Deep neural network model construction with interactive code reuse and automatic code transformation. (13th August 2019)

• Record Type: Journal Article
• Authors: Lee, Keon Myung; Park, Ki‐Sun; Hwang, Kyung‐Soon; Kim, Kwang‐Il
• Other Names: Choi Chang guestEditor.; Pop Florin guestEditor.; Huang Jun guestEditor.; Ogiela Marek R. guestEditor.
• Is Part Of: Concurrency and computation. Volume 32:Number 18(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-08-13
• Subjects: code generation -- deep learning -- deep neural network modeling -- graph algorithm -- machine learning; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5480
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Many application developers have been recently interested in applying deep learning techniques to their works but have little knowledge and experience on them. This paper presents the methods for a GUI‐based modeling tool to easily build deep learning models and automatically transform them into executable program codes. For reuse of existing deep learning codes, it introduces a method that imports such a program code, extracts the deep learning model architecture, and transforms it into a graphical representation that can be modified on a graphical interface. Meanwhile, a deep learning model with many layers is difficult to be visualized on a small display screen. To handle the difficulty, it proposes a method to identify the submodules called articulable subgraphs in a deep learning model and to organize the deep learning model into a hierarchical architecture using the nesting relationships of articulable subgraphs. It introduces a method to identify frequent substractures as well as articulable subgraphs as building blocks for deep learning models for levelwise views. The hierarchical representation easily enables to build deep learning models with many layers. The GUI‐based modeling tool employing the proposed methods makes nonexpert developers easily use deep learning techniques in their practical applications.