TensorFlow.js: Machine Learning for the Web and Beyond
TensorFlow.js: Machine Learning for the Web and Beyond
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发表时间：
2019-01
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通讯作者：
D. Smilkov;Nikhil Thorat;Yannick Assogba;Ann Yuan;Nick Kreeger;Ping Yu;Kangyi Zhang;Shanqing Cai;Eric Nielsen;David Soergel;S. Bileschi;Michael Terry;Charles Nicholson;Sandeep N. Gupta;S. Sirajuddin;D. Sculley;R. Monga;G. Corrado;F. Viégas;M. Wattenberg
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作者：
D. Smilkov;Nikhil Thorat;Yannick Assogba;Ann Yuan;Nick Kreeger;Ping Yu;Kangyi Zhang;Shanqing Cai;Eric Nielsen;David Soergel;S. Bileschi;Michael Terry;Charles Nicholson;Sandeep N. Gupta;S. Sirajuddin;D. Sculley;R. Monga;G. Corrado;F. Viégas;M. Wattenberg
Js是一个用JavaScript构建和执行机器学习算法的库。Js模型在Web浏览器和Node.js环境中运行。该库是TensorFlow生态系统的一部分，提供了一组与Python中的API兼容的API，允许在Python和JavaScript生态系统之间移植模型。Js使来自广泛的JavaScript社区的一组新的开发人员能够构建和部署机器学习模型，并支持新的设备上计算类。本文描述了TensorFlow.js的设计、API和实现，并重点介绍了一些有影响力的用例。
TensorFlow.js is a library for building and executing machine learning algorithms in JavaScript. TensorFlow.js models run in a web browser and in the Node.js environment. The library is part of the TensorFlow ecosystem, providing a set of APIs that are compatible with those in Python, allowing models to be ported between the Python and JavaScript ecosystems. TensorFlow.js has empowered a new set of developers from the extensive JavaScript community to build and deploy machine learning models and enabled new classes of on-device computation. This paper describes the design, API, and implementation of TensorFlow.js, and highlights some of the impactful use cases.