Project $2$

Cancer Detection by Deep Neural Networks

In this project, you should try to change the activation functions, number of epochs, and number of dense layers, try two different optimizers such as sgd and nadam, and compare the results with the original notebook. That is to see if your classification report has improved. You should write a one$-$page report for each part explaining how you have changed the existing model.$[17]$ predictions $=$ model.predict$($x$\_$test $)>0\mathrm{.}5$IV $8//8[==============================]-$ es $2$ms$//$ step$[19]$ print$(\text{'}$Accuracy:$\text{'},$ metrics.accuracy$\_$score$($y$\_$true$=$y$\_$test, y$\_$pred$=$predictions$))$rarr Accuracy: $0\mathrm{.}9344978165938864[20]$ print$($metrics$.$classification$\_$report$($y$\_$true$=$y$\_$test, y$\_$pred$=$predictions$))$

$\backslash$table$[[7_7,$precision,recall,f$1-$score,support$],[0,0\mathrm{.}90,0\mathrm{.}82,0\mathrm{.}86,55],[1,0\mathrm{.}94,0\mathrm{.}97,0\mathrm{.}96,174],[$accuracy$,,,0\mathrm{.}93,229],[$macro avg,$0\mathrm{.}92,0\mathrm{.}89,0\mathrm{.}91,229],[$weighted avg,$0\mathrm{.}93,0\mathrm{.}93,0\mathrm{.}93,229]]$