human
I am working on the file "Homework/Principles of Artificial Neural Networks/Week 5 CNN 1/Week5.ipynb".
The first task is:
Vgg
接下来让我们阅读vgg网络的实现代码.VGGNet全部使用3*3的卷积核和2*2的池化核，通过不断加深网络结构来提升性能。Vgg表明了卷积神经网络的深度增加和小卷积核的使用对网络的最终分类识别效果有很大的作用.

下面是一份用于训练cifar10的简化版的vgg代码. 
有时间的同学可以阅读并训练它.
Can you write Python code for it?
 ... Can you provide a the Python code you requested?
assistant
#Here is the Python code you requested.

import math

class VGG(nn.Module):
    def __init__(self, cfg):
        super(VGG, self).__init__()
        self.features = self._make_layers(cfg)
        # linear layer
        self.classifier = nn.Linear(512, 10)

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

    def _make_layers(self, cfg):
        """
        cfg: a list define layers this layer contains
            'M': MaxPool, number: Conv2d(out_channels=number) -> BN -> ReLU
        """
        layers = []
        in_channels = 3
        for x in cfg:
            if x == 'M':
                layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
            else:
                layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1),
                           nn.BatchNorm2d(x),
                           nn.ReLU(inplace=True)]
                in_channels = x
        layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
        return nn.Sequential(*layers)

cfg = {
    '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'],
}
vggnet = VGG(cfg['VGG11'])
print(vggnet)

# Hyper-parameters
num_epochs = 10
lr = 1e-3
# Device configuration
device = torch.device('cuda:0')

# optimizer
optimizer = torch.optim.Adam(vggnet.parameters(), lr=lr)

fit(vggnet, num_epochs, optimizer, device)