Placeholders allow us to feed data to the model during the session. To get more detailed information about it, take a look at my blog post at http://gsrikar.blogspot.com/2017/06/tensorflow-how-to-use-placeholders.html

Placeholders in TensorFlow

'''
Class uses placeholders instead of constants.
'''
import tensorflow as tf



def main():
    '''
    Main Method
    '''
    a = tf.placeholder(tf.float32)
    b = tf.placeholder(tf.float32)
    # Log the tensors
    print('A: ', a)  # A:  Tensor("Placeholder:0", dtype=float32)
    print('B: ', b)  # B:  Tensor("Placeholder_1:0", dtype=float32)

    # Add the two tensors
    adder_node = tf.add(a, b)
    print('Adder Node: ', adder_node)  # Adder Node:  Tensor("Add:0", dtype=float32)

    # Start a session
    with tf.Session() as sess:
        # Add two numbers
        result1 = sess.run(adder_node, {a: 1.3, b: 5})
        # Add two 1x2 matrices
        result2 = sess.run(adder_node, {a: [2, 8], b: [3, 5]})
        # Add two 3x3 matrices
        result3 = sess.run(adder_node, {a: [[1, 7, 9], [4, 3, 6], [5, 1, 9]],
                                        b: [[2, 8, 2], [7, 9, 0], [4, 4, 5]]})
        print('Result, Add 1: ', result1)  # Result, Add 1:  6.3
        print('Result, Add 2: ', result2)  # Result, Add 2:  [ 5.  13.]
        print('Result, Add 3: ', result3)
        # Result, Add 3:  [[  3.  15.  11.]
        #                 [ 11.  12.   6.]
        #                 [  9.   5.  14.]]

if __name__ == "__main__":
    '''
    Starting point
    '''
    # This file is run directly
    main()