Overview

== Open Source Python Deep Learning Library
– 2015 published
– code is hosted on GitHub
– originally a uniform interface for various backend libraries (TensorFlow, Microsoft Cognitive Toolkit, Theano, R, PlaidM)
– it focuses on being user-friendly, modular, and extensible, and Fast and easy prototyping of neural networks,
– Part of the Tensorflow Core API, but was also continued independently
– since version 2.4 Keras refers directly to the implementation of Tensorflow 2
– contains numerous implementations of commonly used neural-network building blocks (layers, activation functions, objectives, optimizers, tools to make working with image and text data)

keras histo

Features

– supports standard, convolutional and recurrent neural networks
– supports common supply layers (dropout, batch normalization, pooling)
– supports multi-input and multi-output training
– Modular design allows the creation of new models by combining cost functions, activation functions or initialization schemes
– enables in-depth learning models on iOS and Android, on the web, on the Java Virtual Machine with the DL4J model import from SkyMind , on clusters of graphics processing units (GPU) and tensor processing units (TPU), Google Cloud with TensorFlow Serving, Rasberry Pi

Keras vs Tensorflow

kerasvstensorflow