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 each number in a list into an operation in a graph and print the output:

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.multiply(x_data, m_const) 
for x_val in x_vals: 
    print(sess.run(my_product, feed_dict={x_data: x_val})) 

 The output of the preceding code is as follows:

3.0 
9.0 
15.0 
21.0 
27.0 

How it works...

The code in this section creates the data and operations on the computational graph. The following figure is what the computational...