def predict(self): """Predicts result from the model.""" params, flags_obj, is_train = self.params, self.flags_obj, False with tf.name_scope("model"): model = transformer.create_model(params, is_train) self._load_weights_if_possible(model, flags_obj.init_weight_path) model.summary() subtokenizer = tokenizer.Subtokenizer(flags_obj.vocab_file) ds = data_pipeline.eval_input_fn(params) ds = ds.map(lambda x, y: x).take(_SINGLE_SAMPLE) ret = model.predict(ds) val_outputs, _ = ret length = len(val_outputs) for i in range(length): translate.translate_from_input(val_outputs[i], subtokenizer)
def predict(self): """Predicts result from the model.""" self.params['train'] = False params = self.params flags_obj = self.flags_obj with tf.name_scope("model"): model = transformer.create_model(params, is_train=False) self._load_weights_if_possible( model, tf.train.latest_checkpoint(self.flags_obj.model_dir)) model.summary() subtokenizer = tokenizer.Subtokenizer(flags_obj.vocab_file) print(params) ds = data_pipeline.eval_input_fn(params) ds = ds.map(lambda x, y: x).take(_SINGLE_SAMPLE) import time start = time.time() ret = model.predict(ds) val_outputs, _ = ret length = len(val_outputs) for i in range(length): translate.translate_from_input(val_outputs[i], subtokenizer) print('\n\n\n', time.time() - start)