利用alexnet训练cifar10数据集来生产epoch图

首先，我们需要导入必要的库和模块：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt

然后，我们可以定义一些超参数和数据增强操作：

# 超参数
num_epochs = 50
batch_size = 64
learning_rate = 0.001

# 数据增强操作
transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

接下来，我们可以加载数据集并进行数据预处理：

# 加载数据集
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
                                         shuffle=False, num_workers=2)

然后，我们可以定义AlexNet模型：

class AlexNet(nn.Module):
    def __init__(self, num_classes=10):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(64, 192, kernel_size=5, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(192, 384, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(384, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
        )
        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(256 * 6 * 6, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, num_classes),
        )

    def forward(self, x):
        x = self.features(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x

接下来，我们可以定义优化器和损失函数：

# 定义优化器和损失函数
net = AlexNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=learning_rate)

然后，我们可以训练模型并绘制epoch图：

# 训练模型
train_losses = []
train_accs = []
test_losses = []
test_accs = []

for epoch in range(num_epochs):
    # 训练模型
    train_loss = 0.0
    train_acc = 0.0
    net.train()
    for i, (inputs, labels) in enumerate(trainloader):
        inputs, labels = inputs.cuda(), labels.cuda()
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        train_loss += loss.item() * inputs.size(0)
        _, predicted = torch.max(outputs.data, 1)
        train_acc += (predicted == labels).sum().item()

    train_loss /= len(trainloader.dataset)
    train_acc /= len(trainloader.dataset)

    train_losses.append(train_loss)
    train_accs.append(train_acc)

    # 测试模型
    test_loss = 0.0
    test_acc = 0.0
    net.eval()
    with torch.no_grad():
        for inputs, labels in testloader:
            inputs, labels = inputs.cuda(), labels.cuda()
            outputs = net(inputs)
            loss = criterion(outputs, labels)

            test_loss += loss.item() * inputs.size(0)
            _, predicted = torch.max(outputs.data, 1)
            test_acc += (predicted == labels).sum().item()

    test_loss /= len(testloader.dataset)
    test_acc /= len(testloader.dataset)

    test_losses.append(test_loss)
    test_accs.append(test_acc)

    # 打印结果
    print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Test Loss: {:.4f}, Test Acc: {:.4f}'
          .format(epoch+1, num_epochs, train_loss, train_acc, test_loss, test_acc))

# 绘制epoch图
plt.plot(train_losses, label='Train Loss')
plt.plot(test_losses, label='Test Loss')
plt.legend()
plt.show()

最后，我们可以得到epoch图，其中蓝色线表示训练损失，橙色线表示测试损失：