目录:
1. 大于、大于等于、小于、小于等于、不相等 2. 最大值,最小值 3. 排序 4. topk
1. 大于、大于等于、小于、小于等于、不相等
python">
x = torch. Tensor( [ [ 2 , 3 , 5 ] , [ 4 , 7 , 9 ] ] )
y = torch. Tensor( [ [ 2 , 4 , 5 ] , [ 4 , 8 , 9 ] ] )
z = torch. Tensor( [ [ 2 , 3 , 5 ] , [ 4 , 7 , 9 ] ] )
print ( torch. eq( x, y) )
print ( torch. equal( x, z) )
print ( torch. equal( x, y) )
print ( torch. ge( x, y) )
print ( torch. gt( x, y) )
print ( torch. le( x, y) )
print ( torch. lt( x, y) )
print ( torch. ne( x, y) )
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
result:
tensor( [ [ True , False , True ] ,
[ True , False , True ] ] )
True
False
tensor( [ [ True , False , True ] ,
[ True , False , True ] ] )
tensor( [ [ False , False , False ] ,
[ False , False , False ] ] )
tensor( [ [ True , True , True ] ,
[ True , True , True ] ] )
tensor( [ [ False , True , False ] ,
[ False , True , False ] ] )
tensor( [ [ False , True , False ] ,
[ False , True , False ] ] )
2. 最大值,最小值
python">x = torch. Tensor( [ [ 2 , 3 , 5 ] , [ 4 , 7 , 9 ] ] )
print ( torch. max ( x) )
print ( torch. max ( x, dim= 1 ) )
print ( torch. min ( x) )
print ( torch. min ( x, dim= 0 ) )
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
result:
tensor( 9 . )
torch. return_types. max (
values= tensor( [ 5 . , 9 . ] ) ,
indices= tensor( [ 2 , 2 ] ) )
tensor( 2 . )
torch. return_types. min (
values= tensor( [ 2 . , 3 . , 5 . ] ) ,
indices= tensor( [ 0 , 0 , 0 ] ) )
3. 排序
python">x = torch. Tensor( [ [ 10 , 3 , 5 ] , [ 4 , 20 , 9 ] ] )
print ( torch. sort( x) )
print ( torch. sort( x, descending= True ) )
print ( torch. sort( x, dim= 0 , descending= True ) )
print ( torch. sort( x, dim= 0 , descending= False ) )
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
result:
torch. return_types. sort(
values= tensor( [ [ 3 . , 5 . , 10 . ] ,
[ 4 . , 9 . , 20 . ] ] ) ,
indices= tensor( [ [ 1 , 2 , 0 ] ,
[ 0 , 2 , 1 ] ] ) )
- - - - - - - - - - - - - -
torch. return_types. sort(
values= tensor( [ [ 10 . , 5 . , 3 . ] ,
[ 20 . , 9 . , 4 . ] ] ) ,
indices= tensor( [ [ 0 , 2 , 1 ] ,
[ 1 , 2 , 0 ] ] ) )
- - - - - - - - - - - - -
torch. return_types. sort(
values= tensor( [ [ 10 . , 20 . , 9 . ] ,
[ 4 . , 3 . , 5 . ] ] ) ,
indices= tensor( [ [ 0 , 1 , 1 ] ,
[ 1 , 0 , 0 ] ] ) )
- - - - - - - - - - - - - -
torch. return_types. sort(
values= tensor( [ [ 4 . , 3 . , 5 . ] ,
[ 10 . , 20 . , 9 . ] ] ) ,
indices= tensor( [ [ 1 , 0 , 0 ] ,
[ 0 , 1 , 1 ] ] ) )
4. topk
python">
x = torch. Tensor( [ [ 10 , 3 , 5 ] , [ 4 , 20 , 9 ] ] )
print ( torch. topk( x, k= 2 ) )
print ( torch. topk( x, k= 2 , largest= False ) )
print ( torch. topk( x, k= 2 , dim= 0 ) )
print ( torch. topk( x, k= 2 , dim= 1 ) )
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
result:
torch. return_types. topk(
values= tensor( [ [ 10 . , 5 . ] ,
[ 20 . , 9 . ] ] ) ,
indices= tensor( [ [ 0 , 2 ] ,
[ 1 , 2 ] ] ) )
torch. return_types. topk(
values= tensor( [ [ 3 . , 5 . ] ,
[ 4 . , 9 . ] ] ) ,
indices= tensor( [ [ 1 , 2 ] ,
[ 0 , 2 ] ] ) )
torch. return_types. topk(
values= tensor( [ [ 10 . , 20 . , 9 . ] ,
[ 4 . , 3 . , 5 . ] ] ) ,
indices= tensor( [ [ 0 , 1 , 1 ] ,
[ 1 , 0 , 0 ] ] ) )
torch. return_types. topk(
values= tensor( [ [ 10 . , 5 . ] ,
[ 20 . , 9 . ] ] ) ,
indices= tensor( [ [ 0 , 2 ] ,
[ 1 , 2 ] ] ) )