  As gradient descent method in deep learning causes a series of questions,
this paper proposes a novel gradient-free deep learning structure. By adding a
new module into traditional Self-Organizing Map and introducing residual into
the map, a Deep Valued Self-Organizing Map network is constructed. And analysis
about the convergence performance of such a deep Valued Self-Organizing Map
network is proved in this paper, which gives an inequality about the designed
parameters with the dimension of inputs and the loss of prediction.
