利用PyTorch使用LSTM
- 2020 年 2 月 17 日
- 筆記
nn.LSTM
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
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])
