A Combined Activation Function for Learning Performance Improvement of CNN Image Classification

Abstract

: With the rise of artificial intelligence, it has unlimited possibilities for machines to replace human work. Aiming at how to improve the learning performance of convolutional neural network (CNN) image classification by changing the activation function, a combined Tanh-relu activation function is proposed based on the single Sigmoid, Tanh and Relu activation functions. Based on CNN-LeNet-5, the size of the convolution kernel and sampling window is changed and the number of layers of the convolutional neural network is reduced. At the same time, the network structure of the LeNet-5 model is improved. On the Mnist handwritten digital dataset, the combined Tanh-relu activation function was compared with a single activation function. The experimental results show that the CNN model with combined Tanh-relu activation function has faster accuracy fitting speed and higher accuracy, improves the convergence speed of loss and enhances the convergence performance of CNN model.