总结了一下AlexNet的模型结构框图以及对应的模型代码

代码

import torch
from torch import nn
import torch.nn.functional as F
class AlexNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.layer1=nn.Sequential(
            nn.Conv2d(in_channels=3,out_channels=96,kernel_size=11,stride=4),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=3,stride=2)
        )
        self.layer2=nn.Sequential(
            nn.Conv2d(in_channels=96,out_channels=256,kernel_size=5,padding=2),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=3,stride=2)
        )
        self.layer3=nn.Sequential(
            nn.Conv2d(in_channels=256,out_channels=384,kernel_size=3,padding=1),
            nn.ReLU()
        )
        self.layer4=nn.Sequential(
            nn.Conv2d(in_channels=384,out_channels=384,kernel_size=3,padding=1),
            nn.ReLU()
        )
        self.layer5=nn.Sequential(
            nn.Conv2d(in_channels=384,out_channels=256,kernel_size=3,padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=3,stride=2)
        )
        self.layer6=nn.Sequential(
            nn.Linear(in_features=6*6*256,out_features=4096),
            nn.ReLU(),
            nn.Dropout()
        )
        self.layer7=nn.Sequential(
            nn.Linear(in_features=4096,out_features=4096),
            nn.ReLU()
        )
        self.layer8=nn.Linear(in_features=4096,out_features=1000)
    def forward(self,x):
        x=self.layer1(x)
        x=self.layer2(x)
        x=self.layer3(x)
        x=self.layer4(x)
        x=self.layer5(x)
        x=x.view(x.size(0),-1)
        x=self.layer6(x)
        x=self.layer7(x)
        x=self.layer8(x)
        output=F.softmax(x,dim=1)
        return output