基于改进激活函数的用于台风等级分类的深度学习模型

针对特定任务中深度学习模型的激活函数不易选取的问题,在分析传统激活函数和现阶段运用比较广泛的激活函数的优缺点的基础上,将Tanh激活函数与广泛使用的ReLU激活函数相结合,构造了一种能够弥补Tanh函数和ReLU函数缺点的激活函数T-ReLU。通过构建台风等级分类的深度学习模型Typ-CNNs,将日本气象厅发布的台风卫星云图作为自建样本数据集,采用几种不同的激活函数进行对比实验,结果显示使用T-ReLU函数得到的台风等级分类的测试精度比使用ReLU激活函数的测试精度高出1.124%,比使用Tanh函数的测试精度高出2.102%；为了进一步验证结果的可靠性,采用MNIST通用数据集进行激活函数的对比实验,最终使用T-ReLU函数得到99.855%的训练精度和98.620%的测试精度,其优于其他激活函数的效果。

Deep Learning Model for Typhon Grade Classification Based on Improved Activation Function

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.