transformer模型图像分类cifar 10

可以使用Transformer模型来进行CIFAR-10图像分类，以下是一个使用PyTorch实现的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader

# 定义Transformer模型
class TransformerModel(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim, num_heads, num_layers):
        super(TransformerModel, self).__init__()
        self.model_type = 'Transformer'
        self.pos_encoder = PositionalEncoding(input_dim, dropout=0.1)
        encoder_layers = nn.TransformerEncoderLayer(input_dim, num_heads, hidden_dim, dropout=0.1)
        self.transformer_encoder = nn.TransformerEncoder(encoder_layers, num_layers)
        self.fc = nn.Linear(input_dim, output_dim)

    def forward(self, x):
        x = self.pos_encoder(x)
        x = self.transformer_encoder(x)
        x = x.mean(dim=0)
        x = self.fc(x)
        return x

# 定义位置编码器
class PositionalEncoding(nn.Module):
    def __init__(self, d_model, dropout=0.1, max_len=5000):
        super(PositionalEncoding, self).__init__()
        self.dropout = nn.Dropout(p=dropout)
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0).transpose(0, 1)
        self.register_buffer('pe', pe)

    def forward(self, x):
        x = x + self.pe[:x.size(0), :]
        return self.dropout(x)

# 加载数据集
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5,), (0.5,))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = DataLoader(trainset, batch_size=64,
                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = DataLoader(testset, batch_size=64,
                         shuffle=False, num_workers=2)

# 定义超参数
input_dim = 3 * 32 * 32
hidden_dim = 256
output_dim = 10
num_heads = 8
num_layers = 4
lr = 0.001
num_epochs = 10

# 初始化模型和优化器
model = TransformerModel(input_dim, hidden_dim, output_dim, num_heads, num_layers)
optimizer = optim.Adam(model.parameters(), lr=lr)

# 训练模型
for epoch in range(num_epochs):
    model.train()
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        inputs = inputs.view(-1, input_dim)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = nn.CrossEntropyLoss()(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

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

    print('Epoch %d, Test accuracy: %d %%' % (epoch + 1, 100 * correct / total))

在这个示例中，我们使用了一个4层的Transformer模型来进行CIFAR-10图像分类，其中每层都包含8个注意力头和256个隐藏单元。我们使用Adam优化器来训练模型，学习率为0.001，迭代10个周期。在每个周期结束时，我们对模型进行测试，并输出测试准确率。