IMAGE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS BY DIFFERENT ACTIVATION FUNCTIONS
DOI:
https://doi.org/10.17605/OSF.IO/C93GRKeywords:
Image classification,, Convolotion neural network,, AccuracyAbstract
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.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Under the Creative Commons Attribution- 4.0 International License (CC BY-4.0 DEED).
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
