tensorflow/models · error · ValueError

model_export_path must be specified.

Error message

model_export_path must be specified.

What it means

Error "model_export_path must be specified." thrown in tensorflow/models.

Source

Thrown at official/legacy/bert/model_saving_utils.py:47

  Args:
      model_export_path: Path to which exported model will be saved.
      model: Keras model object to export.
      checkpoint_dir: Path from which model weights will be loaded, if
        specified.
      restore_model_using_load_weights: Whether to use checkpoint.restore() API
        for custom checkpoint or to use model.load_weights() API. There are 2
        different ways to save checkpoints. One is using tf.train.Checkpoint and
        another is using Keras model.save_weights(). Custom training loop
        implementation uses tf.train.Checkpoint API and Keras ModelCheckpoint
        callback internally uses model.save_weights() API. Since these two API's
        cannot be used toghether, model loading logic must be take into account
        how model checkpoint was saved.

  Raises:
    ValueError when either model_export_path or model is not specified.
  """
  if not model_export_path:
    raise ValueError('model_export_path must be specified.')
  if not isinstance(model, tf_keras.Model):
    raise ValueError('model must be a tf_keras.Model object.')

  if checkpoint_dir:
    if restore_model_using_load_weights:
      model_weight_path = os.path.join(checkpoint_dir, 'checkpoint')
      assert tf.io.gfile.exists(model_weight_path)
      model.load_weights(model_weight_path)
    else:
      checkpoint = tf.train.Checkpoint(model=model)

      # Restores the model from latest checkpoint.
      latest_checkpoint_file = tf.train.latest_checkpoint(checkpoint_dir)
      assert latest_checkpoint_file
      logging.info('Checkpoint file %s found and restoring from '
                   'checkpoint', latest_checkpoint_file)
      checkpoint.restore(
          latest_checkpoint_file).assert_existing_objects_matched()

View on GitHub (pinned to e006f5f0d5)

When it happens

Trigger: Thrown at official/legacy/bert/model_saving_utils.py:47 when the library encounters an invalid state.

Common situations: See trigger scenarios.

AI-assisted analysis of tensorflow/models@e006f5f0d5 (2026-08-24). Data as JSON: /api/errors/1788120c31f289ad. Report an issue: GitHub.