【PyTorch API】 nn.RNN 和 nn.LSTM 介绍和代码详解-Toy模板网

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1. nn.RNN 构建单向 RNN

torch.nn.RNN 的 PyTorch 链接：torch.nn.RNN(*args, **kwargs)

nn.RNN 的用法和输入输出参数的介绍直接看代码：

import torch
import torch.nn as nn

# 单层单向 RNN
embed_dim = 5    # 每个输入元素的特征维度,如每个单词用长度为 5 的特征向量表示
hidden_dim = 6   # 隐状态的特征维度,如每个单词在隐藏层中用长度为 6 的特征向量表示
rnn_layers = 4   # 循环层数
rnn = nn.RNN(input_size=embed_dim, hidden_size=hidden_dim, num_layers=rnn_layers, batch_first=True)

# 输入
batch_size = 2
sequence_length = 3    # 输入的序列长度，如 i love you 的序列长度为 3,每个单词用长度为 feature_num 的特征向量表示
input = torch.randn(batch_size, sequence_length, embed_dim)
h0 = torch.randn(rnn_layers, batch_size, hidden_dim)

# output 表示隐藏层在各个 time step 上计算并输出的隐状态
# hn 表示所有掩藏层的在最后一个 time step 隐状态, 即单词 you 的隐状态
output, hn = rnn(input, h0)

print(f"output = {output}")
print(f"hn = {hn}")
print(f"output.shape = {output.shape}")     # torch.Size([2, 3, 6])    [batch_size, sequence_length, hidden_dim]
print(f"hn.shape = {hn.shape}")             # torch.Size([4, 2, 6])    [rnn_layers, batch_size, hidden_dim]


"""
output = tensor([[[-0.3727, -0.2137, -0.3619, -0.6116, -0.1483,  0.8292],
         [ 0.1138, -0.6310, -0.3897, -0.5275,  0.2012,  0.3399],
         [-0.0522, -0.5991, -0.3114, -0.7089,  0.3824,  0.1903]],

        [[ 0.1370, -0.6037,  0.3906, -0.5222,  0.8498,  0.8887],
         [-0.3463, -0.3293, -0.1874, -0.7746,  0.2287,  0.1343],
         [-0.2588, -0.4145, -0.2608, -0.3799,  0.4464,  0.1960]]],
       grad_fn=<TransposeBackward1>)
       
hn = tensor([[[-0.2892,  0.7568,  0.4635, -0.2106, -0.0123, -0.7278],
         [ 0.3492, -0.3639, -0.4249, -0.6626,  0.7551,  0.9312]],

        [[ 0.0154,  0.0190,  0.3580, -0.1975, -0.1185,  0.3622],
         [ 0.0905,  0.6483, -0.1252,  0.3903,  0.0359, -0.3011]],

        [[-0.2833, -0.3383,  0.2421, -0.2168, -0.6694, -0.5462],
         [ 0.2976,  0.0724, -0.0116, -0.1295, -0.6324, -0.0302]],

        [[-0.0522, -0.5991, -0.3114, -0.7089,  0.3824,  0.1903],
         [-0.2588, -0.4145, -0.2608, -0.3799,  0.4464,  0.1960]]],
       grad_fn=<StackBackward0>)

output.shape = torch.Size([2, 3, 6])
hn.shape = torch.Size([4, 2, 6])
"""

需要特别注意的是 nn.RNN 的第二个输出 hn 表示所有掩藏层的在最后一个 time step 隐状态，听起来很难理解，看下面的红色方框内的数据就懂了。即 output[:, -1, :] = hn[-1, : , :]

这里 hn 保存了四次循环中最后一个 time step 隐状态的数值，以输入 i love you 为了，hn 保存的是单词 you 的隐状态。

【PyTorch API】 nn.RNN 和 nn.LSTM 介绍和代码详解,# PyTorch算子/函数用法,rnn,lstm,pytorch,深度学习

2. nn.LSTM 构建单向 LSTM

torch.nn.RNN 的 PyTorch 链接：torch.nn.LSTM(*args, **kwargs)

nn.LSTM 的用法和输入输出参数的介绍直接看代码：

import torch
import torch.nn as nn


batch_size = 4
seq_len = 3      # 输入的序列长度
embed_dim = 5    # 每个输入元素的特征维度
hidden_size = 5 * 2    # 隐状态的特征维度，根据工程经验可取 hidden_size = embed_dim * 2
num_layers = 2    # LSTM 的层数，一般设置为 1-4 层；多层 LSTM 的介绍可以参考 https://blog.csdn.net/weixin_41041772/article/details/88032093

lstm = nn.LSTM(input_size=embed_dim, hidden_size=hidden_size, num_layers=num_layers, batch_first=True)

# h0 可以缺省
input = torch.randn(batch_size, seq_len, embed_dim)

"""
output 表示隐藏层在各个 time step 上计算并输出的隐状态
hn 表示所有隐藏层的在最后一个 time step 隐状态, 即单词 you 的隐状态；所以 hn 与句子长度 seq_len 无关
hn[-1] 表示最后一个隐藏层的在最后一个 time step 隐状态，即 LSTM 的输出
cn 表示句子的最后一个单词的细胞状态；所以 cn 与句子长度 seq_len 无关
其中 output[:, -1, :] = hn[-1,:,:]
"""
output, (hn, cn) = lstm(input)


print(f"output.shape = {output.shape}")   # torch.Size([4, 3, 10])
print(f"hn.shape = {hn.shape}")           # torch.Size([2, 4, 10])
print(f"cn.shape = {cn.shape}")           # torch.Size([2, 4, 10])

print(f"output = {output}")
print(f"hn = {hn}")
print(f"output[:, -1, :] = {output[:, -1, :]}")
print(f"hn[-1,:,:] = {hn[-1,:,:]}")


