ones

paddle. ones ( 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]

Create a Tensor of specified shape and dtype and fill it with 1.

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, the elements of it should be integers 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 output Tensor, it should be one of bool, float16, float32, float64, int32 and int64. If it is set to None, the data type will be float32.

  • 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

A Tensor of data type dtype with shape shape and all elements are 1.

Return type

Tensor

Examples

>>> import paddle

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

>>> # shape is a Tensor
>>> shape = paddle.to_tensor([3, 2])
>>> data2 = paddle.ones(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.ones(shape=shape)
>>> print(data3.numpy())
[[1. 1.]
 [1. 1.]
 [1. 1.]]

Used in the guide/tutorials