pytorch实现transformer回归代码

Transformer是一种流行的神经网络模型，用于自然语言处理任务，如机器翻译、文本生成等。而在PyTorch中实现Transformer模型也比较简单，以下是一个简单的Transformer回归代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader

class CustomDataset(Dataset):
    def __init__(self, x, y):
        self.x = x
        self.y = y

    def __len__(self):
        return len(self.x)

    def __getitem__(self, idx):
        return self.x[idx], self.y[idx]

class TransformerRegressor(nn.Module):
    def __init__(self, input_dim, output_dim, hidden_dim, n_layers, dropout):
        super(TransformerRegressor, self).__init__()

        self.input_dim = input_dim
        self.output_dim = output_dim
        self.hidden_dim = hidden_dim
        self.n_layers = n_layers
        self.dropout = dropout

        self.embedding = nn.Linear(input_dim, hidden_dim)
        self.transformer_layer = nn.TransformerEncoderLayer(hidden_dim, nhead=8, dim_feedforward=2048, dropout=dropout)
        self.transformer = nn.TransformerEncoder(self.transformer_layer, num_layers=n_layers)
        self.output_layer = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        x = self.embedding(x)
        x = x.transpose(0, 1)
        x = self.transformer(x)
        x = x.transpose(0, 1)
        x = self.output_layer(x)
        return x.squeeze()

# 实例化数据集和数据加载器
x_train = torch.randn(1000, 10)
y_train = torch.randn(1000, 1)
dataset = CustomDataset(x_train, y_train)
dataloader = DataLoader(dataset, batch_size=32)

# 实例化模型、损失函数和优化器
model = TransformerRegressor(input_dim=10, output_dim=1, hidden_dim=128, n_layers=3, dropout=0.2)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 开始训练
for epoch in range(10):
    for i, (x_batch, y_batch) in enumerate(dataloader):
        optimizer.zero_grad()
        y_pred = model(x_batch)
        loss = criterion(y_pred, y_batch)
        loss.backward()
        optimizer.step()

    print(f"Epoch {epoch+1}, Loss: {loss.item():.4f}")

# 预测并输出结果
x_test = torch.randn(10, 10)
y_pred = model(x_test)
print(y_pred)

该代码中使用了PyTorch的`nn.TransformerEncoder`和`nn.TransformerEncoderLayer`来实现Transformer模型，同时使用了一个自定义数据集和数据加载器来加载数据。在训练过程中，使用了MSE损失函数和Adam优化器。最终输出了对一些测试数据的预测结果。如果你有任何疑问或者需要进一步的解释，请随时提出。