Concatenates tensors along one dimension.
meridian
.
backend
.
concatenate
(
values
,
axis
,
name
=
'concat'
)
See also tf.tile
, tf.stack
, tf.repeat
.
Concatenates the list of tensors values
along dimension axis
. If values[i].shape = [D0, D1, ... Daxis(i), ...Dn]
, the concatenated
result has shape
[D0, D1, ... Raxis, ...Dn]
where
Raxis = sum(Daxis(i))
That is, the data from the input tensors is joined along the axis
dimension.
The number of dimensions of the input tensors must match, and all dimensions
except the axis
must be equal.
For example:
>>>
t1
=
[[
1
,
2
,
3
],
[
4
,
5
,
6
]]
>>>
t2
=
[[
7
,
8
,
9
],
[
10
,
11
,
12
]]
>>>
tf
.
concat
([
t1
,
t2
],
0
)
< tf
.
Tensor
:
shape
=(
4
,
3
),
dtype
=
int32
,
numpy
=
array
([[
1
,
2
,
3
],
[
4
,
5
,
6
],
[
7
,
8
,
9
],
[
10
,
11
,
12
]],
dtype
=
int32
)
>
>>>
tf
.
concat
([
t1
,
t2
],
1
)
< tf
.
Tensor
:
shape
=(
2
,
6
),
dtype
=
int32
,
numpy
=
array
([[
1
,
2
,
3
,
7
,
8
,
9
],
[
4
,
5
,
6
,
10
,
11
,
12
]],
dtype
=
int32
)
>
As in Python, the axis
could also be negative numbers. Negative axis
are interpreted as counting from the end of the rank, i.e., axis + rank(values)
-th dimension.
For example:
>>>
t1
=
[[[
1
,
2
],
[
2
,
3
]],
[[
4
,
4
],
[
5
,
3
]]]
>>>
t2
=
[[[
7
,
4
],
[
8
,
4
]],
[[
2
,
10
],
[
15
,
11
]]]
>>>
tf
.
concat
([
t1
,
t2
],
-
1
)
< tf
.
Tensor
:
shape
=(
2
,
2
,
4
),
dtype
=
int32
,
numpy
=
array
([[[
1
,
2
,
7
,
4
],
[
2
,
3
,
8
,
4
]],
[[
4
,
4
,
2
,
10
],
[
5
,
3
,
15
,
11
]]],
dtype
=
int32
)
>
tf
.
concat
([
tf
.
expand_dims
(
t
,
axis
)
for
t
in
tensors
],
axis
)
can be rewritten as
tf
.
stack
(
tensors
,
axis
=
axis
)
Args
0-D
int32
Tensor
. Dimension along which to concatenate. Must be
in the range [-rank(values), rank(values))
. As in Python, indexing for
axis is 0-based. Positive axis in the rage of [0, rank(values))
refers
to axis
-th dimension. And negative axis refers to axis +
rank(values)
-th dimension.
Returns
A
Tensor
resulting from concatenation of the input tensors.