简要概括就是：
F.relu当函数用，nn.ReLU当模块定义用。
F.relu()一般在forward中用，nn.ReLU()在定义网络结构的时候用。
比如：
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import torch.nn as nn import torch.nn.functional as F class NET1(nn.Module): def __init__(self): super(NET1, self).__init__() self.conv = nn.Conv2d(3, 16, 3, 1, 1) self.bn = nn.BatchNorm2d(16) self.re = nn.ReLU() # 模块的激活函数 def foreward(self, x): out = self.conv(x) out = self.bn(x) out = self.re() return out class NET2(nn.Module): def __init__(self): super(NET2, self).__init__() self.conv = nn.Conv2d(3, 16, 3, 1, 1) self.bn = nn.BatchNorm2d(16) def foreward(self, x): out = self.conv(x) out = self.bn(x) out = F.relu(out) # 函数的激活函数 return out net1 = NET1() net2 = NET2() print(net1) print(net2) |
注意：当用print(net)输出时，会有nn.ReLU()层，而F.ReLU()是没有输出的。