atleast_2d

paddle. atleast_2d ( *inputs, name=None ) [source]

Convert inputs to tensors and return the view with at least 2-dimension. Two or high-dimensional inputs are preserved.

The following diagram illustrates the behavior of atleast_2d on different dimensional inputs for the following cases:

A 0-dim tensor input.

A 0-dim tensor and a 1-dim tensor input.

A 0-dim tensor and a 3-dim tensor input.

In each case, the function returns the tensors (or a list of tensors) in views with at least 2 dimensions.

Parameters

inputs (Tensor|list(Tensor)) – One or more tensors. The data type is float16, float32, float64, int16, int32, int64, int8, uint8, complex64, complex128, bfloat16 or bool.
name (str|None, optional) – Name for the operation (optional, default is None). For more information, please refer to api_guide_Name.

Returns

One Tensor, if there is only one input. List of Tensors, if there are more than one inputs.

Examples

           >>> import paddle

>>> # one input
>>> x = paddle.to_tensor(123, dtype='int32')
>>> out = paddle.atleast_2d(x)
>>> print(out)
Tensor(shape=[1, 1], dtype=int32, place=Place(cpu), stop_gradient=True,
[[123]])

>>> # more than one inputs
>>> x = paddle.to_tensor(123, dtype='int32')
>>> y = paddle.to_tensor([1.23], dtype='float32')
>>> out = paddle.atleast_2d(x, y)
>>> print(out)
[Tensor(shape=[1, 1], dtype=int32, place=Place(cpu), stop_gradient=True,
[[123]]), Tensor(shape=[1, 1], dtype=float32, place=Place(cpu), stop_gradient=True,
[[1.23000002]])]

>>> # more than 2-D input
>>> x = paddle.to_tensor(123, dtype='int32')
>>> y = paddle.to_tensor([[[1.23]]], dtype='float32')
>>> out = paddle.atleast_2d(x, y)
>>> print(out)
[Tensor(shape=[1, 1], dtype=int32, place=Place(cpu), stop_gradient=True,
[[123]]), Tensor(shape=[1, 1, 1], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[1.23000002]]])]

          