Computes the mean of elements across dimensions of a tensor.
meridian
.
backend
.
reduce_mean
(
input_tensor
,
axis
=
None
,
keepdims
=
False
,
name
=
None
)
Reduces input_tensor
along the dimensions given in axis
by computing the
mean of elements across the dimensions 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:
>>>
x
=
tf
.
constant
([[
1.
,
1.
],
[
2.
,
2.
]])
>>>
tf
.
reduce_mean
(
x
)
< tf
.
Tensor
:
shape
=
(),
dtype
=
float32
,
numpy
=
1.5
>
>>>
tf
.
reduce_mean
(
x
,
0
)
< tf
.
Tensor
:
shape
=
(
2
,),
dtype
=
float32
,
numpy
=
array
([
1.5
,
1.5
],
dtype
=
float32
)
>
>>>
tf
.
reduce_mean
(
x
,
1
)
< tf
.
Tensor
:
shape
=
(
2
,),
dtype
=
float32
,
numpy
=
array
([
1.
,
2.
],
dtype
=
float32
)
>
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.mean
Please note that np.mean
has a dtype
parameter that could be used to
specify the output type. By default this is dtype=float64
. On the other
hand, tf.reduce_mean
has an aggressive type inference from input_tensor
,
for example:
>>>
x
=
tf
.
constant
([
1
,
0
,
1
,
0
])
>>>
tf
.
reduce_mean
(
x
)
< tf
.
Tensor
:
shape
=
(),
dtype
=
int32
,
numpy
=
0
>
>>>
y
=
tf
.
constant
([
1.
,
0.
,
1.
,
0.
])
>>>
tf
.
reduce_mean
(
y
)
< tf
.
Tensor
:
shape
=
(),
dtype
=
float32
,
numpy
=
0.5
>
