Dropout Probability Estimation in Convolutional Neural Networks by the Enhanced Bat Algorithm

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. Overfitting 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.