|Original author(s)||François Chollet|
|Initial release||27 March 2015|
2.2.0 / 7 June 2018
Keras is an open source neural network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit or Theano. 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), 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 a standalone machine-learning framework. It offers a higher-level, more intuitive set of abstractions that make it easy to develop deep learning models regardless of the computational backend used. Microsoft added a CNTK backend to Keras as well, available as of CNTK v2.0.
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. The code is hosted on GitHub, and community support forums include the GitHub issues page, and a Slack channel.
It also allows use of distributed training of deep learning models on clusters of Graphics Processing Units (GPU).
- "Keras backends". keras.io. Retrieved 2018-02-23.
- "Keras Documentation". keras.io. Retrieved 2016-09-18.
- Chollet GitHub Comment
- CNTK Keras GitHub Issue
- alexeyo. "CNTK_2_0_Release_Notes". docs.microsoft.com. Retrieved 2017-06-14.
- "Why use Keras?". keras.io. Retrieved 2018-02-23.
- 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.