我正在实现一个继承自tf.layers.Layer的自定义递归类，当使用双向包装时，出现错误：
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-3-7bd5b5269810> in <module>
----> 1 a = TimeDistributed(Bidirectional(char_recurrent_cell))
~/opt/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/wrappers.py in __init__(self, layer, merge_mode, weights, backward_layer, **kwargs)
434 if backward_layer is None:
435 self.backward_layer = self._recreate_layer_from_config(
--> 436 layer, go_backwards=True)
437 else:
438 self.backward_layer = backward_layer
~/opt/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/wrappers.py in _recreate_layer_from_config(self, layer, go_backwards)
493 config = layer.get_config()
494 if go_backwards:
--> 495 config['go_backwards'] = not config['go_backwards']
496 if 'custom_objects' in tf_inspect.getfullargspec(
497 layer.__class__.from_config).args:
KeyError: 'go_backwards'
这是图层本身的代码：
class RecurrentConfig(BaseLayer):
'''Basic configurable recurrent layer'''
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.layers: List[layers.Layer] = stack_layers(self.params,
self.num_layers,
self.layer_name)
def call(self, inputs: np.ndarray) -> layers.Layer:
'''This function is a sequential/functional call to this layers logic
Args:
inputs: Array to be processed within this layer
Returns:
inputs processed through this layer'''
processed = inputs
for layer in self.layers:
processed = layer(processed)
return processed
@staticmethod
def default_params() -> Dict[Any, Any]:
return{
'units': 32,
'recurrent_initializer': 'glorot_uniform',
'dropout': 0,
'recurrent_dropout': 0,
'activation': None,
'return_sequences': True
}
我试图将go_backwards添加到调用get_config（）时检索到的配置中，但这会导致另一个错误：
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-7bd5b5269810> in <module>
----> 1 a = TimeDistributed(Bidirectional(char_recurrent_cell))
~/opt/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/wrappers.py in __init__(self, layer, merge_mode, weights, backward_layer, **kwargs)
430 # Recreate the forward layer from the original layer config, so that it will
431 # not carry over any state from the layer.
--> 432 self.forward_layer = self._recreate_layer_from_config(layer)
433
434 if backward_layer is None:
~/opt/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/keras/layers/wrappers.py in _recreate_layer_from_config(self, layer, go_backwards)
506 return layer.__class__.from_config(config, custom_objects=custom_objects)
507 else:
--> 508 return layer.__class__.from_config(config)
509
510 @tf_utils.shape_type_conversion
~/opt/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py in from_config(cls, config)
517 A layer instance.
518 """
--> 519 return cls(**config)
520
521 def compute_output_shape(self, input_shape):
~/nlpv3-general/nlp-lib/src/main/python/mosaix_py/mosaix_learn/layers/recurrent_layers.py in __init__(self, *args, **kwargs)
12 '''Basic configurable recurrent layer'''
13 def __init__(self, *args, **kwargs):
---> 14 super().__init__(*args, **kwargs)
15 self.layers: List[layers.Layer] = stack_layers(self.params,
16 self.num_layers,
~/nlpv3-general/nlp-lib/src/main/python/mosaix_py/mosaix_learn/layers/base_layer.py in __init__(self, params, mode, layer_name, num_layers, cust_name, **kwargs)
17 cust_name: str = '',
18 **kwargs):
---> 19 super().__init__(params, mode, **kwargs)
20 self.layer_name = layer_name
21 self.cust_name = cust_name
~/nlpv3-general/nlp-lib/src/main/python/mosaix_py/mosaix_learn/configurable.py in __init__(self, params, mode, **kwargs)
61
62 def __init__(self, params: Dict[AnyStr, Any], mode: ModeKeys, **kwargs):
---> 63 super().__init__(**kwargs) #type: ignore
64 self._params = _parse_params(params, self.default_params())
65 self._mode = mode
~/opt/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
455 self._self_setattr_tracking = False # pylint: disable=protected-access
456 try:
--> 457 result = method(self, *args, **kwargs)
458 finally:
459 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
~/opt/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py in __init__(self, trainable, name, dtype, dynamic, **kwargs)
184 }
185 # Validate optional keyword arguments.
--> 186 generic_utils.validate_kwargs(kwargs, allowed_kwargs)
187
188 # Mutable properties
~/opt/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/keras/utils/generic_utils.py in validate_kwargs(kwargs, allowed_kwargs, error_message)
716 for kwarg in kwargs:
717 if kwarg not in allowed_kwargs:
--> 718 raise TypeError(error_message, kwarg)
TypeError: ('Keyword argument not understood:', 'go_backwards')
以下是一个小的虚拟示例，它将复制我的问题：
import tensorflow as tf
class DummyLayer(tf.keras.layers.Layer):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.a = tf.keras.layers.LSTM(2)
def call(inputs):
return self.a(inputs)
tf.keras.layers.Bidirectional(DummyLayer())
版本信息为： tf_version：'2.1.0-dev20191125' git_version：“ v1.12.1-19144-gf39f4ea3fa”