torch LSTM 气温

您好！如果您想使用PyTorch中的LSTM模型来预测气温，我可以为您提供一些代码示例。首先，您需要导入必要的库并准备您的数据。假设您已经有一个包含气温数据的时间序列数据集。以下是一个简单的示例：

import torch
import torch.nn as nn

# 准备数据
data = [30, 32, 35, 28, 24, 26, 30, 32, 35, 28, 24, 26]  # 假设这是气温数据
input_seq = torch.tensor(data[:-1]).reshape(-1, 1, 1)  # 输入序列 (num_samples, seq_len, input_size)
target_seq = torch.tensor(data[1:]).reshape(-1, 1, 1)   # 目标序列 (num_samples, seq_len, input_size)

# 定义LSTM模型
class LSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers):
        super(LSTMModel, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers)
        self.fc = nn.Linear(hidden_size, input_size)

    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(1), self.hidden_size).to(x.device)
        c0 = torch.zeros(self.num_layers, x.size(1), self.hidden_size).to(x.device)
        out, _ = self.lstm(x, (h0, c0))
        out = self.fc(out)
        return out

# 初始化模型并定义训练参数
input_size = 1
hidden_size = 16
num_layers = 1
model = LSTMModel(input_size, hidden_size, num_layers)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

# 训练模型
num_epochs = 1000
for epoch in range(num_epochs):
    model.train()
    outputs = model(input_seq)
    loss = criterion(outputs, target_seq)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if (epoch+1) % 100 == 0:
        print(f'Epoch {epoch+1}/{num_epochs}, Loss: {loss.item()}')

# 使用模型进行预测
model.eval()
with torch.no_grad():
    predicted_seq = model(input_seq)

print('Predicted Sequence:', predicted_seq.reshape(-1).tolist())

请注意，这只是一个简单的示例，您可能需要根据您的具体数据和需求进行调整。希望这可以帮助到您！如果您有任何其他问题，请随时提问。