1. Tensor 歸并運算函數(shù)

此類操作會使輸出形狀小于輸入形狀，并可以沿著某一維度進行指定操作，如加法， 既可以計算整個 tensor 的和，也可以計算 tensor 每一行或者 每一列的和，

常用歸并操作如下表所示：

2. 使用示例

2.1 torch.mean

In [1]: import torch as tIn [11]: a = t.Tensor([[2,4], [1, 3]])In [12]: a
Out[12]: 
tensor([[2., 4.],[1., 3.]])In [13]: t.mean(a)
Out[13]: tensor(2.5000)In [14]: t.mean(a,dim=0)
Out[14]: tensor([1.5000, 3.5000])In [15]: t.mean(a,dim=1)
Out[15]: tensor([3., 2.])

2.2 torch.sum

In [1]: import torch as tIn [11]: a = t.Tensor([[2,4], [1, 3]])In [12]: a
Out[12]: 
tensor([[2., 4.],[1., 3.]])In [16]: a.sum()
Out[16]: tensor(10.)In [17]: a.sum(dim=0)
Out[17]: tensor([3., 7.])In [18]: a.sum(dim=1)
Out[18]: tensor([6., 4.])In [19]: 

2.3 torch.median

In [1]: import torch as tIn [11]: a = t.Tensor([[2,4], [1, 3]])In [12]: a
Out[12]: 
tensor([[2., 4.],[1., 3.]])In [19]: a.median()
Out[19]: tensor(2.)In [20]: a.median(dim=0)
Out[20]: 
torch.return_types.median(
values=tensor([1., 3.]),
indices=tensor([1, 1]))In [21]: a.median(dim=1)
Out[21]: 
torch.return_types.median(
values=tensor([2., 1.]),
indices=tensor([0, 0]))

2.4 torch.mode

In [1]: import torch as tIn [11]: a = t.Tensor([[2,4], [1, 3]])In [12]: a
Out[12]: 
tensor([[2., 4.],[1., 3.]])In [29]: a.mode()
Out[29]: 
torch.return_types.mode(
values=tensor([2., 1.]),
indices=tensor([0, 0]))In [30]: a.mode(dim=0)
Out[30]: 
torch.return_types.mode(
values=tensor([1., 3.]),
indices=tensor([1, 1]))In [31]: a.mode(dim=1)
Out[31]: 
torch.return_types.mode(
values=tensor([2., 1.]),
indices=tensor([0, 0]))

2.5 torch.norm

In [1]: import torch as tIn [11]: a = t.Tensor([[2,4], [1, 3]])In [12]: a
Out[12]: 
tensor([[2., 4.],[1., 3.]])In [22]: a.norm()
Out[22]: tensor(5.4772)In [23]: a.norm(dim=0)
Out[23]: tensor([2.2361, 5.0000])In [24]: a.norm(dim=1)
Out[24]: tensor([4.4721, 3.1623])

2.6 torch.dist

In [1]: import torch as tIn [11]: a = t.Tensor([[2,4], [1, 3]])In [12]: a
Out[12]: 
tensor([[2., 4.],[1., 3.]])In [28]: a.dist(t.Tensor([1,2]))
Out[28]: tensor(2.4495)

2.7 torch.std

In [1]: import torch as tIn [11]: a = t.Tensor([[2,4], [1, 3]])In [12]: a
Out[12]: 
tensor([[2., 4.],[1., 3.]])In [32]: a.std()
Out[32]: tensor(1.2910)In [33]: a.std(dim=0)
Out[33]: tensor([0.7071, 0.7071])In [34]: a.std(dim=1)
Out[34]: tensor([1.4142, 1.4142])

2.8 torch.var

In [1]: import torch as tIn [11]: a = t.Tensor([[2,4], [1, 3]])In [12]: a
Out[12]: 
tensor([[2., 4.],[1., 3.]])In [35]: a.var()
Out[35]: tensor(1.6667)In [36]: a.var(dim=0)
Out[36]: tensor([0.5000, 0.5000])In [37]: a.var(dim=1)
Out[37]: tensor([2., 2.])

2.9 torch.cumsum

In [1]: import torch as tIn [11]: a = t.Tensor([[2,4], [1, 3]])In [12]: a
Out[12]: 
tensor([[2., 4.],[1., 3.]])In [39]: a.cumsum(dim=0)
Out[39]: 
tensor([[2., 4.],[3., 7.]])In [40]: a.cumsum(dim=1)
Out[40]: 
tensor([[2., 6.],[1., 4.]])

2.10 torch.cumprod

In [1]: import torch as tIn [11]: a = t.Tensor([[2,4], [1, 3]])In [12]: a
Out[12]: 
tensor([[2., 4.],[1., 3.]])In [41]: a.cumprod(dim=0)
Out[41]: 
tensor([[ 2.,  4.],[ 2., 12.]])In [42]: a.cumprod(dim=1)
Out[42]: 
tensor([[2., 8.],[1., 3.]])

總結

以上是生活随笔為你收集整理的PyTorch 笔记（07）— Tensor 的归并运算（torch.mean、sum、median、mode、norm、dist、std、var、cumsum、cumprod）的全部內容，希望文章能夠幫你解決所遇到的問題。

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