Research on deep neural network model construction and overfitting
Tonghan Li, Hui Zhang, Linchang Fan, Hao Henry Wang, Qian Liu · International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022) · 2022
In recent years, deep neural network has been widely used in image recognition, natural language processing, computer vision and other fields, but it is prone to overfitting during network training. To solve this problem, this paper uses TensorFlow2.0 framework to construct multilayer perceptron deep network for Fashion-MNIST dataset, and uses dropout algorithm to solve the overfitting problem in the process of network training. The research results show that the dropout algorithm is applied to the deep neural network, which can make the deep neural network model have strong generalization ability and can effectively solve the overfitting problem of the training network. The research on overfitting problem has important practical significance for reducing the identification error of deep network.