PyTorch学习笔记之Variable_and_function_cat

application 1

 from torch.autograd import Variable
 import torch
 b = Variable(torch.FloatTensor([64, 100, 43]))
 print(b)
 '''
 Variable containing:
   64
  100
   43
 [torch.FloatTensor of size 3]
 '''

application 2

 from torch.autograd import Variable
 import torch

 a1 = Variable(torch.randn(64, 100, 43))
 # print(a)
 a2 = Variable(torch.randn(64, 100, 44))
 a3 = Variable(torch.randn(64, 100, 45))
 a = [a1, a2, a3]

 # method 1
 a = torch.cat((i.data for i in a), 2)
 # method 2
 a = torch.cat(a, 2)
 print(a)
 '''
 Variable containing:
 ( 0 ,.,.) =
  -8.0339e-01 -1.0054e+00 -3.8750e-01  ...  -8.1924e-01 -5.0174e-01  1.2302e+00
  -2.4266e+00  3.8993e-01  9.6254e-01  ...  -2.6002e-01  3.8668e-01 -1.0960e+00
  -9.8618e-01  1.5732e-01 -7.6385e-01  ...  -9.6085e-01  4.7323e-01  1.0353e+00
                  ...                   ⋱                   ...
 (63 ,.,.) =
  -8.9123e-01  8.9774e-01 -1.1528e+00  ...   2.8538e-01 -1.3932e+00 -3.7968e-01
   1.0256e+00  2.1746e+00  6.9736e-01  ...   1.2657e+00 -1.0258e+00  5.7128e-01
  -1.0966e+00  7.1878e-01  3.5268e-01  ...   3.5731e-01  1.9369e+00 -7.3994e-01
 [torch.FloatTensor of size 64x100x132]
 '''

function_cat()

 >>> x = torch.randn(2, 3)
 >>> x

          0.5983 -0.0341  2.4918
          1.5981 -0.5265 -0.8735
         [torch.FloatTensor of size 2x3]

 >>> torch.cat((x, x, x), 0)

          0.5983 -0.0341  2.4918
          1.5981 -0.5265 -0.8735
          0.5983 -0.0341  2.4918
          1.5981 -0.5265 -0.8735
          0.5983 -0.0341  2.4918
          1.5981 -0.5265 -0.8735
         [torch.FloatTensor of size 6x3]

 >>> torch.cat((x, x, x), 1)

          0.5983 -0.0341  2.4918  0.5983 -0.0341  2.4918  0.5983 -0.0341  2.4918
          1.5981 -0.5265 -0.8735  1.5981 -0.5265 -0.8735  1.5981 -0.5265 -0.8735
         [torch.FloatTensor of size 2x9]