请使用pytorch 编写LSTM时间序列预测代码包含激活函数lru的配置

以下是一个使用PyTorch编写的LSTM时间序列预测代码，其中包含了使用lru激活函数的配置。

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

# Set random seed for reproducibility
torch.manual_seed(0)

# Define LSTM model
class LSTM(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(LSTM, self).__init__()
        self.hidden_size = hidden_size
        self.lstm = nn.LSTM(input_size, hidden_size)
        self.fc = nn.Linear(hidden_size, output_size)
        self.activation = nn.LeakyReLU()

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

# Generate sample data
data = np.sin(np.arange(1000)*0.1) + np.random.normal(0, 0.1, 1000)

# Define input and target sequences
seq_length = 20
input_seq = []
target_seq = []
for i in range(len(data)-seq_length):
    input_seq.append(data[i:i+seq_length])
    target_seq.append(data[i+seq_length])

# Convert input and target sequences to PyTorch tensors
input_seq = torch.Tensor(input_seq).unsqueeze(2)
target_seq = torch.Tensor(target_seq).unsqueeze(1)

# Split data into training and validation sets
train_input = input_seq[:800]
train_target = target_seq[:800]
val_input = input_seq[800:]
val_target = target_seq[800:]

# Define model, loss function, and optimizer
model = LSTM(1, 16, 1)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.01)

# Train model
num_epochs = 1000
for epoch in range(num_epochs):
    optimizer.zero_grad()
    output = model(train_input)
    loss = criterion(output, train_target)
    loss.backward()
    optimizer.step()
    if epoch % 100 == 0:
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

# Evaluate model on validation set
model.eval()
with torch.no_grad():
    val_output = model(val_input)
    val_loss = criterion(val_output, val_target)
    print('Validation Loss: {:.4f}'.format(val_loss.item()))

# Plot predicted vs actual values
import matplotlib.pyplot as plt
plt.plot(val_target.numpy(), label='Actual')
plt.plot(val_output.numpy(), label='Predicted')
plt.legend()
plt.show()

在这个代码中，我们使用了LeakyReLU激活函数，它是lru函数的一种变体。我们在LSTM类的构造函数中定义了这个激活函数，并在forward方法中将它应用到全连接层输出的结果上。

在训练过程中，我们使用均方误差损失函数和Adam优化器来训练模型。我们还将数据集分成了训练集和验证集，以帮助我们评估模型的性能。

最后，我们将模型应用于验证集，并将其预测结果与实际结果进行比较。我们使用matplotlib库绘制了预测值和实际值之间的比较图表