How to tune number of epochs? #1021

Hi, may I ask how I can find best number of epochs (not max_epochs) through cross validation? Is it possible to do something like:

from sklearn.model_selection import GridSearchCV, cross_validate
from skorch import NeuralNetClassifier

net = NeuralNetClassifier(...)
params = {
	'n_epochs': [10, 20, 30, 40, 50],
}
gs = GridSearchCV(
	estimator=net,
	param_grid=params,
	refit=True,
	cv=inner_cv,
	scoring='accuracy',
)
test_predictions = cross_validate(
	gs,
	X,
	y,
	cv=outer_cv,
	scoring=scoring,
	verbose=0,
	n_jobs=n_cores_cv
)
...

If it is possible, may I ask how the models are evaluated on each n_epochs? Is an individual model fitted for each n_epochs, or the valid metrics are calculated and recorded along the training epochs of a single model, so that the model will be fitted only once for one split of dataset?

Thanks!