from sklearn.datasets import load$\_$iris

import numpy as np

from sklearn.model$\_$selection import train$\_$test$\_$split

iris $=$ load$\_$iris$()$

feature $=$ iris.data$[$:$100]$

ans $=$ iris.target$[$:$100]$

X$\_$train, X$\_$test, y$\_$train, y$\_$test $=$ train$\_$test$\_$split$($feature$,$ ans, test$\_$size$=0\mathrm{.}3,$ random$\_$state$=1)$

Feature values are petal length, petal width, calyx length, and calyx width.

Ans value is the variety of flowers $(0$: setosa, $1$:versicolor$)$

Data for Learning: X$\_$train, y$\_$train

Data for testing: X$\_$test, y$\_$test

$*$my question$*$: Plz complete the code to train the artificial neural network and the code that outputs accuracy when verifying its performance $,$

$($accuracy: the percentage of predictions correct out of $30$ test samples$)$