Reshapes a tensor.
meridian
.
backend
.
reshape
(
tensor
,
shape
,
name
=
None
)
Given tensor
, this operation returns a new tf.Tensor
that has the same
values as tensor
in the same order, except with a new shape given by shape
.
>>>
t1
=
[[
1
,
2
,
3
],
...
[
4
,
5
,
6
]]
>>>
print
(
tf
.
shape
(
t1
).
numpy
())
[
2
3
]
>>>
t2
=
tf
.
reshape
(
t1
,
[
6
])
>>>
t2
< tf
.
Tensor
:
shape
=(
6
,),
dtype
=
int32
,
numpy
=
array
([
1
,
2
,
3
,
4
,
5
,
6
],
dtype
=
int32
)
>
>>>
tf
.
reshape
(
t2
,
[
3
,
2
])
< tf
.
Tensor
:
shape
=(
3
,
2
),
dtype
=
int32
,
numpy
=
array
([[
1
,
2
],
[
3
,
4
],
[
5
,
6
]],
dtype
=
int32
)
>
The tf.reshape
does not change the order of or the total number of elements
in the tensor, and so it can reuse the underlying data buffer. This makes it
a fast operation independent of how big of a tensor it is operating on.
>>> tf.reshape([1, 2, 3], [2, 2])
Traceback (most recent call last):
...
InvalidArgumentError: Input to reshape is a tensor with 3 values, but the
requested shape has 4
To instead reorder the data to rearrange the dimensions of a tensor, see tf.transpose
.
>>>
t
=
[[
1
,
2
,
3
],
...
[
4
,
5
,
6
]]
>>>
tf
.
reshape
(
t
,
[
3
,
2
]).
numpy
()
array
([[
1
,
2
],
[
3
,
4
],
[
5
,
6
]],
dtype
=
int32
)
>>>
tf
.
transpose
(
t
,
perm
=[
1
,
0
]).
numpy
()
array
([[
1
,
4
],
[
2
,
5
],
[
3
,
6
]],
dtype
=
int32
)
If one component of shape
is the special value -1, the size of that
dimension is computed so that the total size remains constant. In particular,
a shape
of [-1]
flattens into 1-D. At most one component of shape
can
be -1.
>>>
t
=
[[
1
,
2
,
3
],
...
[
4
,
5
,
6
]]
>>>
tf
.
reshape
(
t
,
[
-
1
])
< tf
.
Tensor
:
shape
=(
6
,),
dtype
=
int32
,
numpy
=
array
([
1
,
2
,
3
,
4
,
5
,
6
],
dtype
=
int32
)
>
>>>
tf
.
reshape
(
t
,
[
3
,
-
1
])
< tf
.
Tensor
:
shape
=(
3
,
2
),
dtype
=
int32
,
numpy
=
array
([[
1
,
2
],
[
3
,
4
],
[
5
,
6
]],
dtype
=
int32
)
>
>>>
tf
.
reshape
(
t
,
[
-
1
,
2
])
< tf
.
Tensor
:
shape
=(
3
,
2
),
dtype
=
int32
,
numpy
=
array
([[
1
,
2
],
[
3
,
4
],
[
5
,
6
]],
dtype
=
int32
)
>
tf.reshape(t, [])
reshapes a tensor t
with one element to a scalar.
>>> tf.reshape([7], []).numpy().item()
7
More examples:
>>>
t
=
[
1
,
2
,
3
,
4
,
5
,
6
,
7
,
8
,
9
]
>>>
print
(
tf
.
shape
(
t
).
numpy
())
[
9
]
>>>
tf
.
reshape
(
t
,
[
3
,
3
])
< tf
.
Tensor
:
shape
=(
3
,
3
),
dtype
=
int32
,
numpy
=
array
([[
1
,
2
,
3
],
[
4
,
5
,
6
],
[
7
,
8
,
9
]],
dtype
=
int32
)
>
>>>
t
=
[[[
1
,
1
],
[
2
,
2
]],
...
[[
3
,
3
],
[
4
,
4
]]]
>>>
print
(
tf
.
shape
(
t
).
numpy
())
[
2
2
2
]
>>>
tf
.
reshape
(
t
,
[
2
,
4
])
< tf
.
Tensor
:
shape
=(
2
,
4
),
dtype
=
int32
,
numpy
=
array
([[
1
,
1
,
2
,
2
],
[
3
,
3
,
4
,
4
]],
dtype
=
int32
)
>
>>>
t
=
[[[
1
,
1
,
1
],
...
[
2
,
2
,
2
]],
...
[[
3
,
3
,
3
],
...
[
4
,
4
,
4
]],
...
[[
5
,
5
,
5
],
...
[
6
,
6
,
6
]]]
>>>
print
(
tf
.
shape
(
t
).
numpy
())
[
3
2
3
]
>>>
#
Pass
'
[
-
1
]
'
to
flatten
't'
.
>>>
tf
.
reshape
(
t
,
[
-
1
])
< tf
.
Tensor
:
shape
=(
18
,),
dtype
=
int32
,
numpy
=
array
([
1
,
1
,
1
,
2
,
2
,
2
,
3
,
3
,
3
,
4
,
4
,
4
,
5
,
5
,
5
,
6
,
6
,
6
],
dtype
=
int32
)
>
>>>
#
--
Using
-
1
to
infer
the
shape
--
>>>
#
Here
-
1
is
inferred
to
be
9
:
>>>
tf
.
reshape
(
t
,
[
2
,
-
1
])
< tf
.
Tensor
:
shape
=(
2
,
9
),
dtype
=
int32
,
numpy
=
array
([[
1
,
1
,
1
,
2
,
2
,
2
,
3
,
3
,
3
],
[
4
,
4
,
4
,
5
,
5
,
5
,
6
,
6
,
6
]],
dtype
=
int32
)
>
>>>
#
-
1
is
inferred
to
be
2
:
>>>
tf
.
reshape
(
t
,
[
-
1
,
9
])
< tf
.
Tensor
:
shape
=(
2
,
9
),
dtype
=
int32
,
numpy
=
array
([[
1
,
1
,
1
,
2
,
2
,
2
,
3
,
3
,
3
],
[
4
,
4
,
4
,
5
,
5
,
5
,
6
,
6
,
6
]],
dtype
=
int32
)
>
>>>
#
-
1
is
inferred
to
be
3
:
>>>
tf
.
reshape
(
t
,
[
2
,
-
1
,
3
])
< tf
.
Tensor
:
shape
=(
2
,
3
,
3
),
dtype
=
int32
,
numpy
=
array
([[[
1
,
1
,
1
],
[
2
,
2
,
2
],
[
3
,
3
,
3
]],
[[
4
,
4
,
4
],
[
5
,
5
,
5
],
[
6
,
6
,
6
]]],
dtype
=
int32
)
>
Returns
A
Tensor
. Has the same type as tensor
.