如何自定义 Keras 层名称并使其自动递增 layer.name

Question

我目前正在尝试使用名称为自定义激活的多个层cust_sig。但是当我尝试编译模型时，我收到一个 ValueError 错误，因为多个层具有相同的名称cust_sig。我知道我可以手动更改每个图层的名称，但想知道是否可以_1, _2, ...像内置图层一样自动添加到名称中。模型定义如下。

# Creating a model
from tensorflow.python.keras import keras
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Dense

# Custom activation function
from tensorflow.python.keras.layers import Activation
from tensorflow.python.keras import backend as K
from keras.utils.generic_utils import get_custom_objects

def custom_activation(x):
    return (K.sigmoid(x) * 5) - 1

get_custom_objects().update({'custom_activation': Activation(custom_activation)})

data_format = 'channels_first'

spec_input = keras.layers.Input(shape=(1, 3, 256), name='spec')
x = keras.layers.Flatten(data_format)(spec_input)

for layer in range(3):
  x = Dense(512)(x)
  x = Activation('custom_activation', name='cust_sig')(x)

out = Dense(256, activation="sigmoid", name='out')(x)
model = Model(inputs=spec_input, outputs=out)

错误信息如下所示

Traceback (most recent call last):
  File "/home/xyz/anaconda3/envs/ctf/lib/python3.7/site-packages/tensorflow/python/training/tracking/base.py", line 457, in _method_wrapper
    result = method(self, *args, **kwargs)
  File "/home/xyz/anaconda3/envs/ctf/lib/python3.7/site-packages/tensorflow/python/keras/engine/network.py", line 315, in _init_graph_network
    self.inputs, self.outputs)
  File "/home/xyz/anaconda3/envs/ctf/lib/python3.7/site-packages/tensorflow/python/keras/engine/network.py", line 1861, in _map_graph_network
    str(all_names.count(name)) + ' times in the model. '
ValueError: The name "cust_sig" is used 3 times in the model. All layer names should be unique.

Answer 1

下面应该做：

def custom_activation(x):
    return (K.sigmoid(x) * 5) - 1

class CustSig(Layer):
    def __init__(self, my_activation, **kwargs):
        super(CustSig, self).__init__(**kwargs)
        self.supports_masking = True
        self.activation = my_activation

    def call(self, inputs):
        return self.activation(inputs)

    def get_config(self):
        config = {'activation': activations.serialize(self.activation)}
        base_config = super(Activation, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))

    def compute_output_shape(self, input_shape):
        return input_shape

解释：

从源码来看，自动命名的工作原理如下：

if not name:
  self._name = backend.unique_object_name(
      generic_utils.to_snake_case(self.__class__.__name__),
      zero_based=zero_based)
else:
  self._name = name

Keras 图表会检查是否存在与您定义的对象同名的现有对象 - 如果存在，则继续增加 1，直到没有匹配的对象为止。问题是，您不能指定name=，因为这会消除根据上述条件的自动命名。

唯一的解决方法可能是使用所需名称作为类名定义您自己的自定义激活层，如上所述，其表现如下：

ipt = Input(shape=(1, 3, 256), name='spec')
x   = Flatten('channels_last')(ipt)
for _ in range(3):
    x   = Dense(512)(x)
    x   = CustSig(custom_activation)(x)
out = Dense(256, activation='sigmoid', name='out')(x)

model = Model(ipt, out)

print(model.layers[3].name)
print(model.layers[5].name)
print(model.layers[7].name)

cust_sig
cust_sig_1
cust_sig_2