nn.functional.binary_cross_entropy¶

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

The binary_cross_entropy function computes the binary cross-entropy loss for binary classification tasks.

Function Signature¶

def binary_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 containing probabilities of shape (N, …), where N is the batch size.
target (Tensor):
The target tensor of shape matching input_ with binary values (0 or 1).
weight (Tensor | None, optional):
A manual rescaling weight tensor of shape matching input_. 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 binary cross-entropy loss as a scalar or tensor, depending on the reduction method.

Examples¶

>>> import lucid.nn.functional as F
>>> input_ = Tensor([0.8, 0.2])
>>> target = Tensor([1.0, 0.0])
>>> loss = F.binary_cross_entropy(input_, target)
>>> print(loss)
Tensor(...)  # Scalar loss value