Neural Network with unknown number of Neurons in output layer

Question

Is it possible to design a network with an unknown number of neurons in the output layer?

I am trying to solve a classification problem, where I use motorcycles' exterior color, interior color, and make to predict the type of damages that are going to show on them.

Since I don't know what the possible damage combinations are going to look like (ex: 2 scratches and 1 scuff or 3 scratches and 1 scuff), then it is not possible for me to know how many output neurons I have in my network.

This is my first ever network. I am trying to imagine what the input and output layers look like. The output layer seems to contain "n" neurons.

Is there a solution for this?

Answer 1

To deal with this as a classification problem you need to define your possible class combinations before hand, else how would you define your training ground truth.

It seems like you are dealing with a regression problem here since you are estimating the amount of two variables scratch and scuff, rather than if a scratch of scruff exist or not, and should try formulating it as such.

Answer 2

If there are 100 types of damages, then each combination can be represented as "$n_1$ scratches, $n_2$ dings, $n_3$ damages of type 3 ... $n_{100}$ damages of type 100". Your task is to estimate $n_1$, ..., $n_{100}$ from the input data. This leads to a neural network which has 100 output neurons. Unless only one damage of type $t$ can exist, the activation function for the unit $t$ should be linear rather than log-sigmoidal.