Function learning_phase_scope

tensorflow/python/keras/backend.py:357–397

Provides a scope within which the learning phase is equal to `value`. The learning phase gets restored to its original value upon exiting the scope. Arguments: value: Learning phase value, either 0 or 1 (integers). Yields: None. Raises: ValueError: if `value` is neither `0`

(value)

@keras_export('keras.backend.learning_phase_scope')
@tf_contextlib.contextmanager
def learning_phase_scope(value):
  """Provides a scope within which the learning phase is equal to `value`.

  The learning phase gets restored to its original value upon exiting the scope.

  Arguments:
     value: Learning phase value, either 0 or 1 (integers).

  Yields:
    None.

  Raises:
     ValueError: if `value` is neither `0` nor `1`.
  """
  global _GRAPH_LEARNING_PHASES  # pylint: disable=global-variable-not-assigned
  if value not in {0, 1}:
    raise ValueError('Expected learning phase to be 0 or 1.')

  with ops.init_scope():
    if context.executing_eagerly():
      previous_eager_value = _GRAPH_LEARNING_PHASES.get(
          _DUMMY_EAGER_GRAPH, None)
    previous_graph_value = _GRAPH_LEARNING_PHASES.get(get_graph(), None)

  try:
    set_learning_phase(value)
    yield
  finally:
    # Restore learning phase to initial value.
    with ops.init_scope():
      if context.executing_eagerly():
        if previous_eager_value is not None:
          _GRAPH_LEARNING_PHASES[_DUMMY_EAGER_GRAPH] = previous_eager_value
        elif _DUMMY_EAGER_GRAPH in _GRAPH_LEARNING_PHASES:
          del _GRAPH_LEARNING_PHASES[_DUMMY_EAGER_GRAPH]

      graph = get_graph()
      if previous_graph_value is not None:
        _GRAPH_LEARNING_PHASES[graph] = previous_graph_value
      elif graph in _GRAPH_LEARNING_PHASES:
        del _GRAPH_LEARNING_PHASES[graph]


@tf_contextlib.contextmanager