https:\u002F\u002Ftwitter.com\u002FImperialDSI\u002Fstatus\u002F923928895325442049). \nThis project can also be found at [OpenI](https:\u002F\u002Fgit.openi.org.cn\u002FTensorLayer\u002Ftensorlayer3.0) and [Gitee](https:\u002F\u002Fgitee.com\u002Forganizations\u002FTensorLayer).\n\n# News\n\n- 🔥 [TensorLayerX](https:\u002F\u002Fgithub.com\u002Ftensorlayer\u002Ftensorlayerx) is a Unified Deep Learning and Reinforcement Learning Framework for All Hardwares, Backends and OS. The current version supports TensorFlow, Pytorch, MindSpore, PaddlePaddle, OneFlow and Jittor as the backends, allowing users to run the code on different hardware like Nvidia-GPU and Huawei-Ascend.\n- TensorLayer is now in [OpenI](https:\u002F\u002Fgit.openi.org.cn\u002FTensorLayer\u002Ftensorlayer3.0)\n- Reinforcement Learning Zoo: [Low-level APIs](https:\u002F\u002Fgithub.com\u002Ftensorlayer\u002Ftensorlayer\u002Ftree\u002Fmaster\u002Fexamples\u002Freinforcement_learning) for professional usage, [High-level APIs](https:\u002F\u002Fgithub.com\u002Ftensorlayer\u002FRLzoo) for simple usage, and a corresponding [Springer textbook](http:\u002F\u002Fspringer.com\u002Fgp\u002Fbook\u002F9789811540943)\n- [Sipeed Maxi-EMC](https:\u002F\u002Fgithub.com\u002Fsipeed\u002FMaix-EMC): Run TensorLayer models on the **low-cost AI chip** (e.g., K210) (Alpha Version)\n\n\u003C!-- 🔥 [NNoM](https:\u002F\u002Fgithub.com\u002Fmajianjia\u002Fnnom): Run TensorLayer quantized models on the **MCU** (e.g., STM32) (Coming Soon) -->\n\n# Design Features\n\nTensorLayer is a new deep learning library designed with simplicity, flexibility and high-performance in mind.\n\n- ***Simplicity*** : TensorLayer has a high-level layer\u002Fmodel abstraction which is effortless to learn. You can learn how deep learning can benefit your AI tasks in minutes through the massive [examples](https:\u002F\u002Fgithub.com\u002Ftensorlayer\u002Fawesome-tensorlayer).\n- ***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.\n- ***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).\n\nTensorLayer stands at a unique spot in the TensorFlow wrappers. Other wrappers like Keras and TFLearn\nhide many powerful features of TensorFlow and provide little support for writing custom AI models. Inspired by PyTorch, TensorLayer APIs are simple, flexible and Pythonic,\nmaking it easy to learn while being flexible enough to cope with complex AI tasks.\nTensorLayer has a fast-growing community. It has been used by researchers and engineers all over the world, including those from  Peking University,\nImperial College London, UC Berkeley, Carnegie Mellon University, Stanford University, and companies like Google, Microsoft, Alibaba, Tencent, Xiaomi, and Bloomberg.\n\n# Multilingual Documents\n\nTensorLayer has extensive documentation for both beginners and professionals. The documentation is available in\nboth English and Chinese.\n\n[![English Documentation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdocumentation-english-blue.svg)](https:\u002F\u002Ftensorlayer.readthedocs.io\u002F)\n[![Chinese Documentation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdocumentation-%E4%B8%AD%E6%96%87-blue.svg)](https:\u002F\u002Ftensorlayercn.readthedocs.io\u002F)\n[![Chinese Book](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fbook-%E4%B8%AD%E6%96%87-blue.svg)](http:\u002F\u002Fwww.broadview.com.cn\u002Fbook\u002F5059\u002F)\n\nIf you want to try the experimental features on the the master branch, you can find the latest document\n[here](https:\u002F\u002Ftensorlayer.readthedocs.io\u002Fen\u002Flatest\u002F).\n\n# Extensive Examples\n\nYou can find a large collection of examples that use TensorLayer in [here](examples\u002F) and the following space:\n\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Ftensorlayer\u002Fawesome-tensorlayer\u002Fblob\u002Fmaster\u002Freadme.md\" target=\"\\_blank\">\n\t\u003Cdiv align=\"center\">\n\t\t\u003Cimg src=\"img\u002Fawesome-mentioned.png\" width=\"40%\"\u002F>\n\t\u003C\u002Fdiv>\n\u003C\u002Fa>\n\n# Getting Start\n\nTensorLayer 2.0 relies on TensorFlow, numpy, and others. To use GPUs, CUDA and cuDNN are required.