Keras

Keras
Original author(s) François Chollet
Developer(s) various
Initial release 27 March 2015 (2015-03-27)
Stable release
2.0.2 / 21 March 2017 (2017-03-21)
Development status Active
Written in Python
Platform Cross-platform
Type Neural Networks
License MIT
Website keras.io

Keras is an open source neural network library written in Python. It is capable of running on top of MXNet, Deeplearning4j, Tensorflow, CNTK or Theano.[1][2] Designed to enable fast experimentation with deep neural networks, it focuses on being minimal, modular and extensible. It was developed as part of the research effort of project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System),[3] and its primary author and maintainer is François Chollet, a Google engineer.

In 2017, Google's TensorFlow team decided to support Keras in TensorFlow's core library. Chollet explained that Keras was conceived to be an interface rather than an end-to-end machine-learning framework. It presents a higher-level, more intuitive set of abstractions that make it easy to configure neural networks regardless of the backend scientific computing library.[4] Microsoft has been working to add a CNTK backend to Keras as well and the functionality is currently in beta release with CNTK v2.0 .[5][6]

Features

The library 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. The code is hosted on GitHub, and community support forums include the GitHub issues page, a Gitter channel and a Slack channel.

Traction

As of 16 September 2016, Keras is the second-fastest growing deep learning framework after Google's TensorFlow, and the third largest after TensorFlow and Caffe.[7]

See also

References

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