In Keras if there are two dense layers in a neural network, then all neurons of first layer are connected to ALL neurons of second layer. Can I delete few connections from the dense layer based on certain criteria on weight, such that the resultant is a sparse layer in which all neurons in first layer are not connected to all neurons in second layer??
I tried to reduce the weights that were below threshold to zero. But this did not serve the purpose of deleting / removing the weight connections from the network because after I re-trained the network,, the weights that were forced to be zero regained some values due to gradient descent.