Convolution Neural Network Hyperparameter Optimization Using Simplified Swarm Optimization
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Hyperparameter OptimizationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Optimization of Convolutional Neural Network Using the Linearly Decreasing Weight Particle Swarm Optimization
Convolutional neural network (CNN) is one of the most frequently used deep learning techniques. Various forms of models have been proposed and im-proved for learning at CNN. When learning with CNN, it is necessary to det…
Hyperparameter OptimizationImplementation of Parallel Simplified Swarm Optimization in CUDA
As the acquisition cost of the graphics processing unit (GPU) has decreased, personal computers (PC) can handle optimization problems nowadays. In optimization computing, intelligent swarm algorithms (SIAs) method is sui…
GPUHow to Prove the Optimized Values of Hyperparameters for Particle Swarm Optimization?
In recent years, several swarm intelligence optimization algorithms have been proposed to be applied for solving a variety of optimization problems. However, the values of several hyperparameters should be determined. Fo…
PositionA Novel Simplified Swarm Optimization for Generalized Reliability Redundancy Allocation Problem
Network systems are commonly used in various fields, such as power grid, Internet of Things (IoT), and gas networks. Reliability redundancy allocation problem (RRAP) is a well-known reliability design tool, which needs t…
Neural Architecture Search using Particle Swarm and Ant Colony Optimization
Neural network models have a number of hyperparameters that must be chosen along with their architecture. This can be a heavy burden on a novice user, choosing which architecture and what values to assign to parameters. …
Experimental DesignNeural Architecture SearchTransfer Learning