Balancing self-driving training data - Python plays GTA p.10
753Welcome to Part 10 of the Python Plays: Grand Theft Auto V tutorial series, where we're working on creating a self-driving car in the game.
Before we get into the neural network model, and training it, one other thing to think about is that, chances are, the vast majority of our moves are going to be forward. If we throw data at a neural network that is, for example, 80% biased towards this, the neural network will learn to always predict that class, EXCEPT in cases where it's seen that it is not. The problem here is that the network will almost certainly overfit. So, in training and validation, you might see that you're accuracy is 99%, so surely it's not just only predicting that 80% class, but, then, you throw some out out sample data at the network, or even attempt to actually use it, and you're baffled by the results! Well, you over fit and then created a bunch of rules basically for the edge cases in a case of overfitment.
Text tutorials and sample code: https://pythonprogramming.net/game-frames-open-cv-python-plays-gta-v/
Project Github: https://github.com/sentdex/pygta5
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