Method __init__

tensorflow/python/training/optimizer.py:363–409

Create a new Optimizer. This must be called by the constructors of subclasses. Args: use_locking: Bool. If True apply use locks to prevent concurrent updates to variables. name: A non-empty string. The name to use for accumulators created for the optimizer.

(self, use_locking, name)

  GATE_GRAPH = 2

  def __init__(self, use_locking, name):
    """Create a new Optimizer.

    This must be called by the constructors of subclasses.

    Args:
      use_locking: Bool. If True apply use locks to prevent concurrent updates
        to variables.
      name: A non-empty string.  The name to use for accumulators created
        for the optimizer.

    Raises:
      ValueError: If name is malformed.
    """
    if not name:
      raise ValueError("Must specify the optimizer name")
    self._use_locking = use_locking
    self._name = name
    # Dictionary of slots.
    #  {slot_name :
    #      {_var_key(variable_to_train): slot_for_the_variable, ... },
    #   ... }
    self._slots = {}
    self._non_slot_dict = {}
    # For implementing Trackable. Stores information about how to restore
    # slot variables which have not yet been created
    # (trackable._CheckpointPosition objects).
    #  {slot_name :
    #      {_var_key(variable_to_train): [checkpoint_position, ... ], ... },
    #   ... }
    self._deferred_slot_restorations = {}

    self._loss_scale = None
    if 'TF_ENABLE_AUTO_MIXED_PRECISION_LOSS_SCALING' in os.environ:
      if os.environ['TF_ENABLE_AUTO_MIXED_PRECISION_LOSS_SCALING'] == "1":
        self._loss_scale = loss_scale_module.DynamicLossScale()
        self._track_trackable(self._loss_scale, 'loss_scale')
    elif os.environ.get('TF_ENABLE_AUTO_MIXED_PRECISION') == "1":
      self._loss_scale = loss_scale_module.DynamicLossScale()
      self._track_trackable(self._loss_scale, 'loss_scale')

    # TODO(isaprykin): When using a DistributionStrategy, and when an
    # optimizer is created in each replica, it might be dangerous to
    # rely on some Optimizer methods.  When such methods are called on a
    # per-replica optimizer, an exception needs to be thrown.  We do
    # allow creation per-replica optimizers however, because the
    # compute_gradients()->apply_gradients() sequence is safe.

  def get_name(self):
    return self._name