Tensorflow-Lastdaten: schlechte Marschalldaten
Ich möchte FaceNet in Keras laden, erhalte jedoch Fehler. Die modale Datei facenet_keras.h5 ist fertig, kann aber nicht geladen werden.
Sie können facenet_keras.h5 über diesen Link erhalten:
https://drive.google.com/drive/folders/1pwQ3H4aJ8a6yyJHZkTwtjcL4wYWQb7bn
Meine Tensorflow-Version ist:
tensorflow.__version__
'2.2.0'
und wenn ich Daten laden möchte:
from tensorflow.keras.models import load_model
load_model('facenet_keras.h5')
Erhalte diesen Fehler:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-6-2a20f38e8217> in <module>
----> 1 load_model('facenet_keras.h5')
~/.local/lib/python3.8/site-packages/tensorflow/python/keras/saving/save.py in load_model(filepath, custom_objects, compile)
182 if (h5py is not None and (
183 isinstance(filepath, h5py.File) or h5py.is_hdf5(filepath))):
--> 184 return hdf5_format.load_model_from_hdf5(filepath, custom_objects, compile)
185
186 if sys.version_info >= (3, 4) and isinstance(filepath, pathlib.Path):
~/.local/lib/python3.8/site-packages/tensorflow/python/keras/saving/hdf5_format.py in load_model_from_hdf5(filepath, custom_objects, compile)
175 raise ValueError('No model found in config file.')
176 model_config = json.loads(model_config.decode('utf-8'))
--> 177 model = model_config_lib.model_from_config(model_config,
178 custom_objects=custom_objects)
179
~/.local/lib/python3.8/site-packages/tensorflow/python/keras/saving/model_config.py in model_from_config(config, custom_objects)
53 '`Sequential.from_config(config)`?')
54 from tensorflow.python.keras.layers import deserialize # pylint: disable=g-import-not-at-top
---> 55 return deserialize(config, custom_objects=custom_objects)
56
57
~/.local/lib/python3.8/site-packages/tensorflow/python/keras/layers/serialization.py in deserialize(config, custom_objects)
103 config['class_name'] = _DESERIALIZATION_TABLE[layer_class_name]
104
--> 105 return deserialize_keras_object(
106 config,
107 module_objects=globs,
~/.local/lib/python3.8/site-packages/tensorflow/python/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
367
368 if 'custom_objects' in arg_spec.args:
--> 369 return cls.from_config(
370 cls_config,
371 custom_objects=dict(
~/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/network.py in from_config(cls, config, custom_objects)
984 ValueError: In case of improperly formatted config dict.
985 """
--> 986 input_tensors, output_tensors, created_layers = reconstruct_from_config(
987 config, custom_objects)
988 model = cls(inputs=input_tensors, outputs=output_tensors,
~/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/network.py in reconstruct_from_config(config, custom_objects, created_layers)
2017 # First, we create all layers and enqueue nodes to be processed
2018 for layer_data in config['layers']:
-> 2019 process_layer(layer_data)
2020 # Then we process nodes in order of layer depth.
2021 # Nodes that cannot yet be processed (if the inbound node
~/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/network.py in process_layer(layer_data)
1999 from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top
2000
-> 2001 layer = deserialize_layer(layer_data, custom_objects=custom_objects)
2002 created_layers[layer_name] = layer
2003
~/.local/lib/python3.8/site-packages/tensorflow/python/keras/layers/serialization.py in deserialize(config, custom_objects)
103 config['class_name'] = _DESERIALIZATION_TABLE[layer_class_name]
104
--> 105 return deserialize_keras_object(
106 config,
107 module_objects=globs,
~/.local/lib/python3.8/site-packages/tensorflow/python/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
367
368 if 'custom_objects' in arg_spec.args:
--> 369 return cls.from_config(
370 cls_config,
371 custom_objects=dict(
~/.local/lib/python3.8/site-packages/tensorflow/python/keras/layers/core.py in from_config(cls, config, custom_objects)
988 def from_config(cls, config, custom_objects=None):
989 config = config.copy()
--> 990 function = cls._parse_function_from_config(
991 config, custom_objects, 'function', 'module', 'function_type')
992
~/.local/lib/python3.8/site-packages/tensorflow/python/keras/layers/core.py in _parse_function_from_config(cls, config, custom_objects, func_attr_name, module_attr_name, func_type_attr_name)
1040 elif function_type == 'lambda':
1041 # Unsafe deserialization from bytecode
-> 1042 function = generic_utils.func_load(
1043 config[func_attr_name], globs=globs)
1044 elif function_type == 'raw':
~/.local/lib/python3.8/site-packages/tensorflow/python/keras/utils/generic_utils.py in func_load(code, defaults, closure, globs)
469 except (UnicodeEncodeError, binascii.Error):
470 raw_code = code.encode('raw_unicode_escape')
--> 471 code = marshal.loads(raw_code)
472 if globs is None:
473 globs = globals()
ValueError: bad marshal data (unknown type code)
Dankeschön.
Antworten
Die möglichen Lösungen für diesen Fehler sind unten aufgeführt:
Das wurde
Modelmöglicherweise erstellt und gespeichertPython 2.xund Sie verwenden es möglicherweisePython 3.x. Die Lösung besteht darin, dasselbe zu verwenden,Python Versionmit dem das gewesenModelistBuiltundSaved.Verwenden Sie dieselbe Version von
Keras(und möglicherweisetensorflow), auf der sich Ihr Modell befandBuiltundSaved.Die
Saved Modelkönnen benutzerdefinierte Objekte enthalten. In diesem Fall müssen Sie das Modell mithilfe des Codes laden.new_model = tf.keras.models.load_model('model.h5', custom_objects={'CustomLayer': CustomLayer})Wenn Sie
architectureden Code neu erstellen können (dh Sie haben den ursprünglichen Code, der zum Generieren verwendet wurde), können Sie denmodelCode aus diesem Code instanziieren und dannmodel.load_weights('your_model_file.hdf5')zum Laden der Gewichte verwenden. Dies ist keine Option, wenn Sie nicht über den Code verfügen, mit dem das Original erstellt wurdearchitecture.
Weitere Informationen finden Sie in dieser Github-Ausgabe . Weitere Informationen zu Saving and Loading the Modelwith Custom Objectsfinden Sie in dieser Tensorflow-Dokumentation und in dieser Antwort zum Stapelüberlauf .