new_contents: from keras.models import load_model
  
  import pandas as pd
  import numpy as np
  
  import sys
  import os
  
  # change dir to train file, for environment
  os.chdir('../../chunk/')
  
  # add path
  sys.path.append('../')
  sys.path.append('../tools')
  
  from tools import load_data
  from tools import prepare
  
  epoch = sys.argv[1]
  test = sys.argv[2]
  
  path = './model/word-hash-auto-encoder-128/hidden_model_epoch_%s.h5'%epoch
  
  model = load_data(path)
  
  train_data, dev_data = load_data.load_chunk(dataset='train.txt', split_rate=0.9)
  test_data = load_data.load_chunk(dataset='test.txt')
  
  train_word = []
  dev_word = []
  test_word = []
  
  # all word
  [train_word.extend(list(each[0])) for each in train_data]
  [dev_word.extend(list(each[0])) for each in dev_data]
  [test_word.extend(list(each[0])) for each in test_data]
  
  train_word = [each.strip().lower() for each in train_word]
  dev_word = [each.strip().lower() for each in dev_word]
  test_word = [each.strip().lower() for each in test_word]
  
  train_word = list(set(train_word))
  dev_word = list(set(dev_word))
  test_word = list(set(test_word))
  
  if test=='dev':
      word = dev_word[:20]
  elif test='test':
      word = test_word[:20]
  else:
      word = train_word[:20]
  
  word_hashing = prepare.prepare_chunk_encoder(batch=word)
  word_hashing = word_hashing.toarray()
  output = model.predict_on_batch(word_hashing)
  
  while True:
      number = input('please input word index: ')
      for i, e in word_hashing[number]:
          if e==1:
              print(i)
      print('word_hasing'+ '-'*60)
  
      threshold = input('please input threshold: ')
      for i, e in output[number]:
          if e>=threshold:
              print(i)
      print('output'+ '-'*60)
subject: add auto test