Neural networks with block diagonal inner product layers: a look at neural network architecture through the lens of random matrices

Abstract

Two difficulties continue to burden deep learning researchers and users: (1) neural networks are cumbersome tools, and (2) the activity of the fully connected (FC) layers remains mysterious. We make contributions to these two issues by considering a modified version of the FC layer we call a block diagonal inner product (BDIP) layer. These modified layers have weight matrices that are block diagonal, turning a single FC layer into a set of densely connected neuron groups; they can be achieved by either initializing a purely block diagonal weight matrix or by iteratively pruning off-diagonal block entries. This idea is a natural extension of group, or depthwise separable, convolutional layers. This method condenses network storage and speeds up the run time without significant adverse effect on the testing accuracy, addressing the first problem. Looking at the distribution of the weights through training when varying the number of blocks in a layer gives insight into the second problem. We observe that, even after thousands of training iterations, inner product layers have singular value distributions that resemble that of truly random matrices with iid entries and that each block in a BDIP layer behaves like a smaller copy. For network architectures differing only by the number of blocks in one inner product layer, the ratio of the variance of the weights remains approximately constant for thousands of iterations, that is, the relationship in structure is preserved in the parameter distribution.