text: from tensorflow.keras.models import load_model
  import tensorflow.keras as keras
  from tensorflow.keras.datasets import mnist
  from tensorflow.keras import backend as K
  
  # input image dimensions
  img_rows, img_cols = 28, 28
  
  num_classes = 10
  
  # the data, split between train and test sets
  (x_train, y_train), (x_test, y_test) = mnist.load_data()
  
  if K.image_data_format() == 'channels_first':
      x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
      x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
      input_shape = (1, img_rows, img_cols)
  else:
      x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
      x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
      input_shape = (img_rows, img_cols, 1)
  
  x_train = x_train.astype('float32')
  x_test = x_test.astype('float32')
  x_train /= 255
  x_test /= 255
  
  # convert class vectors to binary class matrices
  y_train = keras.utils.to_categorical(y_train, num_classes)
  y_test = keras.utils.to_categorical(y_test, num_classes)
  
  
  # to load saved model
  filepath = 'tf2_mnist_model.hdf5'
  model = load_model(filepath)
  score = model.evaluate(x_test, y_test, verbose = 0)
  print('Test loss:', score[0])
  print('Test accuracy:', score[1])
  