Variable delta in TF

deltas.py

import numpy as np
import tensorflow as tf

def main():
    var1 = tf.Variable(np.array([[1, 2, 3], [4, 5, 6]]), dtype=tf.float32)
    var2 = tf.Variable(np.array([[1, 2, 2], [5, 7, 6]]), dtype=tf.float32)
    loss = tf.losses.mean_squared_error(var1, var2)

    joined_vars = tf.concat([tf.reshape(x, [-1]) for x in tf.trainable_variables()], axis=0)
    joined_backup = tf.Variable(np.zeros([int(x) for x in joined_vars.get_shape()]),
                                dtype=joined_vars.dtype,
                                trainable=False)
    make_backup = tf.assign(joined_backup, joined_vars)

    with tf.control_dependencies([make_backup]):
        optim = tf.train.AdamOptimizer(learning_rate=1e-1).minimize(loss)

    with tf.control_dependencies([optim]):
        new_joined = tf.concat([tf.reshape(x, [-1]) for x in tf.trainable_variables()], axis=0)
        delta = new_joined - joined_backup
        total_sq_delta = tf.reduce_sum(tf.square(delta))

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for _ in range(30):
            _, _, scalar_delta = sess.run([make_backup, optim, total_sq_delta])
            print(scalar_delta)

main()