Q$3.$ Train the perceptron network given the implemented learning rule. First, define the following as your training set.

Then, initialize the weights for your network randomly.

Next, pass the first data point to your perceptron, to get the predicted output $($a$).$

Next, pass the following to the learning rule model: a $($predicted output$),t($actual target output$),w($current weight$)$ and b $($current bias$).$

The learning rule function will return the update weight and bias.

Repeat this step for each data point in your training set.

Stop when $w$ and $b$ converges.

You may need to iterate over the training set multiple times before $w$ and $b$ converge.

Report the updated values for the first five updates in the table below $[1$: $0\mathrm{.}5$ for table $+0\mathrm{.}5$ code marks$]$

$\backslash$table$[[$Pass$,$w$,$b$],[0($random values$),,],[1,,],[2,,],[3,,],[4,,],[5,,]]$