I want to train an MLP to multiply two inputs. The easiest approach is to generate a vast dataset of numbers and their multiplication and train the network on them.

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I did this as a test and the MLP worked nearly flawless and long as I gave it numbers inside the training range but as soon as I deviated from the training domain the network started malfunctioning.

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I wanted to know if there is an architecture which can learn the semantics of multiplication and act properly outside its training domain.

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Thanks