卷积神经网络-VGG

柔情只为你懂 2023-02-11 10:22 35阅读 0赞

import torch
    import torch.nn as nn
    
    class VGG(nn.Module):
        def __init__(self, class_num):
            super(VGG, self).__init__()
            
            self.features = nn.Sequential(
                nn.Conv2d(3, 64, kernel_size=3, padding=1),
                nn.ReLU(True),
                nn.Conv2d(64, 64, kernel_size=3, padding=1),
                nn.MaxPool2d(kernel_size=2, stride=2),
                
                nn.Conv2d(64, 128, kernel_size=3, padding=1),
                nn.ReLU(True),
                nn.Conv2d(128, 128, kernel_size=3, padding=1),
                nn.MaxPool2d(kernel_size=2, stride=2),
                
                nn.Conv2d(128, 256, kernel_size=3, padding=1),
                nn.ReLU(True),
                nn.Conv2d(256, 256, kernel_size=3, padding=1),
                nn.ReLU(True),
                nn.Conv2d(256, 256, kernel_size=3, padding=1),
                nn.ReLU(True),
                nn.MaxPool2d(kernel_size=2, stride=2),
                
                nn.Conv2d(256, 512, kernel_size=3, padding=1),
                nn.ReLU(True),
                nn.Conv2d(512, 512, kernel_size=3, padding=1),
                nn.ReLU(True),
                nn.Conv2d(512, 512, kernel_size=3, padding=1),
                nn.ReLU(True),
                nn.MaxPool2d(kernel_size=2, stride=2),
                
                nn.Conv2d(256, 512, kernel_size=3, padding=1),
                nn.ReLU(True),
                nn.Conv2d(512, 512, kernel_size=3, padding=1),
                nn.ReLU(True),
                nn.Conv2d(512, 512, kernel_size=3, padding=1),
                nn.ReLU(True),
                nn.MaxPool2d(kernel_size=2, stride=2),
            
            )
            self.classifier = nn.Sequential(
                nn.Linear(512*7*7, 4096),
                nn.ReLU(True),
                nn.Dropout(),
                nn.Linear(4096, 4096),
                nn.ReLU(True),
                nn.Dropout(),
                nn.Linear(4096, class_num),
            
            )
            
            #self._initialize_weights()
            
        def forward(self, x):
            x = self.features(x)
            x = x.view(x.size(0), -1)
            x = self.classifier(x)
            return x
        
    model = VGG(1000)
    print(model)