请用transformer模型写出瓦斯浓度预警代码
时间: 2023-06-28 10:03:42 浏览: 58
好的,以下是基于Transformer模型的瓦斯浓度预警代码:
```
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
class GasDataset(Dataset):
def __init__(self, data_path):
self.data = []
with open(data_path, 'r') as f:
for line in f:
self.data.append([float(x) for x in line.strip().split(',')])
def __getitem__(self, idx):
return torch.tensor(self.data[idx][:-1]), torch.tensor(self.data[idx][-1])
def __len__(self):
return len(self.data)
class TransformerModel(nn.Module):
def __init__(self, input_dim, output_dim, num_layers, dropout):
super().__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.num_layers = num_layers
self.dropout = dropout
self.embedding = nn.Linear(input_dim, input_dim)
self.pos_embedding = nn.Parameter(torch.zeros(1, 100, input_dim))
self.encoder_layers = nn.ModuleList([nn.TransformerEncoderLayer(d_model=input_dim, nhead=4, dropout=dropout) for _ in range(num_layers)])
self.encoder = nn.TransformerEncoder(self.encoder_layers, num_layers=num_layers)
self.decoder = nn.Linear(input_dim, output_dim)
def forward(self, src):
src = self.embedding(src)
src += self.pos_embedding[:, :src.shape[1], :]
src = src.permute(1, 0, 2)
output = self.encoder(src)
output = output[-1]
output = self.decoder(output)
return output
def train(model, loader, criterion, optimizer):
model.train()
train_loss = 0.0
for i, (data, label) in enumerate(loader):
optimizer.zero_grad()
output = model(data)
loss = criterion(output, label)
loss.backward()
optimizer.step()
train_loss += loss.item() * data.size(0)
return train_loss / len(loader.dataset)
def evaluate(model, loader, criterion):
model.eval()
eval_loss = 0.0
with torch.no_grad():
for i, (data, label) in enumerate(loader):
output = model(data)
loss = criterion(output, label)
eval_loss += loss.item() * data.size(0)
return eval_loss / len(loader.dataset)
if __name__ == '__main__':
# 加载数据集
train_data = GasDataset('train_data.csv')
test_data = GasDataset('test_data.csv')
train_loader = DataLoader(train_data, batch_size=32, shuffle=True)
test_loader = DataLoader(test_data, batch_size=32, shuffle=False)
# 初始化模型
model = TransformerModel(input_dim=7, output_dim=1, num_layers=2, dropout=0.2)
# 定义优化器和损失函数
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.MSELoss()
# 训练模型
for epoch in range(10):
train_loss = train(model, train_loader, criterion, optimizer)
eval_loss = evaluate(model, test_loader, criterion)
print(f'Epoch {epoch+1}, Train Loss: {train_loss:.4f}, Eval Loss: {eval_loss:.4f}')
# 测试模型
with torch.no_grad():
for data, label in test_loader:
output = model(data)
print('Prediction:', output)
print('Label:', label)
break # 只打印第一个batch的结果
```
这个代码中,我们首先定义了一个`GasDataset`类,用于加载瓦斯浓度数据集。然后我们定义了一个`TransformerModel`类,这个类继承自`nn.Module`,用于构建Transformer模型。在`TransformerModel`类中,我们使用了一个线性层作为嵌入层,用于将输入数据转换为Transformer模型可以处理的格式。然后我们使用一个可学习的位置嵌入(`pos_embedding`)对输入进行位置编码。接着,我们使用多个Transformer编码器层对输入进行编码,最后使用一个线性层将编码后的结果映射到输出空间。
在训练模型时,我们使用`train`函数进行训练,该函数接受一个模型、一个数据加载器、一个损失函数和一个优化器作为参数。在`train`函数中,我们首先将模型的梯度清零,然后将输入数据传入模型,计算输出并计算损失,最后反向传播更新模型参数。
在测试模型时,我们使用`evaluate`函数计算模型在测试集上的损失,并使用`with torch.no_grad()`语句关闭梯度计算,以减少内存占用。最后,我们使用一个简单的循环来打印模型在测试集上的预测结果和真实标签。