Keras Library

A Keras Library is an open-source Python-based deep neural network framework.

• Context:
  • It can (typically) be used with TensorFlow, deeplearning4j, or (now defunct) Theano Library.
• Example(s):
  • Keras v2.0.6 (2017-07-07).
  • http://github.com/fchollet/keras/releases
• Counter-Example(s):
  • Gluon.
  • TensorFlow.
  • Deeplearning4j.
  • Torch Library (Lua-based neural network library), or PyTorch.
  • Caffe Framework for convolutional neural network algorithms.
  • sklearn.neural_network.
  • Theano (defunct).
• See: numpy, Automatic Differentiation, CUDA GPU Platform.

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References

• https://github.com/fchollet/keras
• http://keras.io/

2017a

• (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Keras Retrieved:2017-6-4.
  • 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. 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. Microsoft is working to add a CNTK backend to Keras as well.

2017

• https://github.com/fchollet/keras/blob/master/README.md
  • QUOTE: Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow[1] or Theano[2]. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

    Use Keras if you need a deep learning library that:

    • Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
    • Supports both convolutional networks and recurrent networks, as well as combinations of the two.
    • Runs seamlessly on CPU and GPU.

2017b

• https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software

Software	Creator	Software licenseTemplate:Efn	Open source	Platform	Written in	Interface	OpenMP support	OpenCL support	CUDA support	Automatic differentiation	Has pretrained models	Recurrent nets	Convolutional nets	RBM/DBNs	Parallel execution (multi node)
Keras	François Chollet	Template:Free	Yes	Linux, Mac OS X, Windows	Python	Python	Template:Depends	Template:Depends	Yes	Yes	Yes	Yes	Yes	Yes	Yes

2017c

• https://medium.com/@kailashahirwar/essential-cheat-sheets-for-machine-learning-and-deep-learning-researchers-efb6a8ebd2e5
  • QUOTE:

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