requirements:

Train the perceptron with the given dataset and return the updated weights. This function should go over the data points in the dataset one$-$by$-$one, call forward$\_$pass$()$ function

then call the backward$\_$propagation$()$ function to update the weights. The function should go over the dataset the number of iterations passed as a parameter.

Parameters:

$-$ x: Input data. a $2$D array with K rows and n columns where K is the number of datapoints in the dataset and n is the number of features

$-$ weights: Weights matrix. A $1-$dimensional array of size $($n$+1)$ where n is the number of features, including the bias term.

$-$ learning$\_$rate: the learning rate used in the training $($alpha$)$

$-$ iterations: the number of iterations of the

Returns:

$-$ updated$\_$weights: Updated weights matrix.

Training $($Forward & backward propagation$)$

In $[]$: import numpy as $np$

def train$\_$perceptron$($x$,$y$,$ weights, learning$\_$rate, iterations$)$: $"""$

Train the perceptron with the given dataset and return the $u$ pdated weights. This function should go over the data points in the dataset one$-$by$-$one, call forward$\_$pass$()$ function

then call the backward$\_$propagation$()$ function to update the weights. The function should go over the dataset the number of i terations passed as a parameter.

Parameters:

$x$ : Input data. a $2D$ array with $K$ rows and $n$ columns where $K$ is the number of datapoints in the dataset and $n$ is the number of features

weights: Weights matrix. A $1-$dimensional array of size $1)$ where $n$ is the number of features, including the bias term. ha$)$

learning$\_$rate: the learning rate used in the training $($alp

iterations: the number of iterations of the

Returns:

updated$\_$weights: Updated weights matrix.

$""$t

# Write your code here

return weights

Use the following cell to test your solution in the previous cell. Here is a list of correct answers:

$\backslash$table$[[$Index of the example,Updated Weights$],[$Updated weights$[$datapoint$(1)],\left[\begin{array}{cccc}0\mathrm{.}5& 0\mathrm{.}3& 0\mathrm{.}2& 0\mathrm{.}1\end{array}\right]$