lucid.empty

lucid.empty(*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.empty(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 empty function generates a tensor of the specified shape, with uninitialized values in its memory. This function does not set the values to zero or any other specific value.

Function Signature

def empty(
    *args: int | tuple[int, ...],
    dtype: type = _base_dtype,
    requires_grad: bool = False,
    keep_grad: bool = False,
    device: _DeviceType = "cpu",
) -> Tensor

Parameters

  • shape (int or tuple of int): The dimensions of the tensor to generate. Can be a variable number of integer arguments for multidimensional tensors or a single tuple specifying the shape.

  • dtype (type, optional): The data type of the uninitialized tensor. Defaults to _base_dtype.

  • requires_grad (bool, optional): If set to True, the resulting tensor will track gradients for automatic differentiation. Defaults to False.

  • keep_grad (bool, optional): Determines whether gradient history should persist across multiple operations. Defaults to False.

Returns

  • Tensor: A tensor of the specified shape with uninitialized values.

Example

>>> import lucid
>>> x = lucid.empty(2, 3)
>>> print(x)
Tensor([[ 6.949e-310,  6.949e-310,  0.000e+000],
        [ 0.000e+000,  0.000e+000,  0.000e+000]], grad=None)

The memory is uninitialized, so values are arbitrary. To track gradients, set requires_grad=True:

>>> y = lucid.empty((3, 2), requires_grad=True)
>>> print(y.requires_grad)
True

Note

  • The empty function does not clear the memory where the tensor is allocated, so the values in the tensor are arbitrary. If you need a tensor with zeros, consider using lucid.zeros instead.