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 :

- 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 –**

**True**

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
True
>>> new_node_2.graph is tf.get_default_graph()
False
```

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 .

Data Science Learner has started creating tutorials on tensorflow and Deep Learning concepts . If you do not want to miss them ,Immediately subscribe **Data Science Learner** . You will get latest update on Deep Learning .

**Thanks **

**Data Science Learner Team **

#### Join our list

Subscribe to our mailing list and get interesting stuff and updates to your email inbox.