import tensorflow as tf
from tensorflow.contrib import graph_editor as ge

g = tf.Graph()

with g.as_default():
    a = tf.constant(1.0, shape=[2, 3], name="a")
    b = tf.constant(2.0, shape=[2, 3], name="b")
    a_pl = tf.placeholder(dtype=tf.float32)
    b_pl = tf.placeholder(dtype=tf.float32)
    c = tf.add(a_pl, b_pl, name='c')

    print g.get_operations()
    with tf.Session(graph=g) as sess:
        #print sess.run(c) # this prints out a lot of error, since input to c (a_pl and b_pl) are not fed any values
        print sess.run(c, feed_dict={a_pl:[[1, 1, 1], [1, 1, 1]], b_pl:[[2, 2, 2], [2, 2, 2]]}) # we could feed values...

    # after changing the inputs of the c to a and b --> disconnects a_pl and b_pl from c
    ge.swap_inputs(c.op, [a, b])
    print g.get_operations()
    with tf.Session(graph=g) as sess:
        print sess.run(c)

    # after replacing the inpus of the c to a and b
    c_1 = ge.graph_replace(c, {a: a_pl, b: b_pl}) # this appends a new node called c_1 to graph with inputs a_pl and b_pl
    print g.get_operations()
    with tf.Session(graph=g) as sess:
        print sess.run(c)
        #print sess.run(c_1) # this prints out a lot of error, since input to c_1 (a_pl and b_pl) are not fed any values
        print sess.run(c_1, feed_dict={a_pl:[[1, 1, 1], [1, 1, 1]], b_pl:[[2, 2, 2], [2, 2, 2]]}) # we could feed values...