Python EncoderDecoderModelAttention.forward_implement Examples

Example #1

File: app.py Project: SnowMasaya/Chainer-Slack-Twitter-Dialogue

 def __predict_sentence(self, src_batch):
     """
     predict sentence
     :param src_batch: get the source sentence
     :return:
     """
     dialogue = EncoderDecoderModelAttention(self.parameter)
     src_vocab = Vocabulary.load(self.model_name + '.srcvocab')
     trg_vocab = Vocabulary.load(self.model_name + '.trgvocab')
     model = AttentionDialogue.load_spec(self.model_name + '.spec', self.XP)
     serializers.load_hdf5(self.model_name + '.weights', model)
     hyp_batch = dialogue.forward_implement(src_batch, None, src_vocab, trg_vocab, model, False, self.generation_limit)
     return hyp_batch

Example #2

File: app_attention.py Project: zkbreeze/Chainer-Slack-Twitter-Dialogue

 def __predict_sentence(self, src_batch):
     """
     predict sentence
     :param src_batch: get the source sentence
     :return:
     """
     dialogue = EncoderDecoderModelAttention(self.parameter)
     src_vocab = Vocabulary.load(self.model_name + '.srcvocab')
     trg_vocab = Vocabulary.load(self.model_name + '.trgvocab')
     model = AttentionDialogue.load_spec(self.model_name + '.spec', self.XP)
     serializers.load_hdf5(self.model_name + '.weights', model)
     hyp_batch = dialogue.forward_implement(src_batch, None, src_vocab,
                                            trg_vocab, model, False,
                                            self.generation_limit)
     return hyp_batch