编写一个基于pytorch的lstm模型股票预测代码,要求输入特征为4,序列长度为20,批次大小为128

可以使用如下代码构建一个基于PyTorch的LSTM股票预测模型：import torch
import torch.nn as nnclass LSTM_StockPredictor(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, output_size):
super(LSTM_StockPredictor, 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):
# Set initial hidden and cell states
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device) # Forward propagate LSTM
out, _ = self.lstm(x, (h0,c0)) # out: tensor of shape (batch_size, seq_length, hidden_size) # Decode the hidden state of the last time step
out = self.fc(out[:,-1,:])
return out# 初始化参数
input_size=4
hidden_size=32
num_layers=2
output_size=1# 实例化模型
model = LSTM_StockPredictor(input_size, hidden_size, num_layers, output_size)# 设置超参数
num_epochs = 10
learning_rate = 0.001# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)# 迭代训练模型
for epoch in range(num_epochs):
# 准备输入和输出
inputs = torch.from_numpy(X_train).float().to(device)
targets = torch.from_numpy(y_train).float().to(device)
# 正向传播
outputs = model(inputs)
# 计算损失
loss = criterion(outputs, targets)
# 清空上一步梯度
optimizer.zero_grad()
# 反向传播
loss.backward()
# 更新参数
optimizer.step()