nn.init.normal

lucid.nn.init.normal(tensor: Tensor, mean: int | float | complex = 0.0, std: int | float | complex = 1.0) → None

The normal function fills the input tensor with values sampled from a normal distribution \(\mathcal{N}(\mu, \sigma^2)\), where \(\mu\) is the mean and \(\sigma\) is the standard deviation.

Function Signature

def normal(tensor: Tensor, mean: _Scalar = 0.0, std: _Scalar = 1.0) -> None

Parameters

  • tensor (Tensor): The tensor to be initialized.

  • mean (_Scalar, optional): The mean of the normal distribution. Defaults to 0.0.

  • std (_Scalar, optional): The standard deviation of the normal distribution. Defaults to 1.0.

Returns

  • None: The function modifies the tensor in-place with new values sampled from the normal distribution.

Examples

>>> import lucid
>>> from lucid.nn.init import normal
>>> tensor = lucid.zeros((3, 3))
>>> normal(tensor, mean=0, std=1)
>>> print(tensor)
Tensor([[ 0.423, -0.234,  0.678],
        [-0.123,  0.654, -0.543],
        [ 0.543, -0.345,  0.234]], requires_grad=False)