It supports other common utility layers like dropout, batch normalization, and pooling. In addition to standard neural networks, Keras has support for convolutional and recurrent neural networks. The code is hosted on GitHub, and community support forums include the GitHub issues page, and a Slack channel. Keras 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 to simplify the coding necessary for writing deep neural network code. Chollet is also the author of the Xception deep neural network model. 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. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible. As of version 2.4, only TensorFlow is supported. Up until version 2.3, Keras supported multiple backends, including TensorFlow, Microsoft Cognitive Toolkit, Theano, and PlaidML. Keras acts as an interface for the TensorFlow library. Keras is an open-source software library that provides a Python interface for artificial neural networks.
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