Early Stopping Criteria for Levenberg-Marquardt Based Neural Network Training Optimization
Azizah Suliman, Batyrkhan Sultanovich Omarov · International Journal of Engineering & Technology · 2018
In this research we train a direct distributed neural network using Levenberg-Marquardt algorithm. In order to prevent overtraining, we proposed correctly recognized image percentage based on early stop condition and conduct the experiments with different stop thresholds for image classification problem. Experiment results show that the best early stop condition is 93% and other increase in stop threshold can lead to decrease in the quality of the neural network. The correct choice of early stop condition can prevent overtraining which led to the training of a neural network with considerable number of hidden neurons.