Model config serialization

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get_config method

Model.get_config()

Returns the config of the object.

An object config is a Python dictionary (serializable) containing the information needed to re-instantiate it.

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from_config method

Model.from_config(config, custom_objects=None)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Arguments

• config: A Python dictionary, typically the output of get_config.

Returns

A layer instance.

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clone_model function

keras.models.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).

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, new Input objects will be created.
• clone_function: Callable to be used to clone each layer in the target model (except Input 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.layers.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)

Using a clone_function to make a model deterministic by setting the random seed everywhere:

def clone_function(layer):
    config = layer.get_config()
    if "seed" in config:
        config["seed"] = 1337
    return layer.__class__.from_config(config)

new_model = clone_model(model)

Note that subclassed models cannot be cloned by default, 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())

In the case of a subclassed model, you cannot using a custom clone_function.