请帮我编写处理灰度图的四分类PYTORCH卷积神经网络，要求三层卷积层三层全连接层

好的，以下是处理灰度图的四分类 PyTorch 卷积神经网络：

import torch.nn as nn

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1)
        self.fc1 = nn.Linear(128 * 7 * 7, 512)
        self.fc2 = nn.Linear(512, 128)
        self.fc3 = nn.Linear(128, 4)
        self.relu = nn.ReLU()
        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.dropout = nn.Dropout(p=0.5)

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.conv2(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.conv3(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.dropout(x)
        x = self.fc2(x)
        x = self.relu(x)
        x = self.dropout(x)
        x = self.fc3(x)
        return x

这个模型共有三个卷积层和三个全连接层，其中每个卷积层都有 ReLU 激活函数和最大池化层，每个全连接层都有 ReLU 激活函数和 dropout 层。最后一层全连接层的输出是四个分类的概率，可以使用交叉熵损失函数进行训练。