reshape

paddle. reshape ( x, shape, name=None ) [source]

Changes the shape of x without changing its data.

Note that the output Tensor will share data with origin Tensor and doesn’t have a Tensor copy in dygraph mode. If you want to use the Tensor copy version, please use Tensor.clone like reshape_clone_x = x.reshape([-1]).clone().

Some tricks exist when specifying the target shape.

    1. -1 means the value of this dimension is inferred from the total element number of x and remaining dimensions. Thus one and only one dimension can be set -1.

    1. 0 means the actual dimension value is going to be copied from the corresponding dimension of x. The index of 0s in shape can not exceed the dimension of x.

Here are some examples to explain it.

    1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape is [6, 8], the reshape operator will transform x into a 2-D tensor with shape [6, 8] and leaving x’s data unchanged.

    1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape specified is [2, 3, -1, 2], the reshape operator will transform x into a 4-D tensor with shape [2, 3, 4, 2] and leaving x’s data unchanged. In this case, one dimension of the target shape is set to -1, the value of this dimension is inferred from the total element number of x and remaining dimensions.

    1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape is [-1, 0, 3, 2], the reshape operator will transform x into a 4-D tensor with shape [2, 4, 3, 2] and leaving x’s data unchanged. In this case, besides -1, 0 means the actual dimension value is going to be copied from the corresponding dimension of x.

The following figure illustrates the first example – a 3D tensor of shape [2, 4, 6] is transformed into a 2D tensor of shape [6, 8], during which the order and values of the elements in the tensor remain unchanged. The elements in the two subdiagrams correspond to each other, clearly demonstrating how the reshape API works.

legend of reshape API
Parameters

  • x (Tensor) – An N-D Tensor. The data type is float16, float32, float64, int16, int32, int64, int8, uint8, complex64, complex128, bfloat16 or bool.

  • shape (list|tuple|Tensor) – Define the target shape. At most one dimension of the target shape can be -1. The data type is int32 . If shape is a list or tuple, each element of it should be integer or Tensor with shape []. If shape is a Tensor, it should be an 1-D Tensor .

  • name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.

Returns

Tensor, A reshaped Tensor with the same data type as x.

Examples

>>> import paddle

>>> x = paddle.rand([2, 4, 6], dtype="float32")
>>> positive_four = paddle.full([1], 4, "int32")

>>> out = paddle.reshape(x, [-1, 0, 3, 2])
>>> print(out.shape)
[2, 4, 3, 2]

>>> out = paddle.reshape(x, shape=[positive_four, 12])
>>> print(out.shape)
[4, 12]

>>> shape_tensor = paddle.to_tensor([8, 6], dtype=paddle.int32)
>>> out = paddle.reshape(x, shape=shape_tensor)
>>> print(out.shape)
[8, 6]
>>> # out shares data with x in dygraph mode
>>> x[0, 0, 0] = 10.
>>> print(out[0, 0])
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
10.)