lucid.random.randn¶

lucid.random.randn(*shape: int, requires_grad: bool = False, keep_grad: bool = False, device: Literal['cpu', 'gpu'] = 'cpu') → Tensor¶
lucid.random.randn(shape: list[int] | tuple[int], requires_grad: bool = False, keep_grad: bool = False, device: Literal['cpu', 'gpu'] = 'cpu') → Tensor

The randn function generates a tensor of the specified shape, filled with random values sampled from a standard normal distribution (mean = 0, variance = 1).

Function Signature¶

def randn(
    *shape: int,
    requires_grad: bool = False,
    keep_grad: bool = False,
    device: _DeviceType = "cpu",
) -> Tensor

Parameters¶

shape (int): The shape of the output tensor, specified as one or more integers.
requires_grad (bool, optional): If 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 shape filled with random values drawn from a standard normal distribution.

Example¶

>>> import lucid
>>> x = lucid.random.randn(2, 3)
>>> print(x)
Tensor([[ 0.78357324, -0.26325684,  0.22752314],
        [-0.68811989, -1.01051757,  0.41808962]], grad=None)

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

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

Note

The random values are drawn from a standard normal distribution.
Use lucid.random.seed to ensure reproducibility of random values.
Since the output values are continuous, backpropagation through this function is not defined.