Paper
10 May 2019 Deep learning visual programming
Author Affiliations +
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.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dorra Mahouachi and Moulay A. Akhloufi "Deep learning visual programming", Proc. SPIE 11013, Disruptive Technologies in Information Sciences II, 1101308 (10 May 2019); https://doi.org/10.1117/12.2519882
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CITATIONS
Cited by 1 patent.
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KEYWORDS
Computer programming

Neural networks

Visualization

Clouds

Computer vision technology

Machine learning

Machine vision

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