Loss functions play an important role in the training of artificial neural networks (ANNs), and can affect the generalisation ability of the ANN model, among other properties. Specifically, it has been shown that the cross entropy and sum squared error loss functions result in different training dynamics, and exhibit different properties that are complementary to one another. It has previously been suggested that a hybrid of the entropy and sum squared error loss functions could combine the advantages of the two functions, while limiting their disadvantages. The effectiveness of such hybrid loss functions is investigated in this study. It is shown that hybridisation of the two loss functions improves the generalisation ability of the ANNs on all problems considered. The hybrid loss function that starts training with the sum squared error loss function and later switches to the cross entropy error loss function is shown to either perform the best on average, or to not be significantly different than the best loss function tested for all problems considered. This study shows that the minima discovered by the sum squared error loss function can be further exploited by switching to cross entropy error loss function. It can thus be concluded that hybridisation of the two loss functions could lead to better performance in ANNs.

翻译：损失功能在人工神经网络(ANNs)的培训中起着重要作用,并可能影响ANN模型的普及能力。具体地说,已经证明交叉环流和总正方差损失功能导致不同的培训动态,并显示出不同的属性,这些功能是相辅相成的。以前曾指出,对正方差和正方差损失功能的混合作用可以结合这两种功能的优势,同时限制其劣势。本研究报告调查了这种混合损失功能的有效性。事实证明,两种损失功能的混合作用提高了ANNS在所考虑的所有问题上的普遍化能力。开始以正方差错误功能进行训练的混合损失函数,以及后来开始的跨方差错损失函数的开关,要么平均表现最佳,要么与所考虑的所有问题所测试的最佳损失函数没有重大差别。这项研究表明,通过转换为交叉错误损失功能,可以进一步利用由总正方差损失函数发现的微值。因此可以断定,两种损失函数的混合作用在ANNS中会更好表现。