从一维张量中提取前 k 个值索引

Question

给定 Torch 中的一维张量 (torch.Tensor)，其中包含可以比较的值（比如浮点数），我们如何提取 top-k[ 的索引=16=] 该张量中的值？

除了暴力方法，我正在寻找一些 API 调用，Torch/lua 提供，可以有效地执行此任务。

Answer 1

只需遍历张量和运行你的比较：

require 'torch'

data = torch.Tensor({1,2,3,4,505,6,7,8,9,10,11,12})
idx  = 1
max  = data[1]

for i=1,data:size()[1] do
   if data[i]>max then
      max=data[i]
      idx=i
   end
end

print(idx,max)

--编辑-- 回应您的编辑：使用此处记录的 torch.max 操作：https://github.com/torch/torch7/blob/master/doc/maths.md#torchmaxresval-resind-x-dim ...

y, i = torch.max(x, 1) returns the largest element in each column (across rows) of x, and a Tensor i of their corresponding indices in x

Answer 2

截至拉取请求 #496 Torch now includes a built-in API named torch.topk。示例：

> t = torch.Tensor{9, 1, 8, 2, 7, 3, 6, 4, 5}

-- obtain the 3 smallest elements
> res = t:topk(3)
> print(res)
 1
 2
 3
[torch.DoubleTensor of size 3]

-- you can also get the indices in addition
> res, ind = t:topk(3)
> print(ind)
 2
 4
 6
[torch.LongTensor of size 3]

-- alternatively you can obtain the k largest elements as follow
-- (see the API documentation for more details)
> res = t:topk(3, true)
> print(res)
 9
 8
 7
[torch.DoubleTensor of size 3]

在撰写本文时，CPU 实现遵循 sort and narrow approach (there are plans to improve it in the future). That being said an optimized GPU implementation for cutorch is currently being reviewed。

Answer 3

您可以使用topk函数。

例如：

import torch

t = torch.tensor([5.7, 1.4, 9.5, 1.6, 6.1, 4.3])

values,indices = t.topk(2)

print(values)
print(indices)

结果：

tensor([9.5000, 6.1000])
tensor([2, 4])

从一维张量中提取前 k 个值索引

Extracting the top-k value-indices from a 1-D Tensor

python

lua

torch

pytorch