动手学深度学习_VggNet

VggNet 是 AlexNet 的改进，将 AlexNet 网络中的 5x5 或 11x11 的卷积核全都替换成 3x3 的大小；maxpoolin 改成了 2x2 的大小；另外对于部分卷积和Pooling进行块的封装，使用循环来构建复杂的框架。

上图是Vgg16的结构图，可以看到一共有 5 块，前两块是2层卷积+一层maxpooling，后三层是3层卷积+一层maxpooling。

具体设计网络的时候可以参考下面的表格，十分清晰。

def Conv3x3BNReLU(in_channels,out_channels):
    return nn.Sequential(
        nn.Conv2d(in_channels=in_channels,out_channels=out_channels,kernel_size=3,stride=1,padding=1),
        nn.BatchNorm2d(out_channels),
        nn.ReLU6(inplace=True)
    )
class VGG(nn.Module):
    def __init__(self, block_nums,num_classes=10):
        super(VGG, self).__init__()
        self.stage1 = self._make_layers(in_channels=1, out_channels=64, block_num=block_nums[0])
        self.stage2 = self._make_layers(in_channels=64, out_channels=128, block_num=block_nums[1])
        self.stage3 = self._make_layers(in_channels=128, out_channels=256, block_num=block_nums[2])
        self.stage4 = self._make_layers(in_channels=256, out_channels=512, block_num=block_nums[3])
        self.stage5 = self._make_layers(in_channels=512, out_channels=512, block_num=block_nums[4])
        self.classifier = nn.Sequential(
            nn.Linear(in_features=512*7*7,out_features=4096),
            nn.Dropout(p=0.2),
            nn.Linear(in_features=4096, out_features=4096),
            nn.Dropout(p=0.2),
            nn.Linear(in_features=4096, out_features=num_classes)
        )
        self._init_params()
    def _make_layers(self, in_channels, out_channels, block_num):
        layers = []
        layers.append(Conv3x3BNReLU(in_channels,out_channels))
        for i in range(1,block_num):
            layers.append(Conv3x3BNReLU(out_channels,out_channels))
        layers.append(nn.MaxPool2d(kernel_size=2,stride=2, ceil_mode=False))
        return nn.Sequential(*layers)
    def _init_params(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
    def forward(self, x):
        x = self.stage1(x)
        x = self.stage2(x)
        x = self.stage3(x)
        x = self.stage4(x)
        x = self.stage5(x)
        x = x.view(x.size(0),-1)
        out = self.classifier(x)
        return out
def VGG16():
    block_nums = [2, 2, 3, 3, 3]
    model = VGG(block_nums)
    return model
def VGG19():
    block_nums = [2, 2, 4, 4, 4]
    model = VGG(block_nums)
    return model

 打印一下看看网络具体结构吧

Sequential output shape:     torch.Size([1, 64, 112, 112])
Sequential output shape:     torch.Size([1, 128, 56, 56])
Sequential output shape:     torch.Size([1, 256, 28, 28])
Sequential output shape:     torch.Size([1, 512, 14, 14])
Sequential output shape:     torch.Size([1, 512, 7, 7])
Flatten output shape:        torch.Size([1, 25088])
Linear output shape:         torch.Size([1, 4096])
ReLU output shape:   torch.Size([1, 4096])
Dropout output shape:        torch.Size([1, 4096])
Linear output shape:         torch.Size([1, 4096])
ReLU output shape:   torch.Size([1, 4096])
Dropout output shape:        torch.Size([1, 4096])
Linear output shape:         torch.Size([1, 10])