帮我写一个transformer做7特征与1标签拟合的网络，放在gpu训练

好的，我可以帮你设计一个基于Transformer的神经网络，用于将7个输入特征映射到1个输出标签。以下是一个简单的实现示例：

import torch
import torch.nn as nn
import torch.optim as optim

class TransformerModel(nn.Module):
    def __init__(self, num_features, num_labels, hidden_size, num_layers, num_heads, dropout):
        super(TransformerModel, self).__init__()
        self.num_features = num_features
        self.num_labels = num_labels
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.num_heads = num_heads
        self.dropout = dropout
        
        # Embedding layers
        self.embedding = nn.Linear(num_features, hidden_size)
        self.positional_encoding = nn.Parameter(torch.zeros(1, num_features, hidden_size))
        
        # Transformer layers
        self.transformer = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(hidden_size, num_heads, hidden_size, dropout),
            num_layers
        )
        
        # Output layer
        self.out = nn.Linear(hidden_size, num_labels)
        
    def forward(self, x):
        # Embedding
        x = self.embedding(x) + self.positional_encoding
        
        # Transformer
        x = self.transformer(x)
        
        # Output
        x = self.out(x.mean(dim=0))
        return x

# Define hyperparameters
num_features = 7
num_labels = 1
hidden_size = 128
num_layers = 4
num_heads = 8
dropout = 0.1

# Instantiate the model
model = TransformerModel(num_features, num_labels, hidden_size, num_layers, num_heads, dropout)

# Prepare the data
train_data = torch.randn(1000, num_features)
train_labels = torch.randn(1000, num_labels)

# Define the loss function and optimizer
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters())

# Train the model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
train_data, train_labels = train_data.to(device), train_labels.to(device)
model.train()
for epoch in range(100):
    optimizer.zero_grad()
    outputs = model(train_data)
    loss = criterion(outputs, train_labels)
    loss.backward()
    optimizer.step()
    print('Epoch {}: loss={}'.format(epoch+1, loss.item()))

在这个例子中，我们使用了 PyTorch 中的 `nn.TransformerEncoder` 类来定义Transformer层，并将其堆叠在一起以形成完整的Transformer模型。我们使用 `nn.Linear` 和 `nn.Parameter`来定义输入嵌入层和位置编码。

为了训练模型，我们使用了均方误差损失函数，并使用Adam优化器进行优化。我们还使用了GPU来加速训练过程。

注意，在实际应用中，您可能需要根据您的数据和任务的特定要求对模型进行微调。