剛踩的坑, 差點就哭出來了TT. --- 我明明加了一百個dropout, 為什麼結果一點都沒變

使用F.dropout ( nn.functional.dropout )的時候需要設定它的training這個狀態引數與模型整體的一致.

比如:

Class DropoutFC(nn.Module):
    def __init__(self):
        super(DropoutFC, self).__init__()
        self.fc = nn.Linear(100,20)

    def forward(self, input):
        out = self.fc(
 
input)
        out = F.dropout(out, p=0.5)
        return out

Net = DropoutFC()
Net.train()

# train the Net

這段程式碼中的F.dropout實際上是沒有任何用的, 因為它的training狀態一直是預設值False. 由於F.dropout只是相當於引用的一個外部函式, 模型整體的training狀態變化也不會引起F.dropout這個函式的training狀態發生變化. 所以, 此處的out = F.dropout(out) 就是 out = out. Ref: https://github.com/pytorch/pyto

正確的使用方法如下, 將模型整體的training狀態引數傳入dropout函式

Class DropoutFC(nn.Module):
   def __init__(self):
       super(DropoutFC, self).__init__()
       self.fc = nn.Linear(100,20)

   def forward(self, input):
       out = self.fc(input)
       out = F.dropout(out, p=0.5, training
 
=self.training)
       return out

Net = DropoutFC()
Net.train()

# train the Net

Class DropoutFC(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 = DropoutFC()
Net.train()

# train the Net