Analysis and Comparison of Convolution Layer in Deep Convolution Neural Network
| Autor: | Yi-Chun Hu, 胡依淳 |
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| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 With the rapid development of information technology, big data has become mainstream, and many identification systems have been greatly affected. Therefore, deep learning requires a large database learning model and thus becomes the mainstream. Deep learning can take advantage of the characteristics of robots to automatically learn to task objectives, and thus deep learning of this architecture has become a very popular technology in academics. Nowadays, neural networks are popular in the field of visual imaging. The best performing model is the convolutional neural network. The progress of deep learning is related to Convolutional Neural Networks (CNN). Convolutional neural networks, also known as CNNs or ConvNets, are the main developments in the field of deep neural networks, and can even be more accurate than humans in image recognition. If there is any way to live up to the expectations of deep learning, convolutional neural networks are definitely the first choice. A key part of the convolutional neural network is the weight value in the kernel of the convolutional layer. There are usually three ways to change the convolutional layer in the convolutional neural network. Kernel size, activation function, and convolution use the number of kernels. The neural network may have different activation function selection or kernel size. The accuracy is not high. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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