pytorch 中nn.Dropout的使用说明(使用说明)

看代码吧~

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：

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)

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