用python帮我写一个基于transformer的图像分类

好的，这是一个使用 transformer 模型进行图像分类的简单代码示例：

``` import torch import torch.nn as nn import torchvision.transforms as transforms import torchvision.datasets as datasets

加载数据集

train_dataset = datasets.CIFAR10(root='./data', train=True, transform=transforms.ToTensor(), download=True) test_dataset = datasets.CIFAR10(root='./data', train=False, transform=transforms.ToTensor(), download=True)

定义数据加载器

train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True) test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=64, shuffle=False)

定义transformer模型

class Transformer(nn.Module): def init(self): super(Transformer, self).init() self.layer1 = nn.Linear(33232, 128) self.layer2 = nn.Linear(128, 64) self.layer3 = nn.Linear(64, 10)

def forward(self, x):
    x = x.reshape(x.size(0), -1)
    x = torch.relu(self.layer1(x))
    x = torch.relu(self.layer2(x))
    x = self.layer3(x)
    return x
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model = Transformer()

定义损失函数和优化器

criterion = nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

训练模型

for epoch in range(10): for i, (images, labels) in enumerate(train_loader): outputs = model(images) loss = criterion(outputs, labels)

optimizer.zero_grad()
    loss.backward()
    optimizer.step()if (i+1) % 100 == 0:
        print (f'Epoch [{epoch+1}/10], Step [{i+1}/{len(train_loader)}], Loss: {loss.item()}')
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测试模型

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

print(f'Accuracy of the model on the test
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