nn.functional.huber_loss¶

lucid.nn.functional.huber_loss(input_: Tensor, target: Tensor, delta: float = 1.0, reduction: Literal['mean', 'sum'] | None = 'mean') → Tensor¶

The huber_loss function computes the Huber loss, which is less sensitive to outliers than the MSE loss.

Function Signature¶

def huber_loss(
    input_: Tensor,
    target: Tensor,
    delta: float = 1.0,
    reduction: _ReductionType | None = "mean",
) -> Tensor

Parameters¶

input_ (Tensor):
The input tensor of shape (N, …), where N is the batch size.
target (Tensor):
The target tensor of shape matching input_.
delta (float, optional):
The threshold at which to change between squared error and absolute error. Default: 1.0.
reduction (str | None, optional):
Specifies the reduction to apply: ‘mean’, ‘sum’, or ‘none’. Default: ‘mean’.

Returns¶

Tensor:
The computed Huber loss as a scalar or tensor, depending on the reduction method.

Examples¶

>>> import lucid.nn.functional as F
>>> input_ = Tensor([1.0, 2.0, 3.0])
>>> target = Tensor([1.5, 2.5, 3.5])
>>> loss = F.huber_loss(input_, target)
>>> print(loss)
Tensor(...)  # Scalar loss value