Particle Swarm Optimization Based Approach for Finding Optimal Values of Convolutional Neural Network Parameters

Convolutional Neural Networks (CNNs) have demonstrated great potential in complex image classification problems in past few years. CNNs have a large number of parameters and the system accuracy depends directly on the selection of these parameters. With diverse parameters, selection of optimal parameter remains a trial and error, ad hoc or expert's mercy. In practice, optimal parameter selection remains the biggest obstacle in designing a real-world application using CNN. Convolutional neural network's performance is highly affected by its parameters. A novel approach is proposed in this paper to select convolutional neural network parameters in an image classification task. The proposed approach incorporated particle swarm optimization to select the parameters of the convolutional network. Two datasets, one benchmark CIFAR-IO and one real world application dataset, road-side vegetation dataset, were selected to evaluate the proposed approach. It is demonstrated that proposed approach efficiently explores the solution space, and determines the best combination of parameters. Extensive experiments, along with the statistical tests, revealed that proposed approach is an effective technique for automatically optimizing CNN's parameters.