lucid.random.uniform¶

lucid.random.uniform(low: int | float | complex = 0, high: int | float | complex = 1, size: int | list[int] | tuple[int] = 1, requires_grad: bool = False, keep_grad: bool = False, device: Literal['cpu', 'gpu'] = 'cpu') → Tensor¶

The uniform function generates a tensor of the specified shape, filled with random values drawn from a uniform distribution over the interval \([low, high)\).

Function Signature¶

def uniform(
    low: float,
    high: float,
    size: int | tuple[int, ...],
    requires_grad: bool = False,
    keep_grad: bool = False,
    device: _DeviceType = "cpu",
) -> Tensor

Parameters¶

low (float): The lower bound of the interval from which the random values are drawn (inclusive).
high (float): The upper bound of the interval from which the random values are drawn (exclusive).
size (int or tuple of int): The dimensions of the tensor to generate. Can be a single integer for a 1D tensor or a tuple for multidimensional tensors.
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 shape size with random values uniformly distributed in \([low, high)\).

Example¶

>>> import lucid
>>> x = lucid.random.uniform(0, 10, (2, 3))
>>> print(x)
Tensor([[2.345, 7.654, 5.123],
        [6.234, 1.543, 9.876]], grad=None)

By default, the generated tensor does not track gradients. Set requires_grad=True to enable gradient tracking:

>>> y = lucid.random.uniform(-1, 1, (3, 2), requires_grad=True)
>>> print(y.requires_grad)
True

Note

The random values are drawn from a uniform distribution over the interval \([low, high)\), which is useful for parameter initialization in neural networks and general-purpose random number generation.
Use lucid.random.seed to ensure reproducibility of random values.