RNN pytorch代码

下面是一个使用PyTorch定义RNN的示例代码：

import torch
import torch.nn as nn

# 定义RNN类
class RNN(nn.Module):
    def __init__(self, input_size, output_size, hidden_dim, n_layers):
        super(RNN, self).__init__()
        
        self.hidden_dim = hidden_dim
        self.n_layers = n_layers
        
        # 创建RNN层
        self.rnn = nn.RNN(input_size, hidden_dim, n_layers, batch_first=True)
        
        # 最后的全连接层
        self.fc = nn.Linear(hidden_dim, output_size)
        
    def forward(self, x):
        batch_size = x.size(0)
        
        # 初始化隐藏状态
        hidden = self.init_hidden(batch_size)
        
        # 前向传播
        out, hidden = self.rnn(x, hidden)
        
        # 提取最后一个时间步的输出
        out = out[:, -1, :]
        
        # 通过全连接层得到最终输出
        out = self.fc(out)
        
        return out
    
    def init_hidden(self, batch_size):
        # 初始化隐藏状态
        hidden = torch.zeros(self.n_layers, batch_size, self.hidden_dim)
        return hidden

# 创建RNN实例
input_size = 1
output_size = 1
hidden_dim = 32
n_layers = 1
rnn = RNN(input_size, output_size, hidden_dim, n_layers)

# 打印RNN的结构
print(rnn)

这段代码首先定义了一个自定义的RNN类，继承自`nn.Module`。在类的`__init__`方法中，定义了RNN的结构，包括输入大小、隐藏状态的特征数、RNN的层数等。然后在`forward`方法中，进行了前向传播计算。最后，在`init_hidden`方法中，初始化了隐藏状态。

你可以根据自己的需求修改这段代码中的超参数，比如`input_size`、`output_size`、`hidden_dim`和`n_layers`等。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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