请问这个两个AlexNet的pytorch代码有什么区别？

Question

class AlexNet(nn.Module):
    def __init__(self, num_classes=10):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
        nn.Conv2d(3, 64, 3, 2, 1),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(2),
        nn.Conv2d(64, 192, kernel_size=3, padding=1),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(2),
        nn.Conv2d(192, 384, 3, 1, ),
        nn.ReLU(inplace=True),
        nn.Conv2d(384, 256, 3, 1),
        nn.ReLU(inplace=True),
        nn.Conv2d(256, 256, 3, 1),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(2))

        self.classifier = nn.Sequential(
        nn.Linear(256*2*2, 4096),
        nn.ReLU(inplace=True),
        nn.Dropout(0.5),
        nn.Linear(4096, 4096),
        nn.ReLU(inplace=True),
        nn.Dropout(0.5),
        nn.Linear(4096, 10))

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), 256*2*2)
        x = self.classifier(x)
        return x

和

class AlexNet(nn.Module):
    def __init__(self, num_classes=10):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2),
            nn.Conv2d(64, 192, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2),
            nn.Conv2d(192, 384, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(384, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2),
        )
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(256 * 2 * 2, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, 10),
        )

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), 256 * 2 * 2)
        x = self.classifier(x)
        return x

第二个可以运行 ，第一个会提示RuntimeError:sizes must be non-negative

Answer 1

https://blog.csdn.net/qq_43257281/article/details/99618258