关键词: 卷积神经网络, 权值参数, 抑制系数, 泛化能力, 鲁棒性

Abstract: Convolutional Neural Network（CNN） is an important research direction for deep learning. Because its model is complex and difficult to train, designing a better training method for CNN has always been a research hotspot. It analyzes the influence of its weights on the training results based on the trained CNN model, and confirms that the greater weights have greater impact on the network performance. The performance of the model mainly depends on the few large weights. Weight Restrain of CNN（WR-CNN） training method is proposed, which adjusts the weight updating strategy during model training and sets a suppression coefficient related to the value of the weight. The coefficient adjusts the weight increment of the back propagation to achieve the purpose of controlling the distribution of connection with a large weight. The experimental results show that the WR-CNN training method can effectively improve the training performance of CNN. Under different experimental conditions, the method reduces the error rate of the CNN model by 1.8%~5.0%, the sensitivity of the model to the connection with a lager weight is significantly reduced and the model generalization ability and robustness are improved. The WR-CNN training method can be used to re-optimize the trained CNN model.

Key words: convolutional neural network, weight parameters, suppression coefficient, generalization ability, robustness