Placeholders

While constants allow us to provide a value at the time of defining the tensor, the placeholders allow us to create tensors whose values can be provided at runtime. TensorFlow 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)

We see the following output:

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

Now let's define an operation using these placeholders:

op4 = p1 * p2

TensorFlow allows using shorthand symbols for various operations. In the earlier example, p1 * p2 is shorthand for tf.multiply(p1,p2):

print('run(op4,{p1:2.0, p2:3.0}) : ',tfs.run(op4,{p1:2.0, p2:3.0}))

The preceding command runs the op4 in the TensorFlow Session, feeding the Python dictionary (the second argument to the run() operation) with values for p1 and p2.

The output is as follows: 

run(op4,{p1:2.0, p2:3.0}) :  6.0

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

print('run(op4,feed_dict = {p1:3.0, p2:4.0}) : ',
      tfs.run(op4, feed_dict={p1: 3.0, p2: 4.0}))

The output is as follows:

run(op4,feed_dict = {p1:3.0, p2:4.0}) :  12.0

Let's look at one last example, with a vector being fed to the same operation:

print('run(op4,feed_dict = {p1:[2.0,3.0,4.0], p2:[3.0,4.0,5.0]}) : ',
    tfs.run(op4,feed_dict = {p1:[2.0,3.0,4.0], p2:[3.0,4.0,5.0]}))

The output is as follows:

run(op4,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.