Abstract:

Among the problems involving image classification, the classification method based on convolutional neural networks is preferred. In order to solve the problems of poor processing capacity and low classification accuracy, a new type of image classification model of convolutional neural network is proposed. The new Inception module is added on the basis of the traditional network model, which enhances the transmission of the feature information of the model and improves the ability of feature expression. Additionally, the performance of the model and the classification accuracy are improved by activation function, data enhancement, batch regularization, initial weight optimization and Adadelta optimization method. The new network model based on the new initial module is used to test the data on the CIFAR-10 data set, and compared with the traditional network model method, which proves that the modulated model effectives improves the network performance.

Key words: convolution neural network, image classification, new Inception module, data augmentation, weight initialization, Adadelta optimization method