A lightweight CNN model and its application in intelligent practical teaching evaluation

Yi He,Tianli Li,J Joo

doi:10.1051/matecconf/202030905016

Abstract

In this paper, we propose a lightweight CNN model. Firstly, we standardize the existing CNN model structure based on the minimum computing unit, and second we apply a parameter control solution to solve the problem of parameter redundancy in the model. At last we build a lightweight nonaligned CNN model. The experimental results show that the model parameters can be reduced by more than 50% when the test error is almost the same. Through deep learning, the proposed model is applied to the practical teaching system to achieve the intelligent evaluation effect of the practical teaching process, while improve the quality and efficiency of teaching.

Highlights

Convolutional neural network (CNN) model [1] has significant advantages in feature extraction, and is widely used in computer vision fields such as image classification [2], target detection [3], style recognition [4], scene segmentation [5]
The experimental results show that compared with DenseNet40, this method can reduce the model parameters by more than 50% on the premise that the test error is less than 1%
Through deep learning based on the application of the proposed model, we can realize the intelligent evaluation of the practical teaching operation process, and according to the operation progress and accuracy of the students’ completion of the practical task, we give the final score of the practical course by the weight formula in the teaching

Summary

Introduction

Convolutional neural network (CNN) model [1] has significant advantages in feature extraction, and is widely used in computer vision fields such as image classification [2], target detection [3], style recognition [4], scene segmentation [5]. Through deep learning based on the application of the proposed model, we can realize the intelligent evaluation of the practical teaching operation process, and according to the operation progress and accuracy of the students’ completion of the practical task, we give the final score of the practical course by the weight formula in the teaching. It makes the evaluation of practical courses more efficient, optimizes the process of teachers’ management, supervision and evaluation of teaching, improves the quality of formative assessment of practical teaching, and improves the construction of information and intelligence of practical teaching

Design of LCNN model

Experimental environment and data

The application of LCNN in intelligent practical teaching evaluation

Findings

Conclusion