Performance Optimization of Convolutional Neural Networks in Image Classification

Haoyu Ma

doi:10.62051/ijcsit.v4n2.27

Authors

Haoyu Ma

DOI:

https://doi.org/10.62051/ijcsit.v4n2.27

Keywords:

Convolutional neural networks (CNN), Image classification, Loss function, Stochastic gradient Descent (SGD), Deep learning, Optimization algorithms, Feature extraction

Abstract

With the development of deep learning, CNNs have become mainstream in image classification. However, CNN performance is affected by factors like loss function choice and optimization algorithm. This paper aims to improve CNN performance in image classification by optimizing SGD. Firstly, we analyze different loss functions and propose a suitable optimization strategy. Second, we explore SGD and its variants' roles in CNN training, focusing on accelerating convergence and improving accuracy. Experiments show the proposed scheme effectively enhances CNN performance, providing a reliable solution for image classification. This study supports CNN applications in image recognition and lays a foundation for exploring more efficient optimization methods.

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References

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