Multilayer Perceptron with Adaptive Learning Rate and Activation Functions: An Enhanced Convolutional Neural Network Approach for Zhuang Ethnic Cultural Image Classification

This paper focuses on the classification and recognition of Zhuang ethnic cultural landscape images using Convolutional Neural Network (CNN) technology. As an integral part of China's diverse ethnic culture, Zhuang cultural landscapes encompass unique architectural styles, artistic forms, and historical significance. Traditional image processing techniques face challenges in accuracy and efficiency when dealing with such specialized images. Therefore, this paper proposes a Multilayer Perceptron (MLP) model combined with adaptive learning rates and activation functions, aimed at enhancing the performance of CNNs in Zhuang cultural landscape image classification. We thoroughly discuss the importance of adaptive learning rates and activation functions, providing details of the model's implementation and performance evaluation. Our experimental results demonstrate the effectiveness and superiority of the proposed model in handling the classification tasks of Zhuang cultural landscape images.