In [1]:

    

import tensorflow as tf

with tf.name_scope("Scope_A"):
    a = tf.add(1, 2, name="A_add")
    b = tf.mul(a, 3, name="A_mul")

with tf.name_scope("Scope_B"):
    c = tf.add(4, 5, name="B_add")
    d = tf.mul(c, 6, name="B_mul")

e = tf.add(b, d, name="output")


    

In [2]:

    

writer = tf.train.SummaryWriter('./name_scope_1', graph=tf.get_default_graph())


    

In [3]:

    

writer.close()


    

In [4]:

    

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("Transformation"):

        with tf.name_scope("A"):
            A_mul = tf.mul(in_1, const)
            A_out = tf.sub(A_mul, in_1)

        with tf.name_scope("B"):
            B_mul = tf.mul(in_2, const)
            B_out = tf.sub(B_mul, in_2)

        with tf.name_scope("C"):
            C_div = tf.div(A_out, B_out)
            C_out = tf.add(C_div, const)

        with tf.name_scope("D"):
            D_div = tf.div(B_out, A_out)
            D_out = tf.add(D_div, const)

    out = tf.maximum(C_out, D_out)   

writer = tf.train.SummaryWriter('./name_scope_2', graph=graph)
writer.close()


    

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