LSTM详解
LSTM实现
笔记摘抄

1. nn.LSTM

1.1 lstm=nn.LSTM(input_size, hidden_size, num_layers)

lstm=nn.LSTM(input_size, hidden_size, num_layers)

参数：

• input_size：输入特征的维度， 一般rnn中输入的是词向量，那么 input_size 就等于一个词向量的维度，即feature_len;

• hidden_size：隐藏层神经元个数，或者也叫输出的维度（因为rnn输出为各个时间步上的隐藏状态）;

• num_layers：网络的层数;

1.2 out, (h_t, c_t) = lstm(x, [h_t0, c_t0])

• x：[seq_len, batch, feature_len]

• h/c：[num_layer, batch, hidden_len]

• out：[seq_len, batch, hidden_len]

import torch
from torch import nn

# 4层的LSTM,输入的每个词用100维向量表示,隐藏单元和记忆单元的尺寸是20
lstm = nn.LSTM(input_size=100, hidden_size=20, num_layers=4)

# 3句话，每句10个单词，每个单词的词向量维度(长度)100
x = torch.rand(10, 3, 100) 

# 不传入h_0和c_0则会默认初始化
out, (h, c) = lstm(x)

print(out.shape)     # torch.Size([10, 3, 20])
print(h.shape)       # torch.Size([4, 3, 20])
print(c.shape)       # torch.Size([4, 3, 20])

2. nn.LSTMCell

• nn.LSTMCell 与 nn.LSTM 的区别 和 nn.RNN 与 nn.RNNCell 的区别一样。

2.1 nn.LSTMCell()

• 初始化方法和上面一样。

2.2 h_t, c_t = lstmcell(x_t, [h_t-1, c_t-1])

• \(x_t\)：[batch, feature_len]表示t时刻的输入

• \(h_{t-1}, c_{t-1}\)：[batch, hidden_len]，\(t-1\)时刻本层的隐藏单元和记忆单元

多层LSTM类似下图：(下图是多层RNN)

import torch
from torch import nn

# 单层LSTM
# 1层的LSTM，输入的每个词用100维向量表示，隐藏单元和记忆单元的尺寸是20
cell = nn.LSTMCell(input_size=100, hidden_size=20)

# seq_len=10个时刻的输入,每个时刻shape都是[batch,feature_len]
# x = [torch.randn(3, 100) for _ in range(10)]
x = torch.randn(10, 3, 100)

# 初始化隐藏单元h和记忆单元c,取batch=3
h = torch.zeros(3, 20)
c = torch.zeros(3, 20)

# 对每个时刻,传入输入xt和上个时刻的h和c
for xt in x:
    b, c = cell(xt, (h, c))
    
print(b.shape)    # torch.Size([3, 20])
print(c.shape)    # torch.Size([3, 20]) 

# 两层LSTM
# 输入的feature_len=100,变到该层隐藏单元和记忆单元hidden_len=30
cell_L0 = nn.LSTMCell(input_size=100, hidden_size=30)
# hidden_len从L0层的30变到这一层的20
cell_L1 = nn.LSTMCell(input_size=30, hidden_size=20)     

# 分别初始化L0层和L1层的隐藏单元h 和 记忆单元C,取batch=3
h_L0 = torch.zeros(3, 30)
C_L0 = torch.zeros(3, 30)

h_L1 = torch.zeros(3, 20)
C_L1 = torch.zeros(3, 20)

x = torch.randn(10, 3, 100)

for xt in x:
    h_L0, C_L0 = cell_L0(xt, (h_L0, C_L0))   # L0层接受xt输入
    h_L1, C_L1 = cell_L1(h_L0, (h_L1, C_L1)) # L1层接受L0层的输出h作为输入
    
print(h_L0.shape, C_L0.shape)   # torch.Size([3, 30]) torch.Size([3, 30]) 
print(h_L1.shape, C_L1.shape)   # torch.Size([3, 20]) torch.Size([3, 20])

标签：20,入门,nn,torch,Pytorch,L0,LSTM,size
来源： https://www.cnblogs.com/douzujun/p/13373156.html