Функция Keras get_config для класса слоя

Question

Я построил модель для решения проблемы с несколькими метками. Модель работает нормально, однако проблема возникает, когда я загружаю модель, используя метод load_from_json().

Модель принимает последовательный ввод и выводит метку или выводит список меток.

Layer (type)                 Output Shape              Param #   
=================================================================
input_5 (InputLayer)         (None, 150)               0         
_________________________________________________________________
embedding_layer (Embedding)  (None, 150, 300)          30000300  
_________________________________________________________________
bidirectional_lstm (Bidirect (None, 150, 400)          801600    
_________________________________________________________________
batch_normalization (BatchNo (None, 150, 400)          1600      
_________________________________________________________________
attention_multi_with_context (31, None, 400)           172800    
_________________________________________________________________
dropout_5 (Dropout)          (31, None, 400)           0         
_________________________________________________________________
paralleldenseblock_5 (Parall (None, 31)                1246231   
=================================================================

Параллельный плотный блочный слой создает плотный слой / слои для каждой метки над уровнем внимания, выполняется операция конкатенации для каскадирования всех выходных данных в одну матрицу. который дает матрицу баллов.

Проблема возникает, когда я загружаю модель из JSON. get_config() Функция не записана надлежащим образом. и я не сделал, как это написать.

Я делюсь сценарием класса Parallel_dense_block

class ParallelDenseBlock(Model):

    def __init__(self, nb_classes):
        super(ParallelDenseBlock, self).__init__(name='')
        self.nb_classes = nb_classes
        self.dense_layers = []
        for i in range(nb_classes): #create dense nn for each class
            d1 = Dense(100, activation='sigmoid') #200 ->100
            setattr(self, d1.name, d1)
            d2 = Dense(1, activation='sigmoid') #100 -> 1 
            setattr(self, d2.name, d2)
            self.dense_layers.append([d1.name,d2.name])

    def call(self, input_tensor, training=False):
        x = input_tensor[0]
        layer = self.dense_layers[0]
        x = getattr(self,layer[0])(x)
        merged = getattr(self,layer[1])(x)
        for i in range(1,self.nb_classes):
            # add more layer
            x = input_tensor[i]
            layer = self.dense_layers[i]
            x = getattr(self,layer[0])(x)
            merged = concatenate([merged, getattr(self,layer[1])(x)], axis=-1)
        return merged

    def compute_output_shape(self, input_shape):
        self.output_dim = [input_shape[1], self.nb_classes]
        return input_shape[1], self.nb_classes
    def get_config(self):
        config={}
        config['output_dim'] = self.output_dim
        config['nb_classes'] = self.nb_classes
        config["layers"]=self.dense_layers
        #base_config = super(ParallelDenseBlock, self).get_config()
        return dict(list(config.items()))

ошибка

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-29-53501cecf94a> in <module>
      5 loaded_model_json = json_file.read()
      6 json_file.close()
----> 7 loaded_model = model_from_json(loaded_model_json,custom_objects={"AttentionMultiWithContext":AttentionMultiWithContext,"ParallelDenseBlock":ParallelDenseBlock})

~/anaconda3/envs/py36/lib/python3.6/site-packages/keras/engine/saving.py in model_from_json(json_string, custom_objects)
    662     config = json.loads(json_string)
    663     from ..layers import deserialize
--> 664     return deserialize(config, custom_objects=custom_objects)
    665 
    666 

~/anaconda3/envs/py36/lib/python3.6/site-packages/keras/layers/__init__.py in deserialize(config, custom_objects)
    166                                     module_objects=globs,
    167                                     custom_objects=custom_objects,
--> 168                                     printable_module_name='layer')

~/anaconda3/envs/py36/lib/python3.6/site-packages/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    145                     config['config'],
    146                     custom_objects=dict(list(_GLOBAL_CUSTOM_OBJECTS.items()) +
--> 147                                         list(custom_objects.items())))
    148             with CustomObjectScope(custom_objects):
    149                 return cls.from_config(config['config'])

~/anaconda3/envs/py36/lib/python3.6/site-packages/keras/engine/network.py in from_config(cls, config, custom_objects)
   1054         # First, we create all layers and enqueue nodes to be processed
   1055         for layer_data in config['layers']:
-> 1056             process_layer(layer_data)
   1057 
   1058         # Then we process nodes in order of layer depth.

~/anaconda3/envs/py36/lib/python3.6/site-packages/keras/engine/network.py in process_layer(layer_data)
   1040 
   1041             layer = deserialize_layer(layer_data,
-> 1042                                       custom_objects=custom_objects)
   1043             created_layers[layer_name] = layer
   1044 

~/anaconda3/envs/py36/lib/python3.6/site-packages/keras/layers/__init__.py in deserialize(config, custom_objects)
    166                                     module_objects=globs,
    167                                     custom_objects=custom_objects,
--> 168                                     printable_module_name='layer')

~/anaconda3/envs/py36/lib/python3.6/site-packages/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    145                     config['config'],
    146                     custom_objects=dict(list(_GLOBAL_CUSTOM_OBJECTS.items()) +
--> 147                                         list(custom_objects.items())))
    148             with CustomObjectScope(custom_objects):
    149                 return cls.from_config(config['config'])

~/anaconda3/envs/py36/lib/python3.6/site-packages/keras/engine/network.py in from_config(cls, config, custom_objects)
   1054         # First, we create all layers and enqueue nodes to be processed
   1055         for layer_data in config['layers']:
-> 1056             process_layer(layer_data)
   1057 
   1058         # Then we process nodes in order of layer depth.

~/anaconda3/envs/py36/lib/python3.6/site-packages/keras/engine/network.py in process_layer(layer_data)
   1034                 ValueError: In case of improperly formatted `layer_data` dict.
   1035             """
-> 1036             layer_name = layer_data['name']
   1037 
   1038             # Instantiate layer.

TypeError: list indices must be integers or slices, not str