Either each layer is a homeomorphism, or the layer’s weight matrix has determinant 0. If it is a homemorphism, AAA is still surrounded by BBB, and a line can’t separate them. But suppose it has a determinant of 0: then the dataset gets collapsed on some axis. Since we’re dealing with something homeomorphic to the original dataset, AAA is surrounded by BBB, and collapsing on any axis means we will have some points of AAA and BBB mix and become impossible to distinguish between.
拿着样是不是神经网络每层神经元的个数都是一个:第一个隐层神经元个数最多,然后依次下降?并且第一层最多。是这样么?