參考鏈接

因?yàn)樽约褐皩τ趕queeze 以及unsqueeze應(yīng)用較多，這里不再贅述，只給一個(gè)簡單的例子

>>> 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()###這里就是默認(rèn)壓縮維數(shù)是1的維度 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維增加維數(shù)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]) >>>

• 接下來看下expand~的用法
  expand()
  這個(gè)函數(shù)的作用就是對指定的維度進(jìn)行數(shù)值大小的改變。只能改變維大小為1的維，否則就會報(bào)錯(cuò)。不改變的維可以傳入-1或者原來的數(shù)值。

torch.Tensor.expand(*sizes) → Tensor

pytorch官方文檔

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

下面給出例子

>>> 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)##注意奧，expand只可以在維數(shù)是1 的維數(shù)擴(kuò)展，不可以在其他不是1 的維度上擴(kuò)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
完畢，發(fā)現(xiàn)學(xué)習(xí)代碼的時(shí)候，直接test好方便奧

總結(jié)

以上是生活随笔為你收集整理的关于squeeze unsqueeze 以及expand的学习的全部內(nèi)容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯(cuò)，歡迎將生活随笔推薦給好友。