Machine Learning with TensorFlow 1.x

Saving the model for ongoing use

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