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
代码语言：javascript复制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
代码语言：javascript复制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
代码语言：javascript复制 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])
