用lstm预测血糖的pytorch代码

以下是使用 PyTorch 实现 LSTM 预测血糖的示例代码：

import torch
import torch.nn as nn

class LSTMModel(nn.Module):

    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super().__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, input_seq):
        lstm_out, (h_n, c_n) = self.lstm(input_seq)
        out = self.fc(lstm_out[:, -1, :])
        return out.view(-1)

# 设置超参数
input_size = 1
hidden_size = 64
num_layers = 2
output_size = 1
lr = 0.001
num_epochs = 50

# 加载数据集
train_data = torch.Tensor([[1.2], [2.4], [2.5], [3.1], [4.5], [3.3], [6.5], [4.3], [5.2], [5.5], [6.1], [7.3], [8.1], [6.9], [9.2]])
train_labels = torch.Tensor([[1.4], [1.8], [2.0], [2.2], [2.8], [2.5], [3.2], [2.7], [2.9], [3.0], [3.1], [3.3], [3.5], [3.4], [3.8]])

# 初始化模型
model = LSTMModel(input_size, hidden_size, num_layers, output_size)

# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)

# 训练模型
for epoch in range(num_epochs):
    optimizer.zero_grad()
    y_pred = model(train_data)
    loss = criterion(y_pred, train_labels)
    loss.backward()
    optimizer.step()
    if epoch % 10 == 0:
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

# 预测新数据
with torch.no_grad():
    test_data = torch.Tensor([[10.0], [11.0], [12.0]])
    pred = model(test_data)
    print(pred)

这个示例代码定义了一个 LSTM 模型，输入为单个特征（即血糖），然后使用 MSE 作为损失函数进行训练，最终预测新数据。注意，由于这只是示例代码，实际情况下需要更加复杂的数据预处理、超参数选择等操作。