I am currently trying to have multiple layers with a customized activation with the name cust_sig. But when I try to compile the model, I get a ValueError raised as multiple layers have the same name cust_sig. I am aware that I can manually change the name for every layer but wanted to know if there is something that can be done to automatically have _1, _2, ... added to the name as it does for in-built layers. The model definition can be found below.
# 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)
The error message is shown below
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.
Below should do:
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
From source code, automatic naming works as follows:
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
The Keras graph is checked for existing objects with the same name as the object you're defining - if any exist, continues to increment by 1 until none match. The catch is, you cannot specify name=, as that eliminates automatic naming per above conditional.
The only workaround is likely defining your own custom activation layer using desired name as class name as above, which manifests itself as follows:
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