new_contents: from scipy.io import loadmat
  import numpy as np
  import keras
  from keras.preprocessing.image import load_img, img_to_array
  from keras.models import Sequential
  from keras.layers import Activation
  from keras.layers import Dense, Dropout, Flatten
  from keras.layers import Conv2D, MaxPooling2D
  
  
  num_classes = 10
  
  # input image dimensions
  img_rows, img_cols = 124, 124
  input_shape = (img_rows, img_cols, 1)
  # input_shape = (1, img_rows, img_cols)
  
  data_dir = 'data'
   
  
  def create_model():
  
      model = Sequential()
  
      model.add(Conv2D(32, kernel_size=(3, 3),
                   activation='relu',
                   input_shape=input_shape))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.25))
      model.add(Flatten())
      model.add(Dense(128, activation='relu'))
      model.add(Dropout(0.5))
      model.add(Dense(num_classes, activation='softmax'))
      
      model.compile(loss=keras.losses.categorical_crossentropy,
                      optimizer=keras.optimizers.Adadelta(),
                      metrics=['accuracy'])
      
      # model.compile(loss='categorical_crossentropy',
      #           optimizer='adam',
      #           metrics=['accuracy'])
      
      return model
  
  
  def load_data(mat_file_dir):
      x_img = []
      y_gender = []
  
      meta = loadmat(mat_file_dir)
      img_paths = meta["full_path"]
      genders   = meta["gender"]
   
      for i, path in enumerate(img_paths):
          print('i:', i)
          if i == 1:
              continue
          if i == 3:
              continue
          if i == 10:
              continue
          if i == 16:
              continue
          absPath = data_dir + '/' + path.strip()
          print('loading:', absPath)
          img = load_img(absPath, target_size=(img_rows, img_cols))
          x_img.append( img_to_array(img) )
  
          print('gender:', genders[0][i])
          y_gender.append( genders[0][i] )
  
      return np.array(x_img), np.array(y_gender)
  
  # model = create_model()
  # print(model)
  
  # meta = loadmat('modified_wiki.mat')
  
  x, y = load_data('modified_wiki.mat')
  print('x:', x)
  print('y:', y)
  
subject: Add load image and create array data