DeepVisual: A visual programming tool for deep learning systems

Chao Xie, Hua Qi, Lei Ma, Jianjun Zhao

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

2 被引用数 (Scopus)

抄録

As deep learning (DL) opens the way to many technological innovations in a wild range of fields, more and more researchers and developers from diverse domains start to take advantage of DLs. In many circumstances, a developer leverages a DL framework and programs the training software in the form of source code (e.g., Python, Java). However, not all of the developers across domains are skilled at programming. It is highly desirable to provide a way so that a developer could focus on how to design and optimize their DL systems instead of spending too much time on programming. To simplify the programming process towards saving time and effort especially for beginners, we propose and implement DeepVisual, a visual programming tool for the design and development of DL systems. DeepVisual represents each layer of a neural network as a component. A user can drag-and-drop components to design and build a DL model, after which the training code is automatically generated. Moreover, DeepVisual supports to extract the neural network architecture on the given source code as input. We implement DeepVisual as a PyCharm plugin and demonstrate its usefulness on two typical use cases.

本文言語英語
ホスト出版物のタイトルProceedings - 2019 IEEE/ACM 27th International Conference on Program Comprehension, ICPC 2019
出版社IEEE Computer Society
ページ130-134
ページ数5
ISBN(電子版)9781728115191
DOI
出版ステータス出版済み - 5 2019
イベント27th IEEE/ACM International Conference on Program Comprehension, ICPC 2019 - Montreal, カナダ
継続期間: 5 25 2019 → …

出版物シリーズ

名前IEEE International Conference on Program Comprehension
2019-May

会議

会議27th IEEE/ACM International Conference on Program Comprehension, ICPC 2019
国/地域カナダ
CityMontreal
Period5/25/19 → …

All Science Journal Classification (ASJC) codes

  • ハードウェアとアーキテクチャ
  • ソフトウェア

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