I have a neural network which is trying to predict two types of functions in a noisy setting. The input is an array, and the output is also an array. The two types of functions I am trying to predict are the following:
- a linear function with negative slope
- a significantly steeper exponential function.
Example inputs for each would look like
- [10, 9, 8, 7]
- [11, 1, 0.1, 0]
Sometimes my neural network predicts well, such as the following: Linear
Other times my neural network completely messes up and predicts the exponential decay instead: Poor Prediction on Linear
As you can imagine, this makes the final result a very poor one.
I'm using a NN architecture of NCF because I'm setting up from a matrix setting. That is, the arrays I have generated come from a noisy matrix setting of low rank.
Any suggestions to improve the result? When I only use one function (e.g. just linear or just exponential) I get superb results. However, mixing both those functions together randomly causes my neural network to perform poorly, as described in my question.