Function learning_phase

tensorflow/python/keras/backend.py:265–297

Returns the learning phase flag. The learning phase flag is a bool tensor (0 = test, 1 = train) to be passed as input to any Keras function that uses a different behavior at train time and test time. Returns: Learning phase (scalar integer tensor or Python integer).

()

@keras_export('keras.backend.learning_phase')
def learning_phase():
  """Returns the learning phase flag.

  The learning phase flag is a bool tensor (0 = test, 1 = train)
  to be passed as input to any Keras function
  that uses a different behavior at train time and test time.

  Returns:
      Learning phase (scalar integer tensor or Python integer).
  """
  graph = ops.get_default_graph()
  if graph is _GRAPH:
    # Don't enter an init_scope for the learning phase if eager execution
    # is enabled but we're inside the Keras workspace graph.
    learning_phase = symbolic_learning_phase()
    _mark_func_graph_as_unsaveable(graph, learning_phase)
    return learning_phase
  with ops.init_scope():
    # We always check & set the learning phase inside the init_scope,
    # otherwise the wrong default_graph will be used to look up the learning
    # phase inside of functions & defuns.
    #
    # This is because functions & defuns (both in graph & in eager mode)
    # will always execute non-eagerly using a function-specific default
    # subgraph.
    if context.executing_eagerly():
      if _DUMMY_EAGER_GRAPH not in _GRAPH_LEARNING_PHASES:
        # Fallback to inference mode as default.
        return 0
      return _GRAPH_LEARNING_PHASES[_DUMMY_EAGER_GRAPH]
    learning_phase = symbolic_learning_phase()
    _mark_func_graph_as_unsaveable(graph, learning_phase)
    return learning_phase


def global_learning_phase_is_set():