code: def __init__(self, N_train=8192, N_valid=1024, **kwargs):
      super().__init__(**kwargs)
  
      self.batch_size = 64
  
      img_rows = 28
      img_cols = 28
      self.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
          )
          self.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
          )
          self.input_shape = (img_rows, img_cols, 1)
  
      x_train = x_train.astype("float32")
      x_test = x_test.astype("float32")
      # zero-one normalization
      x_train /= 255
      x_test /= 255
  
      # convert class vectors to binary class matrices
      y_train = keras.utils.to_categorical(y_train, self.num_classes)
      y_test = keras.utils.to_categorical(y_test, self.num_classes)
  
      self.x_train, self.y_train = x_train[:N_train], y_train[:N_train]
      self.x_validation, self.y_validation = (
          x_train[-N_valid:],
          y_train[-N_valid:],
      )
      self.x_test, self.y_test = x_test, y_test
  
      self.input_shape = (img_rows, img_cols, 1)
  
file_path: HpBandSter/hpbandster/examples/example_5_keras_worker.py
imports: import ConfigSpace
  import ConfigSpace.hyperparameters
  from hpbandster.core.worker import Worker
  import logging
label_desc: Use image node_id 1 to create a new KerasWorker object from inherited base classes: Worker with example: obj = KerasWorker(N_train, N_valid)