目錄
gather
torch.gather(input,dim,index,out=None)。對指定維進行索引。比如4*3的張量,對dim=1進行索引,那麼index的取值範圍就是0~2.
input是一個張量,index是索引張量。input和index的size要麼全部維度都相同,要麼指定的dim那一維度值不同。輸出爲和index大小相同的張量。
import torch
a=torch.tensor([[.1,.2,.3],
[1.1,1.2,1.3],
[2.1,2.2,2.3],
[3.1,3.2,3.3]])
b=torch.LongTensor([[1,2,1],
[2,2,2],
[2,2,2],
[1,1,0]])
b=b.view(4,3)
print(a.gather(1,b))
print(a.gather(0,b))
c=torch.LongTensor([1,2,0,1])
c=c.view(4,1)
print(a.gather(1,c))
輸出:
tensor([[ 0.2000, 0.3000, 0.2000],
[ 1.3000, 1.3000, 1.3000],
[ 2.3000, 2.3000, 2.3000],
[ 3.2000, 3.2000, 3.1000]])
tensor([[ 1.1000, 2.2000, 1.3000],
[ 2.1000, 2.2000, 2.3000],
[ 2.1000, 2.2000, 2.3000],
[ 1.1000, 1.2000, 0.3000]])
tensor([[ 0.2000],
[ 1.3000],
[ 2.1000],
[ 3.2000]])
squeeze
將維度爲1的壓縮掉。如size爲(3,1,1,2),壓縮之後爲(3,2)
import torch
a=torch.randn(2,1,1,3)
print(a)
print(a.squeeze())
輸出:
tensor([[[[-0.2320, 0.9513, 1.1613]]],
[[[ 0.0901, 0.9613, -0.9344]]]])
tensor([[-0.2320, 0.9513, 1.1613],
[ 0.0901, 0.9613, -0.9344]])
expand
擴展某個size爲1的維度。如(2,2,1)擴展爲(2,2,3)
import torch
x=torch.randn(2,2,1)
print(x)
y=x.expand(2,2,3)
print(y)
輸出:
tensor([[[ 0.0608],
[ 2.2106]],
[[-1.9287],
[ 0.8748]]])
tensor([[[ 0.0608, 0.0608, 0.0608],
[ 2.2106, 2.2106, 2.2106]],
[[-1.9287, -1.9287, -1.9287],
[ 0.8748, 0.8748, 0.8748]]])
sum
size爲(m,n,d)的張量,dim=1時,輸出爲size爲(m,d)的張量
import torch
a=torch.tensor([[[1,2,3],[4,8,12]],[[1,2,3],[4,8,12]]])
print(a.sum())
print(a.sum(dim=1))
輸出:
tensor(60)
tensor([[ 5, 10, 15],
[ 5, 10, 15]])
contiguous
返回一個內存爲連續的張量,如本身就是連續的,返回它自己。一般用在view()函數之前,因爲view()要求調用張量是連續的。可以通過is_contiguous查看張量內存是否連續。
import torch
a=torch.tensor([[[1,2,3],[4,8,12]],[[1,2,3],[4,8,12]]])
print(a.is_contiguous)
print(a.contiguous().view(4,3))
輸出:
<built-in method is_contiguous of Tensor object at 0x7f4b5e35afa0>
tensor([[ 1, 2, 3],
[ 4, 8, 12],
[ 1, 2, 3],
[ 4, 8, 12]])
softmax
假設數組V有C個元素。對其進行softmax等價於將V的每個元素的指數除以所有元素的指數之和。這會使值落在區間(0,1)上,並且和爲1。
import torch
import torch.nn.functional as F
a=torch.tensor([[1.,1],[2,1],[3,1],[1,2],[1,3]])
b=F.softmax(a,dim=1)
print(b)
輸出:
tensor([[ 0.5000, 0.5000],
[ 0.7311, 0.2689],
[ 0.8808, 0.1192],
[ 0.2689, 0.7311],
[ 0.1192, 0.8808]])
max
返回最大值,或指定維度的最大值以及index
import torch
a=torch.tensor([[.1,.2,.3],
[1.1,1.2,1.3],
[2.1,2.2,2.3],
[3.1,3.2,3.3]])
print(a.max(dim=1))
print(a.max())
輸出:
(tensor([ 0.3000, 1.3000, 2.3000, 3.3000]), tensor([ 2, 2, 2, 2]))
tensor(3.3000)
argmax
返回最大值的index
import torch
a=torch.tensor([[.1,.2,.3],
[1.1,1.2,1.3],
[2.1,2.2,2.3],
[3.1,3.2,3.3]])
print(a.argmax(dim=1))
print(a.argmax())
輸出:
tensor([ 2, 2, 2, 2])
tensor(11)