Dropout Probability Estimation in Convolutional Neural Networks by the Enhanced Bat Algorithm
Nebojša Bačanin, Eva Tuba, Timea Bezdan, Ivana Strumberger, Raka Jovanović, Milan Tuba · 2020
In recent years, deep learning has reached exceptional accomplishment in diverse applications, such as visual and speech recognition, natural language processing. The convolutional neural network represents a particular type of neural network commonly used for the task of digital image classification. A common issue in deep neural network models is the high variance problem, or also called over-fitting. Over-fitting occurs when the model fits well with the training data and fails to generalize on new data. To prevent over-fitting, several regularization methods can be used; one such powerful method is the dropout regularization. To find the optimal value of the dropout rate is a very time-consuming process; hence, we propose a model to find the optimal value by utilizing a metaheuristic algorithm instead of a manual search. In this paper, we propose a hybridized bat algorithm to find the optimal dropout probability rate in a convolutional neural network and compare the results to similar techniques. The experimental results show that the proposed hybrid method overperforms other metaheuristic techniques.