MATEC Web Conf.
Volume 309, 20202019 International Conference on Computer Science Communication and Network Security (CSCNS2019)
|Number of page(s)||5|
|Section||Modelling and Simulation|
|Published online||04 March 2020|
A lightweight CNN model and its application in intelligent practical teaching evaluation
City Institute, Dalian University of Technology, Dalian 116600, China
* Corresponding author: Heyi517@dlut.edu.cn
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.
Key words: CNN / Lightweight / Parameter redundancy / Practical teaching / Intelligent evaluation
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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