Splits a tensor value
into a list of sub tensors.
meridian
.
backend
.
split
(
value
,
num_or_size_splits
,
axis
=
0
,
num
=
None
,
name
=
'split'
)
See also tf.unstack
.
If num_or_size_splits
is an int
, then it splits value
along the
dimension axis
into num_or_size_splits
smaller tensors. This requires that value.shape[axis]
is divisible by num_or_size_splits
.
If num_or_size_splits
is a 1-D Tensor (or list), then value
is split into len(num_or_size_splits)
elements. The shape of the i
-th
element has the same size as the value
except along dimension axis
where
the size is num_or_size_splits[i]
.
For example:
>>>
x
=
tf
.
Variable
(
tf
.
random
.
uniform
(
[
5
,
30
]
,
-
1
,
1
))
>>>
>>>
# Split `x` into 3 tensors along dimension 1
>>>
s0
,
s1
,
s2
=
tf
.
split
(
x
,
num_or_size_splits
=
3
,
axis
=
1
)
>>>
tf
.
shape
(
s0
).
numpy
()
array
(
[
5
,
10
]
,
dtype
=
int32
)
>>>
>>>
# Split `x` into 3 tensors with sizes [4, 15, 11] along dimension 1
>>>
split0
,
split1
,
split2
=
tf
.
split
(
x
,
[
4
,
15
,
11
]
,
1
)
>>>
tf
.
shape
(
split0
).
numpy
()
array
(
[
5
,
4
]
,
dtype
=
int32
)
>>>
tf
.
shape
(
split1
).
numpy
()
array
(
[
5
,
15
]
,
dtype
=
int32
)
>>>
tf
.
shape
(
split2
).
numpy
()
array
(
[
5
,
11
]
,
dtype
=
int32
)
Args
Either an
int
indicating the number of splits
along axis
or a 1-D integer Tensor
or Python list containing the sizes
of each output tensor along axis
. If an int
, then it must evenly
divide value.shape[axis]
; otherwise the sum of sizes along the split
axis must match that of the value
.An
int
or scalar int32
Tensor
. The dimension along which
to split. Must be in the range [-rank(value), rank(value))
. Defaults to
0.Optional, an
int
, used to specify the number of outputs when it
cannot be inferred from the shape of size_splits
.
Returns
if
num_or_size_splits
is an int
returns a list of num_or_size_splits
Tensor
objects; if num_or_size_splits
is a 1-D
list or 1-D Tensor
returns num_or_size_splits.get_shape[0]
Tensor
objects resulting from splitting value
.
