Is it a common practice to save and load NN models for multiple sessions in practice?
Yes, of course this is common practice. If you're trying to work with a large net that takes hours to train, it would be totally impractical to do anything other than save weights for future use. In fact, if you're at all concerned about reproducibility of your results, you should save any noteworthy iterations of your net.
I just started learning TF and realized that if I create a model of a NN, and then if I were to create a seession to do something with the NN such as getting an output value given the input value,
and then after the session close, if I were to do something else with the NN again, then create a session for that, then it re-initializes the weights into random, which makes it pointless.
So then is it a common practice to, say in the first session, I save the model, and then in the second session, I would load the weights do something else with the NN?
I understand that TF is intended to be used with 1 session but when dealing with something more complex than a "simple" supervised classification problem, such as reinforcement learning, then I need to use the NN for various way for various reasons.
So coding all of these such that i can execute all the functionalities within 1 session is very tiring and confusing at times. where I would rather create 1 session each to do something with the NN. But is it a poor practice to do so?