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
If youre searching for neural network architecture that have varying number of inputs and outputs, Recurrent Neural Networks, LSTM's .. etc are examples. They are used in Natural Language Processing where the main goal is to examine patterns in sentences.  But I highly doubt that they will work for your use case since no information about it is provided.
Another way would be to create multiple neural networks with a different input/ output sizes, such that the input/output sizes are averages of the groups of similar input/output sizes.