Sometimes the training phase of the network breaks in the middle. It happens
because the synaptical weights are initialized with random values, and
sometimes updating them causes those values to become >= 1. This makes the
output values of the network diverge instead of converging to the desired,
expected values. The library recognizes this behaviour, and when a weight
become >= 1 throws an InvalidSynapticalWeightException. So far there's no
way to prevent this odd, random behaviour. The network implements the usage
of an inertial momentum coefficient to avoid strong oscillations in the
training phase, in order to make this phenomenon rarer, but also using this
mechanism there's a possibility ~ 10% of getting a diverging network, and
so a training phase broken by an InvalidSynapticalWeightException.