帮我编写一段基于pytorch的何凯明初始化网络代码

以下是基于 PyTorch 的何凯明初始化网络代码：

import torch.nn as nn
import torch.nn.init as init

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.relu3 = nn.ReLU(inplace=True)
        self.conv4 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
        self.relu4 = nn.ReLU(inplace=True)
        self.conv5 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.relu5 = nn.ReLU(inplace=True)
        self.conv6 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.relu6 = nn.ReLU(inplace=True)
        self.conv7 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
        self.relu7 = nn.ReLU(inplace=True)
        self.conv8 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.relu8 = nn.ReLU(inplace=True)
        self.fc1 = nn.Linear(512 * 4 * 4, 1024)
        self.relu9 = nn.ReLU(inplace=True)
        self.fc2 = nn.Linear(1024, 1024)
        self.relu10 = nn.ReLU(inplace=True)
        self.fc3 = nn.Linear(1024, 10)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                if m.bias is not None:
                    init.constant_(m.bias, )
            elif isinstance(m, nn.BatchNorm2d):
                init.constant_(m.weight, 1)
                init.constant_(m.bias, )
            elif isinstance(m, nn.Linear):
                init.normal_(m.weight, , .01)
                init.constant_(m.bias, )

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.conv2(x)
        x = self.relu2(x)
        x = nn.MaxPool2d(kernel_size=2, stride=2)(x)
        x = self.conv3(x)
        x = self.relu3(x)
        x = self.conv4(x)
        x = self.relu4(x)
        x = nn.MaxPool2d(kernel_size=2, stride=2)(x)
        x = self.conv5(x)
        x = self.relu5(x)
        x = self.conv6(x)
        x = self.relu6(x)
        x = nn.MaxPool2d(kernel_size=2, stride=2)(x)
        x = self.conv7(x)
        x = self.relu7(x)
        x = self.conv8(x)
        x = self.relu8(x)
        x = nn.MaxPool2d(kernel_size=2, stride=2)(x)
        x = x.view(x.size(), -1)
        x = self.fc1(x)
        x = self.relu9(x)
        x = self.fc2(x)
        x = self.relu10(x)
        x = self.fc3(x)
        return x

希望这段代码能够对你有所帮助！