because we're optimizing the network we're improving its performance eventually though there becomes a point where the training data starts to diverge from the testing data now what happens is that the training day should always continue to fit or the model should always continue to fit the training data because it's still seeing all of the training data it's not being penalized from that except for maybe if you drop out or other means but the testing data it's not seeing so at some point the network is going to start to