Operations in a Computational Graph

Now that we can put objects into our computational graph, we will introduce operations that act on such objects.

Getting ready

To start a graph, we load TensorFlow and create a session, as follows:

import tensorflow as tf
sess = tf.Session()

How to do it…

In this example, we will combine what we have learned and feed in each number in a list to an operation in a graph and print the output:

1. First we declare our tensors and placeholders. Here we will create a numpy array to feed into our operation:

   import numpy as np
   x_vals = np.array([1., 3., 5., 7., 9.])
   x_data = tf.placeholder(tf.float32)
   m_const = tf.constant(3.)
   my_product = tf.mul(x_data, m_const)
   for x_val in x_vals:
       print(sess.run(my_product, feed_dict={x_data: x_val}))
   3.0
   9.0
   15.0
   21.0
   27.0

How it works…

Steps 1 and 2 create the data and operations on the computational graph. Then, in step 3, we feed the data through the graph and print the output. Here is what the computational graph...