As we have learned, every computation in TensorFlow is represented by a computational graph. They consist of several nodes and edges, where nodes are mathematical operations, such as addition and multiplication, and edges are tensors. Computational graphs are very efficient at optimizing resources and promote distributed computing.
A computational graph consists of several TensorFlow operations, arranged in a graph of nodes.
Let's consider a basic addition operation:
import tensorflow as tf
x = 2
y = 3
z = tf.add(x, y, name='Add')
The computational graph for the preceding code would look like the following:
A computational graph helps us to understand the network architecture when we work on building a really complex neural network. For instance, let's consider a simple layer, 
. Its computational graph would be...
