données de charge tensorflow: données de mauvais marshal

Aug 19 2020

Je veux charger FaceNet dans Keras mais j'obtiens des erreurs. le modal facenet_keras.h5 est prêt mais je ne peux pas le charger.

vous pouvez obtenir facenet_keras.h5 à partir de ce lien:

https://drive.google.com/drive/folders/1pwQ3H4aJ8a6yyJHZkTwtjcL4wYWQb7bn

Ma version tensorflow est:

tensorflow.__version__

«2.2.0»

et quand je veux charger des données:

from tensorflow.keras.models import load_model
load_model('facenet_keras.h5')

obtenir cette erreur:

---------------------------------------------------------------------------
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)

Je vous remercie.

Réponses

1 TensorflowSupport Aug 23 2020 at 12:02

Les solutions possibles à cette erreur sont indiquées ci-dessous:

  1. Le Modelpeut avoir été construit et enregistré Python 2.xet vous utilisez peut-être Python 3.x. La solution est d'utiliser la même Python Versionaide que l' Modela été Builtet Saved.

  2. Utilisez la même version de Keras(et, peut-être, tensorflow), sur laquelle votre modèle était Builtet Saved.

  3. Le Saved Modelpeut contenir des objets personnalisés. Si tel est le cas, vous devez charger le modèle à l'aide du code,

    new_model = tf.keras.models.load_model('model.h5', custom_objects={'CustomLayer': CustomLayer})

  4. Si vous pouvez recréer le architecture(c'est-à-dire que vous avez le code original utilisé pour le générer), vous pouvez instancier le à modelpartir de ce code, puis l'utiliser model.load_weights('your_model_file.hdf5')pour charger les poids. Ce n'est pas une option si vous ne disposez pas du code utilisé pour créer l'original architecture.

Pour plus de détails, veuillez consulter ce problème Github . Pour plus de détails concernant Saving and Loading the Modelavec Custom Objects, veuillez consulter cette documentation Tensorflow et cette réponse Stack Overflow .