pytorch 卷积神经网络模板

下面是一个基本的 PyTorch 卷积神经网络模板：

import torch
import torch.nn as nn

class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        
        # 定义卷积层
        self.conv1 = nn.Conv2d(in_channels=3, 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)

        # 定义池化层
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)

        # 定义全连接层
        self.fc1 = nn.Linear(128 * 8 * 8, 1024)
        self.fc2 = nn.Linear(1024, 10)

        # 定义激活函数
        self.relu = nn.ReLU()

    def forward(self, x):
        # 前向传播
        x = self.relu(self.conv1(x))
        x = self.pool(x)
        x = self.relu(self.conv2(x))
        x = self.pool(x)
        x = self.relu(self.conv3(x))
        x = self.pool(x)

        x = x.view(-1, 128 * 8 * 8)
        x = self.relu(self.fc1(x))
        x = self.fc2(x)

        return x

这个模板包含了卷积层、池化层、全连接层和激活函数。可以根据自己的需求修改模型结构。