根据上述数据建立,lstm-transformer模型,不使用tansorflow帮我写出python代码
时间: 2024-09-23 07:09:06 浏览: 59
由于TensorFlow不是必需的,我们可以选择PyTorch库来构建LSTM-Transformer模型。以下是使用PyTorch实现的基本框架,不包含完整的数据预处理部分,假设你已经有了合适的数据格式(例如Pandas DataFrame):
```python
import torch
import torch.nn as nn
import torch.optim as optim
# 定义LSTM层
class LSTMBlock(nn.Module):
def __init__(self, input_size, hidden_size, num_layers):
super(LSTMBlock, self).__init__()
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
def forward(self, x):
h0, c0 = torch.zeros(num_layers, x.size(0), hidden_size).to(device), torch.zeros(num_layers, x.size(0), hidden_size).to(device)
out, _ = self.lstm(x, (h0, c0))
return out[:, -1, :]
# 定义Transformer模块
class TransformerBlock(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward, dropout=0.1):
super(TransformerBlock, self).__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
def forward(self, src):
attn_output, _ = self.self_attn(src, src, src)
attn_output = self.dropout(attn_output)
residual = src
src = self.linear2(self.dropout(torch.relu(self.linear1(attn_output))))
src = src + residual
return src
# 组合LSTM和Transformer
class LSTMTranformerModel(nn.Module):
def __init__(self, input_size, hidden_size, transformer_hidden_size, num_layers, nhead, num_transformer_blocks):
super().__init__()
self.lstm = LSTMBlock(input_size, hidden_size, num_layers)
self.transformer = nn.Sequential(*[TransformerBlock(hidden_size, nhead, transformer_hidden_size) for _ in range(num_transformer_blocks)])
def forward(self, x):
lstm_out = self.lstm(x)
transformer_out = self.transformer(lstm_out.unsqueeze(1)) # Add time dimension
return transformer_out.squeeze(1)
# 设定超参数
input_size = ... # 根据你的输入数据特征设定
hidden_size = ...
transformer_hidden_size = ...
num_layers = ...
nhead = ...
num_transformer_blocks = ...
# 创建模型并设置设备(如GPU)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = LSTMTranformerModel(input_size, hidden_size, transformer_hidden_size, num_layers, nhead, num_transformer_blocks).to(device)
# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 假设train_loader是一个数据加载器
for data in train_loader:
inputs, targets = data[0].to(device), data[1].to(device)
outputs = model(inputs)
loss = criterion(outputs, targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
```
请注意,你需要根据实际的项目需求调整模型结构、超参数以及训练部分。此代码仅用于展示基本框架,实际应用中还需要添加数据预处理、批次处理等步骤。另外,如果你需要使用更复杂的模型,如自注意力机制,可能需要修改TransformerBlock部分。
阅读全文
相关推荐



















