如何用nnAdaptiveAvgPool2d替换下面代码的全连接层class LeNetnnModule def __init__self superLeNetself__init__ selfconv1 = nnSequential nnConv2din_channels=1out_channels=6kernel_size=5stride

将LeNet模型的全连接层替换为nn.AdaptiveAvgPool2d()可以按照以下方式实现：

class LeNet(nn.Module): def init(self): super(LeNet,self).init() self.conv1 = nn.Sequential( nn.Conv2d(in_channels=1,out_channels=6,kernel_size=5,stride=1,padding=2), nn.BatchNorm2d(6), nn.ReLU(), nn.MaxPool2d(kernel_size=2,stride=2) )

    self.conv2 = nn.Sequential(
        nn.Conv2d(in_channels=6,out_channels=16,kernel_size=5,stride=1),
        nn.BatchNorm2d(16),
        nn.ReLU(),
        nn.MaxPool2d(kernel_size=2,stride=2)
    )

    self.conv3 = nn.Sequential(
        nn.Conv2d(in_channels=16,out_channels=120,kernel_size=5),
        nn.BatchNorm2d(120),
        nn.ReLU()
    )

    self.avg_pool = nn.AdaptiveAvgPool2d((1,1))  # 替换全连接层

    self.fc = nn.Linear(120,10)

def forward(self,x):
    x = self.conv1(x)
    x = self.conv2(x)
    x = self.conv3(x)
    x = self.avg_pool(x)
    x = x.view(x.size(0),-1)
    x = self.fc(x)
    return x

这里使用nn.AdaptiveAvgPool2d((1,1))将最后一个卷积层的输出转换为1x1的特征图，然后使用x.view(x.size(0),-1)将特征图展平，最后使用nn.Linear(120,10)作为全连接层替换原有的两个全连接层