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Even though dense networks have lost importance today, they are still used asfinal logic elements. It could be shown that these dense networks can besimplified by the sparse graph interpretation. This in turn shows that theinformation flow between input and output is not optimal with an initializationcommon today. The lightning initialization sets the weights so that completeinformation paths exist between input and output from the start. It turned outthat pure dense networks and also more complex networks with additional layersbenefit from this initialization. The networks accuracy increases faster. Thelightning initialization has two parameters which behaved robustly in the testscarried out. However, especially with more complex networks, an improvementeffect only occurs at lower learning rates, which shows that the initializationretains its positive effect over the epochs with learning rate reduction.

Dense neural networks as sparse graphs and the lightning initialization