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Keras logo.svg
Original author(s)François Chollet [fr]
Initial release27 March 2015; 5 years ago (2015-03-27)
Stable release
2.4.0[1] / 17 June 2020; 6 months ago (17 June 2020)
Repository Edit this at Wikidata
Written inPython
TypeNeural networks

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, R, 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]


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]


Keras claims over 375,000 individual users as of early-2020.[3] Keras was the 10th most cited tool in the KDnuggets 2018 software poll and registered a 22% usage.[9]

See also[edit]


  1. ^ "Release 2.4.0". 17 June 2020. Retrieved 18 June 2020.
  2. ^ "Keras backends". Retrieved 2018-02-23.
  3. ^ a b c "Why use Keras?". Retrieved 2020-03-22.
  4. ^ "R interface to Keras". Retrieved 2020-03-22.
  5. ^ "Keras Documentation". Retrieved 2016-09-18.
  6. ^ Chollet, François (2016). "Xception: Deep Learning with Depthwise Separable Convolutions". arXiv:1610.02357.
  7. ^ "Core - Keras Documentation". Retrieved 2018-11-14.
  8. ^ "Using TPUs | TensorFlow". TensorFlow. Retrieved 2018-11-14.
  9. ^ Piatetsky, Gregory. "Python eats away at R: Top Software for Analytics, Data Science, Machine Learning in 2018: Trends and Analysis". KDnuggets. KDnuggets. Retrieved 30 May 2018.

External links[edit]