METHOD OF TRAINED RECURRENT NEURAL NETWORK DEBUGGING

摘要

FIELD: physics.;SUBSTANCE: invention relates to data processing and can be used for operation with trained neural networks (NN) and their debugging. Method is realized using at least one processor, wherein the RNN is trained on a set of data consisting of sequences of tokens, which are vector representations of elements of said data set, and the method comprises steps of: a) obtaining the aggregation function of latent RNN states for said sequence of tokens; b) searching inside said sequence of tokens of at least one subsequence of tokens and determining for each said subsequence an aggregating function of latent RNN states; c) interpreting RNN by detecting a subsequence of tokens based on the minimum value of the discrepancy measure between the aggregation function values obtained at steps a) and b).;EFFECT: enabling the possibility of estimating the effect of input disturbances on the result of calculating an aggregating function from latent RNN states by minimizing the divergence measure when searching for relevant subsequences of tokens.;3 cl, 8 dwg