[1]费 宁,张浩然.TensorFlow 架构与实现机制的研究[J].计算机技术与发展,2019,29(09):31-34.[doi:10. 3969 / j. issn. 1673-629X. 2019. 09. 006]
　FEI Ning,ZHANG Hao-ran.Research on Tensor Flow Architecture and Mechanism[J].,2019,29(09):31-34.[doi:10. 3969 / j. issn. 1673-629X. 2019. 09. 006]
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TensorFlow 架构与实现机制的研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年09期
页码:
31-34
栏目:
智能、算法、系统工程
出版日期:
2019-09-10

文章信息/Info

Title:
Research on Tensor Flow Architecture and Mechanism
文章编号:
1673-629X(2019)09-0031-04
作者:
费 宁1 ; 张浩然1; 2
1. 南京邮电大学 计算机学院、软件学院,江苏 南京 210003; 2. 大连交通大学 软件学院,辽宁 大连 116028
Author(s):
FEI Ning1 ; ZHANG Hao-ran1; 2
1. School of Computer Science &Technology,School of Software,Nanjing University of Posts &Telecommunications,Nanjing 210003,China; 2. School of Software,Dalian Jiaotong University,Dalian 116028,China
关键词:
TensorFlow; 神经网络; 数据流图; 节点
Keywords:
TensorFlow; neural network; dataflow graphs; node
分类号:
TP31
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 09. 006
摘要:
在大数据时代,云计算和大规模并行处理基础架构的共同发展不仅使得机器学习和深度人工智能有了更为广阔的应用空间,也激发了人工智能框架的快速迭代和部署。TensorFlow 是 Google 发布的开放源代码的深度学习平台,已经在工业界有了广泛的应用。文中从 TensorFlow 平台的设计理念出发,分析了平台的框架和基本结构,对每个模块的功能和应用做了详尽阐述。在此基础上,通过建立一个多层深度学习神经网络,分析了输入层、隐藏层、输出层及激励函数的构建方法。最后在对 TensorFlow 实例运行和调试的基础上,演示了通过 TensorBoard 跟踪程序运行状态和参数调制的方法,给出了一维数据和多维数据的可视化结果。 研究表明,相比较其他学术界的人工智能平台,TensorFlow 有着更好的生态系统,支持更多的硬件架构,具备了一定的实用基础。
Abstract:
In the era of big data,the common development of cloud computing and large-scale parallel processing infrastructure not only makes machine learning and deep artificial intelligence have broader application space, but also stimulates the rapid iteration and deployment of artificial intelligence framework. TensorFlow is an open source deep learning platform released by Google that has been widely used in the industry. Based on the design concept and mechanism of TensorFlow,we analyze the framework and basic structure of the platform and elaborate the functions and applications of each module. By building a multiple layer deep learning program,we investigate the way to construct input layer,hidden layer,output layer,and activation function. Finally,on the basis of running and debugging the TensorFlow instance,the method of tracking the running state and parameter modulation by TensorBoard is demonstrated,and the visualization of one - dimensional data and multidimensional data are presented. Research shows that compared with other academic artificial intelligence platforms,TensorFlow has a better ecosystem,supports more hardware architectures and has a certain practical basis.

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更新日期/Last Update: 2019-09-10