Interrupting neural network training is a fundamental operation that facilitates critical interventions, such as early stopping and dynamic layer-wise analysis, during the training process. Early stopping utilises interruptions to prevent overfitting by terminating training when the performance metrics on a validation set plateau or deteriorate. Similarly, layer-wise analysis during interruptions provides insights into the internal behavior of the network, including weight distributions, gradient flow, and activation patterns, which are essential for diagnosing training inefficiencies or identifying opportunities for optimization. This article proposed a new framework for implementing statistical methods in training sessions. Moreover, the time analysis of opened interruption windows and data conducted for statistical analysis is included, particularly the Kulback-Leibner (KL) divergence. Experiments across multiple datasets demonstrate how this framework enhances both training efficiency and model interpretability while maintaining performance standards.