concatenate、add

concatenate和add是深度学习中常用的两种操作方式。

concatenate操作用于将特征联合，多个卷积特征提取框架提取的特征融合或者是将输出层的信息进行融合。它是通道数的合并，也就是说描述图像本身的特征增加了，而每一特征下的信息是没有增加的。中的代码示例。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *3* [神经网络中concatenate和add层的不同](https://blog.csdn.net/u012193416/article/details/79479935)[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^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"]
- *2* [add 和concatenate的区别](https://blog.csdn.net/qq_44936246/article/details/121068720)[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^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"]
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