# tensorlayer/TensorLayer

Deep Learning and Reinforcement Learning Library for Scientists and Engineers

Repository: https://github.com/tensorlayer/TensorLayer
Canonical: https://ross.abutalabs.com/products/tensorlayer
Homepage: http://tensorlayerx.com
Language: Python
License: NOASSERTION
License Family: other
Topics: tensorlayer, deep-learning, tensorflow, neural-network, reinforcement-learning, artificial-intelligence, gan, a3c, tensorflow-tutorials, dqn, object-detection, chatbot, python, tensorflow-tutorial, imagenet, google
Last push: 2023-02-18T07:58:21+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3739, "days_push": 1292, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 7381, forks 1584 (observed 2026-08-28T04:09:58.665416+00:00)

## What it is
TensorLayer is a TensorFlow-based deep learning and reinforcement learning library offering customizable neural layers for researchers and engineers, with extensive tutorials and example applications. Its successor, TensorLayerX, extends the same API across multiple backends including TensorFlow, PyTorch, PaddlePaddle, MindSpore, OneFlow, and Jittor.

## Use cases
- build and train neural networks on tensorflow with a high-level layer API
- implement reinforcement learning algorithms like DQN and A3C
- train GANs for image generation
- run object detection and imagenet classification experiments
- learn deep learning through tutorials and example code
- write backend-agnostic deep learning code with tensorlayerx

## When to choose
- you work in TensorFlow and want a researcher-friendly high-level API
- you need reference implementations of RL algorithms or GANs
- you want cross-framework portability via TensorLayerX

## When to avoid
- you need a library with active development and recent releases
- you prefer PyTorch-native tooling
- you need production-grade serving rather than research prototyping

## Facets
- artifact type: library
- maturity: maintenance
- function: deep-learning, machine-learning, reinforcement-learning, llm-training
- domain: deep-learning, machine-learning, reinforcement-learning, artificial-intelligence, computer-vision
- platform: python, cross-platform
- tags: tensorflow, neural-networks, gan, reinforcement-learning, tutorials, high-level-api, natural-language-processing, gpu

## Member repositories
- tensorlayer/TensorLayer (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:58.665416+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:38:21.992476+00:00, confidence not recorded.
  - readme: https://github.com/tensorlayer/TensorLayer (fetched 2026-08-28T04:09:58.665416+00:00, sha 8ba7648721ff)
  - homepage: http://tensorlayerx.com (fetched 2026-08-29T08:33:44.803319+00:00, sha b088891bd7d9)
  - site_page: http://tensorlayerx.com/installation_zh.html (fetched 2026-08-29T08:33:44.808020+00:00, sha bec300110d64)
  - registry_pypi: https://pypi.org/pypi/tensorlayer/json (fetched 2026-08-29T08:33:44.809953+00:00, sha 17182056f36a)
- Data as of 2026-08-30T08:39:29.467469+00:00.