Keras
Keras 3 API 문서
Models API
Saving & serialization
Model config serialization

Model config serialization

source

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.

source

from_config method 

Model.from_config(config, custom_objects=None)

Creates an operation from its config.

This method is the reverse of get_config, capable of instantiating the same operation from the config dictionary.

Note: If you override this method, you might receive a serialized dtype config, which is a dict. You can deserialize it as follows:

if "dtype" in config and isinstance(config["dtype"], dict):
    policy = dtype_policies.deserialize(config["dtype"])

Arguments

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

Returns

An operation instance.

source

clone_model function 

keras.models.clone_model(
    model,
    input_tensors=None,
    clone_function=None,
    call_function=None,
    recursive=False,
    **kwargs
)

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 with signature fn(layer) 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 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). Defaults to None.
  • call_function: Callable with signature fn(layer, *args, **kwargs) to be used to call each cloned layer and a set of inputs. It takes the layer instance, the call arguments and keyword arguments, and returns the call outputs. If unspecified, this callable defaults to the regular __call__() method: def fn(layer, *args, **kwargs): return layer(*args, **kwargs). By passing a custom callable, you can insert new layers before or after a given layer. Note: this argument can only be used with Functional models.
  • recursive: Boolean. Whether to recursively clone any Sequential or Functional models encountered in the original Sequential/Functional model. If False, then inner models are cloned by calling clone_function(). If True, then inner models are cloned by calling clone_model() with the same clone_function, call_function, and recursive arguments. Note that in this case, call_function will not be propagated to any Sequential model (since it is not applicable to Sequential models).

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 or call_function modifies a layer or layer call.

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, clone_function=clone_function)

Using a call_function to add a Dropout layer after each Dense layer (without recreating new layers):

def call_function(layer, *args, **kwargs):
    out = layer(*args, **kwargs)
    if isinstance(layer, keras.layers.Dense):
        out = keras.layers.Dropout(0.5)(out)
    return out

new_model = clone_model(
    model,
    clone_function=lambda x: x,  # Reuse the same layers.
    call_function=call_function,
)

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.