TensorFlow: Learning Functions at Scale
TensorFlow: Learning Functions at Scale
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DOI：
10.1145/3022670.2976746
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发表时间：
2016-09-01
影响因子：
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通讯作者：
Abadi, Martin
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文献类型：
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作者：
Abadi, Martin
TensorFlow是一个在大规模和异构环境中运行的机器学习系统。它的计算模型是基于状态可变的图。图形节点可以映射到集群中的不同机器，并且在每个机器内映射到CPU、GPU和其他设备。TensorFlow支持各种应用程序，但它特别针对深度神经网络的训练和推理。它是研究和部署多个领域机器学习系统的平台，例如语音识别、计算机视觉、机器人技术、信息检索和自然语言处理。在这次演讲中，我们描述了TensorFlow并概述了它的一些应用。我们还讨论了TensorFlow和深度学习可能与函数式编程有关的问题。虽然TensorFlow不是纯函数式的，但它的许多用途都涉及优化函数（在训练期间），然后应用这些函数（在推理期间）。这些函数被定义为简单原语的组合（在函数式编程中很常见），内部数据表示是学习的，而不是手动设计的。TensorFlow是与Google Brain团队和其他地方的许多其他人联合开发的。更多信息请访问tensorflow.org。
TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Its computational model is based on dataflow graphs with mutable state. Graph nodes may be mapped to different machines in a cluster, and within each machine to CPUs, GPUs, and other devices. TensorFlow supports a variety of applications, but it particularly targets training and inference with deep neural networks. It serves as a platform for research and for deploying machine learning systems across many areas, such as speech recognition, computer vision, robotics, information retrieval, and natural language processing. In this talk, we describe TensorFlow and outline some of its applications. We also discuss the question of what TensorFlow and deep learning may have to do with functional programming. Although TensorFlow is not purely functional, many of its uses are concerned with optimizing functions (during training), then with applying those functions (during inference). These functions are defined as compositions of simple primitives (as is common in functional programming), with internal data representations that are learned rather than manually designed. TensorFlow is joint work with many other people in the Google Brain team and elsewhere. More information is available at tensorflow.org.