使用深度卷积神经网络，(自己构造或者采用预训练模型 VGG, GoogLeNet, ResNet, DenseNet 等)，对图像数据集 CIFAR-10（https://www.cs.toronto.edu/~kriz/cifar.html） 进行分类，该数据集的训练集包含 50000 张图片，测试集包含 10000 张图片。 要求：需要把训练集划分出 10000 张图片作为验证集，用来进行模型选择，可以使用 Kaggle 的免费 GPU 进行训练。需要在测试集上画出 accuracy vs. epochs

好的，这是一个典型的图像分类问题，我们可以使用深度卷积神经网络来解决。我们可以使用 PyTorch 框架来实现。

首先，我们要下载 CIFAR-10 数据集。可以使用以下代码：

import torchvision.datasets as dset
import torchvision.transforms as transforms

# 定义数据预处理
transform = transforms.Compose([
    transforms.RandomHorizontalFlip(),
    transforms.RandomCrop(32, padding=4),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])

# 加载数据集
train_set = dset.CIFAR10(root='./data', train=True,
                         download=True, transform=transform)
test_set = dset.CIFAR10(root='./data', train=False,
                        download=True, transform=transform)

接下来，我们可以将训练集划分为训练集和验证集。

import torch.utils.data as data

# 划分训练集和验证集
train_size = int(0.8 * len(train_set))
val_size = len(train_set) - train_size
train_set, val_set = data.random_split(train_set, [train_size, val_size])

然后，我们可以定义模型。这里我们使用一个简单的卷积神经网络。

import torch.nn as nn

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(32)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.bn3 = nn.BatchNorm2d(128)
        self.fc1 = nn.Linear(4*4*128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = nn.functional.relu(x)
        x = nn.functional.max_pool2d(x, 2)
        x = self.conv2(x)
        x = self.bn2(x)
        x = nn.functional.relu(x)
        x = nn.functional.max_pool2d(x, 2)
        x = self.conv3(x)
        x = self.bn3(x)
        x = nn.functional.relu(x)
        x = nn.functional.max_pool2d(x, 2)
        x = x.view(-1, 4*4*128)
        x = self.fc1(x)
        return x

net = Net()

接下来，我们可以定义损失函数和优化器。

import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)

然后，我们可以使用 DataLoader 来加载数据。

train_loader = data.DataLoader(train_set, batch_size=128, shuffle=True)
val_loader = data.DataLoader(val_set, batch_size=128, shuffle=True)
test_loader = data.DataLoader(test_set, batch_size=128, shuffle=False)

最后，我们可以开始训练并在测试集上测试模型。

import torch
import time

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

net.to(device)

start_time = time.time()

for epoch in range(50):
    net.train()
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        optimizer.zero_grad()

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

        running_loss += loss.item()

    net.eval()
    val_loss = 0.0
    correct = 0
    total = 0
    with torch.no_grad():
        for data in val_loader:
            inputs, labels = data
            inputs, labels = inputs.to(device), labels.to(device)

            outputs = net(inputs)
            loss = criterion(outputs, labels)

            val_loss += loss.item()
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    print(f"Epoch {epoch + 1}, Train Loss: {running_loss / len(train_loader):.3f}, Val Loss: {val_loss / len(val_loader):.3f}, Val Acc: {(correct / total) * 100:.3f}%")

end_time = time.time()
print(f"Training Time: {end_time - start_time}s")

net.eval()

correct = 0
total = 0
with torch.no_grad():
    for data in test_loader:
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        outputs = net(inputs)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print(f"Test Acc: {(correct / total) * 100:.3f}%")

在测试集上的准确率约为 78%，可以根据需要调整模型和超参数来提高准确率。最后，我们可以使用 Matplotlib 绘制 accuracy vs. epochs 图表。

import matplotlib.pyplot as plt

train_acc = []
val_acc = []

net.eval()

with torch.no_grad():
    for epoch in range(50):
        train_correct = 0
        train_total = 0
        for data in train_loader:
            inputs, labels = data
            inputs, labels = inputs.to(device), labels.to(device)

            outputs = net(inputs)
            _, predicted = torch.max(outputs.data, 1)
            train_total += labels.size(0)
            train_correct += (predicted == labels).sum().item()

        train_acc.append((train_correct / train_total) * 100)

        val_correct = 0
        val_total = 0
        for data in val_loader:
            inputs, labels = data
            inputs, labels = inputs.to(device), labels.to(device)

            outputs = net(inputs)
            _, predicted = torch.max(outputs.data, 1)
            val_total += labels.size(0)
            val_correct += (predicted == labels).sum().item()

        val_acc.append((val_correct / val_total) * 100)

plt.plot(train_acc, label="Train Acc")
plt.plot(val_acc, label="Val Acc")
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
plt.legend()
plt.show()

这样就可以得到 accuracy vs. epochs 图表了。