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

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.