pytorch实现神经网络常见model（LeNet，GoogLeNet，AlexNet，VggNet）

最新推荐文章于 2025-06-27 02:49:51 发布

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最新推荐文章于 2025-06-27 02:49:51 发布 · 6.9k 阅读

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#pytorch #alexnet、vggnet #google、lenet #模型实现

神经网络常见model的实现

lenet
在这里插入图片描述

输入大小为32x32
class LenetNet(nn.Module):
    def __init__(self):
        super(LenetNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5) 
        self.conv2 = nn.Conv2d(6, 16, 5)  
        self.fc1   = nn.Linear(16*5*5, 120)  
        self.fc2   = nn.Linear(120, 84)
        self.fc3   = nn.Linear(84, 10)

    def forward(self, x): 
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) 
        x = F.max_pool2d(F.relu(self.conv2(x)), 2) 
        x = x.view(x.size()[0], -1) 
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)        
        return x

alexnet
在这里插入图片描述

输入大小为224x224
class AlexNet(nn.Module):
    def __init__(self, num_classes=1000):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(64, 192, kernel_size=5, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(192, 384, kernel_si