vgg16模型结构图
时间: 2023-10-29 20:55:32 浏览: 210
VGG16模型的结构图如下所示:
1. 输入层(Input Layer)
2. 卷积层1-1(Convolutional Layer):64个卷积核
3. 卷积层1-2(Convolutional Layer):64个卷积核
4. 池化层1(Pooling Layer)
5. 卷积层2-1(Convolutional Layer):128个卷积核
6. 卷积层2-2(Convolutional Layer):128个卷积核
7. 池化层2(Pooling Layer)
8. 卷积层3-1(Convolutional Layer):256个卷积核
9. 卷积层3-2(Convolutional Layer):256个卷积核
10. 卷积层3-3(Convolutional Layer):256个卷积核
11. 池化层3(Pooling Layer)
12. 卷积层4-1(Convolutional Layer):512个卷积核
13. 卷积层4-2(Convolutional Layer):512个卷积核
14. 卷积层4-3(Convolutional Layer):512个卷积核
15. 池化层4(Pooling Layer)
16. 卷积层5-1(Convolutional Layer):512个卷积核
17. 卷积层5-2(Convolutional Layer):512个卷积核
18. 卷积层5-3(Convolutional Layer):512个卷积核
19. 池化层5(Pooling Layer)
20. 全连接层1(Fully Connected Layer):4096个神经元
21. 全连接层2(Fully Connected Layer):4096个神经元
22. 全连接层3(Fully Connected Layer):1000个神经元(用于分类任务)
请注意,这只是VGG16模型的基本结构图,具体的细节和参数可能会有所不同。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *3* [【深度学习】全面理解VGG16模型](https://blog.csdn.net/Resume_f/article/details/92800997)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
- *2* [VGG-16网络结构详解](https://blog.csdn.net/qq_45998041/article/details/114626473)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
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