因为自己之前对于squeeze 以及unsqueeze应用较多，这里不再赘述，只给一个简单的例子
>>> import torch
>>> a=torch.randn(2,1,10)
>>> a
tensor([[[ 2.0138, 0.5330, 0.1697, -2.1840, 1.1781, -0.2538, -1.9618,
2.5919, -0.1698, 0.7177]],
[[ 1.2393, 0.8537, -0.1364, 0.2114, -0.4427, 0.7169, -0.0189,
-2.8338, 1.0929, 0.5666]]])
>>> print(a.shape)
torch.Size([2, 1, 10])
>>> a.squeeze()###这里就是默认压缩维数是1的维度
tensor([[ 2.0138, 0.5330, 0.1697, -2.1840, 1.1781, -0.2538, -1.9618, 2.5919,
-0.1698, 0.7177],
[ 1.2393, 0.8537, -0.1364, 0.2114, -0.4427, 0.7169, -0.0189, -2.8338,
1.0929, 0.5666]])
>>> b = a.squeeze()
>>> b
tensor([[ 2.0138, 0.5330, 0.1697, -2.1840, 1.1781, -0.2538, -1.9618, 2.5919,
-0.1698, 0.7177],
[ 1.2393, 0.8537, -0.1364, 0.2114, -0.4427, 0.7169, -0.0189, -2.8338,
1.0929, 0.5666]])
>>> b.shape
torch.Size([2, 10])
>>> b.unsqueeze()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: unsqueeze() missing 1 required positional arguments: "dim"
>>> b.unsqueeze(0)###在第0维增加维数1
tensor([[[ 2.0138, 0.5330, 0.1697, -2.1840, 1.1781, -0.2538, -1.9618,
2.5919, -0.1698, 0.7177],
[ 1.2393, 0.8537, -0.1364, 0.2114, -0.4427, 0.7169, -0.0189,
-2.8338, 1.0929, 0.5666]]])
>>> b1 = b.unsqueeze()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: unsqueeze() missing 1 required positional arguments: "dim"
>>> b1 = b.unsqueeze(0)
>>> b1.size
<built-in method size of Tensor object at 0x7fda5d4e03a8>
>>> b1.shape
torch.Size([1, 2, 10])
>>> b2 = b.unsqueeze(1)
>>> b2.shape
torch.Size([2, 1, 10])
>>> b2 = b.unsqueeze(2)
>>> b2.shape
torch.Size([2, 10, 1])
>>>
- 接下来看下expand~的用法
expand()
这个函数的作用就是对指定的维度进行数值大小的改变。只能改变维大小为1的维，否则就会报错。不改变的维可以传入-1或者原来的数值。
torch.Tensor.expand(*sizes) → Tensor
expand(*sizes) → Tensor
Returns a new view of the self tensor with singleton dimensions expanded to a larger size.
**Passing -1 as the size for a dimension means not changing the size of that dimension.**
Tensor can be also expanded to a larger number of dimensions, and the new ones will be appended at the front. For the new dimensions, the size cannot be set to -1.
Expanding a tensor does not allocate new memory, but only creates a new view on the existing tensor where a dimension of size one is expanded to a larger size by setting the stride to 0. Any dimension of size 1 can be expanded to an arbitrary value without allocating new memory.
Parameters
*sizes (torch.Size or int...) – the desired expanded size
下面给出例子
>>> x = torch.tensor([[1], [2], [3]])
>>> x
tensor([[1],
[2],
[3]])
>>> x.shape
torch.Size([3, 1])
>>> x.expand(3,3)
tensor([[1, 1, 1],
[2, 2, 2],
[3, 3, 3]])
>>> x.expand(3,10)
tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
[3, 3, 3, 3, 3, 3, 3, 3, 3, 3]])
>>> x.expand(-1,5)
tensor([[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 3]])
>>> x.expand(6,5)##注意奥，expand只可以在维数是1 的维数扩展，不可以在其他不是1 的维度上扩展
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
RuntimeError: The expanded size of the tensor (6) must match the existing size (3) at non-singleton dimension 0. Target sizes: [6, 5]. Tensor sizes: [3, 1]
>>>
ok
完毕，发现学习代码的时候，直接test好方便奥