Keras is an open-source library that provides a Python interface for artificial neural networks. Keras was first independent software, then integrated into the TensorFlow library, and later supporting more. "Keras 3 is a full rewrite of Keras [and can be used] as a low-level cross-framework language to develop custom components such as layers, models, or metrics that can be used in native workflows in JAX, TensorFlow, or PyTorch — with one codebase."[2] Keras 3 will be the default Keras version for TensorFlow 2.16 onwards, but Keras 2 can still be used.[3]
 | |
| Original author(s) | François Chollet |
|---|---|
| Developer(s) | ONEIROS |
| Initial release | 27 March 2015 |
| Stable release | 3.7.0[1]
/ 26 November 2024 |
| Repository | |
| Written in | Python |
| Platform | Cross-platform |
| Type | Frontend for TensorFlow, JAX or PyTorch (and more) |
| License | Apache 2.0 |
| Website | keras |
History
editThe name 'Keras' derives from the Ancient Greek word κέρας (Keras) meaning 'horn'.[4]
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),[5] and its primary author and maintainer is François Chollet, a Google engineer. Chollet is also the author of the Xception deep neural network model.[6]
Up until version 2.3, Keras supported multiple backends, including TensorFlow, Microsoft Cognitive Toolkit, Theano, and PlaidML.[7][8][9]
As of version 2.4, only TensorFlow was supported. Starting with version 3.0 (as well as its preview version, Keras Core), however, Keras has become multi-backend again, supporting TensorFlow, JAX, and PyTorch.[10]
Features
editKeras 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.[11] 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.[12]
Keras allows users to produce deep models on smartphones (iOS and Android), on the web, or on the Java Virtual Machine.[8] It also allows use of distributed training of deep-learning models on clusters of graphics processing units (GPU) and tensor processing units (TPU).[13]
See also
editReferences
edit- ^ "Release 3.7.0". 26 November 2024. Retrieved 25 December 2024.
- ^ "Keras: Deep Learning for humans". keras.io. Retrieved 2024-04-30.
- ^ "What's new in TensorFlow 2.16". Retrieved 2024-04-30.
- ^ Team, Keras. "Keras documentation: About Keras 3". keras.io. Retrieved 2024-02-10.
- ^ "Keras Documentation". keras.io. Retrieved 2016-09-18.
- ^ Chollet, François (2016). "Xception: Deep Learning with Depthwise Separable Convolutions". arXiv:1610.02357 [cs.CV].
- ^ "Keras backends". keras.io. Retrieved 2018-02-23.
- ^ a b "Why use Keras?". keras.io. Retrieved 2020-03-22.
- ^ "R interface to Keras". keras.rstudio.com. Retrieved 2020-03-22.
- ^ Chollet, François; Usui, Lauren (2023). "Introducing Keras Core: Keras for TensorFlow, JAX, and PyTorch". Keras.io. Retrieved 2023-07-11.
- ^ Ciaramella, Alberto; Ciaramella, Marco (2024). Introduction to Artificial Intelligence: from data analysis to generative AI. ISBN 9788894787603.
- ^ "Core - Keras Documentation". keras.io. Retrieved 2018-11-14.
- ^ "Using TPUs | TensorFlow". TensorFlow. Archived from the original on 2019-06-04. Retrieved 2018-11-14.