"""
output = tensor([[[ 0.0447,  0.0111,  0.0292,  0.0692, -0.0547, -0.0120, -0.0202, -0.0243,  0.1216,  0.0643],
                  [ 0.0780,  0.0279,  0.0231,  0.1061, -0.0819, -0.0027, -0.0269, -0.0509,  0.1800,  0.0921],
                  [ 0.0993,  0.0160,  0.0516,  0.1402, -0.1146, -0.0177, -0.0607, -0.0715,  0.2110,  0.0954]],

                 [[ 0.0542, -0.0053,  0.0415,  0.0899, -0.0561, -0.0376, -0.0327, -0.0276,  0.1159,  0.0545],
                  [ 0.0819, -0.0015,  0.0640,  0.1263, -0.1021, -0.0502, -0.0495, -0.0464,  0.1814,  0.0750],
                  [ 0.0914,  0.0034,  0.0558,  0.1418, -0.1327, -0.0643, -0.0616, -0.0674,  0.2195,  0.0886]],

                 [[ 0.0552, -0.0006,  0.0351,  0.0864, -0.0486, -0.0192, -0.0305, -0.0289,  0.1103,  0.0554],
                  [ 0.0835, -0.0099,  0.0415,  0.1396, -0.0758, -0.0829, -0.0616, -0.0604,  0.1740,  0.0828],
                  [ 0.1202, -0.0113,  0.0570,  0.1608, -0.0836, -0.0801, -0.0792, -0.0874,  0.1923,  0.0829]],

                 [[ 0.0115, -0.0026,  0.0267,  0.0747, -0.0867, -0.0250, -0.0199, -0.0154,  0.1158,  0.0649],
                  [ 0.0628,  0.0003,  0.0297,  0.1191, -0.1028, -0.0342, -0.0509, -0.0496,  0.1759,  0.0831],
                  [ 0.0569,  0.0105,  0.0158,  0.1300, -0.1367, -0.0207, -0.0514, -0.0629,  0.2029,  0.1042]]], grad_fn=<TransposeBackward0>)
            
hn = tensor([[[-0.1933, -0.0058, -0.1237,  0.0348, -0.1394,  0.2403,  0.1591, -0.1143,  0.1211, -0.1971],
              [-0.2387,  0.0433, -0.0296,  0.0877, -0.1198,  0.1919,  0.0832, 0.0738,  0.1907, -0.1807],
              [-0.2174,  0.0721, -0.0447,  0.1081, -0.0520,  0.2519,  0.4040, -0.0033,  0.1378, -0.2930],
              [-0.2130, -0.0404, -0.0588, -0.1346, -0.1865,  0.1032, -0.0269, 0.0265, -0.0664, -0.1800]],

             [[ 0.0993,  0.0160,  0.0516,  0.1402, -0.1146, -0.0177, -0.0607, -0.0715,  0.2110,  0.0954],
              [ 0.0914,  0.0034,  0.0558,  0.1418, -0.1327, -0.0643, -0.0616, -0.0674,  0.2195,  0.0886],
              [ 0.1202, -0.0113,  0.0570,  0.1608, -0.0836, -0.0801, -0.0792, -0.0874,  0.1923,  0.0829],
              [ 0.0569,  0.0105,  0.0158,  0.1300, -0.1367, -0.0207, -0.0514, -0.0629,  0.2029,  0.1042]]], grad_fn=<StackBackward0>)


验证 output[:, -1, :] = hn[-1,:,:]
output[:, -1, :] = tensor([[ 0.0993,  0.0160,  0.0516,  0.1402, -0.1146, -0.0177, -0.0607, -0.0715,0.2110,  0.0954],
                           [ 0.0914,  0.0034,  0.0558,  0.1418, -0.1327, -0.0643, -0.0616, -0.0674,  0.2195,  0.0886],
                           [ 0.1202, -0.0113,  0.0570,  0.1608, -0.0836, -0.0801, -0.0792, -0.0874, 0.1923,  0.0829],
                           [ 0.0569,  0.0105,  0.0158,  0.1300, -0.1367, -0.0207, -0.0514, -0.0629, 0.2029,  0.1042]], grad_fn=<SliceBackward0>)
          
hn[-1,:,:] = tensor([[ 0.0993,  0.0160,  0.0516,  0.1402, -0.1146, -0.0177, -0.0607, -0.0715,0.2110,  0.0954],
                     [ 0.0914,  0.0034,  0.0558,  0.1418, -0.1327, -0.0643, -0.0616, -0.0674,  0.2195,  0.0886],
                     [ 0.1202, -0.0113,  0.0570,  0.1608, -0.0836, -0.0801, -0.0792, -0.0874,  0.1923,  0.0829],
                     [ 0.0569,  0.0105,  0.0158,  0.1300, -0.1367, -0.0207, -0.0514, -0.0629, 0.2029,  0.1042]], grad_fn=<SliceBackward0>)
"""