Managing Nodes Working with multiple graphs in TensorFlow

multiple graphs in TensorFlow featured image

Most of the scenario in development requires only one graph for the computation in tensorflow .  Still there may be some situation where we need more tensor graph for computation  . In this multi graph scenario if you create a new node then It will automatically add to the default graph . Hence if you need to manage multiple graphs in TensorFlow , you need to set them default before creating new nodes for this .

How to add new node with different graphs :

  1. Refer the below code . Here we will see all new created node is associated with default graph .
new_node = tf.Variable(1)
new_node .graph is tf.get_default_graph()

output  –


2. Now we will create a new graph.

graph = tf.Graph()

3. After the creation of new graph , set it as default graph for working temporary and associate a new node with it.

with graph.as_default():
   new_node_2 = tf.Variable(2)

Conclusion –

Every new node is from the default graph. If you create a new graph in TensorFlow , you need to set it default and then add the new node with it. Let’s check the result-

>>> node_2.graph is graph
>>> new_node_2.graph is tf.get_default_graph()

You may see the newly created node is associated with new graph . It is because we have set it as default for a temporary basis. Once the original or previous graph gets back and resume, now on checking the node association with default graph , we get it is not from default graph . I hope this article must help in clearing the concept of  multiple graphs in TensorFlow .

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Meet Abhishek ( Chief Editor) , a data scientist with major expertise in NLP and Text Analytics. He has worked on various projects involving text data and have been able to achieve great results. He is currently manages, where he and his team share knowledge and help others learn more about data science.
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