Translations:Overfitting and Regularization/23/en: Difference between revisions

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Message definition (Overfitting and Regularization)

At test time, all neurons are active but their outputs are scaled by <math>(1 - p)</math> to compensate for the larger number of active units (or equivalently, outputs are scaled by <math>1/(1-p)</math> during training — '''inverted {{Term|dropout}}''').

At test time, all neurons are active but their outputs are scaled by $$ (1 - p) $$ to compensate for the larger number of active units (or equivalently, outputs are scaled by $$ 1/(1-p) $$ during training — inverted dropout).

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	At test time, all neurons are active but their outputs are scaled by <math>(1 - p)</math> to compensate for the larger number of active units (or equivalently, outputs are scaled by <math>1/(1-p)</math> during training — '''inverted dropout''').		At test time, all neurons are active but their outputs are scaled by <math>(1 - p)</math> to compensate for the larger number of active units (or equivalently, outputs are scaled by <math>1/(1-p)</math> during training — '''inverted {{Term\|dropout}}''').