Convolutional neural networks (CNNs) are widely used in image recognition. Numerous CNN models, such as LeNet, AlexNet, VGG, ResNet, and GoogLeNet, have been proposed by increasing the number of layers, to improve the performance of CNNs. However, performance deteriorates beyond a certain number of layers. Hence, hyperparameter optimisation is a more efficient way to improve CNNs. To validate this concept, a new algorithm based on simplified swarm optimisation is proposed to optimise the hyperparameters of the simplest CNN model, which is LeNet. The results of experiments conducted on the MNIST, Fashion MNIST, and Cifar10 datasets showed that the accuracy of the proposed algorithm is higher than the original LeNet model and PSO-LeNet and that it has a high potential to be extended to more complicated models, such as AlexNet.
@article{arxiv.2103.03995,
title = {Convolution Neural Network Hyperparameter Optimization Using Simplified Swarm Optimization},
author = {Wei-Chang Yeh and Yi-Ping Lin and Yun-Chia Liang and Chyh-Ming Lai},
journal= {arXiv preprint arXiv:2103.03995},
year = {2021}
}