Early Stopping in PyTorch to Prevent Overfitting (3.4)

It can be difficult to know how many epochs to train a neural network for. Early stopping stops the neural network from training before it begins to seriously overfitting. Generally too many epochs will result in an overfit neural network and too few will be underfit.

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[zazikel]

One thing has been omitted from this material. Since the validation set affects training, you need an independent test set for final evaluation.

[timstevens]

i meant
the vector of early stopping
can have a component
on the fun basis vector.
depends . . .
if you get my angle.

[timstevens]

well . . .
early stopping
can be fun.

[lakeguy]

Dr. Heaton,
Greetings from a fellow Missourian. I enjoy your videos and continue to learn from you! Can you explain how you determine the optimum batch size? Given a tabular problem, if your model is small enough and your computer's ram and GPU ram are large enough, would you still process in batches? As I understand it, we process in batches when the entire model can't be processed in one batch. Am I on the right track?

Let's create a hypothetical. Assume a pc with 32 gigs of main memory and an nvidia card with 11 gigs of ram (1080ti). How big a model (rows x columns) will fit in memory? When the model is greater than (what's the limiting factor, the GPU?), how do you determine what size batch to use?

Thank you for taking the time to consider and answer my question(s).

JD

[alevida]

Looks like there is an error when you restore the best weights: you are restoring the best_model weights to the variable "model" called inside of the __call__() method, so the restored weights are not getting returned outside of your class

[崔明太]

I feel something wrong in your example, in regression, the val_loss should be less and less, instead of classification. So I think there must a parameter to choose the way to counter

[cookiekhai]

Can I know how to implement this if I were to use tensorflow?

[TheTimtimtimtam]

Appreciate your work Jeff, Thank you.