Keras
Keras is an open-source library that provides a Python
Keras
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
History Initial release 27 March 2015
Stable release 3.8.0[1] / 7 January 2025
The name 'Keras' derives from the Ancient Greek word
Repository github.com/keras-team
κέρας (Keras) meaning 'horn'.[4]
/keras (https://github.com/k
Designed to enable fast experimentation with deep eras-team/keras)
neural networks, Keras focuses on being user-friendly, Written in Python
modular, and extensible. It was developed as part of
Platform Cross-platform
the research effort of project ONEIROS (Open-ended
Type Frontend for TensorFlow,
Neuro-Electronic Intelligent Robot Operating
[5] JAX or PyTorch (and more)
System), and its primary author and maintainer is
François Chollet, a Google engineer. Chollet is also the License Apache 2.0
author of the Xception deep neural network model.[6] Website keras.io (https://keras.io/)
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] It now also supports OpenVINO!.
Features
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.[11] The code is hosted on GitHub, and
community support forums include the GitHub issues page, and a Slack channel.
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
Comparison of deep-learning software
References
1. "Release 3.8.0" (https://github.com/keras-team/keras/releases/tag/v3.8.0). 7 January 2025.
Retrieved 24 January 2025.
2. "Keras: Deep Learning for humans" (https://keras.io/keras_3/). keras.io. Retrieved
2024-04-30.
3. "What's new in TensorFlow 2.16" (https://blog.tensorflow.org/2024/03/whats-new-in-tensorflo
w-216.html). Retrieved 2024-04-30.
4. Team, Keras. "Keras documentation: About Keras 3" (https://keras.io/about/). keras.io.
Retrieved 2024-02-10.
5. "Keras Documentation" (https://keras.io/#why-this-name-keras). keras.io. Retrieved
2016-09-18.
6. Chollet, François (2016). "Xception: Deep Learning with Depthwise Separable
Convolutions". arXiv:1610.02357 (https://arxiv.org/abs/1610.02357) [cs.CV (https://arxiv.org/
archive/cs.CV)].
7. "Keras backends" (https://keras.io/backend/). keras.io. Retrieved 2018-02-23.
8. "Why use Keras?" (https://keras.io/why-use-keras/). keras.io. Retrieved 2020-03-22.
9. "R interface to Keras" (https://keras.rstudio.com/). keras.rstudio.com. Retrieved 2020-03-22.
10. Chollet, François; Usui, Lauren (2023). "Introducing Keras Core: Keras for TensorFlow, JAX,
and PyTorch" (https://keras.io/keras_core/announcement/). Keras.io. Retrieved 2023-07-11.
11. Ciaramella, Alberto; Ciaramella, Marco (2024). Introduction to Artificial Intelligence: from
data analysis to generative AI. ISBN 9788894787603.
12. "Core - Keras Documentation" (https://keras.io/layers/core/). keras.io. Retrieved
2018-11-14.
13. "Using TPUs | TensorFlow" (https://web.archive.org/web/20190604082736/https://www.tens
orflow.org/guide/using_tpu). TensorFlow. Archived from the original (https://www.tensorflow.
org/guide/using_tpu) on 2019-06-04. Retrieved 2018-11-14.
External links
Official website (https://keras.io/)
Keras (https://github.com/keras-team/keras) on GitHub
Retrieved from "https://en.wikipedia.org/w/index.php?title=Keras&oldid=1270879142"
