I built a three-layer neural network (first is 1D convolutional and the remaining two are linear). It takes an input of 5 angles in radians, and outputs two numbers from 0 to 1, which are respectively the probability of failure or success. The NN is trained in a simulation.
The simulation goes this way: it takes 5 angles in radians and calculates the vector sum of 5 vectors having $x$ as module and $\alpha$ as angles (taken from the input). It returns $1$ if the vector sum has a module greater than $y$, or $0$ if it is less than $y$.
My intention is to be able to tell sequences of radians that will generate vectors with a sum greater than $y$ in module from the ones which won't.
Which would be the best configuration to achieve this? Is the configuration I set up (1D convolution layer + 2 linear layers) efficient? If so, would it be easy to find the right size for the convolution? Or should I just remove it?
I noticed that if I change the order of the input angles the output of the simulation will be the same. Is there a particular configuration you should use when dealing with these cases?