Keras is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation.
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Keras is a deep learning library for Theano and TensorFlow. It is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
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. Keras deep learning library allows for easy and fast prototyping (through total modularity, minimalism, and extensibility). It supports both convolutional networks and recurrent networks, as well as combinations of the two.
Keras also supports arbitrary connectivity schemes (including multi-input and multi-output training) and runs seamlessly on CPU and GPU. The core data structure of Keras is a model, a way to organize layers. The main type of model is the Sequential model, a linear stack of layers. Keras’ Guiding principles include Modularity.
A model is understood as a sequence or a graph of standalone, fully-configurable modules that can be plugged together with as little restrictions as possible. In particular, neural layers, cost functions, optimizers, initialization schemes, activation functions, regularization schemes are all standalone modules that users can combine to create new models. Each module should be kept short and simple.
Every piece of code should be transparent upon first reading. No black magic: it hurts iteration speed and ability to innovate. New modules are dead simple to add (as new classes and functions), and existing modules provide ample examples. To be able to easily create new modules allows for total expressiveness, making Keras suitable for advanced research.