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tf.keras.models.clone_model

Clone a Functional or Sequential Model instance.

Used in the notebooks

Used in the guide Used in the tutorials

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).

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).

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())