python加权交叉熵
时间: 2023-08-13 07:10:49 浏览: 77
在Python中,可以使用torch.nn.functional.cross_entropy函数来计算加权交叉熵。该函数的第一个参数是logits,即模型的输出结果,第二个参数是目标标签。如果需要对不同类别的损失进行加权,可以使用weight参数来指定权重。以下是一个示例代码:
```python
import torch
import torch.nn.functional as F
logits = torch.randn(1, 10) # 模型的输出结果
target = torch.tensor(\[3\]) # 目标标签
weights = torch.tensor(\[1.0, 2.0, 1.5, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0\]) # 权重
loss = F.cross_entropy(logits, target, weight=weights)
```
在上述代码中,logits是模型的输出结果,target是目标标签,weights是每个类别的权重。通过调用F.cross_entropy函数并传入相应的参数,即可计算加权交叉熵的损失值。
#### 引用[.reference_title]
- *1* [交叉熵以及通过Python实现softmax_交叉熵(tensorflow验证)](https://blog.csdn.net/qq_40326280/article/details/113527525)[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* *3* [pytorch:交叉熵(cross entropy)](https://blog.csdn.net/weixin_62637793/article/details/121363832)[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 ]