nn.functional.cross_entropy¶

lucid.nn.functional.cross_entropy(input_: Tensor, target: Tensor, weight: Tensor | None = None, reduction: Literal['mean', 'sum'] | None = 'mean', eps: float = 1e-07) → Tensor¶

The cross_entropy function computes the cross-entropy loss for multi-class classification tasks.

Function Signature¶

def cross_entropy(
    input_: Tensor,
    target: Tensor,
    weight: Tensor | None = None,
    reduction: _ReductionType | None = "mean",
    eps: float = 1e-7,
) -> Tensor

Parameters¶

input_ (Tensor):
The input tensor of shape (N, C), where N is the batch size and C is the number of classes.
target (Tensor):
The target tensor of shape (N,) containing class indices in the range [0, C-1].
weight (Tensor | None, optional):
A manual rescaling weight tensor of shape (C,). Default: None.
reduction (str | None, optional):
Specifies the reduction to apply: ‘mean’, ‘sum’, or ‘none’. Default: ‘mean’.
eps (float, optional):
A small constant added for numerical stability. Default: 1e-7.

Returns¶

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

Examples¶

>>> import lucid.nn.functional as F
>>> input_ = Tensor([[2.0, 1.0, 0.1]])
>>> target = Tensor([0])
>>> loss = F.cross_entropy(input_, target)
>>> print(loss)
Tensor(...)  # Scalar loss value