Question

我已经使用来自keras的MobileNet架构使用以下代码定义了一个模型：

model = MobileNet(input_shape=(size, size, 1), alpha=1.0, weights=None, classes=NCATS)
model.compile(optimizer=Adam(lr=0.002), loss='categorical_crossentropy',
              metrics=[categorical_crossentropy, categorical_accuracy, top_3_accuracy])

训练后，我使用来保存模型

model.save(MOD1_PATH)

我现在正在尝试使用加载模型

model = load_model(MOD1_PATH)

但是尝试加载时出现以下TypeError：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-26-2166dee60e7c> in <module>
----> 1 load_model(MOD1_PATH)

/opt/conda/lib/python3.6/site-packages/Keras-2.2.4-py3.6.egg/keras/engine/saving.py in load_model(filepath, custom_objects, compile)
    418     f = H5Dict(filepath, 'r')
    419     try:
--> 420         model = _deserialize_model(f, custom_objects, compile)
    421     finally:
    422         if opened_new_file:

/opt/conda/lib/python3.6/site-packages/Keras-2.2.4-py3.6.egg/keras/engine/saving.py in _deserialize_model(f, custom_objects, compile)
    224         raise ValueError('No model found in config.')
    225     model_config = json.loads(model_config.decode('utf-8'))
--> 226     model = model_from_config(model_config, custom_objects=custom_objects)
    227     model_weights_group = f['model_weights']
    228 

/opt/conda/lib/python3.6/site-packages/Keras-2.2.4-py3.6.egg/keras/engine/saving.py in model_from_config(config, custom_objects)
    457                         '`Sequential.from_config(config)`?')
    458     from ..layers import deserialize
--> 459     return deserialize(config, custom_objects=custom_objects)
    460 
    461 

/opt/conda/lib/python3.6/site-packages/Keras-2.2.4-py3.6.egg/keras/layers/__init__.py in deserialize(config, custom_objects)
     53                                     module_objects=globs,
     54                                     custom_objects=custom_objects,
---> 55                                     printable_module_name='layer')

/opt/conda/lib/python3.6/site-packages/Keras-2.2.4-py3.6.egg/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    143                     config['config'],
    144                     custom_objects=dict(list(_GLOBAL_CUSTOM_OBJECTS.items()) +
--> 145                                         list(custom_objects.items())))
    146             with CustomObjectScope(custom_objects):
    147                 return cls.from_config(config['config'])

/opt/conda/lib/python3.6/site-packages/Keras-2.2.4-py3.6.egg/keras/engine/network.py in from_config(cls, config, custom_objects)
   1020         # First, we create all layers and enqueue nodes to be processed
   1021         for layer_data in config['layers']:
-> 1022             process_layer(layer_data)
   1023         # Then we process nodes in order of layer depth.
   1024         # Nodes that cannot yet be processed (if the inbound node

/opt/conda/lib/python3.6/site-packages/Keras-2.2.4-py3.6.egg/keras/engine/network.py in process_layer(layer_data)
   1006 
   1007             layer = deserialize_layer(layer_data,
-> 1008                                       custom_objects=custom_objects)
   1009             created_layers[layer_name] = layer
   1010 

/opt/conda/lib/python3.6/site-packages/Keras-2.2.4-py3.6.egg/keras/layers/__init__.py in deserialize(config, custom_objects)
     53                                     module_objects=globs,
     54                                     custom_objects=custom_objects,
---> 55                                     printable_module_name='layer')

/opt/conda/lib/python3.6/site-packages/Keras-2.2.4-py3.6.egg/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    145                                         list(custom_objects.items())))
    146             with CustomObjectScope(custom_objects):
--> 147                 return cls.from_config(config['config'])
    148         else:
    149             # Then `cls` may be a function returning a class.

/opt/conda/lib/python3.6/site-packages/Keras-2.2.4-py3.6.egg/keras/engine/base_layer.py in from_config(cls, config)
   1107             A layer instance.
   1108         """
-> 1109         return cls(**config)
   1110 
   1111     def count_params(self):

/opt/conda/lib/python3.6/site-packages/Keras-2.2.4-py3.6.egg/keras/layers/advanced_activations.py in __init__(self, max_value, negative_slope, threshold, **kwargs)
    290                  threshold=0., **kwargs):
    291         super(ReLU, self).__init__(**kwargs)
--> 292         if max_value is not None and max_value < 0.:
    293             raise ValueError('max_value of ReLU layer '
    294                              'cannot be negative value: %s' % str(max_value))

TypeError: '<' not supported between instances of 'dict' and 'float'

有人在加载经过训练的模型之前遇到过这种问题吗？任何帮助，将不胜感激。谢谢。

Answer 1

我在以下方面有同样的问题

Tensorflow 1.11.0 ，

Keras 2.1.6-tf

我在Google合作实验室创建了一个笔记本，结果是一样的。 Google相关配置：

Tensorflow 1.12.0-rc1 ，

Keras 2.1.6-tf

然后我尝试使用以下方法尝试load_model：

Tensorflow 1.9.0 ，

Keras 2.1.6-tf

，一切开始起作用。可能是新版本 Tensorflow

的错误

在Keras模型中加载时发生TypeError

1 个答案: