Placeholders

While constants store the value at the time of defining the tensor, placeholders allow you to create empty tensors so that the values can be provided at runtime. The TensorFlow library provides the tf.placeholder() function with the following signature to create placeholders:

tf.placeholder(
  dtype,
  shape=None,
  name=None
  )

As an example, let's create two placeholders and print them:

p1 = tf.placeholder(tf.float32)
p2 = tf.placeholder(tf.float32)
print('p1 : ', p1)
print('p2 : ', p2)

The following output shows that each placeholder has been created as a tensor:

p1 :  Tensor("Placeholder:0", dtype=float32)
p2 :  Tensor("Placeholder_1:0", dtype=float32)

Let's define an operation using these placeholders:

mult_op = p1 * p2

In TensorFlow, shorthand symbols can be used for various operations. In the preceding code, p1 * p2 is shorthand for tf.multiply(p1,p2):

print('run(mult_op,{p1:13.4, p2:61.7}) : ',tfs.run(mult_op,{p1:13.4, p2:61.7}))

The preceding command runs mult_op in the TensorFlow session and feeds the values dictionary (the second argument to the run() operation) with the values for p1 and p2.

The output is as follows: 

run(mult_op,{p1:13.4, p2:61.7}) :  826.77997

We can also specify the values dictionary by using the feed_dict parameter in the run() operation:

feed_dict={p1: 15.4, p2: 19.5}
print('run(mult_op,feed_dict = {p1:15.4, p2:19.5}) : ',
      tfs.run(mult_op, feed_dict=feed_dict))

The output is as follows:

run(mult_op,feed_dict = {p1:15.4, p2:19.5}) :  300.3

Let's look at one final example, which is of a vector being fed to the same operation:

feed_dict={p1: [2.0, 3.0, 4.0], p2: [3.0, 4.0, 5.0]}
print('run(mult_op,feed_dict={p1:[2.0,3.0,4.0], p2:[3.0,4.0,5.0]}):',
      tfs.run(mult_op, feed_dict=feed_dict))

The output is as follows:

run(mult_op,feed_dict={p1:[2.0,3.0,4.0],p2:[3.0,4.0,5.0]}):[  6.  12.  20.]

The elements of the two input vectors are multiplied in an element-wise fashion.