transformers做cifar10图像分类有loss曲线和准确率曲线并保存模型pytorch

当使用transformers库进行CIFAR-10图像分类时，你可以使用PyTorch中提供的CrossEntropyLoss作为损失函数，并选择Adam或SGD作为优化器。以下是一个简单的示例代码，用于训练和保存模型，并记录训练过程中的损失和准确率。

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.datasets import CIFAR10
from torchvision.transforms import ToTensor, Normalize
from transformers import ViTModel

# 定义模型
model = ViTModel.from_pretrained('google/vit-base-patch16-224')
model.fc = nn.Linear(model.config.hidden_size, 10)

# 加载数据
train_data = CIFAR10(root='./data', train=True, transform=ToTensor(), download=True)
train_loader = DataLoader(train_data, batch_size=64, shuffle=True)

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

# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
    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()

        # 统计损失和准确率
        running_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    # 输出训练结果
    print('Epoch [%d/%d], Loss: %.4f, Accuracy: %.2f%%'
          % (epoch+1, num_epochs, running_loss / len(train_loader), 100 * correct / total))

# 保存模型
torch.save(model.state_dict(), 'cifar10_vit.pth')

在训练过程中，你可以使用tensorboard等工具记录损失和准确率，并可视化损失和准确率曲线。在代码中增加以下内容即可：

from torch.utils.tensorboard import SummaryWriter

# 定义tensorboard writer
writer = SummaryWriter(log_dir='./logs')

# 在每个epoch结束时记录损失和准确率
writer.add_scalar('Loss/train', running_loss / len(train_loader), epoch)
writer.add_scalar('Accuracy/train', 100 * correct / total, epoch)

# 关闭tensorboard writer
writer.close()

最后，你可以使用以下代码加载保存的模型，并进行测试：

# 加载模型
model.load_state_dict(torch.load('cifar10_vit.pth'))

# 加载测试数据
test_data = CIFAR10(root='./data', train=False, transform=ToTensor(), download=True)
test_loader = DataLoader(test_data, batch_size=64, shuffle=False)

# 测试模型
correct = 0
total = 0
with torch.no_grad():
    for inputs, labels in test_loader:
        outputs = model(inputs)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Test Accuracy: %.2f%%' % (100 * correct / total))

希望这些代码对你有所帮助！