Draw a multi$-$layer network with one hidden layer which has three units. Assume

activation functions of the network is a linear function and all weights in the network

are $1.$ Check if the network generates the desired outputs for the following training set.

If not, make a suitable change to handle the problem.Consider the following training set: input values $($x$)$ desired output values $($y$)$ x $1=(-1,-1,-1)1$ x $2=(1,1,1)-1$ Given the learning constant $\backslash$alpha $=0\mathrm{.}1$and initial weight vector w $0=(0\mathrm{.}1,0\mathrm{.}1,0\mathrm{.}1)$: Use the perceptron learning method to find the weight vector for the training set given above for one epoch $($update the weight vector one time for each training pattern$)?1.$ Consider the following training set: $[$ input values $($x$)$ desired output values $($y$)$; x$^1=(-1,-1,-1)1$; x$^2=(1,1,1)-1]$ Given the learning constant $\backslash$alpha $=0\mathrm{.}1$ and initial weight vector w$^0=(0\mathrm{.}1,0\mathrm{.}1,0\mathrm{.}1)$ : Use the perceptron learning method to find the weight vector for the training set given above for one epoch $($update the weight vector one time for each training pattern$)?$