def on_calc_additional_loss(self, trg, generator, generator_loss):
if self.policy_learning is None:
return None
reward = FactoredLossExpr()
reward.add_loss("generator",
-dy.inputTensor(generator_loss.value(), batched=True))
if self.length_prior is not None:
reward.add_loss('length_prior',
self.length_prior.log_ll(self.seg_size_unpadded))
reward_value = reward.value()
if trg.batch_size() == 1:
reward_value = [reward_value]
reward_tensor = dy.inputTensor(reward_value, batched=True)
### Calculate losses
try:
return self.policy_learning.calc_loss(reward_tensor)
finally:
self.reward = reward
if self.train and self.reporter is not None:
self.reporter.report_process(self)
def on_calc_additional_loss(self, trg, generator, generator_loss):
if self.policy_learning is None:
return None
trg_counts = dy.inputTensor([t.len_unpadded() for t in trg],
batched=True)
reward = FactoredLossExpr()
# Adding all reward from the translator
for loss_key, loss_value in generator_loss.get_nobackprop_loss().items(
):
if loss_key == 'mle':
reward.add_loss('mle', dy.cdiv(-loss_value, trg_counts))
else:
reward.add_loss(loss_key, -loss_value)
if self.length_prior is not None:
reward.add_loss('seg_lp',
self.length_prior.log_ll(self.seg_size_unpadded))
reward = dy.inputTensor(reward.value(), batched=True)
### Calculate losses
try:
return self.policy_learning.calc_loss(reward)
finally:
self.reward = reward
if self.reporter is not None:
self.reporter.report_process(self)