Visual Ensemble Analysis to Study the Influence of Hyper-parameters on Training Deep Neural Networks
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Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
A good deep neural network design allows for efficient training and high accuracy. The training step requires a suitable choice of several hyper-parameters. Limited knowledge exists on how the hyper-parameters impact the training process, what is the interplay of multiple hyper-parameters, and what is the interrelation of hyper-parameters and network topology. In this paper, we present a structured analysis towards these goals by investigating an ensemble of training runs.We propose a visual ensemble analysis based on hyper-parameter space visualizations, performance visualizations, and visualizing correlations of topological structures. As a proof of concept, we apply our approach to deep convolutional neural networks.
Description
@inproceedings{10.2312:mlvis.20191160,
booktitle = {Machine Learning Methods in Visualisation for Big Data},
editor = {Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko},
title = {{Visual Ensemble Analysis to Study the Influence of Hyper-parameters on Training Deep Neural Networks}},
author = {Hamid, Sagad and Derstroff, Adrian and Klemm, Sören and Ngo, Quynh Quang and Jiang, Xiaoyi and Linsen, Lars},
year = {2019},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-089-5},
DOI = {10.2312/mlvis.20191160}
}