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12 January 2023 The influence of different parameters on classical neural network fitting
Zhekai Zhu
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Proceedings Volume 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022); 125092B (2023) https://doi.org/10.1117/12.2655885
Event: Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 2022, Guangzhou, China
Abstract
Numerous network models can successfully fit some datasets since the advent of deep learning. The performance of the deep learning model continues to improve, mainly benefiting from the constantly added modules, such as convolution kernels. To quantitatively evaluate the role of these modules, it is essential to research how each module in the network topology affects the network's functionality by developing the standard model in this study. The paper takes LeNet, AlexNet, and VGG19 as examples, considering how each module affects prediction accuracy. In LeNet, AlexNet, or VGG19, different outcomes can be obtained by altering the linear layer of the net's dimension, the convolution kernel size, the dropout layer's percentage, or the pooling layer's deletion. This experiment is based on the CIFAR-10 dataset to test the original network. According to the experimental findings, the network's performance will also depend on the size of the convolution kernel, the dropout ratio, and whether the pooling layer is kept.
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Zhekai Zhu "The influence of different parameters on classical neural network fitting", Proc. SPIE 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 125092B (12 January 2023); https://doi.org/10.1117/12.2655885
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KEYWORDS
Convolution
Neural networks
Neurons
Feature extraction
Image processing
Convolutional neural networks
Data modeling
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