The building of a computational graph is very simple; you go on adding the variables and operations and passing them through (flow the tensors) in the sequence you build your neural network layer by layer. TensorFlow also allows you to use specific devices (CPU/GPU) with different objects of the computation graph using with tf.device(). In our example, the computational graph consists of three nodes, v_1 and v_2 representing the two vectors, and Add is the operation to be performed on them.

Now, to bring this graph to life, we first need to define a session object using tf.Session(); we gave the name sess to our session object. Next, we run it using the run method defined in Session class as follows:

run (fetches, feed_dict=None, options=None, run_metadata) 

This evaluates the tensor in fetches; our example has tensor v_add in fetches. The run method will execute every tensor and every operation in the graph leading to v_add. If instead of v_add, you have v_1 in fetches, the result will be the value of vector v_1:

[1,2,3,4]  

Fetches can be a single tensor/operation object or more, for example, if the fetches is [v_1, v_2, v_add], the output will be the following:

[array([1, 2, 3, 4]), array([2, 1, 5, 3]), array([3, 3, 8, 7])] 

In the same program code, we can have many session objects.