  Reproducibility of a deep-learning fully convolutional neural network is
evaluated by training several times the same network on identical conditions
(database, hyperparameters, hardware) with non-deterministic Graphics
Processings Unit (GPU) operations. The propagation of two-dimensional acoustic
waves, typical of time-space evolving physical systems, is studied on both
recursive and non-recursive tasks. Significant changes in models properties
(weights, featured fields) are observed. When tested on various propagation
benchmarks, these models systematically returned estimations with a high level
of deviation, especially for the recurrent analysis which strongly amplifies
variability due to the non-determinism. Trainings performed with double
floating-point precision provide slightly better estimations and a significant
reduction of the variability of both the network parameters and its testing
error range.
