使用pytorch创建LSTM模型

import torch
import torch.nn as nn

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

input_size = 10  # 输入特征维度
hidden_size = 20  # LSTM隐藏层维度
num_layers = 2  # LSTM层数
output_size = 1  # 输出维度（预测结果维度）
model = LSTMModel(input_size, hidden_size, num_layers, output_size)

criterion = nn.MSELoss()  # 均方误差损失函数
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)  # Adam优化器

# 假设有训练数据X和对应的标签y
# X的形状为(batch_size, sequence_length, input_size)
# y的形状为(batch_size, output_size)

# 将数据转换为torch张量
X = torch.tensor(X, dtype=torch.float32)
y = torch.tensor(y, dtype=torch.float32)

# 训练模型
num_epochs = 100
for epoch in range(num_epochs):
    outputs = model(X)
    loss = criterion(outputs, y)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if (epoch+1) % 10 == 0:
        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')