torch实现手写数字的识别,使用MNIST数据集内容完成手写数字识别 代码

好的，以下是使用 PyTorch 实现手写数字识别的代码：

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)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64,
                                          shuffle=True, num_workers=2)

testset = datasets.MNIST(root='./data', train=False,
                         download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=64,
                                         shuffle=False, num_workers=2)

# 定义模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout2d(0.25)
        self.dropout2 = nn.Dropout2d(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = nn.functional.relu(x)
        x = self.conv2(x)
        x = nn.functional.relu(x)
        x = nn.functional.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = nn.functional.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = nn.functional.log_softmax(x, dim=1)
        return output

net = Net()

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.5)

# 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:    # 每 100 个小批量打印一次损失值
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

print('Finished Training')

# 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))

这个代码中，我们使用了 `torchvision` 中的 `datasets` 和 `transforms` 模块来加载 MNIST 数据集并进行预处理。然后，我们定义了一个卷积神经网络模型并用交叉熵损失函数和随机梯度下降优化器来训练模型。最后，我们用测试集来测试模型的准确率。