Question

with open('2model.json','r') as f:
json = f.read()
model = model_from_json(json)
model.load_weights("color_tensorflow_real_mode.h5")

我在Google colab上训练了一个keras模型。现在无法将其本地加载到我的系统上。收到此错误：ValueError：未知的初始值设定项：GlorotUniform

如何解决这个问题？每次我在colab上制作模型并尝试在本地加载时，我都无法这样做。收到此错误消息：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-17-c3ed162a8277> in <module>()
----> 1 model = model_from_json(json)
      2 model.load_weights("color_tensorflow_real_mode.h5")

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\saving.py in model_from_json(json_string, custom_objects)
    349   config = json.loads(json_string)
    350   from tensorflow.python.keras.layers import deserialize  # pylint: disable=g-import-not-at-top
--> 351   return deserialize(config, custom_objects=custom_objects)
    352 
    353 

~\Anaconda3\lib\site-packages\tensorflow\python\keras\layers\serialization.py in deserialize(config, custom_objects)
     62       module_objects=globs,
     63       custom_objects=custom_objects,
---> 64       printable_module_name='layer')

~\Anaconda3\lib\site-packages\tensorflow\python\keras\utils\generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    171             custom_objects=dict(
    172                 list(_GLOBAL_CUSTOM_OBJECTS.items()) +
--> 173                 list(custom_objects.items())))
    174       with CustomObjectScope(custom_objects):
    175         return cls.from_config(config['config'])

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\network.py in from_config(cls, config, custom_objects)
   1290     # First, we create all layers and enqueue nodes to be processed
   1291     for layer_data in config['layers']:
-> 1292       process_layer(layer_data)
   1293     # Then we process nodes in order of layer depth.
   1294     # Nodes that cannot yet be processed (if the inbound node

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\network.py in process_layer(layer_data)
   1276       from tensorflow.python.keras.layers import deserialize as deserialize_layer  # pylint: disable=g-import-not-at-top
   1277 
-> 1278       layer = deserialize_layer(layer_data, custom_objects=custom_objects)
   1279       created_layers[layer_name] = layer
   1280 

~\Anaconda3\lib\site-packages\tensorflow\python\keras\layers\serialization.py in deserialize(config, custom_objects)
     62       module_objects=globs,
     63       custom_objects=custom_objects,
---> 64       printable_module_name='layer')

~\Anaconda3\lib\site-packages\tensorflow\python\keras\utils\generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    173                 list(custom_objects.items())))
    174       with CustomObjectScope(custom_objects):
--> 175         return cls.from_config(config['config'])
    176     else:
    177       # Then `cls` may be a function returning a class.

~\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in from_config(cls, config)
   1615         A layer instance.
   1616     """
-> 1617     return cls(**config)
   1618 
   1619 

~\Anaconda3\lib\site-packages\tensorflow\python\keras\layers\convolutional.py in __init__(self, filters, kernel_size, strides, padding, data_format, dilation_rate, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, **kwargs)
    464         activation=activations.get(activation),
    465         use_bias=use_bias,
--> 466         kernel_initializer=initializers.get(kernel_initializer),
    467         bias_initializer=initializers.get(bias_initializer),
    468         kernel_regularizer=regularizers.get(kernel_regularizer),

~\Anaconda3\lib\site-packages\tensorflow\python\keras\initializers.py in get(identifier)
    153     return None
    154   if isinstance(identifier, dict):
--> 155     return deserialize(identifier)
    156   elif isinstance(identifier, six.string_types):
    157     config = {'class_name': str(identifier), 'config': {}}

~\Anaconda3\lib\site-packages\tensorflow\python\keras\initializers.py in deserialize(config, custom_objects)
    145       module_objects=globals(),
    146       custom_objects=custom_objects,
--> 147       printable_module_name='initializer')
    148 
    149 

~\Anaconda3\lib\site-packages\tensorflow\python\keras\utils\generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    161       cls = module_objects.get(class_name)
    162       if cls is None:
--> 163         raise ValueError('Unknown ' + printable_module_name + ': ' + class_name)
    164     if hasattr(cls, 'from_config'):
    165       arg_spec = tf_inspect.getfullargspec(cls.from_config)

ValueError: Unknown initializer: GlorotUniform

Stackoverflow要求我添加详细信息，而我无须添加。或者我不确定要添加什么。请帮忙。

Answer 1

通过运行Keras或{，确保您拥有tensorflow和2.4.4（分别为1.11.0和pip install keras tensorflow）的最新版本{1}}。
如果是使用不推荐使用的对象的Google Colab，则可能需要使用自定义对象：

conda install keras tensorflow

不确定这是否是您的情况。

Answer 2

使用

加载模型

 from tensorflow.keras.models import load_model

代替

from keras.models import load_model

我尝试了许多方法，但这是最后可行的方法！

Answer 3

当我尝试本地加载在Colab上训练的模型时，我遇到了类似的错误（未知图层：名称）。我试图更改keras版本，tensorflow版本，conda版本等，但没有任何帮助。我通过将模型的权重保存在Colab上，在本地创建相同的模型并将权重加载到该模型中来解决了这个问题。

我在Google Colab上训练了一个Keras模型。现在无法将其本地加载到我的系统上。

3 个答案: