  Even though dense networks have lost importance today, they are still used as
final logic elements. It could be shown that these dense networks can be
simplified by the sparse graph interpretation. This in turn shows that the
information flow between input and output is not optimal with an initialization
common today. The lightning initialization sets the weights so that complete
information paths exist between input and output from the start. It turned out
that pure dense networks and also more complex networks with additional layers
benefit from this initialization. The networks accuracy increases faster. The
lightning initialization has two parameters which behaved robustly in the tests
carried out. However, especially with more complex networks, an improvement
effect only occurs at lower learning rates, which shows that the initialization
retains its positive effect over the epochs with learning rate reduction.
