16×3与3×16×3区别_4*3与3*4有什么区别-优快云博客

本文通过具体示例展示了如何使用PyTorch进行张量的创建与修改操作，包括初始化张量、批量操作及索引赋值等常用方法。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

import torch
a=[[0.000,-26.109,0.000],[26.031,86.410,1.000],
   [50.608,75.701,1.000],[71.730,87.567,1.000],
   [49.170,89.006,1.000],[23.139,94.790,1.000],
   [61.169,81.634,1.000],[98.050,58.777,1.000],
   [96.885,56.611,1.000],[116.569,53.422,1.000],
   [131.312,13.805,1.000],[111.644,34.630,1.000],
   [93.711,49.091,1.000],[102.388,68.462,1.000],
   [110.784,88.427,1.000],[139.129,89.584,1.000]]
   # [[0.000,-26.109,0.000],[26.031,86.410,1.000],
   # [50.608,75.701,1.000],[71.730,87.567,1.000],
   # [49.170,89.006,1.000],[23.139,94.790,1.000],
   # [61.169,81.634,1.000],[98.050,58.777,1.000],
   # [96.885,56.611,1.000],[116.569,53.422,1.000],
   # [131.312,13.805,1.000],[111.644,34.630,1.000],
   # [93.711,49.091,1.000],[102.388,68.462,1.000],
   # [110.784,88.427,1.000],[139.129,89.584,1.000]],
   # [[0.000,-26.109,0.000],[26.031,86.410,1.000],
   # [50.608,75.701,1.000],[71.730,87.567,1.000],
   # [49.170,89.006,1.000],[23.139,94.790,1.000],
   # [61.169,81.634,1.000],[98.050,58.777,1.000],
   # [96.885,56.611,1.000],[116.569,53.422,1.000],
   # [131.312,13.805,1.000],[111.644,34.630,1.000],
   # [93.711,49.091,1.000],[102.388,68.462,1.000],
   # [110.784,88.427,1.000],[139.129,89.584,1.000]],]

b=torch.FloatTensor(a)
b[6]=0
b[7]=0
# # c=b[:,0:6,:]
# d=b[:,8:,]
# print(b.size())
print(b)

输出结果：
 0.0000  -26.1090    0.0000
  26.0310   86.4100    1.0000
  50.6080   75.7010    1.0000
  71.7300   87.5670    1.0000
  49.1700   89.0060    1.0000
  23.1390   94.7900    1.0000
   0.0000    0.0000    0.0000
   0.0000    0.0000    0.0000
  96.8850   56.6110    1.0000
 116.5690   53.4220    1.0000
 131.3120   13.8050    1.0000
 111.6440   34.6300    1.0000
  93.7110   49.0910    1.0000
 102.3880   68.4620    1.0000
 110.7840   88.4270    1.0000
 139.1290   89.5840    1.0000
[torch.FloatTensor of size 16x3]


另一种：

import torch
a=[[[0.000,-26.109,0.000],[26.031,86.410,1.000],
   [50.608,75.701,1.000],[71.730,87.567,1.000],
   [49.170,89.006,1.000],[23.139,94.790,1.000],
   [61.169,81.634,1.000],[98.050,58.777,1.000],
   [96.885,56.611,1.000],[116.569,53.422,1.000],
   [131.312,13.805,1.000],[111.644,34.630,1.000],
   [93.711,49.091,1.000],[102.388,68.462,1.000],
   [110.784,88.427,1.000],[139.129,89.584,1.000]],
   [[0.000,-26.109,0.000],[26.031,86.410,1.000],
   [50.608,75.701,1.000],[71.730,87.567,1.000],
   [49.170,89.006,1.000],[23.139,94.790,1.000],
   [61.169,81.634,1.000],[98.050,58.777,1.000],
   [96.885,56.611,1.000],[116.569,53.422,1.000],
   [131.312,13.805,1.000],[111.644,34.630,1.000],
   [93.711,49.091,1.000],[102.388,68.462,1.000],
   [110.784,88.427,1.000],[139.129,89.584,1.000]],
   [[0.000,-26.109,0.000],[26.031,86.410,1.000],
   [50.608,75.701,1.000],[71.730,87.567,1.000],
   [49.170,89.006,1.000],[23.139,94.790,1.000],
   [61.169,81.634,1.000],[98.050,58.777,1.000],
   [96.885,56.611,1.000],[116.569,53.422,1.000],
   [131.312,13.805,1.000],[111.644,34.630,1.000],
   [93.711,49.091,1.000],[102.388,68.462,1.000],
   [110.784,88.427,1.000],[139.129,89.584,1.000]]]

b=torch.FloatTensor(a)
b[:,6,:]=0
b[:,7,:]=0
# # c=b[:,0:6,:]
# d=b[:,8:,]
# print(b.size())
print(b)

输出结果：
(0 ,.,.) =
    0.0000 -26.1090    0.0000
   26.0310   86.4100    1.0000
   50.6080   75.7010    1.0000
   71.7300   87.5670    1.0000
   49.1700   89.0060    1.0000
   23.1390   94.7900    1.0000
    0.0000    0.0000    0.0000
    0.0000    0.0000    0.0000
   96.8850   56.6110    1.0000
116.5690   53.4220    1.0000
131.3120   13.8050    1.0000
111.6440   34.6300    1.0000
   93.7110   49.0910    1.0000
102.3880   68.4620    1.0000
110.7840   88.4270    1.0000
139.1290   89.5840    1.0000

(1 ,.,.) =
    0.0000 -26.1090    0.0000
   26.0310   86.4100    1.0000
   50.6080   75.7010    1.0000
   71.7300   87.5670    1.0000
   49.1700   89.0060    1.0000
   23.1390   94.7900    1.0000
    0.0000    0.0000    0.0000
    0.0000    0.0000    0.0000
   96.8850   56.6110    1.0000
116.5690   53.4220    1.0000
131.3120   13.8050    1.0000
111.6440   34.6300    1.0000
   93.7110   49.0910    1.0000
102.3880   68.4620    1.0000
110.7840   88.4270    1.0000
139.1290   89.5840    1.0000

(2 ,.,.) =
    0.0000 -26.1090    0.0000
   26.0310   86.4100    1.0000
   50.6080   75.7010    1.0000
   71.7300   87.5670    1.0000
   49.1700   89.0060    1.0000
   23.1390   94.7900    1.0000
    0.0000    0.0000    0.0000
    0.0000    0.0000    0.0000
   96.8850   56.6110    1.0000
116.5690   53.4220    1.0000
131.3120   13.8050    1.0000
111.6440   34.6300    1.0000
   93.7110   49.0910    1.0000
102.3880   68.4620    1.0000
110.7840   88.4270    1.0000
139.1290   89.5840    1.0000
[torch.FloatTensor of size 3x16x3]