def _setup_layer()

in tensorflow_hub/keras_layer.py [0:0]

• 19 lines of code
• 11 McCabe index (conditional complexity)

  def _setup_layer(self, trainable=False, **kwargs):
    """Constructs keras layer with relevant weights and losses."""
    # Initialize an empty layer, then add_weight() etc. as needed.
    super().__init__(trainable=trainable, **kwargs)

    # Add trainable and non-trainable weights from the callable.
    if hasattr(self._func, "trainable_variables"):
      for v in self._func.trainable_variables:
        self._add_existing_weight(v, trainable=True)
      trainable_variables = {id(v) for v in self._func.trainable_variables}
    else:
      trainable_variables = set()
    if hasattr(self._func, "variables"):
      for v in self._func.variables:
        if id(v) not in trainable_variables:
          self._add_existing_weight(v, trainable=False)

    # Forward the callable's regularization losses (if any).
    if hasattr(self._func, "regularization_losses"):
      for l in self._func.regularization_losses:
        if not callable(l):
          raise ValueError(
              "hub.KerasLayer(obj) expects obj.regularization_losses to be an "
              "iterable of callables, each returning a scalar loss term.")
        self.add_loss(self._call_loss_if_trainable(l))  # Supports callables.