chickadee » nanograd » cross-entropy-loss

Cross-entropy loss with batch support.

pred

predictions tensor

If from-logits=#f

probabilities (softmax already applied)

If from-logits=#t

logits (raw scores, log-softmax applied internally)

target

target tensor

One-hot

same shape as pred

Class indices

(batch_size,) with integer class labels

reduction

'mean (average over batch) or 'sum

from-logits

if true, apply log-softmax to pred first

Input shapes:

• 1D pred (n_classes,): single sample
• 2D pred (batch_size, n_classes): batch of samples

; Single sample with one-hot target
(define loss (cross-entropy-loss probs target))

; Batch with one-hot targets
(define batch-probs (softmax logits axis: -1))
(define batch-loss (cross-entropy-loss batch-probs targets reduction: 'mean))

; Batch with class indices (more memory efficient)
(define class-indices (make-tensor32 (f32vector 0.0 2.0 1.0) '(3)))
(define batch-loss (cross-entropy-loss logits class-indices 
                                       from-logits: #t 
                                       reduction: 'mean))