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The exponential growth of data has sparked computational demands on MLresearch and industry use. Sparsification of hyper-parametrized deep neuralnetworks (DNNs) creates simpler representations of complex data. Past researchhas shown that some sparse networks achieve similar performance as dense ones,reducing runtime and storage. RadiX-Nets, a subgroup of sparse DNNs, maintainuniformity which counteracts their lack of neural connections. Generation,independent of a dense network, yields faster asymptotic training and removesthe need for costly pruning. However, little work has been done on RadiX-Nets,making testing challenging. This paper presents a testing suite for RadiX-Netsin TensorFlow. We test RadiX-Net performance to streamline processing inscalable models, revealing relationships between network topology,initialization, and training behavior. We also encounter "strange models" thattrain inconsistently and to lower accuracy while models of similar sparsitytrain well.

Testing RadiX-Nets: Advances in Viable Sparse Topologies