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'''Keras''' is an [[Open-source software|open-source]] [[Library (computing)|library]] that provides a [[Python (programming language)|Python]] [[Interface (computing)|interface]] for [[artificial neural network]]s. Keras acts as an interface for the [[TensorFlow]] library.
'''Keras''' is an [[Open-source software|open-source]] [[Library (computing)|library]] that provides a [[Python (programming language)|Python]] [[Interface (computing)|interface]] for [[artificial neural network]]s. Keras acts as an interface for the [[TensorFlow]] library.


Up until version 2.3, Keras supported multiple [[Frontend and backend|backends]], including [[TensorFlow]], [[Microsoft Cognitive Toolkit]], [[Theano (software)|Theano]], and [[PlaidML]].<ref>{{Cite web|url=https://keras.io/backend/|title=Keras backends|website=keras.io|access-date=2018-02-23}}</ref><ref name="why-keras">{{Cite web|url=https://keras.io/why-use-keras/|title=Why use Keras?|website=keras.io|access-date=2020-03-22}}</ref><ref>{{Cite web|url=https://keras.rstudio.com/|title=R interface to Keras|website=keras.rstudio.com|access-date=2020-03-22}}</ref> As of version 2.4, only [[TensorFlow]] is supported. Designed to enable fast experimentation with [[Deep learning|deep neural networks]], it 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),<ref>{{Cite web|url=https://keras.io/#why-this-name-keras|title=Keras Documentation|website=keras.io|access-date=2016-09-18}}</ref> and its primary author and maintainer is [[François Chollet]], a [[Google]] engineer. Chollet is also the author of the [[Xception]] deep neural network model.<ref>{{cite arXiv
Up until version 2.3, Keras supported multiple [[Frontend and backend|backends]], including TensorFlow, [[Microsoft Cognitive Toolkit]], [[Theano (software)|Theano]], and [[PlaidML]].<ref>{{Cite web|url=https://keras.io/backend/|title=Keras backends|website=keras.io|access-date=2018-02-23}}</ref><ref name="why-keras">{{Cite web|url=https://keras.io/why-use-keras/|title=Why use Keras?|website=keras.io|access-date=2020-03-22}}</ref><ref>{{Cite web|url=https://keras.rstudio.com/|title=R interface to Keras|website=keras.rstudio.com|access-date=2020-03-22}}</ref> As of version 2.4, only TensorFlow is supported. Designed to enable fast experimentation with [[Deep learning|deep neural networks]], it 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),<ref>{{Cite web|url=https://keras.io/#why-this-name-keras|title=Keras Documentation|website=keras.io|access-date=2016-09-18}}</ref> and its primary author and maintainer is [[François Chollet]], a [[Google]] engineer. Chollet is also the author of the [[Xception]] deep neural network model.<ref>{{cite arXiv
|title=Xception: Deep Learning with Depthwise Separable Convolutions
|title=Xception: Deep Learning with Depthwise Separable Convolutions
|last1=Chollet |first1=François
|last1=Chollet |first1=François

Revision as of 15:16, 9 June 2023

Keras
Original author(s)François Chollet
Developer(s)ONEIROS
Initial release27 March 2015; 9 years ago (2015-03-27)
Stable release
3.5.0[1] / 12 August 2024; 34 days ago (12 August 2024)
Repository
Written inPython
PlatformCross-platform
TypFrontend for TensorFlow
LicenseApache 2.0
Websitekeras.io Edit this on Wikidata

Keras is an open-source library that provides a Python interface for artificial neural networks. Keras acts as an interface for the TensorFlow library.

Up until version 2.3, Keras supported multiple backends, including TensorFlow, Microsoft Cognitive Toolkit, Theano, and PlaidML.[2][3][4] As of version 2.4, only TensorFlow is supported. Designed to enable fast experimentation with deep neural networks, it 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]

Eigenschaften

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.

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.[7]

Keras allows users to produce deep models on smartphones (iOS and Android), on the web, or on the Java Virtual Machine.[3] It also allows use of distributed training of deep-learning models on clusters of graphics processing units (GPU) and tensor processing units (TPU).[8]

See also

References

  1. ^ "Release 3.5.0". 12 August 2024. Retrieved 22 August 2024.
  2. ^ "Keras backends". keras.io. Retrieved 2018-02-23.
  3. ^ a b "Why use Keras?". keras.io. Retrieved 2020-03-22.
  4. ^ "R interface to Keras". keras.rstudio.com. Retrieved 2020-03-22.
  5. ^ "Keras Documentation". keras.io. Retrieved 2016-09-18.
  6. ^ Chollet, François (2016). "Xception: Deep Learning with Depthwise Separable Convolutions". arXiv:1610.02357.
  7. ^ "Core - Keras Documentation". keras.io. Retrieved 2018-11-14.
  8. ^ "Using TPUs | TensorFlow". TensorFlow. Archived from the original on 2019-06-04. Retrieved 2018-11-14.
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