LSTM PYTHON代码

LSTM（长短期记忆）是一种用于处理序列数据的人工神经网络。相较于传统的循环神经网络（RNN），LSTM通过引入门控机制，解决了梯度消失和梯度爆炸的问题，使得网络能够有效地处理长序列数据。

下面是一个简单的LSTM的Python代码示例：

import torch
import torch.nn as nn

# 定义LSTM模型
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

# 定义输入和参数
input_size = 10
hidden_size = 20
num_layers = 2
output_size = 1
batch_size = 16
seq_length = 5

# 随机生成输入数据
x = torch.randn(batch_size, seq_length, input_size)

# 创建LSTM模型实例
lstm_model = LSTMModel(input_size, hidden_size, num_layers, output_size)

# 前向传播
output = lstm_model(x)

# 打印输出
print(output.shape)