Experiment Research on Mobile Terminal Image Scene Recognition Based on optimization

Convolutional neural network (CNN), the network structure of weight sharing, which significantly reduces the complexity of the model and the number of weights, is a research hotspot in the field of speech analysis and image recognition. However, in most cases, insufficient data or small classification differences will result in poor training results: translation invariance and pooling layer. In this paper, the Cross-Entropy overfitting and sample unbalance problems were adjusted by parameter equalization, and the accuracy increased from 51.5% to 87.3%. At the same time, it is verified that in the same datasets, the identification error of the simplified model on the mobile is less than 2%.