 | |
| Original author(s) | François Chollet |
|---|---|
| Developer(s) | ONEIROS |
| Initial release | 27 March 2015 |
| Stable release | 3.0.5[1]
/ 14 February 2024 |
| Repository | |
| Written in | Python |
| Platform | Cross-platform |
| Type | Frontend for TensorFlow |
| License | Apache 2.0 |
| Website | keras |
Keras is an open-source library that provides a Python interface for artificial neural networks. Keras acts as an interface for the TensorFlow library.[citation needed]
The name 'Keras' derives from the Ancient Greek word κέρας (Keras) meaning 'horn'.[2]
Designed to enable fast experimentation with deep neural networks, Keras focuses on being user-friendly, modular, and extensible. It was developed as part of the research effort of project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System),[3] and its primary author and maintainer is François Chollet, a Google engineer. Chollet is also the author of the Xception deep neural network model.[4]
Up until version 2.3, Keras supported multiple backends, including TensorFlow, Microsoft Cognitive Toolkit, Theano, and PlaidML.[5][6][7]
As of version 2.4, only TensorFlow is supported. Starting with version 3.0 (as well as its preview version, Keras Core), however, Keras is to become multi-backend again, supporting TensorFlow, JAX, and PyTorch.[8]
Keras contains numerous implementations of commonly used neural-network building blocks such as layers, objectives, activation functions, optimizers, and a host of tools for working with image and text data to simplify programming in deep neural network area. The code is hosted on GitHub, and community support forums include the GitHub issues page, and a Slack channel.[citation needed]
In addition to standard neural networks, Keras has support for convolutional and recurrent neural networks. It supports other common utility layers like dropout, batch normalization, and pooling.[9]
Keras allows users to produce deep models on smartphones (iOS and Android), on the web, or on the Java Virtual Machine.[6] It also allows use of distributed training of deep-learning models on clusters of graphics processing units (GPU) and tensor processing units (TPU).[10]
