调用卷积神经网络框架实现mnist数据集分类。卷积神经网络框架（包括Alexnet、Vgg、Lenet、Resnet等）可自行选择，优化器策略和学习率也可自行调整，输出分类准确率。

好的，针对您的问题，我可以给出一个使用PyTorch框架实现MNIST数据集分类的示例代码，其中使用了经典的LeNet-5卷积神经网络结构。

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

# 定义LeNet-5卷积神经网络结构
class LeNet5(nn.Module):
    def __init__(self):
        super(LeNet5, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, kernel_size=5)
        self.pool1 = nn.MaxPool2d(kernel_size=2)
        self.conv2 = nn.Conv2d(6, 16, kernel_size=5)
        self.pool2 = nn.MaxPool2d(kernel_size=2)
        self.fc1 = nn.Linear(16 * 4 * 4, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool1(torch.relu(self.conv1(x)))
        x = self.pool2(torch.relu(self.conv2(x)))
        x = x.view(-1, 16 * 4 * 4)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 加载MNIST数据集
batch_size = 64
train_loader = torch.utils.data.DataLoader(
    datasets.MNIST('../data', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(
    datasets.MNIST('../data', train=False, transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=batch_size, shuffle=True)

# 定义损失函数、优化器和学习率
lr = 0.01
momentum = 0.5
model = LeNet5()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum)

# 训练和测试模型
def train(model, device, train_loader, criterion, 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()

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() * data.size(0)
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)

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

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

epochs = 10
for epoch in range(1, epochs + 1):
    train(model, device, train_loader, criterion, optimizer, epoch)
    test(model, device, test_loader)

在运行此代码时，您需要事先安装PyTorch和torchvision包，并确保您的计算机上有可用的GPU（如果您想在GPU上训练模型）。在训练结束后，程序将输出测试集上的分类准确率。