把上面的代码改为三层卷积层

好的，下面是将代码改为三层卷积层的示例代码：

import torch.nn as nn

class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
        self.fc = nn.Linear(64 * 7 * 7, 10)

    def forward(self, x):
        x = nn.functional.relu(self.conv1(x))
        x = nn.functional.max_pool2d(x, kernel_size=2)
        x = nn.functional.relu(self.conv2(x))
        x = nn.functional.max_pool2d(x, kernel_size=2)
        x = nn.functional.relu(self.conv3(x))
        x = nn.functional.max_pool2d(x, kernel_size=2)
        x = x.view(-1, 64 * 7 * 7)
        x = self.fc(x)
        return x

这里我们增加了三个卷积层，每个卷积层都有一个ReLU激活函数和一个2x2的最大池化层。最后我们将输出展平成一维向量，并通过一个全连接层进行分类。