I'm new to Tensorflow and AI, so I'm having trouble researching my question. Either that, or my question hasn't been answered.
I'm trying to make a text classifier to put websites into categories based on their keywords. I have at minimum 5,000 sites and maximum 37,000 sites to train with.
What I'm trying to accomplish is: after the model is trained, I want it to continue to train as it makes predictions about the category a website belongs in.
The keywords that the model is trained on is chosen by clients, so it can always be different than the rest of the websites in its category.
How can I make Tensorflow retrain it's model based on corrections made by me if it's prediction is inaccurate? Basically, to be training for ever.
The key phrase you lack is fine-tuning. This is when you take a model that has finished its customary training (whatever that may be), and needs more work for the application you have in mind. You then give it additional training with new input; when that training has completed (training accuracy plateaus and is close to test accuracy), you then deploy the enhanced model for your purposes.
This is often used in commercial applications -- for instance, when a large predictive model is updated to include the most recent week of customer activity. Another common use is to find a model in a zoo that is trained for something related to the application you want -- perhaps cats v dogs -- and use its recognition of facial features to shorten training for a model to identify two classes of cartoon characters -- perhaps Pokemon v Tiny Toons.
In this latter case, your fine-tuning will almost entirely eliminate what was learned by the last few layers of the model. What you gain is the early-layer abilities to find edges, regions, and features through eyes-nose-mouth combinations. This saves at least 30% of the overall training time.