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: 0input_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.shapetorch.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
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