| XIE Xiaoyan(谢晓燕)*,HE Wanqi*,ZHU Yun**,YU Jinhao*.[J].高技术通讯(英文),2023,29(4):427~433 |
|
| Neural network hyperparameter optimization based onimproved particle swarm optimization |
| |
| DOI： |
| 中文关键词: |
| 英文关键词: hyperparameter optimization, particle swarm optimization (PSO) algorithm, neural
network |
| 基金项目: |
| Author Name | Affiliation | | XIE Xiaoyan(谢晓燕)* | (*School of Computer, Xi’an University of Posts and Telecommunications, Xi’an 710121, P. R. China)
(**School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P. R. China) | | HE Wanqi* | | | ZHU Yun** | | | YU Jinhao* | |
|
| Hits: 287 |
| Download times: 278 |
| 中文摘要: |
| |
| 英文摘要: |
| Hyperparameter optimization is considered as one of the most challenges in deep learning and
dominates the precision of model in a certain. Recent proposals tried to solve this issue through the
particle swarm optimization (PSO), but its native defect may result in the local optima trapped and
convergence difficulty. In this paper, the genetic operations are introduced to the PSO, which makes
the best hyperparameter combination scheme for specific network architecture be located easier. Specifically,
to prevent the troubles caused by the different data types and value scopes, a mixed coding
method is used to ensure the effectiveness of particles. Moreover, the crossover and mutation operations
are added to the process of particles updating, to increase the diversity of particles and avoid
local optima in searching. Verified with three benchmark datasets, MNIST, Fashion-MNIST, and
CIFAR10, it is demonstrated that the proposed scheme can achieve accuracies of 99. 58%,
93. 39%, and 78. 96%, respectively, improving the accuracy by about 0. 1%, 0. 5%, and 2%,
respectively, compared with that of the PSO. |
|
View Full Text
View/Add Comment Download reader |
| Close |
|
|
|
