transformer 预测温度 pytorch

要使用Transformer模型预测温度，您需要准备一个合适的数据集并进行适当的数据预处理。以下是一个基本的示例代码，使用PyTorch和Transformer模型来预测温度。

首先，您需要导入必要的库：

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

import numpy as np

接下来，定义一个自定义的数据集类，用于加载和处理数据：

class TemperatureDataset(Dataset):
    def __init__(self, data):
        self.data = data
    
    def __len__(self):
        return len(self.data)
    
    def __getitem__(self, index):
        return self.data[index]

然后，定义一个Transformer模型类：

class TransformerModel(nn.Module):
    def __init__(self, input_dim, output_dim, hidden_dim, num_layers, num_heads, dropout):
        super(TransformerModel, self).__init__()
        self.encoder_layer = nn.TransformerEncoderLayer(d_model=input_dim, nhead=num_heads)
        self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=num_layers)
        self.fc = nn.Linear(input_dim, hidden_dim)
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(dropout)
        self.output_layer = nn.Linear(hidden_dim, output_dim)
        
    def forward(self, x):
        x = self.transformer_encoder(x)
        x = self.fc(x[:, -1, :])
        x = self.relu(x)
        x = self.dropout(x)
        x = self.output_layer(x)
        
        return x

接下来，定义训练和评估函数：

def train(model, train_loader, criterion, optimizer, device):
    model.train()
    
    for batch in train_loader:
        inputs = batch[:, :-1, :].to(device)
        targets = batch[:, -1, :].to(device)
        
        optimizer.zero_grad()
        outputs = model(inputs)
        
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()

def evaluate(model, val_loader, criterion, device):
    model.eval()
    total_loss = 0
    
    with torch.no_grad():
        for batch in val_loader:
            inputs = batch[:, :-1, :].to(device)
            targets = batch[:, -1, :].to(device)
            
            outputs = model(inputs)
            
            loss = criterion(outputs, targets)
            total_loss += loss.item() * inputs.size(0)
    
    return total_loss / len(val_loader.dataset)

最后，定义训练和评估过程：

def train_and_evaluate(data, input_dim, output_dim, hidden_dim, num_layers, num_heads, dropout, batch_size, num_epochs, lr, device):
    train_data = TemperatureDataset(data[:-100])
    val_data = TemperatureDataset(data[-100:])
    
    train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
    val_loader = DataLoader(val_data, batch_size=batch_size)
    
    model = TransformerModel(input_dim, output_dim, hidden_dim, num_layers, num_heads, dropout).to(device)
    criterion = nn.MSELoss()
    optimizer = optim.Adam(model.parameters(), lr=lr)
    
    best_val_loss = float('inf')
    
    for epoch in range(num_epochs):
        train(model, train_loader, criterion, optimizer, device)
        val_loss = evaluate(model, val_loader, criterion, device)
        
        if val_loss < best_val_loss:
            best_val_loss = val_loss
            torch.save(model.state_dict(), 'best_model.pt')
        
        print(f'Epoch {epoch+1}/{num_epochs}, Training Loss: {train_loss:.4f}, Validation Loss: {val_loss:.4f}')
    
    model.load_state_dict(torch.load('best_model.pt'))
    return model

您可以使用以上代码作为参考，根据您的数据集和需求进行适当的修改。请确保在使用模型进行预测之前，对测试数据进行适当的数据预处理。

希望对您有帮助！