Demo on using control dependencies version 2.

tf_control_dep_demo2.py

import tensorflow as tf

def main1():

    sess = tf.Session()
    x = tf.Variable(1.)
    sess.run(tf.initialize_variables([x]))

    y = x + 3
    assign_op = tf.assign(x, x+1)

    w = tf.identity(x)
    u = x + 0

    with tf.control_dependencies([assign_op]):
        z = tf.identity(x)
        q = x + 0

    print(sess.run([y,z,q,w, u]))
    print(sess.run([y,z,q,w, u]))
    print(sess.run([y,z,q,w, u]))
    print(sess.run([y,z,q,w, u]))


def main2():

    sess = tf.Session()
    x = tf.Variable(1.)
    sess.run(tf.initialize_variables([x]))

    y = x + 3
    with tf.control_dependencies([y]):
        assign_op = tf.assign(x, x+1)


    w = tf.identity(x)
    u = x + 0

    with tf.control_dependencies([assign_op]):
        z = tf.identity(x)
        q = x + 0

    print(sess.run([y,z,q,w, u]))
    print(sess.run([y,z,q,w, u]))
    print(sess.run([y,z,q,w, u]))
    print(sess.run([y,z,q,w, u]))





if __name__ == '__main__':
    main1()
    tf.reset_default_graph()
    print('=')
    main2()
    # TF 1.4
    # prints (although main1 appears to be stochastic!)
    # [4.0, 2.0, 2.0, 2.0, 1.0]
    # [6.0, 3.0, 3.0, 3.0, 3.0]
    # [6.0, 4.0, 4.0, 4.0, 3.0]
    # [8.0, 5.0, 5.0, 5.0, 5.0]
    # =
    # [4.0, 2.0, 2.0, 2.0, 1.0]
    # [5.0, 3.0, 3.0, 3.0, 2.0]
    # [6.0, 4.0, 4.0, 4.0, 3.0]
    # [7.0, 5.0, 5.0, 5.0, 4.0]