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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.

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Aug 10th, 12:00 AM

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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