<p>I'm not sure if you're talking about the design of models with the ability to predict multiple labels or just a problem of implementation.</p>
<p>You can just simply sort the scores of the model's output, and take the top-N highest as the predictions.
But if you're talking about how to design a model, there're lots of work on it. Check out <a href="https://arxiv.org/abs/2009.14119" rel="nofollow noreferrer">this paper</a> for example.</p>
Multiple Output Prediction (Machine Learning)
<p>I'm on the newer side of learning about machine learning (keras/tensorflow) and am curious about how one might set up a network for taking in some input (an &quot;image&quot; with x channels/features) and be able to predict more than one value based off of this input. I've seen regression models, but the ones I've seen only predict a single value and this is not really how I want to set the problem up. I set up a test NN with a Dense layer of how many predictions I would like to make, but these predictions seem to only predict one value that is not necessarily what the output should be (I think the NN optomizes itself to some value and converges to this value). Any help would be greatly appreciated. I'm also new to posting here, so if I can do something to post a better posed question please let me know! The closest thing that I've seen to predicting some sort of tensor is RNN but I am not sure if this will achieve this quite yet.</p>