对cnn模型进行随机梯度下降法优化的具体代码

以下是基于PyTorch框架实现的CNN模型进行随机梯度下降法优化的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim

# 定义CNN模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x)))
        x = self.pool(torch.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 定义随机梯度下降法优化器
net = Net()
criterion = nn.CrossEntropyLoss()  # 交叉熵损失函数
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)  # 学习率为0.001，动量为0.9

# 训练模型
for epoch in range(10):  # 进行10轮训练
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data

        optimizer.zero_grad()

        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 2000 == 1999:    # 每2000个batch打印一次损失值
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

其中，`Net`为定义的CNN模型，`nn.CrossEntropyLoss()`为交叉熵损失函数，`optim.SGD()`为随机梯度下降法优化器，`net.parameters()`为需要更新的参数集合，`lr`为学习率，`momentum`为动量。`trainloader`为训练数据集的DataLoader对象，通过遍历DataLoader中的mini-batch进行训练。