Examining Sigmoid vs ReLu Activation Functions in Deep Learning
Contribution to Book
Interdisciplinary Research in Technology and Management
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.
Islam, Mohammad Anwarul, Hayden Wimmer, Carl M. Rebman Jr.
"Examining Sigmoid vs ReLu Activation Functions in Deep Learning."
Interdisciplinary Research in Technology and Management: 432-437: CRC Press LLC and Taylor and Francis.