使用深度学习库pytorch完成图像分类任务，搭建卷积神经网络的经典模型AlexNet，完成对cifar-10数据的分类任务，记录训练过程中的损失和准确率以及测试集的损失和准确率，并将其可视化，分析结果。 文件夹cifar-10-batches-py包含我们的图像分类问题的数据集,data_batch_1,2..5为训练数据，test_batch为测试数据,为了方便图片转成张量，使用torchvision加载和处理数据集。图像加载预处理示例代码见ex5-1.py。

抱歉，我作为一个语言模型无法执行文件读取和代码运行的操作。但是，我可以提供一些思路和代码示例供您参考。

思路：

1. 导入必要的库和数据集
2. 定义网络模型，这里是AlexNet
3. 定义损失函数和优化器
4. 训练模型并记录训练过程中的损失和准确率
5. 测试模型并记录测试集的损失和准确率
6. 可视化训练过程中的损失和准确率以及测试集的损失和准确率

代码示例：

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

# 定义数据集
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=128,
                                         shuffle=False, num_workers=2)

# 定义网络模型AlexNet
class AlexNet(nn.Module):
    def __init__(self):
        super(AlexNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2)
        self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2)
        self.conv2 = nn.Conv2d(64, 192, kernel_size=5, padding=2)
        self.pool2 = nn.MaxPool2d(kernel_size=3, stride=2)
        self.conv3 = nn.Conv2d(192, 384, kernel_size=3, padding=1)
        self.conv4 = nn.Conv2d(384, 256, kernel_size=3, padding=1)
        self.conv5 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.pool3 = nn.MaxPool2d(kernel_size=3, stride=2)
        self.fc1 = nn.Linear(256 * 2 * 2, 4096)
        self.fc2 = nn.Linear(4096, 4096)
        self.fc3 = nn.Linear(4096, 10)

    def forward(self, x):
        x = self.pool1(torch.relu(self.conv1(x)))
        x = self.pool2(torch.relu(self.conv2(x)))
        x = torch.relu(self.conv3(x))
        x = torch.relu(self.conv4(x))
        x = self.pool3(torch.relu(self.conv5(x)))
        x = x.view(-1, 256 * 2 * 2)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 实例化模型、定义损失函数和优化器
net = AlexNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)

# 定义损失和准确率记录器
train_loss = []
train_acc = []
test_loss = []
test_acc = []

# 训练模型
for epoch in range(5):
    running_loss = 0.0
    correct = 0
    total = 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()
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    train_loss.append(running_loss / len(trainloader))
    train_acc.append(100 * correct / total)

    # 测试模型
    test_running_loss = 0.0
    test_correct = 0
    test_total = 0
    with torch.no_grad():
        for data in testloader:
            images, labels = data
            outputs = net(images)
            loss = criterion(outputs, labels)

            test_running_loss += loss.item()
            _, predicted = torch.max(outputs.data, 1)
            test_total += labels.size(0)
            test_correct += (predicted == labels).sum().item()

        test_loss.append(test_running_loss / len(testloader))
        test_acc.append(100 * test_correct / test_total)

    print('Epoch %d: Train Loss: %.3f | Train Acc: %.3f %% | Test Loss: %.3f | Test Acc: %.3f %%'
          % (epoch + 1, train_loss[-1], train_acc[-1], test_loss[-1], test_acc[-1]))

# 可视化训练过程中的损失和准确率以及测试集的损失和准确率
plt.figure(figsize=(10, 5))
plt.subplot(121)
plt.plot(train_loss, label='Train Loss')
plt.plot(test_loss, label='Test Loss')
plt.legend()
plt.title('Loss')

plt.subplot(122)
plt.plot(train_acc, label='Train Acc')
plt.plot(test_acc, label='Test Acc')
plt.legend()
plt.title('Accuracy')

plt.show()

这是一个简单的示例，你可以根据你的实际需求进行修改和调整。