TensorFlow 1 version | View source on GitHub |
Reshapes a tensor.
tf.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()
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)>
Args | |
---|---|
tensor
|
A Tensor .
|
shape
|
A Tensor . Must be one of the following types: int32 , int64 .
Defines the shape of the output tensor.
|
name
|
Optional string. A name for the operation. |
Returns | |
---|---|
A Tensor . Has the same type as tensor .
|