IMAGE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS BY DIFFERENT ACTIVATION FUNCTIONS

Abstract

With the continuous development of deep learning, convolution neural network with its excellent recognition performance obtains a series of major breakthrough results in target detection, image recognition and other fields. An improved ReLu segmentation correction Activate function is proposed, by improving the traditional convolution neural network, adding the local response normalization layer, and using the maximum stacking and so on. Based on the deep learning consepts, the activation function is used to construct the modified convolution neural network structure model, using the Boat analysis data set as the neural network input for the model training and evaluation. We analyze effects of different neuron activation function on the neural network convergence speed and the accuracy of image classification. The experimental results show that the boat image is classified based on different activation functions used.