编写基于pytorch的手写字符识别，使用卷积神经网络GPU加速

好的，下面是一个基于PyTorch和GPU加速的手写字符识别模型的代码示例。

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，我们可以定义一个包含卷积、池化、全连接层的卷积神经网络模型：

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.dropout1 = nn.Dropout2d(0.25)
        self.dropout2 = nn.Dropout2d(0.5)
        self.fc1 = nn.Linear(1600, 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

接下来，我们可以定义一些超参数和数据处理的方法：

batch_size = 64
epochs = 10
learning_rate = 0.01
momentum = 0.5

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])

然后，我们可以加载MNIST数据集并创建数据加载器：

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

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

接下来，我们可以定义我们的模型、优化器和损失函数：

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = Net().to(device)
optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=momentum)
criterion = nn.NLLLoss()

最后，我们可以开始训练我们的模型：

for epoch in range(epochs):
    train_loss = 0.0
    train_correct = 0
    model.train()

    for data, target in train_loader:
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * data.size(0)
        _, predicted = torch.max(output.data, 1)
        train_correct += (predicted == target).sum().item()

    train_loss = train_loss / len(train_loader.dataset)
    train_accuracy = train_correct / len(train_loader.dataset)

    print('Epoch: {} \tTraining Loss: {:.6f} \tTraining Accuracy: {:.6f}'.format(
        epoch+1, train_loss, train_accuracy))

    test_loss = 0.0
    test_correct = 0
    model.eval()

    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            loss = criterion(output, target)
            test_loss += loss.item() * data.size(0)
            _, predicted = torch.max(output.data, 1)
            test_correct += (predicted == target).sum().item()

    test_loss = test_loss / len(test_loader.dataset)
    test_accuracy = test_correct / len(test_loader.dataset)

    print('Epoch: {} \tTest Loss: {:.6f} \tTest Accuracy: {:.6f}'.format(
        epoch+1, test_loss, test_accuracy))

这样，我们就完成了基于PyTorch和GPU加速的手写字符识别模型的编写。