Recognition and other fields

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ScienceDirect
Procedia Computer Science 131 (2018) 977–984
8th International Congress of Information and Communication Technology (ICICT-2018)

Research on convolutional neural network based on improved Relu piecewise activation function

Guifang Lin ^a, Wei Shen ^a一
^a School Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China




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 Google depth learning platform TensorFlow, the activation function is used to construct the modified convolution neural network structure model, using the CIFAR-10 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 recognition. The experimental results show that using the improved unsaturated nonlinear segment activation function SignReLu, the convergence rate is faster, the gradient vanishing problem is effectively alleviated, and the accuracy of neural network identification is improved obviously.

© 2018 The Authors. Published by Elsevier Ltd.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Selection and peer-review under responsibility of the scientific committee of the 8th International Congress of Information and
Communication Technology.
Keywords: TensorFlow; Convolutional Neural Networks; Depth Learning; Activation Function.