Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-11-29 Cooperative journals: 《计算机应用研究》
Abstract： To improve the correct rate of image classification by convolutional neural network, a multi-model fusion convolutional neural network is proposed after research on the network structure. By extracting the output feature vectors of a single model and then fusion them, the new output feature vectors are obtained, and then a single classifier is set up to classify the images, and the accuracy of the classification is improved. The classification accuracy of single model compare with multi-model fusion, the accuracy of classification of multi-model fusion convolutional neural network is improved. The weight distribution of the last layer of the convolutional neural network is analyzed, and it is found that the weight distribution curve of the same model on different data sets is similar and the weight distribution curve of the network model with better classification effect is more gentle.
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