# Neural networks with not-fixed dimension for input and output

I would like to know if it exists a model/method which can deal with input and output of different dimension.

For example, let us say that the maximum number of info we could have is 6 features and 5 output. Then I could have examples with 4 features and 3 output.

Less input features always relates to less output. And relations stays the same. with only 4 features I have only 4 outputs, and so on.

Most important, it is not that I do not have them for missing knowledge, but because in the same problem dominion I could have all 6 of the features, or less.

It is possibile to create a model which deal with this kind of things ?

The other solution I thought was to just use a simple deep network, with the maximum number of features and output as dimension, and use a value = 0 when I have a missing feature or a missing target. But that destroyed completely the training performances