tensorflow
Any recommended way to save a tf privacy model?
Hi, thanks for the open source, a great work.
If one "naively" saves a tf privacy model, it will face an issue.
Let say, you save the model by TF recommended methods, such as
tf.saved_model.save(model, "model.tfmodel")
or
model.save("model.tfmodel", save_format = 'tf')
It will resulted in an error:
`--------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[19], line 9 ----> 9 loaded_model = tf.keras.models.load_model("model.tfmodel")
File /usr/local/lib/python3.11/dist-packages/keras/src/saving/saving_api.py:262, in load_model(filepath, custom_objects, compile, safe_mode, **kwargs) 254 return saving_lib.load_model( 255 filepath, 256 custom_objects=custom_objects, 257 compile=compile, 258 safe_mode=safe_mode, 259 ) 261 # Legacy case. --> 262 return legacy_sm_saving_lib.load_model( 263 filepath, custom_objects=custom_objects, compile=compile, **kwargs 264 )
File /usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py:70, in filter_traceback.tf.debugging.disable_traceback_filtering()
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb
File /usr/local/lib/python3.11/dist-packages/keras/src/saving/legacy/serialization.py:365, in class_and_config_for_serialized_keras_object(config, module_objects, custom_objects, printable_module_name)
361 cls = object_registration.get_registered_object(
362 class_name, custom_objects, module_objects
363 )
364 if cls is None:
--> 365 raise ValueError(
366 f"Unknown {printable_module_name}: '{class_name}'. "
367 "Please ensure you are using a keras.utils.custom_object_scope "
368 "and that this object is included in the scope. See "
369 "https://www.tensorflow.org/guide/keras/save_and_serialize"
370 "#registering_the_custom_object for details."
371 )
373 cls_config = config["config"]
374 # Check if cls_config is a list. If it is a list, return the class and the
375 # associated class configs for recursively deserialization. This case will
376 # happen on the old version of sequential model (e.g. keras_version ==
377 # "2.0.6"), which is serialized in a different structure, for example
378 # "{'class_name': 'Sequential',
379 # 'config': [{'class_name': 'Embedding', 'config': ...}, {}, ...]}".
ValueError: Unknown optimizer: 'DPOptimizerClass'. Please ensure you are using a keras.utils.custom_object_scope and that this object is included in the scope. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.`
