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always return losses
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1f49300862
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0aa8851878
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@ -195,7 +195,7 @@ class Tagger(TrainablePipe):
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validate_examples(examples, "Tagger.update")
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if not any(len(eg.predicted) if eg.predicted else 0 for eg in examples):
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# Handle cases where there are no tokens in any docs.
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return
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return losses
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set_dropout_rate(self.model, drop)
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tag_scores, bp_tag_scores = self.model.begin_update([eg.predicted for eg in examples])
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for sc in tag_scores:
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@ -233,7 +233,7 @@ class Tagger(TrainablePipe):
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return
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if not any(len(doc) for doc in docs):
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# Handle cases where there are no tokens in any docs.
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return
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return losses
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set_dropout_rate(self.model, drop)
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guesses, backprop = self.model.begin_update(docs)
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target = self._rehearsal_model(examples)
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@ -243,6 +243,7 @@ class Tagger(TrainablePipe):
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if losses is not None:
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losses.setdefault(self.name, 0.0)
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losses[self.name] += (gradient**2).sum()
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return losses
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def get_loss(self, examples, scores):
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"""Find the loss and gradient of loss for the batch of documents and
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@ -116,7 +116,7 @@ cdef class TrainablePipe(Pipe):
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validate_examples(examples, "TrainablePipe.update")
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if not any(len(eg.predicted) if eg.predicted else 0 for eg in examples):
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# Handle cases where there are no tokens in any docs.
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return
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return losses
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set_dropout_rate(self.model, drop)
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scores, bp_scores = self.model.begin_update([eg.predicted for eg in examples])
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loss, d_scores = self.get_loss(examples, scores)
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