training deep leaning models validation accuracy validation precision validation recall validation rocauc similar to we see somewhat better results with than without regularization the gap reduced and the validation auc increased by small fraction with and regularization (individually)we saw the gap between training and validation performance reduced as well as reduced overfitting we now have favorable results for our use case before finalizing the resultslet' add dropout layers listing - adds dropout layer to randomly drop of the input neurons during the learning we add the dropout layer to the input layer as well as the hidden layers listing - dropout regularization #define network with dropout layers class neuralnetwork(nn module)#adding dropout layers within neural network to reduce overfitting def __init__(self)super(__init__(tch manual_seed( self fc nn linear( self fc nn linear( self fc nn linear(