用pytorch训练模型并用tensorboardX记录loss曲线，acc曲线，给出代码演示

好的，下面是一个简单的用PyTorch训练模型并用tensorboardX记录loss曲线和acc曲线的代码演示：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import datasets, transforms

# 定义模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = nn.functional.relu(nn.functional.max_pool2d(self.conv1(x), 2))
        x = nn.functional.relu(nn.functional.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = nn.functional.relu(self.fc1(x))
        x = nn.functional.dropout(x, training=self.training)
        x = self.fc2(x)
        return nn.functional.log_softmax(x, dim=1)

# 加载数据集
train_loader = torch.utils.data.DataLoader(
    datasets.MNIST('../data', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=64, shuffle=True)

test_loader = torch.utils.data.DataLoader(
    datasets.MNIST('../data', train=False, transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=1000, shuffle=True)

# 定义训练函数
def train(model, device, train_loader, optimizer, criterion, epoch, writer):
    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 = criterion(output, target)
        loss.backward()
        optimizer.step()
        writer.add_scalar('train_loss', loss.item(), epoch * len(train_loader) + batch_idx)

# 定义测试函数
def test(model, device, test_loader, criterion, epoch, writer):
    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 += criterion(output, target).item() * len(data)
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    accuracy = correct / len(test_loader.dataset)
    writer.add_scalar('test_loss', test_loss, epoch)
    writer.add_scalar('test_accuracy', accuracy, epoch)

# 初始化模型、优化器、损失函数、设备和tensorboard writer
model = Net().to('cuda')
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
criterion = nn.CrossEntropyLoss()
device = 'cuda'
writer = SummaryWriter()

# 训练和测试模型
for epoch in range(1, 11):
    train(model, device, train_loader, optimizer, criterion, epoch, writer)
    test(model, device, test_loader, criterion, epoch, writer)

writer.close()

运行上述代码后，就可以在tensorboard中看到train_loss、test_loss和test_accuracy的曲线了。