Max-Constraint Margin Loss (module)

Module wrapper for nnf_mc_loss() with configurable parameters. Stores the ancestor matrix R and evaluation mask for reuse across batches.

Usage

nn_mc_loss(
  R,
  to_eval = NULL,
  criterion = nnf_binary_cross_entropy_with_logits,
  reduction = "mean"
)

Arguments

R

Ancestor matrix tensor of shape (1, n_classes, n_classes).

to_eval

Optional logical tensor of shape (n_classes,) indicating which classes to include in loss computation.

criterion

Loss function module or functional to apply after constraint propagation. Default: nn_binary_cross_entropy_with_logits().

reduction

(string, optional): Reduction method: 'none' | 'mean' | 'sum'.

Shape

• Input output: \((N, C)\) where N = batch size, C = number of classes

• Input target: \((N, C)\), same shape as output, binary values

• Output: scalar by default. If reduction = "none", then \((N, C')\) where C' is the number of evaluated classes

See also

nnf_mc_loss(), build_ancestor_matrix_from_outcomes(), get_constr_output()

Examples

if (FALSE) { # \dontrun{
# Build ancestor matrix from hierarchy
R <- build_ancestor_matrix_from_outcomes(my_tree, processed$outcomes, device = "cuda")

# Create loss module
loss_fn <- nn_mc_loss(R = R, reduction = "mean")

# Forward pass
output <- model(x)  # (batch, n_classes)
loss <- loss_fn(output, labels)
loss$backward()
} # }