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Keras allows users to productize deep models on smartphones ([[iOS]] and [[Android (operating system)|Android]]), on the web, or on the [[Java Virtual Machine]].<ref name="why-keras" /> It also allows use of distributed training of deep-learning models on clusters of [[Graphics processing unit|Graphics processing units (GPU)]] and [[Tensor processing unit|tensor processing units (TPU)]].<ref>{{Cite web|url=https://www.tensorflow.org/guide/using_tpu|title=Using TPUs {{!}} TensorFlow|website=TensorFlow|language=en|access-date=2018-11-14}}</ref>
Keras allows users to productize deep models on smartphones ([[iOS]] and [[Android (operating system)|Android]]), on the web, or on the [[Java Virtual Machine]].<ref name="why-keras" /> It also allows use of distributed training of deep-learning models on clusters of [[Graphics processing unit|Graphics processing units (GPU)]] and [[Tensor processing unit|tensor processing units (TPU)]].<ref>{{Cite web|url=https://www.tensorflow.org/guide/using_tpu|title=Using TPUs {{!}} TensorFlow|website=TensorFlow|language=en|access-date=2018-11-14}}</ref>

==Traction==
Keras claims over 375,000 individual users as of early-2020.<ref name="why-keras" /> Keras has been adopted for use in scientific research due to [[Python (programming language)]] and its own ease of use and installation. Keras was the 10th most cited tool in the [[KDnuggets]] 2018 software poll and registered a 22% usage.<ref>{{cite web |last1=Piatetsky |first1=Gregory |title=Python eats away at R: Top Software for Analytics, Data Science, Machine Learning in 2018: Trends and Analysis |url=https://www.kdnuggets.com/2018/05/poll-tools-analytics-data-science-machine-learning-results.html/2 |website=KDnuggets |publisher=KDnuggets |access-date=30 May 2018}}</ref>


==See also==
==See also==

Revision as of 03:08, 23 April 2021

Keras
Original author(s)François Chollet
Developer(s)various
Initial release27 March 2015; 9 years ago (2015-03-27)
Stable release
3.5.0[1] / 12 August 2024; 23 days ago (12 August 2024)
Repository
Written inPython
PlatformCross-platform
TypNeural networks
LicenseMIT
Websitekeras.io Edit this on Wikidata

Keras is an open-source software 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 also is 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 to make working with image and text data easier to simplify the coding necessary for writing deep neural network code. 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 productize 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. Retrieved 2018-11-14.
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