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PyTorch中的LSTM

February 7, 2020 • Read: 3853 • Deep Learning阅读设置

nn.LSTM

PyTorch LSTM API文档

输入数据格式:

  • input:[seq_len, batch, input_size]
  • $h_0$:[num_layers * num_directions, batch, hidden_size]
  • $c_0$:[num_layers * num_directions, batch, hidden_size]

输出数据格式:

  • output:[seq_len, batch, hidden_size * num_directions]
  • $h_n$:[num_layers * num_directions, batch, hidden_size]
  • $c_n$:[num_layers * num_directions, batch, hidden_size]

接下来看个具体的例子

import torch
import torch.nn as nn

lstm = nn.LSTM(input_size=100, hidden_size=20, num_layers=4)
x = torch.randn(10, 3, 100) # 一个句子10个单词,送进去3条句子,每个单词用一个100维的vector表示
out, (h, c) = lstm(x)
print(out.shape, h.shape, c.shape)
# torch.Size([10, 3, 20]) torch.Size([4, 3, 20]) torch.Size([4, 3, 20])

nn.LSTMCell

PyTorch LSTMCell API文档

和RNNCell类似,输入input_size的shape是[batch, input_size],输出$h_t$和$c_t$的shape是[batch, hidden_size]

看个一层的LSTM的例子

import torch
import torch.nn as nn

cell = nn.LSTMCell(input_size=100, hidden_size=20) # one layer LSTM
h = torch.zeros(3, 20)
c = torch.zeros(3, 20)
x = torch.randn(10, 3, 100)
for xt in x:
    h, c = cell(xt, [h, c])
print(h.shape, c.shape) # torch.Size([3, 20]) torch.Size([3, 20])

两层的LSTM例子

 import torch
import torch.nn as nn

cell1 = nn.LSTMCell(input_size=100, hidden_size=30)
cell2 = nn.LSTMCell(input_size=30, hidden_size=20)
h1 = torch.zeros(3, 30)
c1 = torch.zeros(3, 30)
h2 = torch.zeros(3, 20)
c2 = torch.zeros(3, 20)
x = torch.randn(10, 3, 100)
for xt in x:
    h1, c1 = cell1(xt, [h1, c1])
    h2, c2 = cell2(h1, [h2, c2])
print(h2.shape, c2.shape) # torch.Size([3, 20]) torch.Size([3, 20])
Last Modified: February 8, 2020
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4 Comments
  1. dsy dsy

    学到了,我自己编写一个LSTM实现算法,不知道对不对,看到博主用LSTMCell,看到了测试的希望

  2. noao noao

    x = torch.randn(10, 3, 100) # 一个句子10个单词,送进去3条句子,每个单词用一个100维的vector表示

    请问,为什么我在调试的时候看到是:(batch_size批次大小,max_len句子长度,词向量维度),和这里说的前两维反了,求正解.

    1. mathor mathor

      @noao正常来说,大部分情况下我们确实会将句子处理成[batch_size, max_len, dim]这样的维度,但是PyTorch中的RNN类模型,默认的输入格式是[max_len, batch_size, dim],所以你才会觉得前两个维度反了,是因为默认就需要max_len在前面

    2. noao noao

      @mathor好的 谢谢博主~