基于pytorch的手写数字

基于PyTorch的手写数字识别，可以使用深度学习技术来实现。一种常见的方法是使用卷积神经网络（Convolutional Neural Network, CNN），它可以对图像进行高效的特征提取和分类。

在PyTorch中，可以使用torchvision包来构建和训练CNN模型。该包提供了MNIST数据集，这是一个常用的手写数字识别数据集。你可以使用该数据集来训练一个基于PyTorch的手写数字识别模型。

以下是一个简单的基于PyTorch的手写数字识别示例代码：

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

# 定义CNN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)

# 定义训练函数
def train(model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = nn.functional.nll_loss(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()))

# 定义测试函数
def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += nn.functional.nll_loss(output, target, reduction='sum').item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)

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

# 加载MNIST数据集
train_dataset = datasets.MNIST(root='./data', train=True,
                               transform=transforms.Compose([
                                   transforms.ToTensor(),
                                   transforms.Normalize((0.1307,), (0.3081,))
                               ]),
                               download=True)

test_dataset = datasets.MNIST(root='./data', train=False,
                              transform=transforms.Compose([
                                  transforms.ToTensor(),
                                  transforms.Normalize((0.1307,), (0.3081,))
                              ]))

# 定义训练参数
batch_size = 64
epochs = 10
lr = 0.01

# 加载数据集
train_loader = torch.utils.data.DataLoader(train_dataset,
                                           batch_size=batch_size,
                                           shuffle=True)

test_loader = torch.utils.data.DataLoader(test_dataset,
                                          batch_size=batch_size,
                                          shuffle=False)

# 初始化模型和优化器
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Net().to(device)
optimizer = optim.SGD(model.parameters(), lr=lr)

# 训练模型并测试
for epoch in range(1, epochs + 1):
    train(model, device, train_loader, optimizer, epoch)
    test(model, device, test_loader)