How to choose feedforward architecture for few number of features but very large instance?
Is your set of possible labels discrete or continuous?
If it's discrete and you have L possible different labels, then you should have L units in the output layer (then do a softmax if you want probabilities).
If it's continuous, then you should have 1 output unit.
Lastly, the number of units in the input layer is equal to the flattened dimension of one instance of the input. So if your data is 100 dimensions, then you must have 100 units in the input layer.