Computes the tf.math.minimum
of elements across dimensions of a tensor.
meridian
.
backend
.
reduce_min
(
input_tensor
,
axis
=
None
,
keepdims
=
False
,
name
=
None
)
This is the reduction operation for the elementwise tf.math.minimum
op.
Reduces input_tensor
along the dimensions given in axis
.
Unless keepdims
is true, the rank of the tensor is reduced by 1 for each
of the entries in axis
, which must be unique. If keepdims
is true, the
reduced dimensions are retained with length 1.
If axis
is None, all dimensions are reduced, and a
tensor with a single element is returned.
For example:
>>>
a
=
tf
.
constant
([
...
[[
1
,
2
],
[
3
,
4
]],
...
[[
1
,
2
],
[
3
,
4
]]
...
])
>>>
tf
.
reduce_min
(
a
)
< tf
.
Tensor
:
shape
=
(),
dtype
=
int32
,
numpy
=
1
>
Choosing a specific axis returns minimum element in the given axis:
>>>
b
=
tf
.
constant
([[
1
,
2
,
3
],
[
4
,
5
,
6
]])
>>>
tf
.
reduce_min
(
b
,
axis
=
0
)
< tf
.
Tensor
:
shape
=
(
3
,),
dtype
=
int32
,
numpy
=
array
([
1
,
2
,
3
],
dtype
=
int32
)
>
>>>
tf
.
reduce_min
(
b
,
axis
=
1
)
< tf
.
Tensor
:
shape
=
(
2
,),
dtype
=
int32
,
numpy
=
array
([
1
,
4
],
dtype
=
int32
)
>
Setting keepdims
to True
retains the dimension of input_tensor
:
>>>
tf
.
reduce_min
(
a
,
keepdims
=
True
)
< tf
.
Tensor
:
shape
=(
1
,
1
,
1
),
dtype
=
int32
,
numpy
=
array
([[[
1
]]],
dtype
=
int32
)
>
>>>
tf
.
math
.
reduce_min
(
a
,
axis
=
0
,
keepdims
=
True
)
< tf
.
Tensor
:
shape
=(
1
,
2
,
2
),
dtype
=
int32
,
numpy
=
array
([[[
1
,
2
],
[
3
,
4
]]],
dtype
=
int32
)
>
Args
The dimensions to reduce. If
None
(the default), reduces all
dimensions. Must be in the range [-rank(input_tensor),
rank(input_tensor))
.
Returns
The reduced tensor.
numpy compatibility
Equivalent to np.min
