看代码吧~
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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()
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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：
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import torch
import torch.nn as nn
class 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)
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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