training deep leaning models the gap between training and validation performance has reducedwe can see similar performance across both datasets finallylet' combine all three types of regularizers and study the effect on model performance listing - demonstrates and dropout regularization listing - and dropout regularization #create network with dropout layer class neuralnetwork(nn module)def __init__(self)super(__init__(tch manual_seed( self fc nn linear( self fc nn linear( self fc nn linear( self relu nn relu(self out nn linear( self final nn sigmoid(self drop nn dropout( #dropout layer def forward(selfx)op self drop( #dropout for input layer op self fc (opop self relu(opop self drop(op#dropout for hidden layer op self fc (opop self relu(opop self drop(op#dropout for hidden layer op self fc (opop self relu(opop self drop(op#dropout for hidden layer op self out(op