code: def data():
      
      from keras.datasets import mnist
      from keras.utils import np_utils
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
      x_train = x_train.reshape(60000, 784)
      x_test = x_test.reshape(10000, 784)
      x_train = x_train.astype('float32')
      x_test = x_test.astype('float32')
      x_train /= 255
      x_test /= 255
      nb_classes = 10
      y_train = np_utils.to_categorical(y_train, nb_classes)
      y_test = np_utils.to_categorical(y_test, nb_classes)
      return x_train, y_train, x_test, y_test
docs: Data providing function:
  
      Make sure to have every relevant import statement included here and return data as
      used in model function below. This function is separated from model() so that hyperopt
      won't reload data for each evaluation run.