Modern Smart City applications draw on the need for requirements that are safe, reliable and sustainable, as such these applications have a need to utilise machine-learning mechanisms such that they are consistent with public liability. Machine and deep learning networks, therefore, are required to be in a form that is safe and deterministic in their development and also in their deployment. The viability of non-random weight initialisation schemes in neural networks make the network more deterministic in learning sessions which is a desirable property in safety critical systems where deep learning is applied to smart city applications and where public liability is a concern. The paper uses a variety of schemes over number ranges and gradients and achieved a 98.09% accuracy figure, + 0.126% higher than the original random number scheme at 97.964%. The paper highlights that in this case, it is the number range and not the gradient that is affecting the achieved accuracy most dominantly, although there can be a coupling of number range with activation functions used. Unexpectedly in this paper, an effect of numerical instability was discovered from run to run when run on a multi-core CPU. The paper also has shown the enforcement of consistent deterministic results on an multi-core CPU by defining atomic critical code regions, and that aids repeatable information assurance in model fitting (or learning sessions). That enforcement of consistent repeatable determinism has also a benefit to accuracy even for the random schemes, and a highest score of 98.29%, + 0.326% higher than the baseline was achieved. However, also the non-random initialisation scheme causes weight arrangements after learning to be more structured which has benefits for validation in safety critical applications.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)