old_contents: import numpy as np
  import argparse
  from keras.models import load_model
  from keras.preprocessing.image import load_img, img_to_array
  
  ### Configuration
  img_rows, img_cols = 128, 128
  model_dir = 'trained_model.h5'
  
  
  def get_args():
      parser = argparse.ArgumentParser(description='Predict gender which the person\'s in the image is.')
      parser.add_argument('--input', '-i', type=str, required=True, help='path to input an image')
      return parser.parse_args()
  
  
  args = get_args()
  img_path = args.input
  
  
  print('')
  print('### Loading img:', img_path)
  img = load_img(img_path, target_size=(img_rows, img_cols))
  img_arr = img_to_array(img)
  img_array = np.array([img_arr])
  
  
  print('')
  print('### Loading model:', model_dir)
  model = load_model(model_dir)
  model.summary()
  
  
  print('')
  print('### Prediction')
  print('********************')
  print('*   0 for female   *')
  print('*   1 for male     *')
  print('********************')
  results = model.predict(img_array, batch_size=img_array.shape[0])
  for res in results:
      print('Result:')
      print('   Female:', res[0] * 100)
      print('   Male  :', res[1] * 100)
  
new_contents: import numpy as np
  import argparse
  from keras.models import load_model
  from keras.preprocessing.image import load_img, img_to_array
  
  ### Configuration
  img_rows, img_cols = 128, 128
  model_dir = 'trained_model.h5'
  
  
  def get_args():
      parser = argparse.ArgumentParser(description='Predict gender which the person\'s in the image is.')
      parser.add_argument('--input', '-i', type=str, required=True, help='path to input an image')
      return parser.parse_args()
  
  
  args = get_args()
  img_path = args.input
  
  
  print('')
  print('### Loading img:', img_path)
  img = load_img(img_path, target_size=(img_rows, img_cols))
  img_arr = img_to_array(img)
  img_array = np.array([img_arr])
  img_array = img_array.astype('float32')
  img_array /= 255
  
  
  print('')
  print('### Loading model:', model_dir)
  model = load_model(model_dir)
  model.summary()
  
  
  print('')
  print('### Prediction')
  print('********************')
  print('*   0 for female   *')
  print('*   1 for male     *')
  print('********************')
  results = model.predict(img_array, batch_size=img_array.shape[0])
  for res in results:
      print('Result:')
      print('   Female:', res[0] * 100)
      print('   Male  :', res[1] * 100)
  
subject: Normalize prediction image data