Transformer架构估产模型代码

以下是一个简单的使用Transformer架构进行产量预测的Python代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

# 定义Transformer模型
class TransformerModel(nn.Module):
    def __init__(self, input_size, output_size, hidden_size, num_layers, dropout):
        super(TransformerModel, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.encoder = nn.Linear(input_size, hidden_size)
        self.decoder = nn.Linear(hidden_size, output_size)
        self.transformer = nn.Transformer(d_model=hidden_size, nhead=4, num_encoder_layers=num_layers, num_decoder_layers=num_layers, dim_feedforward=hidden_size*2, dropout=dropout)
        
    def forward(self, src):
        src = self.encoder(src)
        tgt = torch.zeros_like(src)
        tgt[:-1] = src[1:]
        output = self.transformer(src.unsqueeze(0), tgt.unsqueeze(0))
        output = self.decoder(output.squeeze(0))
        return output
    
# 定义产量数据
data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])

# 定义模型和优化器
model = TransformerModel(1, 1, 64, 2, 0.1)
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.MSELoss()

# 训练模型
for epoch in range(100):
    optimizer.zero_grad()
    input_seq = torch.FloatTensor(data[:-1]).unsqueeze(1)
    target_seq = torch.FloatTensor(data[1:]).unsqueeze(1)
    output_seq = model(input_seq)
    loss = criterion(output_seq, target_seq)
    loss.backward()
    optimizer.step()
    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, 100, loss.item()))

# 预测未来的产量
future_seq = np.array([16, 17, 18, 19, 20])
input_seq = torch.FloatTensor(data).unsqueeze(1)
output_seq = model(input_seq)
future_output = model(torch.FloatTensor(future_seq).unsqueeze(1))
print('Predicted Data:', torch.cat((output_seq, future_output), dim=0).detach().numpy().flatten())

在上面的代码中，我们首先定义了一个Transformer模型，包括一个编码器、一个解码器和一个Transformer层。然后，我们定义了一组简单的产量数据。接着，我们定义了模型和优化器，并在数据上进行训练。最后，我们使用训练完成的模型来预测未来的产量数据。