使用PyTorch手动搭建AlexNet网络进行图像分类实践代码

在PyTorch中，构建AlexNet网络是一个相对复杂的任务，因为这涉及到卷积层、池化层、全连接层以及Dropout等组件。以下是一个简单的示例，展示了如何手动实现AlexNet的基本结构：

import torch
import torch.nn as nn
import torch.nn.functional as F

# AlexNet的结构定义
class AlexNet(nn.Module):
    def __init__(self):
        super(AlexNet, self).__init__()
        
        # 定义卷积块（convolutional blocks）
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 96, kernel_size=11, stride=4, padding=2),  # 输入通道数，输出通道数，kernel size, stride, padding
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=3, stride=2)  # 下采样
        )

        self.conv2 = nn.Sequential(
            nn.Conv2d(96, 256, kernel_size=5, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=3, stride=2)
        )

        self.conv3 = nn.Sequential(
            nn.Conv2d(256, 384, kernel_size=3, padding=1),
            nn.ReLU()
        )
        self.conv4 = nn.Sequential(
            nn.Conv2d(384, 384, kernel_size=3, padding=1),
            nn.ReLU()
        )
        self.conv5 = nn.Sequential(
            nn.Conv2d(384, 256, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=3, stride=2)
        )

        # 全连接层
        self.fc1 = nn.Linear(256 * 6 * 6, 4096)  # Flatten output of conv5
        self.drop1 = nn.Dropout(p=0.5)
        self.fc2 = nn.Linear(4096, 4096)
        self.drop2 = nn.Dropout(p=0.5)
        self.fc3 = nn.Linear(4096, 1000)  # 输出层，假设我们有1000个类别

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)
        x = self.conv5(x)
        x = x.view(-1, 256 * 6 * 6)  # Flatten the feature maps
        x = self.fc1(x)
        x = F.relu(x)
        x = self.drop1(x)
        x = self.fc2(x)
        x = F.relu(x)
        x = self.drop2(x)
        x = self.fc3(x)
        return F.log_softmax(x, dim=1)  # 输出的概率分布

# 初始化并实例化模型
alexnet = AlexNet()

# 这部分用于加载数据集和设置优化器，这里简化略去
# criterion = nn.CrossEntropyLoss()  # 损失函数
# optimizer = torch.optim.SGD(alexnet.parameters(), lr=0.001)

在这个例子中，你需要根据实际的数据集和需求调整网络结构，如输入尺寸、类别数等。训练过程则需要将`forward`方法中的数据传递给`optimizer.zero_grad()`、损失函数计算和`.step()`。