语法
tf.reshape(
tensor, shape, name=None
)
参数
返回值
返回一个新的形状为shape的tf.Tensor且具有与tensor以同样的顺序和相同的值。
实例
输入：
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)>
如果shape的一个参数为是-1，则计算该维度的大小，使总大小保持不变。特别是，若shape为[-1]，则将tensor展平为一维。shape的参数最多只能有一个-1。此外，若t为仅含一个元素的tensor，则tf.reshape(t, [])将t转变为一个标量。
函数实现
@tf_export("reshape", v1=["reshape", "manip.reshape"])
@dispatch.add_dispatch_support
def reshape(tensor, shape, name=None): # pylint: disable=redefined-outer-name
r"""Reshapes a tensor.
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`.
"""
result = gen_array_ops.reshape(tensor, shape, name)
tensor_util.maybe_set_static_shape(result, shape)
return result