|---| | [ View source on GitHub](https://github.com/keras-team/keras/tree/v2.15.0/keras/models/cloning.py#L464-L555) | Clone a Functional or Sequential `Model` instance. tf.keras.models.clone_model( model, input_tensors=None, clone_function=None ) 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 becomes 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). Defaults to `None`. |



| ## 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: # 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: new_model = model.__class__.from_config(model.get_config())