Application of deep NN optimized by multi-parameter fusion in ideological and political construction of professional courses in colleges and universities

Abstract

Curriculum ideology and politics is an inherent requirement to achieve the goal of "cultivating morality and cultivating people" in colleges and universities, and it is a beneficial exploration to realize the three-round education. The ideological and political construction of professional courses in colleges and universities not only teaches students knowledge and skills, but also helps students form correct values. Aiming at how to build ideological and political courses in colleges and universities, a design method based on multi-parameter fusion to gradually optimize deep NN is proposed. Firstly, the initial NN model without hidden layer is determined by analyzing the samples and categories, and then the hidden layer is gradually added on the basis of the initial NN to construct a deep NN with multi-parameter fusion optimization. Based on the TensorFlow framework, taking handwritten digit recognition as an example, a deep NN model is gradually designed. During the whole experiment, the network structure, activation function, loss function, optimizer, learning rate and sample batch size are continuously adjusted, and finally a multi-parameter design is designed. The fusion optimized deep NN model with high accuracy provides an effective idea for building a NN. As the learning rate increases, the performance of the NN gradually improves. In the training set and test set, the accuracy rate is almost the highest when the learning rate is 0.3, and the accuracy rate is 93.30% and 92.58% respectively when the number of iterations is 30, which shows that The NN optimized by multi-parameter fusion can be well applied to the ideological and political construction of professional courses in colleges and universities, and has strong application prospects.