tensorflow 加载数据:错误的元帅数据 [英] tensorflow load data: bad marshal data

问题描述

我想在 Keras 中加载 FaceNet，但出现错误.模态 facenet_keras.h5 已准备就绪，但我无法加载它.

I want to load FaceNet in Keras but I am getting errors. the modal facenet_keras.h5 is ready but I can't load it.

您可以从此链接获取 facenet_keras.h5:

you can get facenet_keras.h5 from this link:

https://drive.google.com/drive/folders/1pwQ3H4aJ8a6yyJHZ7bYcT>

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

我的 tensorflow 版本是:

My tensorflow version is:

tensorflow.__version__

'2.2.0'

当我想加载数据时:

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

得到这个错误:

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

谢谢.

推荐答案

此错误的可能解决方案如下所示:

The possible solutions to this error are shown below:

1. Model 可能是在 Python 2.x 中构建和保存的，而您可能正在使用 Python 3.x.解决方案是使用与 Model 已Built 和 Saved 相同的 Python 版本.

1. The Model might have been built and saved in Python 2.x and you might be using Python 3.x. Solution is to use the same Python Version using which the Model has been Built and Saved.

使用相同版本的 Keras(也可能是 tensorflow)，您的模型基于该版本Built 和 <代码>已保存.

Use the same version of Keras (and, may be, tensorflow), on which your Model was Built and Saved.

Saved Model 可能包含自定义对象.如果是这样，您需要使用代码加载模型，

The Saved Model might contain Custom Objects. If so, you need to load the Model using the code,

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

如果您可以重新创建架构(即您有用于生成它的原始代码)，您可以从该代码实例化模型，然后使用model.load_weights('your_model_file.hdf5') 加载权重.如果您没有用于创建原始架构的代码，则这不是一个选项.

If you can recreate the architecture (i.e. you have the original code used to generate it), you can instantiate the model from that code and then use model.load_weights('your_model_file.hdf5') to load in the weights. This isn't an option if you don't have the code used to create the original architecture.

有关更多详细信息，请参阅此 Github 问题.有关使用自定义对象保存和加载模型的更多详细信息，请参阅此Tensorflow 文档 和这个堆栈溢出答案.

For more details, please refer this Github Issue. For more details regarding Saving and Loading the Model with Custom Objects, please refer this Tensorflow Documentation and this Stack Overflow Answer.

这篇关于tensorflow 加载数据:错误的元帅数据的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！