Keras

Keras is a deep learning API written in Python and capable of running on top of either JAX, TensorFlow, or PyTorch.

Keras is:

• Simple — but not simplistic. Keras reduces developer cognitive load to free you to focus on the parts of the problem that really matter.
• Flexible — Keras adopts the principle of progressive disclosure of complexity: simple workflows should be quick and easy, while arbitrarily advanced workflows should be possible via a clear path that builds upon what you've already learned.
• Powerful — Keras provides industry-strength performance and scalability: it is used by organizations including NASA, YouTube, or Waymo.

A superpower for developers.

The purpose of Keras is to give an unfair advantage to any developer looking to ship Machine Learning-powered apps. Keras focuses on debugging speed, code elegance & conciseness, maintainability, and deployability. When you choose Keras, your codebase is smaller, more readable, easier to iterate on. Your models run faster thanks to XLA compilation with JAX and TensorFlow, and are easier to deploy across every surface (server, mobile, browser, embedded) thanks to the serving components from the TensorFlow and PyTorch ecosystems, such as TF Serving, TorchServe, TF Lite, TF.js, and more.