看代码吧~
|
1
2
3
4
5
6
7
8
9
10
11
12
|
Class USeDropout(nn.Module): def __init__(self): super(DropoutFC, self).__init__() self.fc = nn.Linear(100,20) self.dropout = nn.Dropout(p=0.5) def forward(self, input): out = self.fc(input) out = self.dropout(out) return out Net = USeDropout() Net.train() |
示例代码如上，直接调用nn.Dropout即可，但是注意在调用时要将模型参数传入。
补充：Pytorch的nn.Dropout运行稳定性测试
结论：
Pytorch的nn.Dropout在每次被调用时dropout掉的参数都不一样，即使是同一次forward也不同。
如果模型里多次使用的dropout的dropout rate大小相同，用同一个dropout层即可。
如代码所示：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
|
import torchimport torch.nn as nnclass MyModel(nn.Module): def __init__(self): super(MyModel, self).__init__() self.dropout_1 = nn.Dropout(0.5) self.dropout_2 = nn.Dropout(0.5) def forward(self, input): # print(input) drop_1 = self.dropout_1(input) print(drop_1) drop_1 = self.dropout_1(input) print(drop_1) drop_2 = self.dropout_2(input) print(drop_2)if __name__ == '__main__': i = torch.rand((5, 5)) m = MyModel() m.forward(i) |
结果如下：
*\python.exe */model.py
tensor([[0.0000, 0.0914, 0.0000, 1.4095, 0.0000],
[0.0000, 0.0000, 0.1726, 1.3800, 0.0000],
[1.7651, 0.0000, 0.0000, 0.9421, 1.5603],
[1.0510, 1.7290, 0.0000, 0.0000, 0.8565],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000]])
tensor([[0.0000, 0.0000, 0.4722, 1.4095, 0.0000],
[0.0416, 0.0000, 0.1726, 1.3800, 1.3193],
[0.0000, 0.3401, 0.6550, 0.0000, 0.0000],
[1.0510, 1.7290, 1.5515, 0.0000, 0.0000],
[0.6388, 0.0000, 0.0000, 1.0122, 0.0000]])
tensor([[0.0000, 0.0000, 0.4722, 0.0000, 1.2689],
[0.0416, 0.0000, 0.0000, 1.3800, 0.0000],
[0.0000, 0.0000, 0.6550, 0.0000, 1.5603],
[0.0000, 0.0000, 1.5515, 1.4596, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000]])Process finished with exit code 0
以上为个人经验，希望能给大家一个参考，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/qq_38603174/article/details/110631064
