Pruning in Keras
If you set an individual weight to zero won't that prevent it from being updated  during back propagation? Shouldn't thatv weight remain zero from one epoch to the next? That's why you set the initial weights to nonzero values before training. If you want to "remove" an entire node, just set all of the weights on that node's output to zero and that will prevent that nodes from having any affect on the output throughout training.
I'm trying to design a neural network using Keras with priority on prediction performance, and I cannot get sufficiently high accuracy by further reducing the number of layers and nodes per layer. I have noticed that very large portion of my weights are effectively zero (>95%). Is there a way to prune dense layers in hope of reducing prediction time?