Not able usee

[eakarsu]

Thanks for providing good model for image rotation. I would like to use it. But I could not use it. I have updated imports for keras so that it is inside tensorflow. I have copied model and run it in google vertex AI and getting this error. Can you please help?

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ValueError Traceback (most recent call last)

Cell In[32], line 2

  1 model_location = os.path.join('rotnet', 'models', 'rotnet_street_view_resnet50.hdf5')

----> 2 model = load_model(model_location, custom_objects={'angle_error': angle_error})

File /opt/conda/lib/python3.10/site-packages/keras/src/saving/saving_api.py:183, in load_model(filepath, custom_objects, compile, safe_mode)

176     return saving_lib.load_model(

177         filepath,

178         custom_objects=custom_objects,

179         compile=compile,

180         safe_mode=safe_mode,

181     )

182 if str(filepath).endswith((".h5", ".hdf5")):

--> 183 return legacy_h5_format.load_model_from_hdf5(filepath)

184 elif str(filepath).endswith(".keras"):

185     raise ValueError(

186         f"File not found: filepath={filepath}. "

187         "Please ensure the file is an accessible `.keras` "

188         "zip file."

189     )

File /opt/conda/lib/python3.10/site-packages/keras/src/legacy/saving/legacy_h5_format.py:133, in load_model_from_hdf5(filepath, custom_objects, compile)

130 model_config = json_utils.decode(model_config)

132 with saving_options.keras_option_scope(use_legacy_config=True):

--> 133 model = saving_utils.model_from_config(

134         model_config, custom_objects=custom_objects

135     )

137     # set weights

138     load_weights_from_hdf5_group(f["model_weights"], model)

File /opt/conda/lib/python3.10/site-packages/keras/src/legacy/saving/saving_utils.py:85, in model_from_config(config, custom_objects)

 81 # TODO(nkovela): Swap find and replace args during Keras 3.0 release

 82 # Replace keras refs with keras

 83 config = _find_replace_nested_dict(config, "keras.", "keras.")

---> 85 return serialization.deserialize_keras_object(

 86     config,

 87     module_objects=MODULE_OBJECTS.ALL_OBJECTS,

 88     custom_objects=custom_objects,

 89     printable_module_name="layer",

 90 )

File /opt/conda/lib/python3.10/site-packages/keras/src/legacy/saving/serialization.py:495, in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)

490 cls_config = _find_replace_nested_dict(

491     cls_config, "keras.", "keras."

492 )

494 if "custom_objects" in arg_spec.args:

--> 495 deserialized_obj = cls.from_config(

496         cls_config,

497         custom_objects={

498             **object_registration.GLOBAL_CUSTOM_OBJECTS,

499             **custom_objects,

500         },

501     )

502 else:

503     with object_registration.CustomObjectScope(custom_objects):

File /opt/conda/lib/python3.10/site-packages/keras/src/models/model.py:492, in Model.from_config(cls, config, custom_objects)

487 if is_functional_config and revivable_as_functional:

488     # Revive Functional model

489     # (but not Functional subclasses with a custom __init__)

490     from keras.src.models.functional import functional_from_config

--> 492 return functional_from_config(

493         cls, config, custom_objects=custom_objects

494     )

496 # Either the model has a custom __init__, or the config

497 # does not contain all the information necessary to

498 # revive a Functional model. This happens when the user creates

(...)

501 # In this case, we fall back to provide all config into the

502 # constructor of the class.

503 try:

File /opt/conda/lib/python3.10/site-packages/keras/src/models/functional.py:503, in functional_from_config(cls, config, custom_objects)

501 # First, we create all layers and enqueue nodes to be processed

502 for layer_data in config["layers"]:

--> 503 process_layer(layer_data)

505 # Then we process nodes in order of layer depth.

506 # Nodes that cannot yet be processed (if the inbound node

507 # does not yet exist) are re-enqueued, and the process

508 # is repeated until all nodes are processed.

509 while unprocessed_nodes:

File /opt/conda/lib/python3.10/site-packages/keras/src/models/functional.py:483, in functional_from_config..process_layer(layer_data)

479 # Instantiate layer.

480 if "module" not in layer_data:

481     # Legacy format deserialization (no "module" key)

482     # used for H5 and SavedModel formats

--> 483 layer = saving_utils.model_from_config(

484         layer_data, custom_objects=custom_objects

485     )

486 else:

487     layer = serialization_lib.deserialize_keras_object(

488         layer_data, custom_objects=custom_objects

489     )

File /opt/conda/lib/python3.10/site-packages/keras/src/legacy/saving/saving_utils.py:85, in model_from_config(config, custom_objects)

 81 # TODO(nkovela): Swap find and replace args during Keras 3.0 release

 82 # Replace keras refs with keras

 83 config = _find_replace_nested_dict(config, "keras.", "keras.")

---> 85 return serialization.deserialize_keras_object(

 86     config,

 87     module_objects=MODULE_OBJECTS.ALL_OBJECTS,

 88     custom_objects=custom_objects,

 89     printable_module_name="layer",

 90 )

File /opt/conda/lib/python3.10/site-packages/keras/src/legacy/saving/serialization.py:473, in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)

470 if isinstance(identifier, dict):

471     # In this case we are dealing with a Keras config dictionary.

472     config = identifier

--> 473 (cls, cls_config) = class_and_config_for_serialized_keras_object(

474         config, module_objects, custom_objects, printable_module_name

475     )

477     # If this object has already been loaded (i.e. it's shared between

478     # multiple objects), return the already-loaded object.

479     shared_object_id = config.get(SHARED_OBJECT_KEY)

File /opt/conda/lib/python3.10/site-packages/keras/src/legacy/saving/serialization.py:354, in class_and_config_for_serialized_keras_object(config, module_objects, custom_objects, printable_module_name)

350 cls = object_registration.get_registered_object(

351     class_name, custom_objects, module_objects

352 )

353 if cls is None:

--> 354 raise ValueError(

355         f"Unknown {printable_module_name}: '{class_name}'. "

356         "Please ensure you are using a `keras.utils.custom_object_scope` "

357         "and that this object is included in the scope. See "

358         "https://www.tensorflow.org/guide/keras/save_and_serialize"

359         "#registering_the_custom_object for details."

360     )

362 cls_config = config["config"]

363 # Check if `cls_config` is a list. If it is a list, return the class and the

364 # associated class configs for recursively deserialization. This case will

365 # happen on the old version of sequential model (e.g. `keras_version` ==

366 # "2.0.6"), which is serialized in a different structure, for example

367 # "{'class_name': 'Sequential',

368 #   'config': [{'class_name': 'Embedding', 'config': ...}, {}, ...]}".

ValueError: Unknown layer: 'Convolution2D'. 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.