lstm预测pytorch

对于使用LSTM进行预测的PyTorch代码，您可以按照以下步骤进行：

1. 导入所需的库：

import torch
import torch.nn as nn

2. 定义LSTM模型类：

class LSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(LSTMModel, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
        out, _ = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out

3. 实例化模型，定义损失函数和优化器：

input_size = ...  # 输入特征维度
hidden_size = ...  # 隐藏层维度
num_layers = ...  # LSTM层数
output_size = ...  # 输出维度

model = LSTMModel(input_size, hidden_size, num_layers, output_size)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

4. 准备输入数据和目标数据，并进行训练：

num_epochs = ...  # 训练轮数

for epoch in range(num_epochs):
    # 前向传播
    outputs = model(inputs)
    loss = criterion(outputs, targets)

    # 反向传播和优化
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if (epoch+1) % 100 == 0:
        print(f'Epoch: {epoch+1}/{num_epochs}, Loss: {loss.item():.4f}')

这是一个简单的LSTM预测的PyTorch代码示例，您可以根据您的具体需求进行修改和扩展。