def test_policy(self):
event_trigger.set_train(True)
self.model.policy_learning = PolicyGradient(input_dim=3 *
self.layer_dim)
mle_loss = MLELoss()
loss = mle_loss.calc_loss(self.model, self.src[0], self.trg[0])
event_trigger.calc_additional_loss(self.trg[0], self.model, loss)
def test_reinforce_loss(self):
fertility_loss = GlobalFertilityLoss()
mle_loss = MLELoss()
loss = CompositeLoss(pt_losses=[mle_loss, fertility_loss]).calc_loss(
self.model, self.src[0], self.trg[0])
reinforce_loss = event_trigger.calc_additional_loss(
self.trg[0], self.model, loss)
pl = self.model.encoder.policy_learning
# Ensure correct length
src = self.src[0]
mask = src.mask.np_arr
outputs = self.segmenting_encoder.compose_output
actions = self.segmenting_encoder.segment_actions
# Ensure sample == outputs
for i, sample_item in enumerate(actions):
# The last segmentation is 1
self.assertEqual(sample_item[-1], src[i].len_unpadded())
self.assertTrue("mle" in loss.expr_factors)
self.assertTrue("global_fertility" in loss.expr_factors)
self.assertTrue("rl_reinf" in reinforce_loss.expr_factors)
self.assertTrue("rl_baseline" in reinforce_loss.expr_factors)
self.assertTrue("rl_confpen" in reinforce_loss.expr_factors)
# Ensure we are sampling from the policy learning
self.assertEqual(self.model.encoder.segmenting_action,
SegmentingSeqTransducer.SegmentingAction.POLICY)
def calc_loss(self,
model: 'model_base.ConditionedModel',
src: Union[sent.Sentence, 'batcher.Batch'],
trg: Union[sent.Sentence, 'batcher.Batch']) -> losses.FactoredLossExpr:
loss_builder = losses.FactoredLossExpr()
for _ in range(self.repeat):
standard_loss = self.child_loss.calc_loss(model, src, trg)
additional_loss = event_trigger.calc_additional_loss(trg, model, standard_loss)
loss_builder.add_factored_loss_expr(standard_loss)
loss_builder.add_factored_loss_expr(additional_loss)
return loss_builder
def calc_loss_single_batch(self): loss = MLELoss().calc_loss(self.model, self.src[0], self.trg[0]) reinforce_loss = event_trigger.calc_additional_loss(self.trg[0], self.model, loss) return loss, reinforce_loss