Disclosed is a method for dynamically optimizing a loss function by means of error cause reinforcement. An optimized loss function is named as ER-Loss, and is realized by means of adding a penalty term on the basis of an original cross-entropy loss function, the penalty term including the following four modules: a penalty degree amplification factor A; a penalty degree adjustment coefficient Aprob for adjusting the influence of the amplification factor A on the correlation between categories of a data set, and an actual output probability of correlated categories according to the actual situation during a training process, and enabling the amplification factor A to automatically adapt to different stages of training; a correlation combination X between the categories of the data set in each network; and the probability PYijd of correlated categories, which is dynamically adjusted according to each training condition of a model. By means of the present invention, compared with the prior art, an amplification factor A is dynamically adjusted by means of a penalty degree adjustment coefficient Aprob, such that identification is more flexible; moreover, a correlation combination X between categories of a data set in each network is introduced, such that the determination of a category feature by a model is more comprehensive, the object identification precision of the model is improved, the deviation between an obtained result and an actual result is reduced, and the identification accuracy of a deep learning network model can be effectively improved.
METHOD FOR DYNAMICALLY OPTIMIZING LOSS FUNCTION BY MEANS OF ERROR CAUSE REINFORCEMENT