  Recent work has shown that the activation function of the convolutional
neural network can meet the Lipschitz condition, then the corresponding
convolutional neural network structure can be constructed according to the
scale of the data set, and the data set can be trained more deeply, more
accurately and more effectively. In this article, we have accepted the
experimental results and introduced the core block N-Gauss, N-Gauss, and Swish
(Conv1, Conv2, FC1) neural network structure design to train MNIST, CIFAR10,
and CIFAR100 respectively. Experiments show that N-Gauss gives full play to the
main role of nonlinear modeling of activation functions, so that deep
convolutional neural networks have hierarchical nonlinear mapping learning
capabilities. At the same time, the training ability of N-Gauss on simple
one-dimensional channel small data sets is equivalent to the performance of
ReLU and Swish.
