Tensorflow 在keras中，如何克隆具有自定义对象的模型？

我有一个自定义激活的模型。因此,

model2 = keras.models.clone_model(model)

给出了一个错误。我可以使用custom\u objects关键字加载保存的模型，但在clone\u模型上看不到这样的选项。除了重新制作模型和转移权重之外，还有其他方法吗

编辑：

下面是示例代码（玩具问题）：

以及错误转储：

~/.conda/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/models.py in clone_model(model, input_tensors)
    269     return _clone_sequential_model(model, input_tensors=input_tensors)
    270   else:
--> 271     return _clone_functional_model(model, input_tensors=input_tensors)
    272 
    273 

~/.conda/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/models.py in _clone_functional_model(model, input_tensors)
    129       if layer not in layer_map:
    130         # Clone layer.
--> 131         new_layer = layer.__class__.from_config(layer.get_config())
    132         layer_map[layer] = new_layer
    133         layer = new_layer

~/.conda/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in from_config(cls, config)
    400         A layer instance.
    401     """
--> 402     return cls(**config)
    403 
    404   def compute_output_shape(self, input_shape):

~/.conda/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/layers/core.py in __init__(self, units, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, **kwargs)
    920         activity_regularizer=regularizers.get(activity_regularizer), **kwargs)
    921     self.units = int(units)
--> 922     self.activation = activations.get(activation)
    923     self.use_bias = use_bias
    924     self.kernel_initializer = initializers.get(kernel_initializer)

~/.conda/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/activations.py in get(identifier)
    209   if isinstance(identifier, six.string_types):
    210     identifier = str(identifier)
--> 211     return deserialize(identifier)
    212   elif callable(identifier):
    213     return identifier

~/.conda/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/activations.py in deserialize(name, custom_objects)
    200       module_objects=globals(),
    201       custom_objects=custom_objects,
--> 202       printable_module_name='activation function')
    203 
    204 

~/.conda/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    210       if fn is None:
    211         raise ValueError('Unknown ' + printable_module_name + ':' +
--> 212                          function_name)
    213     return fn
    214   else:

ValueError: Unknown activation function:myTanh

这是一个开放的凯拉斯

建议的解决方法是使用

Lambda

层代替

激活

层

---

x=keras.layers.Lambda（我的自定义激活函数）（x）

我通过调用

keras.utils.get_custom_objects().update(custom_objects)

在定义keras必须知道的其他对象之后，正确克隆模型

def lrelu(x, alpha=0.2):
    return tf.nn.relu(x) * (1 - alpha) + x * alpha

custom_object = {
    'lrelu': lrelu,
}
keras.utils.get_custom_objects().update(custom_objects)

---

你能提供一些你遇到的错误的代码和回溯吗？用一个导致错误的例子进行后期编辑。

def lrelu(x, alpha=0.2):
    return tf.nn.relu(x) * (1 - alpha) + x * alpha

custom_object = {
    'lrelu': lrelu,
}
keras.utils.get_custom_objects().update(custom_objects)