Method get_slot

tensorflow/python/training/optimizer.py:857–893

Return a slot named `name` created for `var` by the Optimizer. Some `Optimizer` subclasses use additional variables. For example `Momentum` and `Adagrad` use variables to accumulate updates. This method gives access to these `Variable` objects if for some reason you need them. Us

(self, var, name)

      return apply_fn()

  def get_slot(self, var, name):
    """Return a slot named `name` created for `var` by the Optimizer.

    Some `Optimizer` subclasses use additional variables.  For example
    `Momentum` and `Adagrad` use variables to accumulate updates.  This method
    gives access to these `Variable` objects if for some reason you need them.

    Use `get_slot_names()` to get the list of slot names created by the
    `Optimizer`.

    Args:
      var: A variable passed to `minimize()` or `apply_gradients()`.
      name: A string.

    Returns:
      The `Variable` for the slot if it was created, `None` otherwise.
    """
    # pylint: disable=protected-access
    named_slots = self._slots.get(name, None)
    if not named_slots:
      return None

    if hasattr(var, "_distributed_container"):
      # NOTE: If this isn't patched, then there is no `handle` in
      # `_resource_apply_dense`.
      distributed_container = var._distributed_container()
      assert distributed_container is not None
      if ops.executing_eagerly_outside_functions():
        key = distributed_container._unique_id
      else:
        key = (distributed_container.graph, distributed_container._shared_name)
      # pylint: enable=protected-access
      mirrored_slot = named_slots.get(key, None)
      if mirrored_slot is None: return None
      return mirrored_slot.get(device=var.device)

    return named_slots.get(_var_key(var), None)

  def get_slot_names(self):
    """Return a list of the names of slots created by the `Optimizer`.