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Proceedings Paper • new

Deep learning visual programming
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Paper Abstract

These last years, we have witnessed considerable improvements in machine learning and deep learning. Many advanced techniques are now based on deep neural networks. Although many software libraries are available, the development of deep neural networks requires a good level of mathematical knowledge and high programming skills. In this work, we present a visual tool to help simplify the programming of deep learning networks. The developed framework DeepViP is comprised of a node editor that provides users with a toolbox representing different types of neural layers. It allows the connection between the different blocks and the configuration of important hyper parameters of each layer. Thus, speeding-up experimentation with different architectures. Additionally, the developed solution offers users the possibility to generate a python script of the designed network that can be run using specific libraries such as keras or tensorflow.

Paper Details

Date Published: 10 May 2019
PDF: 10 pages
Proc. SPIE 11013, Disruptive Technologies in Information Sciences II, 1101308 (10 May 2019); doi: 10.1117/12.2519882
Show Author Affiliations
Dorra Mahouachi, Univ. de Moncton (Canada)
Moulay A. Akhloufi, Univ. de Moncton (Canada)


Published in SPIE Proceedings Vol. 11013:
Disruptive Technologies in Information Sciences II
Misty Blowers; Russell D. Hall; Venkateswara R. Dasari, Editor(s)

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