mean = tf.get_variable('mean', [], dtype=tf.float32, initializer=tf.constant_initializer(-3))
    stddev = tf.get_variable('stddev', [], dtype=tf.float32, initializer=tf.constant_initializer(5))
    z = tf.random_normal([], mean, stddev)

    loss = tf.square(z - 5)

    global_step = tf.train.get_or_create_global_step()
    optimizer = tf.train.AdamOptimizer(learning_rate=1e-1)
    train = optimizer.minimize(loss, global_step=global_step)

    session = tf.Session()
    session.run(tf.global_variables_initializer())

    for i in range(10000):
        mean_v, stddev_v, z_v, l, _ = session.run([mean, stddev, z, loss, train])
        logger.info('%f %f %f %f', mean_v, stddev_v, z_v, l)