def _pack_obj(obj):
"""Recursively packs objects.
"""
try:
return copy.deepcopy(obj)
except TypeError:
pass # is this a Keras serializable?
try:
model_metadata = saving_utils.model_metadata(obj)
training_config = model_metadata["training_config"]
model = serialize(obj)
weights = obj.get_weights()
return SavedKerasModel(
cls=obj.__class__,
model=model,
weights=weights,
training_config=training_config,
)
except (TypeError, AttributeError):
pass # try manually packing the object
if hasattr(obj, "__dict__"):
for key, val in obj.__dict__.items():
obj.__dict__[key] = _pack_obj(val)
return obj
if isinstance(obj, (list, tuple)):
obj_type = type(obj)
new_obj = obj_type([_pack_obj(o) for o in obj])
return new_obj
return obj
def pack_keras_model(model_obj, protocol):
"""Pickle a Keras Model.
Arguments:
model_obj: an instance of a Keras Model.
protocol: pickle protocol version, ignored.
Returns
-------
Pickled model
A tuple following the pickle protocol.
"""
if not isinstance(model_obj, Model):
raise TypeError("`model_obj` must be an instance of a Keras Model")
# pack up model
model_metadata = saving_utils.model_metadata(model_obj)
training_config = model_metadata.get("training_config", None)
model = serialize(model_obj)
weights = model_obj.get_weights()
return (unpack_keras_model, (model, training_config, weights))
def __reduce__(self):
model_metadata = saving_utils.model_metadata(self)
training_config = model_metadata.get("training_config", None)
model = serialize(self)
weights = self.get_weights()
return (unpack, (model, training_config, weights))
def _reduce_tf_model(model):
model_metadata = saving_utils.model_metadata(model)
training_config = model_metadata.get("training_config", None)
weights = model.get_weights()
model = serialize(model)
return TensorflowDispatcher._make_model, (model, training_config, weights)
