empty

paddle. empty ( shape: ShapeLike, dtype: DTypeLike | None = None, name: str | None = None, *, out: paddle.Tensor | None = None, device: PlaceLike | None = None, requires_grad: bool = False, pin_memory: bool = False ) → paddle.Tensor [source]

Returns a Tensor with uninitialized data which size is same as shape.

Parameters

  • shape (tuple|list|Tensor) – Shape of the Tensor to be created. The data type is int32 or int64 . If shape is a list or tuple, each element of it should be integer or 0-D Tensor with shape []. If shape is an Tensor, it should be an 1-D Tensor which represents a list.

  • dtype (np.dtype|str, optional) – Data type of the output Tensor which can be bool, float16, float32, float64, int32, int64, complex64, complex128 if dtype is None, the data type of created Tensor use global default dtype (see get_default_dtype for details).

  • name (str|None, optional) – For details, please refer to api_guide_Name. Generally, no setting is required. Default: None.

  • out (Tensor, optional) – The output tensor.

  • device (PlaceLike|None, optional) – The desired device of returned tensor. if None, uses the current device for the default tensor type (see paddle.device.set_device()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. Default: None.

  • requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

  • pin_memory (bool, optional) – If set, return tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False

Returns

Tensor which is created according to shape and dtype, and is uninitialized.

Return type

Tensor

Examples

>>> import paddle

>>> # shape is a list/tuple
>>> data1 = paddle.empty(shape=[3, 2])
>>> print(data1.numpy())
>>> 
[[1. 1.]
 [1. 1.]
 [1. 1.]]

>>> # shape is a Tensor
>>> shape = paddle.to_tensor([3, 2])
>>> data2 = paddle.empty(shape=shape)
>>> print(data2.numpy())
>>> 
[[1. 1.]
 [1. 1.]
 [1. 1.]]

>>> # shape is a Tensor List
>>> shape = [paddle.to_tensor(3), paddle.to_tensor(2)]
>>> data3 = paddle.empty(shape=shape)
>>> print(data3.numpy())
>>> 
[[1. 1.]
 [1. 1.]
 [1. 1.]]