用python帮我写一个基于transformer的图像分类

好的，基于Transformer的图像分类可以参考以下步骤：

1. 数据准备：首先，你需要准备一些图像数据集。你可以使用一些公开数据集，如MNIST、CIFAR-10等。对于每个图像，你需要将其转换为数字张量，并对其进行归一化处理。

2. 模型建立：接下来，你需要建立一个Transformer模型。你可以使用PyTorch或TensorFlow等深度学习框架。

3. 训练模型：在构建好模型后，你需要使用图像数据集训练模型。你可以使用Adam优化器和交叉熵损失函数来进行训练。

4. 模型评估：训练完成后，你需要评估模型的性能。你可以使用测试数据集来评估模型的准确率。

5. 模型应用：最后，你可以将模型应用于新的图像数据集进行分类。

下面是一份Python代码，它可以帮助你建立一个基于Transformer的图像分类器：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
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_layers, num_heads, dropout_prob):
        super().__init__()
        self.encoder_layer = nn.TransformerEncoderLayer(d_model=input_dim, nhead=num_heads, dropout=dropout_prob)
        self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=num_layers)
        self.fc = nn.Linear(hidden_dim, output_dim)
        
    def forward(self, x):
        x = self.transformer_encoder(x)
        x = x.mean(dim=0) # 平均池化
        x = self.fc(x)
        return x

# 数据准备
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])
train_set = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
test_set = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
train_loader = DataLoader(train_set, batch_size=32, shuffle=True)
test_loader = DataLoader(test_set, batch_size=32, shuffle=False)

# 模型建立
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = TransformerModel(input_dim=28*28, hidden_dim=256, output_dim=10, num_layers=2, num_heads=8, dropout_prob=0.1).to(device)

# 训练模型
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        inputs, labels = inputs.view(inputs.size(0), -1).to(device), labels.to(device)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(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

# 模型评估
correct = 0
total = 0
with torch.no_grad():
    for data in test_loader:
        images, labels = data
        images, labels = images.view(images.size(0), -1).to(device), labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))

# 模型应用
test_images = ...
test_images = test_images.view(test_images.size(0), -1).to(device)
predictions = model(test_images)