Computational Graph

Any TensorFlow Core program can be divided into two discrete sections:

• Building the computational graph. A computational graph is nothing but a series of TensorFlow operations arranged into a graph of nodes.
• Running the computational graph. To actually evaluate the nodes, we must run the computational graph within a session. A session encapsulates the control and state of the TensorFlow runtime.

Now, let us write our very first TensorFlow program to understand above concept: 

Output:

Sum of node1 and node2 is: 8

Let us try to understand above code:

• Step 1 : Create a computational graph By creating computational graph, we mean defining the nodes. Tensorflow provides different types of nodes for a variety of tasks. Each node takes zero or more tensors as inputs and produces a tensor as an output.
  • In above program, the nodes node1 and node2 are of tf.constant type. A constant node takes no inputs, and it outputs a value it stores internally. Note that we can also specify the data type of output tensor using dtype argument.

node1 = tf.constant(3, dtype=tf.int32)
node2 = tf.constant(5, dtype=tf.int32)

• node3 is of tf.add type. It takes two tensors as input and returns their sum as output tensor.

node3 = tf.add(node1, node2)

• Step 2 : Run the computational graph In order to run the computational graph, we need to create a session. To create a session, we simply do:

sess = tf.Session()

• Now, we can invoke the run method of session object to perform computations on any node:

print("Sum of node1 and node2 is:",sess.run(node3))

• Here, node3 gets evaluated which further invokes node1 and node2. Finally, we close the session using:

sess.close()

Note: Another(and better) method of working with sessions is to use with block like this:

with tf.Session() as sess:
    print("Sum of node1 and node2 is:",sess.run(node3))

The benefit of this approach is that you do not need to close the session explicitly as it gets automatically closed once control goes out of the scope of with block.

TensorFlow is an open-source machine learning library developed by Google. TensorFlow is used to build and train deep learning models as it facilitates the creation of computational graphs and efficient execution on various hardware platforms. The article provides an comprehensive overview of tensorflow.