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

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")):

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 )

130 model_config = json_utils.decode(model_config)
132 with saving_options.keras_option_scope(use_legacy_config=True):

134 model_config, custom_objects=custom_objects
135 )
137 # set weights
138 load_weights_from_hdf5_group(f["model_weights"], model)

 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.")

 86 config,
 87 module_objects=MODULE_OBJECTS.ALL_OBJECTS,
 88 custom_objects=custom_objects,
 89 printable_module_name="layer",
 90 )

490 cls_config = _find_replace_nested_dict(
491 cls_config, "keras.", "keras."
492 )
494 if "custom_objects" in arg_spec.args:

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):

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

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:

501 # First, we create all layers and enqueue nodes to be processed
502 for layer_data in config["layers"]:

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:

479 # Instantiate layer.
480 if "module" not in layer_data:
481 # Legacy format deserialization (no "module" key)
482 # used for H5 and SavedModel formats

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 )

 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.")

 86 config,
 87 module_objects=MODULE_OBJECTS.ALL_OBJECTS,
 88 custom_objects=custom_objects,
 89 printable_module_name="layer",
 90 )

470 if isinstance(identifier, dict):
471 # In this case we are dealing with a Keras config dictionary.
472 config = identifier

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)

350 cls = object_registration.get_registered_object(
351 class_name, custom_objects, module_objects
352 )
353 if cls is None:

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': ...}, {}, ...]}".