def cross_entropy_logits(logits: JaxArray, labels: JaxArray) -> JaxArray:
"""Computes the softmax cross-entropy loss on n-dimensional data.
Args:
logits: (batch, ..., #class) tensor of logits.
labels: (batch, ..., #class) tensor of label probabilities (e.g. labels.sum(axis=-1) must be 1)
Returns:
(batch, ...) tensor of the cross-entropies for each entry.
"""
return logsumexp(logits, axis=-1) - (logits * labels).sum(-1)
def cross_entropy_logits_sparse(logits: JaxArray,
labels: Union[JaxArray, int]) -> JaxArray:
"""Computes the softmax cross-entropy loss.
Args:
logits: (batch, ..., #class) tensor of logits.
labels: (batch, ...) integer tensor of label indexes in {0, ...,#nclass-1} or just a single integer.
Returns:
(batch,) tensor of the cross-entropies for each entry.
"""
return logsumexp(logits, axis=1) - logits[jn.arange(logits.shape[0]),
labels]
def cross_entropy_logits(logits: JaxArray, labels: JaxArray) -> JaxArray:
"""Computes the softmax cross-entropy loss on n-dimensional data.
Args:
logits: (batch, ..., #class) tensor of logits.
labels: (batch, ..., #class) tensor of label probabilities (e.g. labels.sum(axis=-1) must be 1)
Returns:
(batch, ...) tensor of the cross-entropies for each entry.
"""
FUNC_NAME = 'cross_entropy_logits'
if logits.shape != labels.shape:
warnings.warn(' {} {} : arg1 {} and arg2 {}'.format(
WARN_SHAPE_MISMATCH, FUNC_NAME, logits.shape, labels.shape))
return logsumexp(logits, axis=-1) - (logits * labels).sum(-1)
def cross_entropy_logits_sparse(logits: JaxArray,
labels: Union[JaxArray, int]) -> JaxArray:
"""Computes the softmax cross-entropy loss.
Args:
logits: (batch, ..., #class) tensor of logits.
labels: (batch, ...) integer tensor of label indexes in {0, ...,#nclass-1} or just a single integer.
Returns:
(batch, ...) tensor of the cross-entropies for each entry.
"""
if isinstance(labels, int):
labeled_logits = logits[..., labels]
else:
labeled_logits = jn.take_along_axis(logits, labels[..., None],
-1).squeeze(-1)
return logsumexp(logits, axis=-1) - labeled_logits
