加载保存的BertClassifer模型

Question

我使用了这个colab笔记本中的以下示例：https://github.com/tensorflow/models/blob/master/official/colab/fine_tuning_bert.ipynb

它使用model.save()功能保存微调过的模型。

我尝试使用tf.keras.models.load_model()加载相同的模型，但得到以下错误：

KeyError: 'name'
KeyError                                  Traceback (most recent call last)
in engine
----> 1 model = tf.keras.models.load_model('./saved_model/test')

/home/cdsw/.local/lib/python3.6/site-packages/tensorflow/python/keras/saving/save.py in load_model(filepath, custom_objects, compile)
    183     if isinstance(filepath, six.string_types):
    184       loader_impl.parse_saved_model(filepath)
--> 185       return saved_model_load.load(filepath, compile)
    186 
    187   raise IOError(

/home/cdsw/.local/lib/python3.6/site-packages/tensorflow/python/keras/saving/saved_model/load.py in load(path, compile)
    114   # TODO(kathywu): Add saving/loading of optimizer, compiled losses and metrics.
    115   # TODO(kathywu): Add code to load from objects that contain all endpoints
--> 116   model = tf_load.load_internal(path, loader_cls=KerasObjectLoader)
    117 
    118   # pylint: disable=protected-access

/home/cdsw/.local/lib/python3.6/site-packages/tensorflow/python/saved_model/load.py in load_internal(export_dir, tags, loader_cls)
    605       loader = loader_cls(object_graph_proto,
    606                           saved_model_proto,
--> 607                           export_dir)
    608       root = loader.get(0)
    609       if isinstance(loader, Loader):

/home/cdsw/.local/lib/python3.6/site-packages/tensorflow/python/keras/saving/saved_model/load.py in __init__(self, *args, **kwargs)
    186     self._models_to_reconstruct = []
    187 
--> 188     super(KerasObjectLoader, self).__init__(*args, **kwargs)
    189 
    190     # Now that the node object has been fully loaded, and the checkpoint has

/home/cdsw/.local/lib/python3.6/site-packages/tensorflow/python/saved_model/load.py in __init__(self, object_graph_proto, saved_model_proto, export_dir)
    121       self._concrete_functions[name] = _WrapperFunction(concrete_function)
    122 
--> 123     self._load_all()
    124     self._restore_checkpoint()
    125 

/home/cdsw/.local/lib/python3.6/site-packages/tensorflow/python/keras/saving/saved_model/load.py in _load_all(self)
    207     # loaded from config may create variables / other objects during
    208     # initialization. These are recorded in `_nodes_recreated_from_config`.
--> 209     self._layer_nodes = self._load_layers()
    210 
    211     # Load all other nodes and functions.

/home/cdsw/.local/lib/python3.6/site-packages/tensorflow/python/keras/saving/saved_model/load.py in _load_layers(self)
    307         continue
    308 
--> 309       layers[node_id] = self._load_layer(proto.user_object, node_id)
    310 
    311     for node_id, proto in metric_list:

/home/cdsw/.local/lib/python3.6/site-packages/tensorflow/python/keras/saving/saved_model/load.py in _load_layer(self, proto, node_id)
    333     # Detect whether this object can be revived from the config. If not, then
    334     # revive from the SavedModel instead.
--> 335     obj, setter = self._revive_from_config(proto.identifier, metadata, node_id)
    336     if obj is None:
    337       obj, setter = revive_custom_object(proto.identifier, metadata)

/home/cdsw/.local/lib/python3.6/site-packages/tensorflow/python/keras/saving/saved_model/load.py in _revive_from_config(self, identifier, metadata, node_id)
    350     else:
    351       obj = (
--> 352           self._revive_graph_network(metadata, node_id) or
    353           self._revive_layer_from_config(metadata, node_id))
    354 

/home/cdsw/.local/lib/python3.6/site-packages/tensorflow/python/keras/saving/saved_model/load.py in _revive_graph_network(self, metadata, node_id)
    386     else:
    387       model = models_lib.Functional(
--> 388           inputs=[], outputs=[], name=config['name'])
    389 
    390     # Record this model and its layers. This will later be used to reconstruct

KeyError: 'name'

有人可以建议我做错了什么，或者我是否应该做一些其他的事情来加载这个模型？谢谢!

Answer 1

由于所提供的笔记本不再可用，我将假定您正在使用这个(官方的？) BERT implementation for thensorflow，因为我在使用它时遇到了同样的问题。

由于subclassed model和(可能) this layer中缺少适当的get_config方法，您无法加载模型。您需要编辑BERT编码器类(如果您可以这样做)。

我在尝试修改BERT model并应用屏蔽语言建模训练时遇到了这个问题。我发现由于前面提到的KeyError: 'name'，我无法加载检查点。我发现了三个问题：

1. 经过大量测试后，我发现在保存过程中产生警告的几个层，警告声明Custom mask layers require a config and must override...我修复了我的实现中的层，然而问题是我意识到KeyError: 'name'来自模型的get_config implementation，它没有产生tf.keras.Model原始配置和new config dict。在这种特殊情况下，这不是由于缺少层配置(通常是这种情况)。我在implementation:

中修补了子类模型的get_config方法

def get_config(self):
    # Implement get_config to enable serialization. This is NOT optional.
    base_config = super(LayoutEncoder, self).get_config()
    return dict(list(self._config.items()) + list(base_config.items()))

1. 解算KeyError: 'name' (使用第2点中的代码。)创建了一个新的更可怕的错误(在加载/保存期间也是如此)。出现了500多行包含WARNING:tensorflow:Unresolved object in checkpoint...和Inconsistent references when loading the checkpoint into this object graph...的错误/警告，但加载仍然失败。我发现这是由于BERT模型跟踪各层的方式造成的(第484-491行)。这些层在保存时也被序列化了。我不确定为什么要将这些成员添加到编码器类中，但是通过删除它们，我能够在没有任何其他问题的情况下保存和加载模型。

很抱歉有这么长的历史，但是，嘿，这是解释它的唯一方法…

Answer 2

正如它所说的，name没有被定义。model = tf.keras.models.load_model('./saved_model/test')指向路径(文件夹) ./saved_model/test，而不是模型的路径。

Answer 3

我在这里记录了我的KeyError经验(以及我找到的解决方案)：

https://github.com/tensorflow/models/issues/8692#issuecomment-727033061

这是由于自定义层的get_config()没有使用父级的基本配置的糟糕实现，该基本配置包含keras重新加载所必需的name密钥。