vision transformer公式
时间: 2023-08-21 08:03:07 浏览: 53
回答: Vision Transformer的公式可以表示为:
\\[
\text{{output}} = \text{{MLP}}(\text{{LayerNorm}}(\text{{Attention}}(\text{{input}})) + \text{{input}})
\\]
其中,\text{{input}}表示输入的特征向量,\text{{Attention}}表示注意力机制,\text{{LayerNorm}}表示层归一化操作,\text{{MLP}}表示多层感知机。这个公式表示了Vision Transformer的整体框架,通过注意力机制对输入特征进行加权融合,然后通过多层感知机进行非线性变换,最后再加上输入特征本身,得到最终的输出。\[1\]\[2\]\[3\]
#### 引用[.reference_title]
- *1* *3* [Vision Transformer详解(附代码)](https://blog.csdn.net/qq_38406029/article/details/122157116)[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^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
- *2* [一文详解Vision Transformer(附代码)](https://blog.csdn.net/m0_59596990/article/details/122589854)[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^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
[ .reference_list ]