Gist explains how to perform Arithmetic operations in Tensorflow and show it in a computational graph. For more detailed explanation, check out the my blog post https://gsrikar.blogspot.com/2017/07/arithmetic-operations-tensorflow.html

Arithmetic Operations in TensorFlow

'''
Class performs arithmetic operations on multiple tensors and
shows the Computational graph with the help of Tensorboard.

Uses constants for arithmetic operations
'''
import tensorflow as tf



def main():
    '''
    Main Method
    '''
    # Create tensors
    node1 = tf.constant(35)
    node2 = tf.constant(76)
    node3 = tf.constant(11)
    node4 = tf.constant(10)
    node5 = tf.constant(100)

    print('Node 1: ', node1)  # Tensor("Const:0", shape=(), dtype=int32)
    print('Node 2: ', node2)  # Tensor("Const_1:0", shape=(), dtype=int32)
    print('Node 3: ', node3)  # Tensor("Const_2:0", shape=(), dtype=int32)
    print('Node 4: ', node4)  # Tensor("Const_3:0", shape=(), dtype=int32)
    print('Node 5: ', node5)  # Tensor("Const_4:0", shape=(), dtype=int32)

    # Add node1 and node2
    node_add = tf.add(node1, node2)
    print('Node Add: ', node_add)  # Tensor("Add:0", shape=(), dtype=int32)

    # Subtract node_add and node3
    node_subtract = tf.subtract(node_add, node3)
    print('Node Subtract: ', node_subtract)  # Tensor("Sub:0", shape=(), dtype=int32)

    # Multiply node_subtract and node4
    node_multiply = tf.multiply(node_subtract, node4)
    print('Node Multiply: ', node_multiply)  # Tensor("Mul:0", shape=(), dtype=int32)

    # Divide node_multiply and node5
    node_divide = tf.divide(node_multiply, node5)
    print('Node Divide: ', node_divide)  # Tensor("truediv:0", shape=(), dtype=float64)

    # Output node or Final node
    output = node_add + node_subtract + node_multiply
    print('Ouptut Node: ', output)  # Tensor("add_1:0", shape=(), dtype=int32)

    # Start the session
    with tf.Session() as sess:
        # Output value
        output_value = sess.run(output)
        print('Session: Output value: ', output_value)  # Output value:  1211
        # Create a summary
        summary = tf.Summary(value=[
            tf.Summary.Value(tag="summary_basic_math", simple_value=output_value)
        ])
        # Create the event file inside logs directory
        writer = tf.summary.FileWriter("logs/", sess.graph)
        # Add the summary to the writer
        writer.add_summary(summary)



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