I am training a convLSTM with a dropout layer (with prob 0.5).
If I train over more than 5 epochs I notice that the network starts to overfit: my validation set loss becomes stationary while the train loss keeps going down with every epoch.
And if I train for 20 or more epochs the gap between the validation and train loss is quite substantial. At the same time precision-recall curve becomes much more stable (i.e. monotonic) if i train with a large number of epochs (e.g. 20). Why is that? Is this behaviour a common occurrence?