To save variables from the tensor flow session for future use, you can use the Saver() function. Let's start by creating a saver variable right after the writer variable:

    writer = tf.summary.FileWriter(log_location, session.graph)
    saver = tf.train.Saver(max_to_keep=5)

Then, in the training loop, we will add the following code to save the model after every model_saving_step:

 if step % model_saving_step == 0 or step == num_steps + 1: 
   path = saver.save(session, os.path.join(log_location,  
 "model.ckpt"), global_step=step) 
   logmanager.logger.info('Model saved in file: %s' % path) 

After that, whenever we want to restore the model using the saved model, we can easily create a new Saver() instance and use the restore function as follows:

 checkpoint_path = tf.train.latest_checkpoint(log_location) 
 restorer = tf.train.Saver() 
 with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer()) 
    restorer.restore(sess, checkpoint_path) 

In the preceding code, we use the tf.train.latest_checkpoint so that TensorFlow will automatically choose the latest model checkpoint. Then, we create a new Saver instance named restore. Finally, we can use the restore function to load the saved model to the session graph:

    restorer.restore(sess, checkpoint_path) 

You should note that we must restore after we run the tf.global_variables_initializer. Otherwise, the loaded variables will be overridden by the initializer.