DeepGraph: A PyCharm Tool for Visualizing and Understanding Deep Learning Models

Qiang Hu, Lei Ma, Jianjun Zhao

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

6 被引用数 (Scopus)

抄録

As more and more domain specific big data become available, there comes a strong need on the fast development and deployment of deep learning (DL) systems with high quality for domain specific applications, including many safety-critical scenarios. In traditional software engineering, software visualization plays an important role to enhance developers' performance with many tools available. However, there are limited visualization supports existing for DL systems, especially in integrated development environments (IDEs) that allow a developer to visualize the source code of a deep neural network (DNN) and its graph architecture. In this paper, we propose DeepGraph, a visualization tool for visualizing and understanding a deep neural network. DeepGraph analyzes the training program to construct the graph representation of a DNN, and establishes and maintains the linkage (mapping) between the source code of the training program and its corresponding neural network architecture. We implemented DeepGraph as a PyCharm plugin and performed preliminary empirical study to demonstrate its usefulness for understanding deep nueral networks.

本文言語英語
ホスト出版物のタイトルProceedings - 25th Asia-Pacific Software Engineering Conference, APSEC 2018
出版社IEEE Computer Society
ページ628-632
ページ数5
ISBN(電子版)9781728119700
DOI
出版ステータス出版済み - 7 2 2018
イベント25th Asia-Pacific Software Engineering Conference, APSEC 2018 - Nara, 日本
継続期間: 12 4 201812 7 2018

出版物シリーズ

名前Proceedings - Asia-Pacific Software Engineering Conference, APSEC
2018-December
ISSN(印刷版)1530-1362

会議

会議25th Asia-Pacific Software Engineering Conference, APSEC 2018
国/地域日本
CityNara
Period12/4/1812/7/18

All Science Journal Classification (ASJC) codes

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