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Optimizing a Specialized Convolutional Neural Network in a Supercomputing Environment Using Tensorflow and VisIt

Published: 26 July 2020 Publication History

Abstract

We describe hyper-parameter optimization and training for a novel convolutional neural network that identifies neural axons in 3D volume electron microscope data for a larval zebrafish. Training a convolutional neural network to detect image components in three dimensional space has a variety of applications. There are many parts of a network that can be tuned to work most effectively in a supercomputing environment. We optimized run parameters including batch sizes, thread counts, and epoch sizes as well as hyper-parameters such as drop-out rates. Once we had improved the speed and accuracy of the network we created a large three dimensional volume to visualize the errors of the network. We added new samples to our training set based on the larger volume and improved the accuracy of the network using those samples. Adapting the network to our supercomputing environment and adjusting the dropout layers increased the accuracy and efficiency of the network. Being able to accurately detect neurons in three dimensional space shows potential for other deep learning applications.

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cover image ACM Conferences
PEARC '20: Practice and Experience in Advanced Research Computing 2020: Catch the Wave
July 2020
556 pages
ISBN:9781450366892
DOI:10.1145/3311790
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 26 July 2020

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  1. 3D
  2. classifier
  3. neural networks

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Overall Acceptance Rate 133 of 202 submissions, 66%

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