关于torch.nn.LSTM()的输入和输出

主角torch.nn.LSTM()

初始化时要传入的参数

 |  Args:
 |      input_size: The number of expected features in the input `x`
 |      hidden_size: The number of features in the hidden state `h`
 |      num_layers: Number of recurrent layers. E.g., setting ``num_layers=2``
 |          would mean stacking two LSTMs together to form a `stacked LSTM`,
 |          with the second LSTM taking in outputs of the first LSTM and
 |          computing the final results. Default: 1
 |      bias: If ``False``, then the layer does not use bias weights `b_ih` and `b_hh`.
 |          Default: ``True``
 |      batch_first: If ``True``, then the input and output tensors are provided
 |          as `(batch, seq, feature)` instead of `(seq, batch, feature)`.
 |          Note that this does not apply to hidden or cell states. See the
 |          Inputs/Outputs sections below for details.  Default: ``False``
 |      dropout: If non-zero, introduces a `Dropout` layer on the outputs of each
 |          LSTM layer except the last layer, with dropout probability equal to
 |          :attr:`dropout`. Default: 0
 |      bidirectional: If ``True``, becomes a bidirectional LSTM. Default: ``False``
 |      proj_size: If ``> 0``, will use LSTM with projections of corresponding size. Default: 0

input_size：一般是词嵌入的大小

hidden_size：隐含层的维度

num_layers：默认是1，单层LSTM

bias：是否使用bias

batch_first：默认为False，如果设置为True，则表示第一个维度表示的是batch_size

dropout：直接看英文吧

bidirectional：默认为False，表示单向LSTM，当设置为True，表示为双向LSTM，一般和num_layers配合使用（需要注意的是当该项设置为True时，将num_layers设置为1，表示由1个双向LSTM构成）

模型输入输出-单向LSTM

import torch
import torch.nn as nn
import numpy as np

inputs_numpy = np.random.random((64,32,300))
inputs = torch.from_numpy(inputs_numpy).to(torch.float32)
inputs.shape

torch.Size([64, 32, 300])：表示[batchsize, max_length, embedding_size]

hidden_size = 128
lstm = nn.LSTM(300, 128, batch_first=True, num_layers=1)
output, (hn, cn) = lstm(inputs)
print(output.shape)
print(hn.shape)
print(cn.shape)

torch.Size([64, 32, 128])

torch.Size([1, 64, 128])

torch.Size([1, 64, 128])

说明：

output：保存了每个时间步的输出，如果想要获取最后一个时间步的输出，则可以这么获取：output_last = output[:,-1,:]

h_n：包含的是句子的最后一个单词的隐藏状态，与句子的长度seq_length无关

c_n：包含的是句子的最后一个单词的细胞状态，与句子的长度seq_length无关

另外：最后一个时间步的输出等于最后一个隐含层的输出

output_last = output[:,-1,:]
hn_last = hn[-1]
print(output_last.eq(hn_last))

模型输入输出-双向LSTM

首先我们要明确：

output ：（seq_len, batch, num_directions * hidden_size）

h_n：(num_layers * num_directions, batch, hidden_size)

c_n ：（num_layers * num_directions, batch, hidden_size）

其中num_layers表示层数，这里是1，num_directions表示方向数，由于是双向的，这里是2，也是，我们就有下面的结果：

import torch
import torch.nn as nn
import numpy as np

inputs_numpy = np.random.random((64,32,300))
inputs = torch.from_numpy(inputs_numpy).to(torch.float32)
inputs.shape
hidden_size = 128
lstm = nn.LSTM(300, 128, batch_first=True, num_layers=1, bidirectional=True)
output, (hn, cn) = lstm(inputs)
print(output.shape)
print(hn.shape)
print(cn.shape)

torch.Size([64, 32, 256])

torch.Size([2, 64, 128])

torch.Size([2, 64, 128])

这里面的hn包含两个元素，一个是正向的隐含层输出，一个是方向的隐含层输出。

#获取反向的最后一个output
output_last_backward = output[:,0,-hidden_size:]
#获反向最后一层的hn
hn_last_backward = hn[-1]

#反向最后的output等于最后一层的hn
print(output_last_backward.eq(hn_last_backward))

#获取正向的最后一个output
output_last_forward = output[:,-1,:hidden_size]
#获取正向最后一层的hn
hn_last_forward = hn[-2]
# 反向最后的output等于最后一层的hn
print(output_last_forward.eq(hn_last_forward))

https://www.cnblogs.com/LiuXinyu12378/p/12322993.html

https://blog.csdn.net/m0_45478865/article/details/104455978

https://blog.csdn.net/foneone/article/details/104002372