TASK$-4($Combination of the "BASICS" of ANNs and loops, being powerful enhancement of the simulation methods$)$

Consider recursive feedforward deep ANN with one input layer, $800$ hidden layers and one output layer. $[$An example of the recursive ANN with only THREE hidden layersis shown in Figure.$]$

Assume, the same sets of weights and biases for ALL nodes of the ANN.

Use the following data $($pay attention to the signs in numbers$)$:

$*$ recursive given weights: w$11=0\mathrm{.}3$; w$12=0\mathrm{.}6$; w$21=0\mathrm{.}2$; w$22=0\mathrm{.}8$;

$*$ recursive given biases: b$1=15$; b$2=5$;

$*$ given inputs: x$1=1$; x$2=2.$

For simplicity, assume, that the recursive activation function for each node is an identity function, i$.$e$.$ a linear function with UNIT coefficient: g$($x$)=$x$.$

Determine the value of y$2$ the second output of this deep ANN.

Figure$-4$: An example of the recurrent ANN with one input layer, only THREE hidden layers and one output layer. Note, that this is an illustration only, as in case of your assignment task, the number of hidden layers is much larger!

PLEASE, READ ADDITIONAL RECOMMENDATIONS AND ENTER YOUR ANSWER for TASK$-4$ BELOW:$*$

Select the choice $($in the multiple choice answer fields below$),$ most closely matching your numerical answer.

$10$ points

$-500$

$-450$

$-304$

$-275$

$-107$

$120$