MENU

PyTorch中的LSTM

February 7, 2020 • Read: 97 • Deep Learning

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])
Archives Tip
QR Code for this page
Tipping QR Code
Leave a Comment