pytorch的squeeze、cat函数

最新推荐文章于 2025-04-12 11:26:12 发布

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最新推荐文章于 2025-04-12 11:26:12 发布

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本文链接：https://blog.youkuaiyun.com/abc781cba/article/details/79663190

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本文介绍了PyTorch中的squeeze函数，用于去除大小为1的维度，以及cat函数的使用，用于将多个张量沿特定维度连接起来。分别通过行、列和不变的实例展示了squeeze的效果，并对cat函数进行了说明。

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1 squeeze(): 去除size为1的维度，包括行和列。至于维度大于等于2时，squeeze()不起作用。

行、例：

>>> torch.rand(4, 1, 3)

(0 ,.,.) =
  0.5391  0.8523  0.9260

(1 ,.,.) =
  0.2507  0.9512  0.6578

(2 ,.,.) =
  0.7302  0.3531  0.9442

(3 ,.,.) =
  0.2689  0.4367  0.6610
[torch.FloatTensor of size 4x1x3]

>>> torch.rand(4, 1, 3).squeeze()

 0.0801  0.4600  0.1799
 0.0236  0.7137  0.6128
 0.0242  0.3847  0.4546
 0.9004  0.5018  0.4021
[torch.FloatTensor of size 4x3]

列、例：

>>> torch.rand(4, 3, 1)

(0 ,.,.) =
  0.7013
  0.9818
  0.9723

(1 ,.,.) =
  0.9902
  0.8354
  0.3864

(2 ,.,.) =
  0.4620
  0.0844
  0.5707

(3 ,.,.) =
  0.5722
  0.2494
  0.5815
[torch.FloatTensor of size 4x3x1]

>>> torch.rand(4, 3, 1).squeeze()

 0.8784  0.6203  0.8213
 0.7238  0.5447  0.8253
 0.1719  0.7830  0.1046
 0.0233  0.9771  0.2278
[torch.FloatTensor of size 4x3]

不变、例：

>>> torch.rand(4, 3, 2)

(0 ,.,.) =
  0.6618  0.1678
  0.3476  0.0329
  0.1865  0.4349

(1 ,.,.) =
  0.7588  0.8972
  0.3339  0.8376
  0.6289  0.9456

(2 ,.,.) =
  0.1392  0.0320
  0.0033  0.0187
  0.8229  0.0005

(3 ,.,.) =
  0.2327  0.6264
  0.4810  0.6642
  0.8625  0.6334
[torch.FloatTensor of size 4x3x2]

>>> torch.rand(4, 3, 2).squeeze()

(0 ,.,.) =
  0.0593  0.8910
  0.9779  0.1530
  0.9210  0.2248

(1 ,.,.) =
  0.7938  0.9362
  0.1064  0.6630
  0.9321  0.0453

(2 ,.,.) =
  0.0189  0.9187
  0.4458  0.9925
  0.9928  0.7895

(3 ,.,.) =
  0.5116  0.7253
  0.0132  0.6673
  0.9410  0.8159
[torch.FloatTensor of size 4x3x2]

2 cat函数

>>> t1=torch.FloatTensor(torch.randn(2,3))
>>> t1

-1.9405  1.2009  0.0018
 0.9463  0.4409 -1.9017
[torch.FloatTensor of size 2x3]

>>> t2=torch.FloatTensor(torch.randn(2,2))
>>> t2

 0.0942  0.1581
 1.1621  1.2617
[torch.FloatTensor of size 2x2]

>>> torch.cat((t1, t2), 1)

-1.9405  1.2009  0.0018  0.0942  0.1581
 0.9463  0.4409 -1.9017  1.1621  1.2617
[torch.FloatTensor of size 2x5]