tensorflow/models · error · ValueError

%s already registered in LEGACY_OPTIMIZERS_CLS.

Error message

%s already registered in LEGACY_OPTIMIZERS_CLS.

What it means

Error "%s already registered in LEGACY_OPTIMIZERS_CLS." thrown in tensorflow/models.

Source

Thrown at official/modeling/optimization/optimizer_factory.py:96

                           optimizer_config_cls: Union[
                               tf_keras.optimizers.Optimizer,
                               tf_keras.optimizers.legacy.Optimizer,
                               tf_keras.optimizers.experimental.Optimizer
                           ],
                           use_legacy_optimizer: bool = True):
  """Register customize optimizer cls.

  The user will still need to subclass data classes in
  configs.optimization_config to be used with OptimizerFactory.

  Args:
    key: A string to that the optimizer_config_cls is registered with.
    optimizer_config_cls: A class which inherits tf_keras.optimizers.Optimizer.
    use_legacy_optimizer: A boolean that indicates if using legacy optimizers.
  """
  if use_legacy_optimizer:
    if key in LEGACY_OPTIMIZERS_CLS:
      raise ValueError('%s already registered in LEGACY_OPTIMIZERS_CLS.' % key)
    LEGACY_OPTIMIZERS_CLS[key] = optimizer_config_cls
  else:
    if key in NEW_OPTIMIZERS_CLS:
      raise ValueError('%s already registered in NEW_OPTIMIZERS_CLS.' % key)
    NEW_OPTIMIZERS_CLS[key] = optimizer_config_cls


class OptimizerFactory:
  """Optimizer factory class.

  This class builds learning rate and optimizer based on an optimization config.
  To use this class, you need to do the following:
  (1) Define optimization config, this includes optimizer, and learning rate
      schedule.
  (2) Initialize the class using the optimization config.
  (3) Build learning rate.
  (4) Build optimizer.

View on GitHub (pinned to e006f5f0d5)

When it happens

Trigger: Thrown at official/modeling/optimization/optimizer_factory.py:96 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/878c9e7fd72b82d0. Report an issue: GitHub.