Examining Sigmoid vs ReLu Activation Functions in Deep Learning

Authors

Mohammad Anwarul Islam, Georgia Southern UniversityFollow
Hayden Wimmer, Georgia Southern UniversityFollow
Carl M. Rebman Jr, University of San DiegoFollow

Document Type

Contribution to Book

Publication Date

2021

Publication Title

Interdisciplinary Research in Technology and Management

Abstract

In recent years, deep learning has been considered to be a solution for many different problems such as natural language processing, pattern recognition, image detection and image classification. Artificial neural networks (ANN) are one of the deep learning models developed to address these problems. This study presents a Convolutional Neural Network (CNN) with LeNet architecture for image classification. Tests were conducted on the Caltech-101 datasets to determine the effectiveness of the CNN model. Over 1260 images were used and results indicate that the CNN with LeNEt was more accurate in image classification.

Comments

Georgia Southern University faculty members, Mohammad Anwarul Islam and Hayden Wimmer, co-authored Examining Sigmoid vs ReLu Activation Functions in Deep Learning.

Recommended Citation

Islam, Mohammad Anwarul, Hayden Wimmer, Carl M. Rebman Jr. 2021. "Examining Sigmoid vs ReLu Activation Functions in Deep Learning." Interdisciplinary Research in Technology and Management: 432-437: CRC Press LLC and Taylor and Francis.
https://digitalcommons.georgiasouthern.edu/math-sci-facpubs/784

Copyright

Taylor and Francis Publishers Rights and Permissions