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train deep network with lstm layer with missing (NaN) data input and target (not synced)

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is it possible to train a lstm network with NaN values on input and target data like we can with timedelay nets (in this case there exists the fixunknowns function?
I tried a simple case adding just 1 NaN value and the training didn't converged....
Is there a "fixunknown function" for deep networks...

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