长短期记忆神经网络与GRU的pytorch实现

长短期记忆网络（LSTM）和门控循环单元网络（GRU）是循环神经网络（RNN）的两个变种，用于处理时序数据。在PyTorch中，可以使用torch.nn模块来实现LSTM和GRU。

下面是LSTM和GRU的PyTorch实现示例：

1. LSTM的PyTorch实现：

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

# 创建LSTM模型实例
input_size = 10
hidden_size = 20
num_layers = 2
output_size = 1
lstm_model = LSTMModel(input_size, hidden_size, num_layers, output_size)

2. GRU的PyTorch实现：

import torch
import torch.nn as nn

# 定义GRU模型
class GRUModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(GRUModel, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.gru = nn.GRU(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)
        out, _ = self.gru(x, h0)
        out = self.fc(out[:, -1, :])
        return out

# 创建GRU模型实例
input_size = 10
hidden_size = 20
num_layers = 2
output_size = 1
gru_model = GRUModel(input_size, hidden_size, num_layers, output_size)