def run()

in example_zoo/tensorflow/models/keras_cifar_main/official/resnet/keras/keras_cifar_main.py [0:0]
75 lines of code
9 McCabe index (conditional complexity)

def run(flags_obj):
  """Run ResNet Cifar-10 training and eval loop using native Keras APIs.

  Args:
    flags_obj: An object containing parsed flag values.

  Raises:
    ValueError: If fp16 is passed as it is not currently supported.

  Returns:
    Dictionary of training and eval stats.
  """
  if flags_obj.enable_eager:
    tf.enable_eager_execution()

  dtype = flags_core.get_tf_dtype(flags_obj)
  if dtype == 'fp16':
    raise ValueError('dtype fp16 is not supported in Keras. Use the default '
                     'value(fp32).')

  data_format = flags_obj.data_format
  if data_format is None:
    data_format = ('channels_first'
                   if tf.test.is_built_with_cuda() else 'channels_last')
  tf.keras.backend.set_image_data_format(data_format)

  if flags_obj.use_synthetic_data:
    input_fn = keras_common.get_synth_input_fn(
        height=cifar_main.HEIGHT,
        width=cifar_main.WIDTH,
        num_channels=cifar_main.NUM_CHANNELS,
        num_classes=cifar_main.NUM_CLASSES,
        dtype=flags_core.get_tf_dtype(flags_obj))
  else:
    input_fn = cifar_main.input_fn

  train_input_dataset = input_fn(
      is_training=True,
      data_dir=flags_obj.data_dir,
      batch_size=flags_obj.batch_size,
      num_epochs=flags_obj.train_epochs,
      parse_record_fn=parse_record_keras)

  eval_input_dataset = input_fn(
      is_training=False,
      data_dir=flags_obj.data_dir,
      batch_size=flags_obj.batch_size,
      num_epochs=flags_obj.train_epochs,
      parse_record_fn=parse_record_keras)

  strategy = distribution_utils.get_distribution_strategy(
      num_gpus=flags_obj.num_gpus,
      turn_off_distribution_strategy=flags_obj.turn_off_distribution_strategy)

  strategy_scope = keras_common.get_strategy_scope(strategy)

  with strategy_scope:
    optimizer = keras_common.get_optimizer()
    model = resnet_cifar_model.resnet56(classes=cifar_main.NUM_CLASSES)

    model.compile(loss='categorical_crossentropy',
                  optimizer=optimizer,
                  metrics=['categorical_accuracy'])

  time_callback, tensorboard_callback, lr_callback = keras_common.get_callbacks(
      learning_rate_schedule, cifar_main.NUM_IMAGES['train'])

  train_steps = cifar_main.NUM_IMAGES['train'] // flags_obj.batch_size
  train_epochs = flags_obj.train_epochs

  if flags_obj.train_steps:
    train_steps = min(flags_obj.train_steps, train_steps)
    train_epochs = 1

  num_eval_steps = (cifar_main.NUM_IMAGES['validation'] //
                    flags_obj.batch_size)

  validation_data = eval_input_dataset
  if flags_obj.skip_eval:
    tf.keras.backend.set_learning_phase(1)
    num_eval_steps = None
    validation_data = None

  history = model.fit(train_input_dataset,
                      epochs=train_epochs,
                      steps_per_epoch=train_steps,
                      callbacks=[
                          time_callback,
                          lr_callback,
                          tensorboard_callback
                      ],
                      validation_steps=num_eval_steps,
                      validation_data=validation_data,
                      verbose=2)
  eval_output = None
  if not flags_obj.skip_eval:
    eval_output = model.evaluate(eval_input_dataset,
                                 steps=num_eval_steps,
                                 verbose=1)
  stats = keras_common.build_stats(history, eval_output, time_callback)
  return stats