tensorflow2

import numpy as np
import tensorflow as tf

graph = tf.Graph()

with graph.as_default():
#    in_1 = tf.placeholder(tf.float32, shape=[], name="input_a")
#    in_2 = tf.placeholder(tf.float32, shape=[], name="input_b")
#    const = tf.constant(3, dtype=tf.float32, name="static_value")

    with tf.name_scope("variables"):
        # variable to keep track of how many times the graph has been run
        global_step = tf.Variable(0, dtype=tf.int32, trainable=False, name="global_step")
        # variable that keeps track of the sum of all output values over time
        total_output = tf.Variable(0.0, dtype=tf.float32, trainable=False, name= "total_output")

    with tf.name_scope("transformation"):

        # separate input layer
        with tf.name_scope("input"):
            # create input placeholder - takes in a vector
            a = tf.placeholder(tf.float32, shape=[None], name="input_placeholder_a")

        # separate middle layer
        with tf.name_scope("intermediate_layer"):
            b = tf.reduce_prod(a, name="product_b")
            c = tf.reduce_sum(a, name="sum_c")

        # separate output layer
        with tf.name_scope("output"):
            output = tf.add(b, c, name="output")

    with tf.name_scope("update"):
        # increment the total_output variable by the latest output
        update_total = total_output.assign_add(output)
        # increment the global_step variable, should be run whenever the graph is run
        increment_step = global_step.assign_add(1)


writer = tf.summary.FileWriter('./name_scope_2', graph=graph)
writer.close()