Keras

Keras is an open-source library that makes it easy to work with artificial neural networks using the Python programming language. It was created to help people build and train neural networks without needing to write complex code from scratch.

Keras started as an independent software project but later became part of the TensorFlow library, making it even more powerful and widely used. This integration allowed Keras to take advantage of TensorFlow’s capabilities, such as running on different devices like computers and smartphones.

Recently, Keras was updated to version 3, which is a complete rewrite. This new version works across multiple frameworks, meaning developers can use the same code with JAX, TensorFlow, or PyTorch. Keras 3 will become the default version for TensorFlow starting with version 2.16, although the older Keras 2 version will still be available for those who prefer it. This flexibility makes Keras a valuable tool for anyone interested in machine learning and artificial intelligence.

History

The name 'Keras' comes from an Ancient Greek word meaning 'horn'. Keras was created to make working with deep neural networks easier and more user-friendly. It was developed by François Chollet, a former Google engineer, as part of a research project.

Keras used to support many different systems for running neural networks, but from version 2.4 to 3.0, it only worked with TensorFlow. Starting with version 3.0, Keras now supports TensorFlow, JAX, PyTorch, and OpenVINO again, making it more flexible.

Features

Keras is a tool that helps people create and use neural networks, which are like computer brains that can learn from data. It includes many ready-made pieces, such as layers and functions, to make building these networks easier. You can find the code on GitHub and get help from other users there.

Keras works with different types of neural networks, including special ones for images and sequences. It can run on many devices, like smartphones on iOS and Android, websites, or computer clusters using graphics processing units (GPU) and tensor processing units (TPU).