Abstract: Aiming at the issue that it is difficult to select the activation function in deep learning model for specific task,on the basis of analyzing the advantages and disadvantages of traditional activation function and the popular activation function at the present stage,this paper constructed an activation function T-ReLU which can make up for the shortcomings of Tanh function and ReLU function by combining the Tanh activation function with the widely used ReLU function.By constructing the deep learning model Typ-CNNs for typhoon grade classification,using the Typhoon satellite image published by the Japan Meteorological Agency as the self-built sample data,this paper made use of several different activation functions to conduct comparison experiments.The results show that the test accuracy of typhoon grade classification using the T-ReLU function is 1.124% higher than that of using ReLU activation function,which is 2.102% higher than that of using Tanh function.In order to further verify the reliability of the results,the MNIST general data set was utilized to carry out the comparison experiment of activation function.The final results show that 99.855% training accuracy and 98.620% test accuracy can be obtained by using T-ReLU function,and it performs better than other activation functions.

Key words: Activation function, Convolution neural network, Deep learning, MNIST dataset, Typhoon grade