cnn手写数字识别pytorch

你可以使用PyTorch来实现CNN（卷积神经网络）手写数字识别。以下是一个简单的示例代码：

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

# 定义CNN模型
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
        self.relu = nn.ReLU()
        self.maxpool = nn.MaxPool2d(kernel_size=2)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.fc1 = nn.Linear(64*7*7, 128)
        self.fc2 = nn.Linear(128, 10)
        
    def forward(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.conv2(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        return x

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

# 定义训练和测试数据加载器
batch_size = 64
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)

# 初始化模型和优化器
model = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
num_epochs = 10
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)

for epoch in range(num_epochs):
    for batch_idx, (data, targets) in enumerate(train_loader):
        data = data.to(device)
        targets = targets.to(device)
        
        # 前向传播
        scores = model(data)
        loss = criterion(scores, targets)
        
        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
    print(f'Epoch {epoch+1}/{num_epochs}, Loss: {loss.item():.4f}')

# 在测试集上评估模型
def test_model(model, test_loader):
    model.eval()
    num_correct = 0
    num_samples = 0
    
    with torch.no_grad():
        for data, targets in test_loader:
            data = data.to(device)
            targets = targets.to(device)
            
            scores = model(data)
            _, predictions = scores.max(1)
            num_correct += (predictions == targets).sum()
            num_samples += predictions.size(0)
    
    accuracy = num_correct / num_samples
    print(f'Test Accuracy: {accuracy*100:.2f}%')

test_model(model, test_loader)

这是一个简单的CNN模型，可用于MNIST手写数字识别任务。你可以根据需要调整模型结构和超参数来提高准确率。