Keras - NN prediction of error in metrological measurement. : <p>I have example data which contain coordinates of points on the x-y plane (for 
example 2.0000 , 4.0000), next using monte carlo method, a small random error 
is added to those coordinates to simulate a set of points measured by a 
metrological machine.</p>

<p>This may sound trivial, but Im not really sure what to do next with this data, Im trying to build a model which predicts the error in measurement, but I have problem in visualizing the whole concept, ie. should the input layer of the network have neurons which receive real coordinates of the points and 
simulated coordinates, or simulated ones only? Or maybe I should estimate 
measurement error for each simulated point and and use it with coordinates of 
those points in the input layer? Also, how many neurons the network should 
have on the output layer and how should I interpret that data? I know this 
probably isnt the best description of the problem, but I am a complete 
begginner in this field, so any theoretical help or practical examples will be greatly appreciated. </p>
