def loss(top_out, targets, model_hparams, vocab_size, weights_fn):
"""Compute loss numerator and denominator for one shard of output."""
del vocab_size # unused arg
logits = top_out
logits = common_attention.maybe_upcast(logits, hparams=model_hparams)
return common_layers.padded_cross_entropy(
logits,
targets,
model_hparams.label_smoothing,
weights_fn=weights_fn)
def loss(self, top_out, targets, weights_fn=None):
"""Compute loss numerator and denominator for one shard of output."""
logits = top_out
if weights_fn is None:
weights_fn = self.targets_weights_fn
logits = common_attention.maybe_upcast(logits, hparams=self._model_hparams)
return common_layers.padded_cross_entropy(
logits,
targets,
self._model_hparams.label_smoothing,
weights_fn=weights_fn)
def generic_loss(top_out, targets, model_hparams, vocab_size, weights_fn):
"""Compute loss numerator and denominator for one shard of output."""
del vocab_size # unused arg
logits = top_out
logits = common_attention.maybe_upcast(logits, hparams=model_hparams)
cutoff = getattr(model_hparams, "video_modality_loss_cutoff", 0.0)
return common_layers.padded_cross_entropy(logits,
targets,
model_hparams.label_smoothing,
cutoff=cutoff,
weights_fn=weights_fn,
reduce_sum=False)