Abstract
Next‐generation networks are data‐driven by design but face uncertainty due to various changing user group patterns and the hybrid nature of infrastructures running these systems. Meanwhile, the amount of data gathered in the computer system is increasing. How to classify and process the massive data to reduce the amount of data transmission in the network is a very worthy problem. Recent research uses deep learning to propose solutions for these and related issues. However, deep learning faces problems like overfitting that may undermine the effectiveness of its applications in solving different network problems. This paper considers the overfitting problem of convolutional neural network (CNN) models in practical applications. An algorithm for maximum pooling dropout and weight attenuation is proposed to avoid overfitting. First, design the maximum value pooling dropout in the pooling layer of the model to sparse the neurons and then introduce the regularization based on weight attenuation to reduce the complexity of the model when the gradient of the loss function is calculated by backpropagation. Theoretical analysis and experiments show that the proposed method can effectively avoid overfitting and can reduce the error rate of data set classification by more than 10% on average than other methods. The proposed method can improve the quality of different deep learning‐based solutions designed for data management and processing in next‐generation networks.
Highlights
At present, the direction supported by the internet is changing from consumption to production, but the network architecture based on TCP/IP cannot adapt to this change in scalability, security, and other aspects
Based on the advantages of these new technologies, this paper proposes a data-driven network architecture, which is aimed at solving the problem of massive data filtering and classification in the development of the emerging future network, and the specific contents are as follows: based on the full analysis of the characteristics of Convolutional neural network (CNN) and the current research on CNN model overfitting by scholars at home and abroad, this paper proposes an algorithm for the maximum pooling dropout and weight attenuation overfitting problem, and through the theoretical derivation, as well as the image data collected in the network for classification experiment comparison, it proves that this method can effectively avoid overfitting in the training process of CNN model and improve the generalization ability of the model
In order to verify the performance and effect of the method proposed in this paper, the CNN method is based on the improvement of the immune system proposed in literature [16], the dropout method proposed by Hinton et al in literature [21], and the random pooling method proposed in [33] and the method in this paper for comparison experiments, in which ICNN proposed an improved CNN network method based on the immune system for problems such as overfitting, this method is tested in the data set
Summary
The direction supported by the internet is changing from consumption to production, but the network architecture based on TCP/IP cannot adapt to this change in scalability, security, and other aspects. Based on the advantages of these new technologies, this paper proposes a data-driven network architecture, which is aimed at solving the problem of massive data filtering and classification in the development of the emerging future network, and the specific contents are as follows: based on the full analysis of the characteristics of CNN and the current research on CNN model overfitting by scholars at home and abroad, this paper proposes an algorithm for the maximum pooling dropout and weight attenuation overfitting problem, and through the theoretical derivation, as well as the image data collected in the network for classification experiment comparison, it proves that this method can effectively avoid overfitting in the training process of CNN model and improve the generalization ability of the model.
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