tensorflow yük verileri: bozuk mareşal verileri

Aug 19 2020

FaceNet'i Keras'ta yüklemek istiyorum ama hatalar alıyorum. modal facenet_keras.h5 hazır ama yükleyemiyorum.

facenet_keras.h5'i bu bağlantıdan alabilirsiniz:

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

Tensorflow sürümüm:

tensorflow.__version__

"2.2.0"

ve verileri yüklemek istediğimde:

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

bu hatayı al:

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

teşekkür ederim.

Yanıtlar

1 TensorflowSupport Aug 23 2020 at 12:02

Bu hatanın olası çözümleri aşağıda gösterilmiştir:

  1. Oluşturulmuş Modelve kaydedilmiş Python 2.xolabilir ve kullanıyor olabilirsiniz Python 3.x. Çözüm aynı kullanmaktır Python Versionhangi kullanılarak Modelolmuştur Builtve Saved.

  2. Modelinizin bulunduğu ve Modelinizin aynı sürümünü Keras(ve, olabilir tensorflow) kullanın .BuiltSaved

  3. Saved ModelÖzel Nesneleri içerebilir. Eğer öyleyse, Modeli kodu kullanarak yüklemeniz gerekir,

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

  4. Yeniden oluşturabiliyorsanız architecture(yani, onu oluşturmak için kullanılan orijinal koda sahipseniz), modelbu koddan örnek oluşturabilir ve ardından model.load_weights('your_model_file.hdf5')ağırlıkları yüklemek için kullanabilirsiniz . Orijinali oluşturmak için kullanılan koda sahip değilseniz bu bir seçenek değildir architecture.

Daha fazla ayrıntı için lütfen bu Github Sorununa bakın . İle ilgili daha ayrıntılı bilgi için Saving and Loading the Modelbirlikte Custom Objects, bu başvurun Tensorflow Belgeleri ve bu yığın taşması Cevap .