Tensorboard. Duplicated graph node: one unconnected with placeholders and one connected - python-3.x

Question

I'm trying to visualize my model graph on TensorBoard. I'm using Keras 2.1.5 and tensorflow-gpu 1.13.1. My model is a concatenation of convolutional layers and, at the end, a custom layer where I make some operations with tensors.
Everything works fine, although I defined some prints at the end and at the beggining of my custom layer and then check that Python enters two time my code when I just call it once.
I'm running some trainings and check the model graph on TensorBoard and then found out something I haven't seen in any other example on the web:
Graph of custom layer
There is a connected graph of my custom layer (trans2img) and another one unconnected and with empty placeholders as inputs. I don't understand the reason.
Here is a simple example as my code:
def custom_layer(inputs):
with tf.name_scope('trans2img'):
a = inputs[0]
def some_operation(a):
with tf.name_scope('op1'):
b = 2*a
return b
def some_other_op(b, c):
with tf.name_scope('op2'):
d = b/c
return d
b = some_operation(a)
d = some_other_op(b, inputs[1])
return d
After that, in my network definition file, I load this custom layer as from custom_layer import custom_layer, and then use it as a Lambda layer:
net = Lambda(custom_layer)[branch1, branch2]
I don't know if it is because the way I define the inner operations in my custom_layer or the way I call them. I would like to know how to interpret this second unconnected graph I get and if it's an indicator of unnineficientcode. I would appreciate any clue and help.

Answer 1

This issue happens when you are using multiple tf scope.
For each scope it creates a new op with "op_{integer}".
You need to make use of "absolute_name_scope(scope)" to resolve your issue.
Please refer to below link on how to make use of it.
https://github.com/tensorflow/tensorflow/issues/9545
https://github.com/tensorflow/tensorflow/pull/23250/commits/1169eaca048b660905fc5776e617cc824a19b125