History
The name 'Keras' derives from the Ancient Greek word κέρας (Keras) meaning 'horn'. [4]
Designed to enable fast experimentation with deep neural networks, Keras 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), [5] and its primary author and maintainer is François Chollet, who was a Google engineer until leaving the company in 2024. [6] Chollet is also the author of the Xception deep neural network model. [7]
Up until version 2.3, Keras supported multiple backends, including TensorFlow, Microsoft Cognitive Toolkit, Theano, and PlaidML. [8] [9] [10]
From version 2.4 up until version 3.0, only TensorFlow was supported. Starting with version 3.0 (as well as its preview version, Keras Core), however, Keras has become multi-backend again, supporting TensorFlow, JAX, and PyTorch. [11] It now also supports OpenVINO!.
Features
Keras contains numerous implementations of commonly used neural-network building blocks such as layers, objectives, activation functions, optimizers, and a host of tools for working with image and text data to simplify programming in deep neural network area. [12] The code is hosted on GitHub, and community support forums include the GitHub issues page. [13]
In addition to standard neural networks, Keras has support for convolutional and recurrent neural networks. It supports other common utility layers like dropout, batch normalization, and pooling. [14]
Keras allows users to produce deep models on smartphones (iOS and Android), on the web, or on the Java Virtual Machine. [9] It also allows use of distributed training of deep-learning models on clusters of graphics processing units (GPU) and tensor processing units (TPU). [15]
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