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Paper Type
ERF
Abstract
Activation functions are a very crucial part of convolutional neural networks (CNN) because to a very large extent, they determine the performance of the CNN model. Various activation functions have been developed over the years and the choice of activation function to use in a given model is usually a matter of trial and error. In this paper, we evaluate some of the most-used activation functions and how they impact the time to train a CNN model and the performance of the model. We make recommendations for the best activation functions to use based on the results of our experiment.
Recommended Citation
Onwujekwegn, Gerald and Yoon, Victoria Y, "Analyzing the Impacts of Activation Functions on the Performance of Convolutional Neural Network Models" (2020). AMCIS 2020 Proceedings. 4.
https://aisel.aisnet.org/amcis2020/ai_semantic_for_intelligent_info_systems/ai_semantic_for_intelligent_info_systems/4
Analyzing the Impacts of Activation Functions on the Performance of Convolutional Neural Network Models
Activation functions are a very crucial part of convolutional neural networks (CNN) because to a very large extent, they determine the performance of the CNN model. Various activation functions have been developed over the years and the choice of activation function to use in a given model is usually a matter of trial and error. In this paper, we evaluate some of the most-used activation functions and how they impact the time to train a CNN model and the performance of the model. We make recommendations for the best activation functions to use based on the results of our experiment.
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