def calc_loss(self, translator, src, trg):
search_outputs = translator.generate_search_output(src, self.search_strategy)
sign = -1 if self.inv_eval else 1
total_loss = FactoredLossExpr()
for search_output in search_outputs:
self.eval_score = []
for trg_i, sample_i in zip(trg, search_output.word_ids):
# Removing EOS
sample_i = self.remove_eos(sample_i.tolist())
ref_i = trg_i.words[:trg_i.len_unpadded()]
score = self.evaluation_metric.evaluate_one_sent(ref_i, sample_i)
self.eval_score.append(sign * score)
self.reward = dy.inputTensor(self.eval_score, batched=True)
# Composing losses
loss = FactoredLossExpr()
if self.baseline is not None:
baseline_loss = []
losses = []
for state, logsoft, mask in zip(search_output.state,
search_output.logsoftmaxes,
search_output.mask):
bs_score = self.baseline.transform(state)
baseline_loss.append(dy.squared_distance(self.reward, bs_score))
loss_i = dy.cmult(logsoft, self.reward - bs_score)
valid = list(np.nonzero(mask)[0])
losses.append(dy.cmult(loss_i, dy.inputTensor(mask, batched=True)))
loss.add_loss("reinforce", dy.sum_elems(dy.esum(losses)))
loss.add_loss("reinf_baseline", dy.sum_elems(dy.esum(baseline_loss)))
else:
loss.add_loss("reinforce", dy.sum_elems(dy.cmult(self.true_score, dy.esum(logsofts))))
total_loss.add_factored_loss_expr(loss)
return loss
def calc_loss(self,
model: 'model_base.ConditionedModel',
src: Union[sent.Sentence, 'batchers.Batch'],
trg: Union[sent.Sentence, 'batchers.Batch']):
total_loss = FactoredLossExpr()
for loss, weight in zip(self.losses, self.loss_weight):
total_loss.add_factored_loss_expr(loss.calc_loss(model, src, trg) * weight)
return total_loss
def calc_loss(self,
model: 'model_base.ConditionedModel',
src: Union[sent.Sentence, 'batcher.Batch'],
trg: Union[sent.Sentence, 'batcher.Batch']):
loss_builder = 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
