tensorflow/models · error · ValueError

Optimizer type must be specified

Error message

Optimizer type must be specified

What it means

Error "Optimizer type must be specified" thrown in tensorflow/models.

Source

Thrown at official/modeling/optimization/optimizer_factory.py:154

  optimizer = opt_factory.build_optimizer(lr)
  ```
  """

  def __init__(self, config: opt_cfg.OptimizationConfig):
    """Initializing OptimizerFactory.

    Args:
      config: OptimizationConfig instance contain optimization config.
    """
    self._config = config
    self._optimizer_config = config.optimizer.get()
    self._optimizer_type = config.optimizer.type

    self._use_ema = config.ema is not None
    self._ema_config = config.ema

    if self._optimizer_config is None:
      raise ValueError('Optimizer type must be specified')

    self._lr_config = config.learning_rate.get()
    self._lr_type = config.learning_rate.type

    if self._lr_type is None:
      raise ValueError('Learning rate type must be specified')

    self._warmup_config = config.warmup.get()
    self._warmup_type = config.warmup.type

  def build_learning_rate(self):
    """Build learning rate.

    Builds learning rate from config. Learning rate schedule is built according
    to the learning rate config. If learning rate type is consant,
    lr_config.learning_rate is returned.

    Returns:

View on GitHub (pinned to e006f5f0d5)

When it happens

Trigger: Thrown at official/modeling/optimization/optimizer_factory.py:154 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/1566dc6dc8420ce9. Report an issue: GitHub.