| 1.2.0 / 20 December 2016; 2 months ago (2016-12-20)|
Keras is an open source neural network library written in Python. It is capable of running on top of Deeplearning4j, Tensorflow or Theano. 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), and its primary author and maintainer is François Chollet, a Google engineer.
While Google's TensorFlow team decided to support Keras in TensorFlow's core library, Chollet has said 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. Microsoft is working to add a CNTK backend to Keras as well.
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.
As of Sept 16, 2016, Keras is the second-fastest growing deep learning framework after Google's TensorFlow, and the third largest after TensorFlow and Caffe.Caffe Computer Vision Library
OpenNN, an open source neural networks library written in C++ for deep learning.
Theano, an open source deep learning library for Python.
Torch, an open source framework written in Lua with wide support for machine learning algorithms.