class Net(nn.Module): def __init__(self,input_size,hidden_size,num_layers,output_size,batch_size,seq_length) -> None: super(Net,self).__init__() self.input_size=input_size self.hidden_size=hidden_size self.num_layers=num_layers self.output_size=output_size self.batch_size=batch_size self.seq_length=seq_length self.num_directions=1 # 单向LSTM self.lstm=nn.LSTM(input_size=input_size,hidden_size=hidden_size,num_layers=num_layers,batch_first=True) # LSTM层 self.fc=nn.Linear(hidden_size,output_size) # 全连接层 def forward(self,x): # e.g. x(10,3,100) 三个句子，十个单词，一百维的向量,nn.LSTM(input_size=100,hidden_size=20,num_layers=4) # out.shape=(10,3,20) h/c.shape=(4,b,20) batch_size, seq_len = x.size()[0], x.size()[1] # x.shape=(604,3,3) h_0 = torch.randn(self.num_directions * self.num_layers, x.size(0), self.hidden_size) c_0 = torch.randn(self.num_directions * self.num_layers, x.size(0), self.hidden_size) # output(batch_size, seq_len, num_directions * hidden_size) output, _ = self.lstm(x, (h_0, c_0)) # output(5, 30, 64) pred = self.fc(output) # (5, 30, 1) pred = pred[:, -1, :] # (5, 1) return pred改成python代码

这段代码是一个简单的使用 PyTorch 实现的 LSTM 模型。下面是将其转化为 Python 代码的版本：

import torch
import torch.nn as nn

class Net(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size, seq_length):
        super(Net, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.output_size = output_size
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.num_directions = 1  # 单向LSTM

        # LSTM层
        self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True)

        # 全连接层
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        # x.shape=(10,3,100)，三个句子，十个单词，一百维的向量
        # out.shape=(10,3,20)，h/c.shape=(4,b,20)
        batch_size, seq_len = x.size()[0], x.size()[1]
        h_0 = torch.randn(self.num_directions * self.num_layers, x.size(0), self.hidden_size)
        c_0 = torch.randn(self.num_directions * self.num_layers, x.size(0), self.hidden_size)
        output, _ = self.lstm(x, (h_0, c_0))  # output(batch_size, seq_len, num_directions * hidden_size)
        pred = self.fc(output)  # (5, 30, 1)
        pred = pred[:, -1, :]  # (5, 1)

        return pred