能否生成完整的代码呢，训练数据和标签可以暂时使用torch的随机数代替

当然可以！以下是一个完整的示例代码，展示如何使用PyTorch实现一个简单的Transformer模型，并使用随机数据进行训练：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader

class CustomDataset(Dataset):
    def __init__(self, num_samples, input_dim, output_dim):
        self.num_samples = num_samples
        self.input_dim = input_dim
        self.output_dim = output_dim

    def __len__(self):
        return self.num_samples

    def __getitem__(self, idx):
        input_data = torch.randint(0, self.input_dim, (10,))
        output_data = torch.randint(0, self.output_dim, (10,))
        return input_data, output_data

class Transformer(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim, num_layers, num_heads):
        super(Transformer, self).__init__()

        self.embedding = nn.Embedding(input_dim, hidden_dim)
        self.positional_encoding = PositionalEncoding(hidden_dim)
        
        encoder_layer = nn.TransformerEncoderLayer(hidden_dim, num_heads)
        self.encoder = nn.TransformerEncoder(encoder_layer, num_layers)
        
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        x = self.embedding(x)
        x = self.positional_encoding(x)
        x = self.encoder(x)
        x = self.fc(x)
        return x

class PositionalEncoding(nn.Module):
    def __init__(self, hidden_dim, max_length=1000):
        super(PositionalEncoding, self).__init__()

        pe = torch.zeros(max_length, hidden_dim)
        position = torch.arange(0, max_length).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, hidden_dim, 2) * -(math.log(10000.0) / hidden_dim))

        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)

        self.register_buffer('pe', pe.unsqueeze(0))

    def forward(self, x):
        x = x + self.pe[:, :x.size(1), :]
        return x

# 设置超参数
input_dim = 100
hidden_dim = 256
output_dim = 10
num_layers = 2
num_heads = 4
batch_size = 32
num_epochs = 10

# 创建数据集和数据加载器
dataset = CustomDataset(1000, input_dim, output_dim)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

# 创建模型和优化器
model = Transformer(input_dim, hidden_dim, output_dim, num_layers, num_heads)
optimizer = optim.Adam(model.parameters())

# 训练模型
for epoch in range(num_epochs):
    for batch_inputs, batch_targets in dataloader:
        optimizer.zero_grad()
        outputs = model(batch_inputs)
        loss = nn.CrossEntropyLoss()(outputs.view(-1, output_dim), batch_targets.view(-1))
        loss.backward()
        optimizer.step()

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

# 使用模型进行预测
test_input = torch.randint(0, input_dim, (1, 10))
with torch.no_grad():
    test_output = model(test_input)
    predicted_label = torch.argmax(test_output, dim=2)

print("Test Input:", test_input)
print("Predicted Label:", predicted_label)

上述代码首先定义了一个自定义数据集 `CustomDataset`，其中生成随机的输入数据和标签。然后定义了一个简单的Transformer模型 `Transformer`，包括嵌入层、位置编码层、Transformer编码器和最终的线性层。接着创建了数据加载器、模型和优化器，并进行了训练。最后使用训练好的模型进行预测。

请注意，这只是一个简单的示例，实际使用时可能需要根据具体任务进行调整和扩展。希望这可以帮到你！如果有任何其他问题，请随时提问。