mnist手写数字识别pytorch

好的，下面是一个简单的示例代码，使用PyTorch实现MNIST手写数字识别：

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

# 加载数据集
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
trainset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
testset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True)
testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False)

# 定义模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
        self.fc1 = nn.Linear(64*5*5, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = nn.functional.relu(x)
        x = nn.functional.max_pool2d(x, 2)
        x = self.conv2(x)
        x = nn.functional.relu(x)
        x = nn.functional.max_pool2d(x, 2)
        x = x.view(-1, 64*5*5)
        x = self.fc1(x)
        x = nn.functional.relu(x)
        x = self.fc2(x)
        return nn.functional.log_softmax(x, dim=1)

net = Net()

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

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    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()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

# 测试模型
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))

在这个示例中，我们首先使用`torchvision`加载MNIST数据集，并对数据进行标准化处理。然后我们定义了一个包含两个卷积层和两个全连接层的神经网络，使用ReLU作为激活函数，并使用负对数似然损失作为损失函数，优化器使用SGD。接着我们训练模型并测试模型的准确率。