Pytorch-LSTM

1.nn.LSTM

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

參數：

• input_size：輸入特征的維度， 一般rnn中輸入的是詞向量，那么 input_size 就等於一個詞向量的維度，即feature_len；
• hidden_size：隱藏層神經元個數，或者也叫輸出的維度（因為rnn輸出為各個時間步上的隱藏狀態）；
• num_layers：網絡的層數；

1.2out, (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

lstm = nn.LSTM(input_size=100, hidden_size=20, num_layers=4)   #4層的LSTM,輸入的每個詞用100維向量表示,隱藏單元和記憶單元的尺寸是20

x = torch.randn(10, 3, 100)    #3句話，每句10個單詞，每個單詞表示為長100的向量
out, (h, c) = lstm(x)          #不傳入h_0和c_0則會默認初始化
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與nn.LSTM的區別和nn.RNN與nn.RNNCell的區別一樣。

2.1nn.LSTMCell()

初始化方法和上面一樣。

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

• x_t：[batch, feature_len]表示t時刻的輸入
• h_t-1, c_t-1：[batch, hidden_len] t-1時刻本層的隱藏單元和記憶單元

#單層LSTM
import torch
from torch import nn

cell = nn.LSTMCell(input_size=100, hidden_size=20)       #1層的LSTM，輸入的每個詞用100維向量表示，隱藏單元和記憶單元的尺寸是20

h = torch.zeros(3, 20)                                   #初始化隱藏單元h和記憶單元c,取batch=3
c = torch.zeros(3, 20)

x = [torch.randn(3, 100) for _ in range(10)]             #seq_len=10個時刻的輸入,每個時刻shape都是[batch,feature_len]

for xt in x:                                             #對每個時刻,傳入輸入xt和上個時刻的h和c
    h, c = cell(xt, (h, c))

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


#兩層LSTM
cell_l0 = nn.LSTMCell(input_size=100, hidden_size=30)    #輸入的feature_len=100,變到該層隱藏單元和記憶單元hidden_len=30
cell_l1 = nn.LSTMCell(input_size=30, hidden_size=20)     #hidden_len從l0層的30變到這一層的20

h_l0 = torch.zeros(3, 30)           #分別初始化l0層和l1層的隱藏單元h和記憶單元C,取batch=3
C_l0 = torch.zeros(3, 30)

h_l1 = torch.zeros(3, 20)
C_l1 = torch.zeros(3, 20)

x = [torch.randn(3, 100) for _ in range(10)]             #seq_len=10個時刻的輸入,每個時刻shape都是[batch,feature_len]

for xt in x:
    h_l0, C_l0 = cell_l0(xt, (h_l0, C_l0))               #l0層接受xt輸入
    h_l1, C_l1 = cell_l1(h_l0, (h_l1, C_l1))             #l1層接受l0層的輸出h作為輸入

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])