ClassificationOutputLayer

A classification layer computes the cross-entropy loss for classification and weighted classification tasks with mutually exclusive classes.

For typical classification networks, the classification layer usually follows a softmax layer. In the classification layer,trainNetworktakes the values from the softmax function and assigns each input to one of theKmutually exclusive classes using the cross entropy function for a 1-of-Kcoding scheme[1]:

whereNis the number of samples,Kis the number of classes, $$w_i$$is the weight for classi, $$t_{ni}$$is the indicator that thenth sample belongs to theith class, and $$y_{ni}$$is the output for samplenfor classi, which in this case, is the value from the softmax function. In other words, $$y_{ni}$$is the probability that the network associates thenth input with classi.