Welcome to TensorLayer


TensorLayer is a Deep Learning (DL) and Reinforcement Learning (RL) library extended from Google TensorFlow. It provides popular DL and RL modules that can be easily customized and assembled for tackling real-world machine learning problems.


If you got problem to read the docs online, you could download the repository on GitHub, then go to /docs/_build/html/index.html to read the docs offline. The _build folder can be generated in docs using make html.

Why TensorLayer

TensorLayer grow out from a need to combine the power of TensorFlow with the right building modules for deep neural networks. According to our years of research and practical experiences of tackling real-world machine learning problems, we come up with three design goals for TensorLayer:

  • Simplicity: we make TensorLayer easy to work with by providing mass tutorials that can be deployed and run through in minutes. A TensorFlow user may find it easier to bootstrap with the simple, high-level APIs provided by TensorLayer, and then deep dive into their implementation details if need.
  • Flexibility: developing an effective DL algorithm for a specific domain typically requires careful tunings from many aspects. Without the loss of simplicity, TensorLayer allows users to customize their modules by manipulating the native APIs of TensorFlow (e.g., training parameters, iteration control and tensor components).
  • Performance: TensorLayer aims to provide zero-cost abstraction for TensorFlow. With its first-class support for TensorFlow, it can easily run on either heterogeneous platforms or multiple computation nodes without compromise in performance.

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