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 on 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 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 conv 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?
Thank you all!