DEPENDENCE OF THE INITIAL DISTRIBUTION OF WEIGHTS ON THE TRAINING EFFICIENCY OF DEEP NEURAL NETWORKS
The article is devoted to the influence of the initial weights, during training. Data set is generated by computation methods based on the Lennard-Jones equation. The trained regression model predicts interaction energy between two particles. The sum square error is chosen as the criterion for evaluating of the efficiency. The article presents graphs of the efficiency indicator from the number of training steps, for different regimes. The data obtained in this article will allow to analyze the possibility of fast method designing for complex models.