10. 调用卷积神经网络框架实现mnist数据集分类。

好的，以下是使用PyTorch框架实现MNIST数据集分类的示例代码：

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms

# 定义超参数
BATCH_SIZE = 64
EPOCHS = 10
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 加载数据集并进行预处理
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])

train_set = datasets.MNIST('./data', train=True, download=True, transform=transform)
test_set = datasets.MNIST('./data', train=False, download=True, transform=transform)

train_loader = torch.utils.data.DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=BATCH_SIZE, shuffle=False)

# 定义模型结构
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2(x), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

# 实例化模型并将其移动到GPU上（如果有的话）
model = Net().to(DEVICE)

# 定义优化器和损失函数
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
criterion = nn.CrossEntropyLoss()

# 训练过程
def train(model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 100 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

# 测试过程
def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += criterion(output, target).item() # 将一批的损失相加
            pred = output.argmax(dim=1, keepdim=True) # 找到概率最大的下标
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

# 开始训练和测试
for epoch in range(1, EPOCHS + 1):
    train(model, DEVICE, train_loader, optimizer, epoch)
    test(model, DEVICE, test_loader)

这个示例代码使用了两个卷积层和两个全连接层，可以在MNIST数据集上达到约99%的准确率。