示例代码：

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time    : 2018/9/14 13:33
# @Author  : Seven
# @Site    : 
# @File    : VGG.py
# @Software: PyCharm

import torch.nn as nn

model = {
    'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
    'VGG19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}


class VGG(nn.Module):
    def __init__(self, vgg_name):
        super(VGG, self).__init__()
        self.features = self._make_layers(model[vgg_name])
        self.classifier = nn.Linear(512, 10)

    def forward(self, x):
        network = self.features(x)
        network = network.view(network.size(0), -1)
        out = self.classifier(network)
        return out, network

    @staticmethod
    def _make_layers(models):
        layers = []
        in_channels = 3
        for layer in models:
            if layer == 'M':
                layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
            else:
                layers += [nn.Conv2d(in_channels, layer, kernel_size=3, padding=1),
                           nn.BatchNorm2d(layer),
                           nn.ReLU(inplace=True)]
                in_channels = layer
        layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
        return nn.Sequential(*layers)