TensorView: Visualizing the training of convolutional neural network using paraview

Xinyu Chen; Qiang Guan; Xin Liang; Li Ta Lo; Simon Su; Trilce Estrada; James Ahrens

doi:10.1145/3154842.3154846

TensorView: Visualizing the training of convolutional neural network using paraview

Xinyu Chen, Qiang Guan, Xin Liang, Li Ta Lo, Simon Su, Trilce Estrada, James Ahrens

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Convolutional Neural Networks(CNNs) have been widely used in visual recognition tasks recently. Previous works visualize learning features at different layers to help people to understand how CNNs learn visual recognition tasks. However they only provide qualitative description and do not help to accelerate the training process. We present TensorView to enable Paraview to visualize the evolution of CNNs. TensorView provides both qualitative and quantitative visualization that help understand the learning procedure, tune the learning parameters, direct merging and pruning of neural networks.

Original language	English
Title of host publication	DIDL 2017 - Proceedings of the 1st Workshop on Distributed Infrastructures for Deep Learning, Part of Middleware 2017
Pages	11-16
Number of pages	6
ISBN (Electronic)	9781450351690
DOIs	https://doi.org/10.1145/3154842.3154846
State	Published - Dec 11 2017
Event	1st Workshop on Distributed Infrastructures for Deep Learning, DIDL 2017 - Las Vegas, United States Duration: Dec 11 2017 → Dec 15 2017

Publication series

Name	DIDL 2017 - Proceedings of the 1st Workshop on Distributed Infrastructures for Deep Learning, Part of Middleware 2017

Conference

Conference	1st Workshop on Distributed Infrastructures for Deep Learning, DIDL 2017
Country/Territory	United States
City	Las Vegas
Period	12/11/17 → 12/15/17

Bibliographical note

Publisher Copyright:
© 2017 Copyright held by the owner/author(s).

Keywords

Convolutional networks
Paraview
Visualization

ASJC Scopus subject areas

Computer Networks and Communications
Hardware and Architecture
Information Systems
Software

Access to Document

10.1145/3154842.3154846

Cite this

Chen, X., Guan, Q., Liang, X., Lo, L. T., Su, S., Estrada, T., & Ahrens, J. (2017). TensorView: Visualizing the training of convolutional neural network using paraview. In DIDL 2017 - Proceedings of the 1st Workshop on Distributed Infrastructures for Deep Learning, Part of Middleware 2017 (pp. 11-16). (DIDL 2017 - Proceedings of the 1st Workshop on Distributed Infrastructures for Deep Learning, Part of Middleware 2017). https://doi.org/10.1145/3154842.3154846

Chen, Xinyu ; Guan, Qiang ; Liang, Xin et al. / TensorView : Visualizing the training of convolutional neural network using paraview. DIDL 2017 - Proceedings of the 1st Workshop on Distributed Infrastructures for Deep Learning, Part of Middleware 2017. 2017. pp. 11-16 (DIDL 2017 - Proceedings of the 1st Workshop on Distributed Infrastructures for Deep Learning, Part of Middleware 2017).

@inproceedings{4639ce629cb441b0a85e88fd1c7d9a9a,

title = "TensorView: Visualizing the training of convolutional neural network using paraview",

abstract = "Convolutional Neural Networks(CNNs) have been widely used in visual recognition tasks recently. Previous works visualize learning features at different layers to help people to understand how CNNs learn visual recognition tasks. However they only provide qualitative description and do not help to accelerate the training process. We present TensorView to enable Paraview to visualize the evolution of CNNs. TensorView provides both qualitative and quantitative visualization that help understand the learning procedure, tune the learning parameters, direct merging and pruning of neural networks.",

keywords = "Convolutional networks, Paraview, Visualization",

author = "Xinyu Chen and Qiang Guan and Xin Liang and Lo, {Li Ta} and Simon Su and Trilce Estrada and James Ahrens",

note = "Publisher Copyright: {\textcopyright} 2017 Copyright held by the owner/author(s).; 1st Workshop on Distributed Infrastructures for Deep Learning, DIDL 2017 ; Conference date: 11-12-2017 Through 15-12-2017",

year = "2017",

month = dec,

day = "11",

doi = "10.1145/3154842.3154846",

language = "English",

series = "DIDL 2017 - Proceedings of the 1st Workshop on Distributed Infrastructures for Deep Learning, Part of Middleware 2017",

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booktitle = "DIDL 2017 - Proceedings of the 1st Workshop on Distributed Infrastructures for Deep Learning, Part of Middleware 2017",

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Chen, X, Guan, Q, Liang, X, Lo, LT, Su, S, Estrada, T & Ahrens, J 2017, TensorView: Visualizing the training of convolutional neural network using paraview. in DIDL 2017 - Proceedings of the 1st Workshop on Distributed Infrastructures for Deep Learning, Part of Middleware 2017. DIDL 2017 - Proceedings of the 1st Workshop on Distributed Infrastructures for Deep Learning, Part of Middleware 2017, pp. 11-16, 1st Workshop on Distributed Infrastructures for Deep Learning, DIDL 2017, Las Vegas, United States, 12/11/17. https://doi.org/10.1145/3154842.3154846

TensorView: Visualizing the training of convolutional neural network using paraview. / Chen, Xinyu; Guan, Qiang; Liang, Xin et al.
DIDL 2017 - Proceedings of the 1st Workshop on Distributed Infrastructures for Deep Learning, Part of Middleware 2017. 2017. p. 11-16 (DIDL 2017 - Proceedings of the 1st Workshop on Distributed Infrastructures for Deep Learning, Part of Middleware 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Estrada, Trilce

AU - Ahrens, James

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AB - Convolutional Neural Networks(CNNs) have been widely used in visual recognition tasks recently. Previous works visualize learning features at different layers to help people to understand how CNNs learn visual recognition tasks. However they only provide qualitative description and do not help to accelerate the training process. We present TensorView to enable Paraview to visualize the evolution of CNNs. TensorView provides both qualitative and quantitative visualization that help understand the learning procedure, tune the learning parameters, direct merging and pruning of neural networks.

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Chen X, Guan Q, Liang X, Lo LT, Su S, Estrada T et al. TensorView: Visualizing the training of convolutional neural network using paraview. In DIDL 2017 - Proceedings of the 1st Workshop on Distributed Infrastructures for Deep Learning, Part of Middleware 2017. 2017. p. 11-16. (DIDL 2017 - Proceedings of the 1st Workshop on Distributed Infrastructures for Deep Learning, Part of Middleware 2017). doi: 10.1145/3154842.3154846

TensorView: Visualizing the training of convolutional neural network using paraview

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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