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.