def _check_trainability()

in tensorflow_hub/keras_layer.py [0:0]

• 15 lines of code
• 12 McCabe index (conditional complexity)

  def _check_trainability(self):
    """Raises or logs errors for unuspported uses of trainable=True."""
    if not self.trainable: return  # Nothing to do.

    # Training is only supported when calling a reusable TF2 SavedModel through
    # its @tf.function __call__. Trying to train through a signature is likely
    # to go wrong beyond the most simple cases due to a number of pitfalls:
    # - No good support for train vs inference mode. TF1 Hub format used
    #   graph versions identified by tags, but this was not a general
    #   standard for SavedModels, and TF2 can no longer save with tags.
    # - No support for update ops. TF1 Hub format had them in the UPDATE_OPS
    #   collection, but collections are no longer loaded in TF2. General
    #   SavedModel signatures had no support for them.
    # - No support for regularization losses (same story).
    # - A SavedModel without @tf.function __call__ will likely also not
    #   provide a trainable_variables attribute.
    if self._is_hub_module_v1:
      raise ValueError(
          "Setting hub.KerasLayer.trainable = True is unsupported when "
          "loading from the TF1 Hub format.")
    elif self._signature:
      raise ValueError(
          "Setting hub.KerasLayer.trainable = True is unsupported when "
          "calling a SavedModel signature.")
    # Having zero trainable variables in an otherwise trainable model
    # is suspicious but may be valid as a boundary case, so we just log,
    # but at most once per layer instance.
    if not self.trainable_weights:
      if not hasattr(self, "_already_logged_trainable_with_zero_weights"):
        logging.error(
            "hub.KerasLayer is trainable but has zero trainable weights.")
        setattr(self, "_already_logged_trainable_with_zero_weights", True)