写一个基于pyotrch的手写字母识别系统

好的，下面是一个基于PyTorch的手写字母识别系统的示例代码。

首先，我们需要导入必要的库：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms

然后，我们定义一些超参数：

batch_size = 64
learning_rate = 0.01
num_epochs = 10

接着，我们下载MNIST数据集并定义数据转换：

train_dataset = datasets.MNIST(root='data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='data', train=False, transform=transforms.ToTensor())

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

然后，我们定义一个简单的卷积神经网络（CNN）模型：

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2)
        self.relu1 = nn.ReLU()
        self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2)
        self.relu2 = nn.ReLU()
        self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(7*7*32, 10)
        
    def forward(self, x):
        out = self.conv1(x)
        out = self.relu1(out)
        out = self.maxpool1(out)
        out = self.conv2(out)
        out = self.relu2(out)
        out = self.maxpool2(out)
        out = out.view(out.size(0), -1)
        out = self.fc1(out)
        return out

接着，我们定义损失函数和优化器：

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

然后，我们训练模型：

for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))

最后，我们测试模型并计算准确率：

with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        outputs = model(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: {} %'.format(100 * correct / total))

这就是一个基于PyTorch的手写字母识别系统的示例代码。