View source on GitHub |
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).
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())