old_contents: """Test ImageNet model.
  
  Author: Yuhuang Hu
  Email : duguyue100@gmail.com
  """
  
  from __future__ import print_function
  import os
  import argparse
  import numpy as np
  
  from keras.models import model_from_json
  
  home_path = os.environ["HOME"]
  config_path = os.path.join(home_path, ".snntoolbox")
  data_dir = os.path.join(config_path, "datasets")
  
  
  def model_evaluation(model_name, data_path):
      """Evaluate ImageNet model.
  
      Parameters
      ----------
      model_name : str
          the name of the model
      """
      model_json = os.path.join(config_path, model_name+".json")
      model_data = os.path.joinjoin(config_path, model_name+".h5")
  
      data_path = os.path.join(data_dir, "imagenet", "X_norm.npz")
      label_path = os.path.join(data_dir, "imagenet", "Y_norm.npz")
  
      data = np.load(data_path)["arr_0"]
      label = np.load(label_path)["arr_0"]
  
      json_file = open(model_json, 'r')
      model = model_from_json(json_file.read())
      model.load_weights(model_data)
  
      score = model.evaluate(data, label, batch_size=1000)
  
      print('Test score:', score[0])
      print('Test accuracy:', score[1])
  
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser(description="Model Visualization \
                                       by Yuhuang Hu")
      parser.add_argument("-m", "--model-name", type=str,
                          default="imagenet",
                          help="The name of the model")
      parser.add_argument("-d", "--data-path", type=str,
                          default="imagenet.npz",
                          help="The dataset set.")
      args = parser.parse_args()
      model_evaluation(**vars(args))
  
new_contents: """Test ImageNet model.
  
  Author: Yuhuang Hu
  Email : duguyue100@gmail.com
  """
  
  from __future__ import print_function
  import os
  import argparse
  import numpy as np
  
  from keras.models import model_from_json
  
  home_path = os.environ["HOME"]
  config_path = os.path.join(home_path, ".snntoolbox")
  data_dir = os.path.join(config_path, "datasets")
  
  
  def model_evaluation(model_name, data_path):
      """Evaluate ImageNet model.
  
      Parameters
      ----------
      model_name : str
          the name of the model
      """
      model_json = os.path.join(config_path, model_name+".json")
      model_data = os.path.join(config_path, model_name+".h5")
  
      data_path = os.path.join(data_dir, data_path, "X_norm.npz")
      label_path = os.path.join(data_dir, data_path, "Y_norm.npz")
  
      print ("[MESSAGE] Loading data...")
      data = np.load(data_path)["arr_0"]
      label = np.load(label_path)["arr_0"]
      print ("[MESSAGE] Data loaded.")
  
      print ("[MESSAGE] Loading model...")
      json_file = open(model_json, 'r')
      model = model_from_json(json_file.read())
      model.load_weights(model_data)
      print ("[MESSAGE] Model Loaded...")
  
      print ("[MESSAGE] Evaluating model...")
      score = model.evaluate(data, label, batch_size=1000)
      print ("[MESSAGE] Model evaluated...")
  
      print('Test score:', score[0])
      print('Test accuracy:', score[1])
  
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser(description="Model Visualization \
                                       by Yuhuang Hu")
      parser.add_argument("-m", "--model-name", type=str,
                          default="imagenet",
                          help="The name of the model")
      parser.add_argument("-d", "--data-path", type=str,
                          default="imagenet",
                          help="The dataset set.")
      args = parser.parse_args()
      model_evaluation(**vars(args))
  
subject: update imagenet test script