Function clone_model

tensorflow/python/keras/models.py:367–409

Clone any `Model` instance. Model cloning is similar to calling a model on new inputs, except that it creates new layers (and thus new weights) instead of sharing the weights of the existing layers. Arguments: model: Instance of `Model` (could be a functional model or a Seq

(model, input_tensors=None, clone_function=None)

@keras_export('keras.models.clone_model')
def clone_model(model, input_tensors=None, clone_function=None):
  """Clone any `Model` instance.

  Model cloning is similar to calling a model on new inputs,
  except that it creates new layers (and thus new weights) instead
  of sharing the weights of the existing layers.

  Arguments:
      model: Instance of `Model`
          (could be a functional model or a Sequential model).
      input_tensors: optional list of input tensors or InputLayer objects
          to build the model upon. If not provided,
          placeholders will be created.
      clone_function: Callable to be used to clone each layer in the target
          model (except `InputLayer` instances). It takes as argument the layer
          instance to be cloned, and returns the corresponding layer instance to
          be used in the model copy. If unspecified, this callable defaults to
          the following serialization/deserialization function:
          `lambda layer: layer.__class__.from_config(layer.get_config())`.
          By passing a custom callable, you can customize your copy of the
          model, e.g. by wrapping certain layers of interest (you might want to
          replace all `LSTM` instances with equivalent
          `Bidirectional(LSTM(...))` instances, for example).

  Returns:
      An instance of `Model` reproducing the behavior
      of the original model, on top of new inputs tensors,
      using newly instantiated weights. The cloned model might behave
      differently from the original model if a custom clone_function
      modifies the layer.

  Raises:
      ValueError: in case of invalid `model` argument value.
  """
  if clone_function is None:
    clone_function = _clone_layer

  if isinstance(model, Sequential):
    return _clone_sequential_model(
        model, input_tensors=input_tensors, layer_fn=clone_function)
  else:
    return _clone_functional_model(
        model, input_tensors=input_tensors, layer_fn=clone_function)


# "Clone" a subclassed model by reseting all of the attributes.