unsqueeze

paddle. unsqueeze ( x: Tensor, axis: int | Sequence[Tensor | int] | Tensor, name: str | None = None ) → Tensor [source]

Insert single-dimensional entries to the shape of input Tensor x. Takes one required argument axis, a dimension or list of dimensions that will be inserted. Dimension indices in axis are as seen in the output tensor.

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 unsqueeze_clone_x = x.unsqueeze(-1).clone().

Note

Alias Support: The parameter name input can be used as an alias for x, and dim can be used as an alias for axis. For example, unsqueeze(input=tensor_x, dim=1) is equivalent to unsqueeze(x=tensor_x, axis=1).

Parameters

  • x (Tensor) – The input Tensor to be unsqueezed. Supported data type: bfloat16, float16, float32, float64, bool, int8, int32, int64. alias: input.

  • axis (int|list|tuple|Tensor) –

    Indicates the dimensions to be inserted. The data type is int32 .

    If axis is a list or tuple, each element of it should be integer or 0-D Tensor with shape []. If axis is a Tensor, it should be an 1-D Tensor . If axis is negative, axis = axis + ndim(x) + 1.

    System Message: WARNING/2 (/usr/local/lib/python3.10/site-packages/paddle/tensor/manipulation.py:docstring of paddle.tensor.manipulation.unsqueeze, line 16)

    Definition list ends without a blank line; unexpected unindent.

    alias: dim.

  • name (str|None, optional) – Name for this layer. Please refer to api_guide_Name, Default None.

Returns

Tensor, Unsqueezed Tensor with the same data type as input Tensor.

Examples

>>> import paddle

>>> x = paddle.rand([5, 10])
>>> print(x.shape)
[5, 10]

>>> out1 = paddle.unsqueeze(x, axis=0)
>>> print(out1.shape)
[1, 5, 10]

>>> out2 = paddle.unsqueeze(x, axis=[0, 2])
>>> print(out2.shape)
[1, 5, 1, 10]

>>> axis = paddle.to_tensor([0, 1, 2])
>>> out3 = paddle.unsqueeze(x, axis=axis)
>>> print(out3.shape)
[1, 1, 1, 5, 10]

>>> # out1, out2, out3 share data with x in dygraph mode
>>> x[0, 0] = 10.
>>> print(out1[0, 0, 0])
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
10.)
>>> print(out2[0, 0, 0, 0])
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
10.)
>>> print(out3[0, 0, 0, 0, 0])
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
10.)