original_docstring:  do after all training, before every eval. also before stack_sentences 
docstring: do after all training, before every eval.
docstring_tokens: do after all training before every eval .
code: def load_word_vectors(self):
          ''' do after all training, before every eval. also before stack_sentences '''
          self.load_weights_to_matrix()
          if self.embed_mode != 'mod' :
              if self.load_good:
                  self.word_embeddings = self.model.get_layer('embedding_1').get_weights()
                  #print('stage: embedding_1')
              else:
                  print('stage: early load...')
                  exit()
              if self.word_embeddings is None: self.set_embedding_matrix()
          else:
              #print(self.word_embeddings.shape)
              if self.word_embeddings is None: self.set_embedding_matrix()
              self.word_embeddings = np.expand_dims(self.embedding_matrix,0)
code_tokens: def load_word_vectors ( self ) : self . load_weights_to_matrix ( ) if self . embed_mode != 'mod' : if self . load_good : self . word_embeddings = self . model . get_layer ( 'embedding_1' ) . get_weights ( ) else : print ( 'stage: early load...' ) exit ( ) if self . word_embeddings is None : self . set_embedding_matrix ( ) else : if self . word_embeddings is None : self . set_embedding_matrix ( ) self . word_embeddings = np . expand_dims ( self . embedding_matrix , 0 )
short_docstring: do after all training, before every eval.
short_docstring_tokens: do after all training before every eval .
comment: ''' do after all training, before every eval. also before stack_sentences '''$SEP$#print('stage: embedding_1')$SEP$#print(self.word_embeddings.shape)