The cross-entropy loss is a loss function that measures the discrepancy between the predicted results and the actual outcomes of a model . It serves as a crucial metric for optimizing model parameters. A lower value of L is indicative of a narrower gap between the model's predictions and the actual outcomes, thus reflecting superior model performance. The value of L typically declines within the initial epochs, yet prolonging the training duration can further ameliorate the model. Caution is advised as neural network models may reach a saturation point, at which the learning curve will exhibit overfitting.