text: import pickle
  import tensorflow as tf
  import numpy as np
  from keras.layers import Input, Flatten, Dense
  from keras.models import Model
  import win_unicode_console
  
  win_unicode_console.enable()
  flags = tf.app.flags
  FLAGS = flags.FLAGS
  
  # command line flags
  flags.DEFINE_string('training_file', '', "Bottleneck features training file (.p)")
  flags.DEFINE_string('validation_file', '', "Bottleneck features validation file (.p)")
  flags.DEFINE_integer('epochs', 50, "The number of epochs.")
  flags.DEFINE_integer('batch_size', 256, "The batch size.")
  
  
  def load_bottleneck_data(training_file, validation_file):
      """
      Utility function to load bottleneck features.
  
      Arguments:
          training_file - String
          validation_file - String
      """
      print("Training file", training_file)
      print("Validation file", validation_file)
  
      with open(training_file, 'rb') as f:
          train_data = pickle.load(f)
      with open(validation_file, 'rb') as f:
          validation_data = pickle.load(f)
  
      X_train = train_data['features']
      y_train = train_data['labels']
      X_val = validation_data['features']
      y_val = validation_data['labels']
  
      return X_train, y_train, X_val, y_val
  
  
  def main(_):
      # load bottleneck data
      X_train, y_train, X_val, y_val = load_bottleneck_data(FLAGS.training_file, FLAGS.validation_file)
  
      print(X_train.shape, y_train.shape)
      print(X_val.shape, y_val.shape)
  
      nb_classes = len(np.unique(y_train))
  
      # define model
      input_shape = X_train.shape[1:]
      inp = Input(shape=input_shape)
      x = Flatten()(inp)
      x = Dense(nb_classes, activation='softmax')(x)
      model = Model(inp, x)
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
  
      # train model
      model.fit(X_train, y_train, FLAGS.batch_size, FLAGS.epochs, validation_data=(X_val, y_val), shuffle=True)
  
  
  # parses flags and calls the `main` function above
  if __name__ == '__main__':
      tf.app.run()
  