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.
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,
placeholders 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).
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 might behave
differently from the original model if a custom clone_function
modifies the layer.
TensorFlow 1 version
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