写一个FCN网络，用pytorch

可以使用以下代码来实现一个FCN网络：

import torch
import torch.nn as nn

class FCN(nn.Module):
    def __init__(self, num_classes):
        super(FCN, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.conv4 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
        self.conv5 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(512 * 7 * 7, 4096)
        self.fc2 = nn.Linear(4096, 4096)
        self.fc3 = nn.Linear(4096, num_classes)

    def forward(self, x):
        x = self.conv1(x)
        x = nn.ReLU()(x)
        x = self.pool(x)
        x = self.conv2(x)
        x = nn.ReLU()(x)
        x = self.pool(x)
        x = self.conv3(x)
        x = nn.ReLU()(x)
        x = self.pool(x)
        x = self.conv4(x)
        x = nn.ReLU()(x)
        x = self.pool(x)
        x = self.conv5(x)
        x = nn.ReLU()(x)
        x = self.pool(x)
        x = x.view(-1, 512 * 7 * 7)
        x = self.fc1(x)
        x = nn.ReLU()(x)
        x = self.fc2(x)
        x = nn.ReLU()(x)
        x = self.fc3(x)
        return x

这个FCN网络包含了5个卷积层和3个全连接层，可以用于图像分类任务。其中，输入的图像大小为224x224，输出的类别数由num_classes指定。