transformers做cifar10图像分类任务保存loss曲线和准确率曲线并保存模型pytorch
时间: 2024-02-11 09:08:50 浏览: 30
好的,我可以帮您解决这个问题。首先,您需要安装 PyTorch 和 Transformers 库。然后,您可以按照以下步骤进行操作:
1. 加载 CIFAR-10 数据集
```python
import torch
import torchvision
import torchvision.transforms as transforms
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=2)
```
2. 定义模型和优化器
```python
from transformers import ViTForImageClassification, ViTConfig
config = ViTConfig.from_pretrained('google/vit-base-patch16-224')
model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224', num_labels=10)
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
```
3. 训练模型并保存 loss 和准确率曲线
```python
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)
loss_list = []
accuracy_list = []
num_epochs = 10
for epoch in range(num_epochs):
running_loss = 0.0
accuracy = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = F.cross_entropy(outputs.logits, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
accuracy += (outputs.logits.argmax(1) == labels).sum().item() / len(labels)
epoch_loss = running_loss / len(trainloader)
epoch_accuracy = accuracy / len(trainloader)
loss_list.append(epoch_loss)
accuracy_list.append(epoch_accuracy)
print('[%d] loss: %.3f accuracy: %.3f' %
(epoch + 1, epoch_loss, epoch_accuracy))
plt.plot(loss_list)
plt.title('Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.savefig('loss.png')
plt.plot(accuracy_list)
plt.title('Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.savefig('accuracy.png')
```
4. 保存模型
```python
torch.save(model.state_dict(), 'model.pth')
```
这样,您就可以得到 loss 和准确率曲线以及保存的模型。请记得将路径更改为您自己的路径。
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