In order to further improve the convergence speed of deep neural network training,using the characteristics of batch normalization(BN)algorithm as reference,a BN algorithm of scale factor regularization is proposed.By applying L2 regularization to the learnable scale factor y in the BN layer,y was attenuated,the gradient upper bound of the parameter was reduced,and the optimization space was smoother.Based on VGG16 Net and AlexNet,the image classification comparison experiments between this algorithm and the BN algorithm were carried out on the cifar10,cifar100 and crack image datasets.The results show that the proposed algorithm not only improves the convergence speed of network training,but also improves the accuracy rate at the same training times.
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