lucid.ones

lucid.ones(*shape: int, dtype: type[bool | int | float | complex] | Numeric | None = None, requires_grad: bool = False, keep_grad: bool = False, device: Literal['cpu', 'gpu'] = 'cpu') → Tensor
lucid.ones(shape: list[int] | tuple[int], /, dtype: type[bool | int | float | complex] | Numeric | None = None, requires_grad: bool = False, keep_grad: bool = False, device: Literal['cpu', 'gpu'] = 'cpu') → Tensor

The ones function creates a tensor filled with ones of the specified shape and data type.

Function Signature

def ones(
    shape: _ShapeLike,
    dtype: Any = np.float32,
    requires_grad: bool = False,
    keep_grad: bool = False,
    device: _DeviceType = "cpu",
) -> Tensor

Parameters

  • shape (_ShapeLike):

    The shape of the output tensor. Can be a list or tuple of integers.

  • dtype (Any, optional):

    The data type of the elements in the tensor. Defaults to np.float32.

  • requires_grad (bool, optional):

    If True, the resulting tensor will be part of the computation graph and capable of tracking gradients. Defaults to False.

  • keep_grad (bool, optional):

    If True, the gradient history will be preserved even if the tensor does not require gradients. Defaults to False.

Returns

  • Tensor:

    A tensor filled with ones, having the specified shape and data type.

Example

>>> import lucid
>>> o = lucid.ones((2, 3))
>>> print(o)
Tensor([[1. 1. 1.]
        [1. 1. 1.]])

Note

  • This function is commonly used for initialization purposes in neural networks or other numerical computations.

  • Gradient-related parameters allow flexibility in gradient tracking for differentiable operations.