|TensorFlow 2 version||View source on GitHub|
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.
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, placeholders will be created.
clone_function: Callable to be used to clone each layer in the target model (except
InputLayerinstances). 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
LSTMinstances 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 might behave
differently from the original model if a custom clone_function
modifies the layer.
ValueError: in case of invalid