text: import keras
  from keras.models import Sequential
  from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
  from keras.utils import np_utils
  import numpy as np
  
  ## Settings in training phase.
  batch_size = 32    # Change batch size based on the capability of your resource.
  num_classes = 10   # MNIST
  epochs = 12
  
  ## Loads MNIST datasets.
  MNIST_shape224 = np.load('04.MNIST_shape224.npz')
  x_train, y_train = MNIST_shape224['x_train'], MNIST_shape224['y_train']
  x_test, y_test = MNIST_shape224['x_test'], MNIST_shape224['y_test']
  
  ## Size of input images.
  img_x, img_y = 224, 224
  input_shape = (img_x, img_y, 1)
  
  ## Set ground truth to be one-hot value.
  y_train = np_utils.to_categorical(y_train, num_classes=num_classes)
  y_test = np_utils.to_categorical(y_test, num_classes=num_classes)
  
  ## Normalize the input images' value.
   # If your memory size of GPU is capable to carry the resource requirement of float32,
   # you can consider to uncomment it.
  # x_train = x_train.astype('float32')
  # x_test = x_test.astype('float32')
  x_train /= 255
  x_test /= 255
  
  ## Initialize Model.
  model = Sequential()
  ## VGG-16.
  model.add(Conv2D(filters=64, kernel_size=(3, 3), strides=1, padding='same', activation='relu', input_shape=input_shape))
  model.add(Conv2D(filters=64, kernel_size=(3, 3), strides=1, padding='same', activation='relu'))
  model.add(MaxPooling2D(pool_size=(2, 2), strides=2))
  model.add(Conv2D(filters=128, kernel_size=(3, 3), strides=1, padding='same', activation='relu'))
  model.add(Conv2D(filters=128, kernel_size=(3, 3), strides=1, padding='same', activation='relu'))
  model.add(MaxPooling2D(pool_size=(2, 2), strides=2))
  model.add(Conv2D(filters=256, kernel_size=(3, 3), strides=1, padding='same', activation='relu'))
  model.add(Conv2D(filters=256, kernel_size=(3, 3), strides=1, padding='same', activation='relu'))
  model.add(Conv2D(filters=256, kernel_size=(3, 3), strides=1, padding='same', activation='relu'))
  model.add(MaxPooling2D(pool_size=(2, 2), strides=2))
  model.add(Conv2D(filters=512, kernel_size=(3, 3), strides=1, padding='same', activation='relu'))
  model.add(Conv2D(filters=512, kernel_size=(3, 3), strides=1, padding='same', activation='relu'))
  model.add(Conv2D(filters=512, kernel_size=(3, 3), strides=1, padding='same', activation='relu'))
  model.add(MaxPooling2D(pool_size=(2, 2), strides=2))
  model.add(Conv2D(filters=512, kernel_size=(3, 3), strides=1, padding='same', activation='relu'))
  model.add(Conv2D(filters=512, kernel_size=(3, 3), strides=1, padding='same', activation='relu'))
  model.add(Conv2D(filters=512, kernel_size=(3, 3), strides=1, padding='same', activation='relu'))
  model.add(MaxPooling2D(pool_size=(2, 2), strides=2))
  ## We changed the fully connected layers in VGG-16.
  model.add(Flatten())
  model.add(Dense(256, activation='relu'))
  model.add(Dropout(0.5))
  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.SGD(), metrics=['accuracy'])
  
  ## Show model summary.
  print(model.summary())
  
  ## Strat to train model.
  train_history = model.fit(x_train, y_train, epochs=epochs,
                            batch_size=batch_size, verbose=1, validation_data=(x_test, y_test))
  
  ## Show results.
  scores = model.evaluate(x_test, y_test, verbose=0)
  print('Test loss:', scores[0])
  print('Test accuracy:', scores[1])
  
  ## Save the configurations and weights of the model we just trained.
  with open('04.MNIST_cnn_model_VGG16.config', 'w') as text_file:
      text_file.write(model.to_json())
  
  model.save_weights('04.MNIST_cnn_model_VGG16.weights')