\n\nInstall TensorFlow:\n\n```bash\npip3 install tensorflow-gpu==2.0.0-rc1 # TensorFlow GPU (version 2.0 RC1)\npip3 install tensorflow # CPU version\n```\n\nInstall the stable release of TensorLayer:\n\n```bash\npip3 install tensorlayer\n```\n\nInstall the unstable development version of TensorLayer:\n\n```bash\npip3 install git+https:\u002F\u002Fgithub.com\u002Ftensorlayer\u002Ftensorlayer.git\n```\n\nIf you want to install the additional dependencies, you can also run\n```bash\npip3 install --upgrade tensorlayer[all]              # all additional dependencies\npip3 install --upgrade tensorlayer[extra]            # only the `extra` dependencies\npip3 install --upgrade tensorlayer[contrib_loggers]  # only the `contrib_loggers` dependencies\n```\n\nIf you are TensorFlow 1.X users, you can use TensorLayer 1.11.0:\n\n```bash\n# For last stable version of TensorLayer 1.X\npip3 install --upgrade tensorlayer==1.11.0\n```\n\n\u003C!---\n## Using Docker\n\nThe [TensorLayer containers](https:\u002F\u002Fhub.docker.com\u002Fr\u002Ftensorlayer\u002Ftensorlayer\u002F) are built on top of the official [TensorFlow containers](https:\u002F\u002Fhub.docker.com\u002Fr\u002Ftensorflow\u002Ftensorflow\u002F):\n\n### Containers with CPU support\n\n```bash\n# for CPU version and Python 2\ndocker pull tensorlayer\u002Ftensorlayer:latest\ndocker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer\u002Ftensorlayer:latest\n\n# for CPU version and Python 3\ndocker pull tensorlayer\u002Ftensorlayer:latest-py3\ndocker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer\u002Ftensorlayer:latest-py3\n```\n\n### Containers with GPU support\n\nNVIDIA-Docker is required for these containers to work: [Project Link](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fnvidia-docker)\n\n```bash\n# for GPU version and Python 2\ndocker pull tensorlayer\u002Ftensorlayer:latest-gpu\nnvidia-docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer\u002Ftensorlayer:latest-gpu\n\n# for GPU version and Python 3\ndocker pull tensorlayer\u002Ftensorlayer:latest-gpu-py3\nnvidia-docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer\u002Ftensorlayer:latest-gpu-py3\n```\n--->\n\n# Performance Benchmark\n\nThe following table shows the training speeds of [VGG16](http:\u002F\u002Fwww.robots.ox.ac.uk\u002F~vgg\u002Fresearch\u002Fvery_deep\u002F) using TensorLayer and native TensorFlow on a TITAN Xp.\n\n|   Mode    |       Lib       |  Data Format  | Max GPU Memory Usage(MB)  |Max CPU Memory Usage(MB) | Avg CPU Memory Usage(MB) | Runtime (sec) |\n| :-------: | :-------------: | :-----------: | :-----------------: | :-----------------: | :-----------------: | :-----------: |\n| AutoGraph | TensorFlow 2.0  | channel last  | 11833 |      2161         |        2136         |      74       |\n|           | TensorLayer 2.0 | channel last  | 11833 |      2187         |        2169         |      76       |\n|   Graph   |      Keras      | channel last  | 8677 |      2580         |        2576         |      101       |\n|   Eager   | TensorFlow 2.0  | channel last  | 8723 |      2052         |        2024         |      97       |\n|           | TensorLayer 2.0 | channel last  | 8723 |      2010         |        2007         |      95       |\n\n# Getting Involved\n\nPlease read the [Contributor Guideline](CONTRIBUTING.md) before submitting your PRs.\n\nWe suggest users to report bugs using Github issues. Users can also discuss how to use TensorLayer in the following slack channel.\n\n\u003Cbr\u002F>\n\n\u003Ca href=\"https:\u002F\u002Fjoin.slack.com\u002Ft\u002Ftensorlayer\u002Fshared_invite\u002FenQtODk1NTQ5NTY1OTM5LTQyMGZhN2UzZDBhM2I3YjYzZDBkNGExYzcyZDNmOGQzNmYzNjc3ZjE3MzhiMjlkMmNiMmM3Nzc4ZDY2YmNkMTY\" target=\"\\_blank\">\n\t\u003Cdiv align=\"center\">\n\t\t\u003Cimg src=\"img\u002Fjoin_slack.png\" width=\"40%\"\u002F>\n\t\u003C\u002Fdiv>\n\u003C\u002Fa>\n\n\u003Cbr\u002F>\n\n# Citing TensorLayer\n\nIf you find TensorLayer useful for your project, please cite the following papers：\n\n```\n@article{tensorlayer2017,\n    author  = {Dong, Hao and Supratak, Akara and Mai, Luo and Liu, Fangde and Oehmichen, Axel and Yu, Simiao and Guo, Yike},\n    journal = {ACM Multimedia},\n    title   = {{TensorLayer: A Versatile Library for Efficient Deep Learning Development}},\n    url     = {http:\u002F\u002Ftensorlayer.org},\n    year    = {2017}\n}\n\n@inproceedings{tensorlayer2021,\n  title={Tensorlayer 3.0: A Deep Learning Library Compatible With Multiple Backends},\n  author={Lai, Cheng and Han, Jiarong and Dong, Hao},\n  booktitle={2021 IEEE International Conference on Multimedia \\& Expo Workshops (ICMEW)},\n  pages={1--3},\n  year={2021},\n  organization={IEEE}\n}\n```\n","TensorLayer 是一个基于 TensorFlow 的深度学习和强化学习库，专为科学家和工程师设计。它提供了丰富的可定制神经网络层，支持快速构建先进的AI模型，并且包含大量教程和应用实例，便于用户上手。该库支持A3C、DQN等算法以及GAN、图像识别等功能，适用于需要高效开发深度学习或强化学习解决方案的场景。TensorLayer因其卓越性能获得了2017年ACM多媒体协会最佳开源软件奖。",2,"2026-06-11 03:24:34","top_topic"]