用pytorch训练mnist

以下是使用PyTorch训练MNIST数据集的示例代码：

首先，导入必要的库：

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

接下来，定义超参数和数据转换：

batch_size = 64
learning_rate = 0.01
num_epochs = 10

# 数据转换
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])

然后，加载训练和测试集：

# 加载训练集和测试集
train_dataset = datasets.MNIST(root='./data', train=True, transform=transform, download=True)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

test_dataset = datasets.MNIST(root='./data', train=False, transform=transform, download=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

接着，定义一个简单的卷积神经网络模型：

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.pool1 = nn.MaxPool2d(2)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.pool2 = nn.MaxPool2d(2)
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 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, 320)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return torch.log_softmax(x, dim=1)

model = Net()

然后，定义损失函数和优化器：

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate)

最后，进行模型训练和测试：

for epoch in range(num_epochs):
    for batch_idx, (data, target) in enumerate(train_loader):
        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()))

    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            output = model(data)
            test_loss += criterion(output, target).item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

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

以上就是使用PyTorch训练MNIST数据集的完整代码。