假设你是一个pytorch，请写出利用tensorbordX显示vgg16训练过程中每一个epoch的损失值和准确率。

好的，以下是一个利用tensorboardX显示VGG16训练过程中每一个epoch的损失值和准确率的示例代码：

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

# 创建tensorboard writer对象
writer = SummaryWriter('logs')

# 定义VGG16模型
model = models.vgg16(pretrained=True)
num_features = model.classifier[0].in_features
model.classifier = nn.Linear(num_features, 2)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# 加载数据集
train_transform = transforms.Compose([
    transforms.RandomResizedCrop(224),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
train_dataset = datasets.ImageFolder('train', transform=train_transform)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    correct = 0
    total = 0
    
    for i, (inputs, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        
        # 记录训练损失和准确率
        running_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
        
    epoch_loss = running_loss / len(train_loader)
    epoch_acc = 100 * correct / total
    
    # 将损失和准确率写入tensorboard
    writer.add_scalar('Loss/train', epoch_loss, epoch)
    writer.add_scalar('Accuracy/train', epoch_acc, epoch)
    
    print('Epoch: {} | Loss: {:.4f} | Acc: {:.4f}'.format(epoch, epoch_loss, epoch_acc))

# 关闭tensorboard writer对象
writer.close()

在上述示例代码中，我们首先创建了一个tensorboard writer对象，然后定义了VGG16模型、损失函数和优化器，以及加载了训练数据集。在每个epoch中，我们记录了训练损失和准确率，并将它们写入tensorboard。最后，我们关闭了tensorboard writer对象。