文章基本信息

标题：Benchmarking open source deep learning frameworks
本地全文：下载
作者：Ghadeer Al-Bdour ; Raffi Al-Qurran ; Mahmoud Al-Ayyoub 等
期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2020
卷号：10
期号：5
页码：5479-5486
DOI：10.11591/ijece.v10i5.pp5479-5486
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：Deep Learning (DL) is one of the hottest fields. To foster the growth of DL, several open source frameworks appeared providing implementations of the most common DL algorithms. These frameworks vary in the algorithms they support and in the quality of their implementations. The purpose of this work is to provide a qualitative and quantitative comparison among three such frameworks: TensorFlow, Theano and CNTK. To ensure that our study is as comprehensive as possible, we consider multiple benchmark datasets from different fields (image processing, NLP, etc.) and measure the performance of the frameworks' implementations of different DL algorithms. For most of our experiments, we find out that CNTK's implementations are superior to the other ones under consideration.
关键词：TensorFlow;Theano;CNTK;Performance Comparison