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Hyperparameter optimization ResNet by improved Beluga Whale Optimization
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AI
DOI：10.1371/journal.pone.0333575.png)
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Abstract
En 中文
The parameter values of neural networks will directly affect the performance of the network, so it is very important to choose the appropriate parameter tuning method to improve the performance of the neural network. In this paper, the improved beluga whale optimization hyperparameter optimization ResNet model is used to construct a new model, EBWO-ResNet. Firstly, in order to solve the problem that the initial population of the original beluga whale optimization is not rich enough, the Tent chaotic map is introduced into the beluga whale optimization, and a new algorithm EBWO is constructed. Secondly, in order to solve the problems of low accuracy and difficult parameter tuning of ResNet, the EBWO algorithm was integrated into ResNet to construct a new model EBWO-ResNet. Finally, in order to verify the effectiveness of the EBWO algorithm, the EBWO algorithm was applied to three engineering problems and compared with other five swarm intelligent algorithms, and in order to verify the effectiveness of the EBWO-ResNet model, EBWO-ResNet was applied to maize disease identification,in order to improve the accuracy of corn identification and ensure corn yield,and the other seven models were compared based on three evaluation indexes. The experimental results show that the EBWO algorithm provides the best solutions in the three engineering problems, and the EBWO-ResNet has the best performance in identifying maize diseases, with an accuracy of 96.3%,which is 0.2-1.5 percentage points higher than that of other models.
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