$($x$1,$ x$2)$ are input features and target classes are elther $+1$ or

$-1$ as shown in the figure.

A$.$ What is the minimum number of hidden layers and

hidden nodes required to classify the following dataset with

$100\%$ accuracy using a fully connected multilayer

perceptron network? Step activation functions are used at

all nodes, l$.$e$.,$ output $=+1$ if total weighted input $$ bias b at

a node, else output $=-1.$

B$.$ Show the minimal network architecture by organizing the

nodes in each layer horizontally. Show the node

representing $1$ at the left on the input layer. Organize the

hidden nodes in ascending order of bias at that node.

Specify all weights and bias values at 'all nodes. Weights can

be only $-2\mathrm{.}5,2\mathrm{.}5$ or $0,$ and bias $+$ve$/-$ve multiples of $2\mathrm{.}5.$