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. `clone_model` will not preserve the uniqueness of shared objects within the model (e.g. a single variable attached to two distinct layers will be restored as two separate variables). Args: 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. """ with generic_utils.DisableSharedObjectScope(): 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)
def clone_model(model, input_tensors=None, clone_function=None): """Clone a Functional or Sequential `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. Note that `clone_model` will not preserve the uniqueness of shared objects within the model (e.g. a single variable attached to two distinct layers will be restored as two separate variables). Args: 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, new `Input` objects 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 may behave differently from the original model if a custom `clone_function` modifies the layer. Example: ```python # Create a test Sequential model. model = keras.Sequential([ keras.Input(shape=(728,)), keras.layers.Dense(32, activation='relu'), keras.layers.Dense(1, activation='sigmoid'), ]) # Create a copy of the test model (with freshly initialized weights). new_model = clone_model(model) ``` Note that subclassed models cannot be cloned, since their internal layer structure is not known. To achieve equivalent functionality as `clone_model` in the case of a subclassed model, simply make sure that the model class implements `get_config()` (and optionally `from_config()`), and call: ```python new_model = model.__class__.from_config(model.get_config()) ``` """ with generic_utils.DisableSharedObjectScope(): 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)