predict.py

import tensorflow as tf
import data
import sys
import model as ml

from configs import DEFINES
	
if __name__ == '__main__':
    tf.logging.set_verbosity(tf.logging.INFO)
    arg_length = len(sys.argv)
    
    if(arg_length < 2):
        raise Exception("Don't call us. We'll call you")
  
    
    char2idx,  idx2char, vocabulary_length = data.load_vocabulary()
    input = ""
    for i in sys.argv[1:]:
        input += i 
        input += " "
        
    print(input)
    # 테스트셋 인코딩 / 디코딩 입력 / 디코딩 출력 만드는 부분이다.
    predic_input_enc, predic_input_enc_length = data.enc_processing([input], char2idx)
    predic_output_dec, predic_output_dec_length = data.dec_input_processing([""], char2idx)
    predic_target_dec = data.dec_target_processing([""], char2idx)

	# 에스티메이터 구성
    classifier = tf.estimator.Estimator(
            model_fn=ml.model,
            model_dir=DEFINES.check_point_path, 
            params={ 
                'hidden_size': DEFINES.hidden_size, 
                'layer_size': DEFINES.layer_size, 
                'learning_rate': DEFINES.learning_rate, 
                'vocabulary_length': vocabulary_length, 
                'embedding_size': DEFINES.embedding_size, 
                'embedding': DEFINES.embedding, 
                'multilayer': DEFINES.multilayer, 
            })

    predictions = classifier.predict(
        input_fn=lambda:data.eval_input_fn(predic_input_enc, predic_output_dec, predic_target_dec, DEFINES.batch_size))
    
    data.pred2string(predictions, idx2char)