1. to_scalar()

 1 import torch
 2 import torch.autograd as autograd
 3 import torch.nn as nn
 4 import torch.optim as optim
 5 from torch.autograd import Variable
 6 
 7 torch.manual_seed(1234)
 8 
 9 def to_scalar(var):
10     return var.view(-1).data.tolist()[0]
11 
12 a = Variable(torch.LongTensor([[1, 3, 5], [2, 4, 5]]))
 
13 print(a)
14 ‘‘‘
15 Variable containing:
16  1  3  5
17  2  4  5
18 [torch.LongTensor of size 2x3]
19 ‘‘‘
20 print(a.view(-1))
21 ‘‘‘
22 Variable containing:
23  1
24  3
25  5
26  2
27  4
28  5
29 [torch.LongTensor of size 6]
30 ‘‘‘
31 print(a.view(-1).data)
32 ‘‘‘
33  1
34  3
35  5
36  2
37  4
38  5
 
39 [torch.LongTensor of size 6]
40 ‘‘‘
41 print(a.view(-1).data.tolist())
42 ‘‘‘
43 [1, 3, 5, 2, 4, 5]
44 ‘‘‘
45 print(a.view(-1).data.tolist()[0])      # 1
46 a = to_scalar(a)
47 print(a)                                # 1

2.

Bi-LSTM+CRF函數分解