Until now, we have been dealing with constants and variables. We can also feed tensors during the execution of a graph. Here we have an example of feeding tensors during execution. For feeding a tensor, first, we have to define the feed object using the tf.placeholder() function.

After defining two feed objects, we can see how to use it inside sess.run():

x = tf.placeholder(tf.float32) 
y = tf.placeholder(tf.float32) 
 
output = tf.mul(input1, input2) 
 
with tf.Session() as sess: 
  print(sess.run([output], feed_dict={x:[8.], y:[2.]})) 
 
# output: 
# [array([ 16.], dtype=float32)] 

Let's start coding using TensorFlow.