transformer多元回归预测
时间: 2023-11-23 09:58:16 浏览: 255
Transformer是一种强大的神经网络模型,可以用于多元回归预测。下面是一个使用Transformer进行多元回归预测的示例代码:
```python
import torch
import torch.nn as nn
import numpy as np
class TransformerModel(nn.Module):
def __init__(self, input_dim, output_dim, nhead, num_layers):
super(TransformerModel, self).__init__()
self.model_type = 'Transformer'
self.src_mask = None
self.pos_encoder = PositionalEncoding(input_dim)
encoder_layers = nn.TransformerEncoderLayer(input_dim, nhead)
self.transformer_encoder = nn.TransformerEncoder(encoder_layers, num_layers)
self.encoder = nn.Linear(input_dim, input_dim)
self.decoder = nn.Linear(input_dim, output_dim)
def forward(self, src):
if self.src_mask is None or self.src_mask.size(0) != len(src):
device = src.device
mask = self._generate_square_subsequent_mask(len(src)).to(device)
self.src_mask = mask
src = self.encoder(src)
src = self.pos_encoder(src)
output = self.transformer_encoder(src, self.src_mask)
output = self.decoder(output)
return output
def _generate_square_subsequent_mask(self, sz):
mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
return mask
class PositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=5000):
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-np.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer('pe', pe)
def forward(self, x):
x = x + self.pe[:x.size(0), :]
return self.dropout(x)
# 定义模型参数
input_dim = 10
output_dim = 1
nhead = 2
num_layers = 2
lr = 0.001
epochs = 100
# 生成随机数据
x = torch.randn(100, input_dim)
y = torch.randn(100, output_dim)
# 初始化模型
model = TransformerModel(input_dim, output_dim, nhead, num_layers)
# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
# 训练模型
for epoch in range(epochs):
optimizer.zero_grad()
output = model(x)
loss = criterion(output, y)
loss.backward()
optimizer.step()
if epoch % 10 == 0:
print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, epochs, loss.item()))
# 使用模型进行预测
test_x = torch.randn(1, input_dim)
pred_y = model(test_x)
print('Predicted value: {:.4f}'.format(pred_y.item()))
```