Keras

Last updated
Keras
Original author(s) François Chollet
Developer(s) ONEIROS
Initial release27 March 2015;9 years ago (2015-03-27)
Stable release
3.7.0 [1] / 26 November 2024;56 days ago (26 November 2024)
Repository
Written in Python
Platform Cross-platform
Type Frontend for TensorFlow, JAX or PyTorch (and more)
License Apache 2.0
Website keras.io   OOjs UI icon edit-ltr-progressive.svg

Keras is an open-source library that provides a Python interface for artificial neural networks. Keras was first independent software, then integrated into the TensorFlow library, and later supporting more. "Keras 3 is a full rewrite of Keras [and can be used] as a low-level cross-framework language to develop custom components such as layers, models, or metrics that can be used in native workflows in JAX, TensorFlow, or PyTorch — with one codebase." [2] Keras 3 will be the default Keras version for TensorFlow 2.16 onwards, but Keras 2 can still be used. [3]

Contents

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, a Google engineer. Chollet is also the author of the Xception deep neural network model. [6]

Up until version 2.3, Keras supported multiple backends, including TensorFlow, Microsoft Cognitive Toolkit, Theano, and PlaidML. [7] [8] [9]

As of version 2.4, 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. [10] 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. [11] The code is hosted on GitHub, and community support forums include the GitHub issues page, and a Slack channel.[ citation needed ]

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. [12]

Keras allows users to produce deep models on smartphones (iOS and Android), on the web, or on the Java Virtual Machine. [8] It also allows use of distributed training of deep-learning models on clusters of graphics processing units (GPU) and tensor processing units (TPU). [13]

See also

Related Research Articles

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<span class="mw-page-title-main">Torch (machine learning)</span> Deep learning software

Torch is an open-source machine learning library, a scientific computing framework, and a scripting language based on Lua. It provides LuaJIT interfaces to deep learning algorithms implemented in C. It was created by the Idiap Research Institute at EPFL. Torch development moved in 2017 to PyTorch, a port of the library to Python.

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<span class="mw-page-title-main">TensorFlow</span> Machine learning software library

TensorFlow is a software library for machine learning and artificial intelligence. It can be used across a range of tasks, but is used mainly for training and inference of neural networks. It is one of the most popular deep learning frameworks, alongside others such as PyTorch and PaddlePaddle. It is free and open-source software released under the Apache License 2.0.

The following tables compare notable software frameworks, libraries, and computer programs for deep learning applications.

<span class="mw-page-title-main">Tensor Processing Unit</span> AI accelerator ASIC by Google

Tensor Processing Unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google for neural network machine learning, using Google's own TensorFlow software. Google began using TPUs internally in 2015, and in 2018 made them available for third-party use, both as part of its cloud infrastructure and by offering a smaller version of the chip for sale.

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Caffe is a deep learning framework, originally developed at University of California, Berkeley. It is open source, under a BSD license. It is written in C++, with a Python interface.

PyTorch is a machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, originally developed by Meta AI and now part of the Linux Foundation umbrella. It is one of the most popular deep learning frameworks, alongside others such as TensorFlow and PaddlePaddle, offering free and open-source software released under the modified BSD license. Although the Python interface is more polished and the primary focus of development, PyTorch also has a C++ interface.

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The Latent Diffusion Model (LDM) is a diffusion model architecture developed by the CompVis group at LMU Munich.

MobileNet is a family of convolutional neural network (CNN) architectures designed for image classification, object detection, and other computer vision tasks. They are designed for small size, low latency, and low power consumption, making them suitable for on-device inference and edge computing on resource-constrained devices like mobile phones and embedded systems. They were originally designed to be run efficiently on mobile devices with TensorFlow Lite.

References

  1. ↑ "Release 3.7.0". 26 November 2024. Retrieved 25 December 2024.
  2. ↑ "Keras: Deep Learning for humans". keras.io. Retrieved 2024-04-30.
  3. ↑ "What's new in TensorFlow 2.16" . Retrieved 2024-04-30.
  4. ↑ Team, Keras. "Keras documentation: About Keras 3". keras.io. Retrieved 2024-02-10.
  5. ↑ "Keras Documentation". keras.io. Retrieved 2016-09-18.
  6. ↑ Chollet, François (2016). "Xception: Deep Learning with Depthwise Separable Convolutions". arXiv: 1610.02357 [cs.CV].
  7. ↑ "Keras backends". keras.io. Retrieved 2018-02-23.
  8. 1 2 "Why use Keras?". keras.io. Retrieved 2020-03-22.
  9. ↑ "R interface to Keras". keras.rstudio.com. Retrieved 2020-03-22.
  10. ↑ Chollet, François; Usui, Lauren (2023). "Introducing Keras Core: Keras for TensorFlow, JAX, and PyTorch". Keras.io. Retrieved 2023-07-11.
  11. ↑ Ciaramella, Alberto; Ciaramella, Marco (2024). Introduction to Artificial Intelligence: from data analysis to generative AI. ISBN   9788894787603.
  12. ↑ "Core - Keras Documentation". keras.io. Retrieved 2018-11-14.
  13. ↑ "Using TPUs | TensorFlow". TensorFlow. Archived from the original on 2019-06-04. Retrieved 2018-11-14.
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