大家好，又见面了，我是你们的朋友全栈君。
关于dropout改进的新技术分享：
dropout改进_jacke121的专栏-CSDN博客_dropout改进
链接：https://www.zhihu.com/question/67209417/answer/302434279
刚踩的坑, 差点就哭出来了TT. — 我明明加了一百个dropout, 为什么结果一点都没变
使用F.dropout ( nn.functional.dropout )的时候需要设置它的training这个状态参数与模型整体的一致.
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,training=self.training, p=0.5)
return out
Net = DropoutFC()
Net.train()
# train the Net这段代码中的F.dropout实际上是没有任何用的, 因为它的training状态一直是默认值False. 由于F.dropout只是相当于引用的一个外部函数, 模型整体的training状态变化也不会引起F.dropout这个函数的training状态发生变化. 所以, 此处的out = F.dropout(out) 就是 out = out. Ref: https://github.com/pytorch/pytorch/blob/master/torch/nn/functional.py#L535
正确的使用方法如下, 将模型整体的training状态参数传入dropout函数
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
或者直接使用nn.Dropout() (nn.Dropout()实际上是对F.dropout的一个包装, 也将self.training传入了) Ref: https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/dropout.py#L46
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()
发布者：全栈程序员-站长，转载请注明出处：https://javaforall.net/131129.html原文链接：https://javaforall.net
