The following diagram represents a feed$-$forward neural network with one hidden layer

A weight on connection between nodes i and $j$ is denoted by $w_{ij},$ such as $w13$ is the weight on the

connection between nodes $1$ and $3.$ The following table lists all the weights in the network:

$w_{13}=-2,w_{35}=1$

$w_{23}=3,w_{45}=-1$

$w_{14}=4,w_{36}=-1$

$w_{24}=-1,w_{46}=1$

Each of the nodes $3,4,5$ and $6$ uses the following activation function:

$(v)=\{\begin{array}{ccc}1& if& v0\\ 0& \text{o}\text{t}\text{h}\text{e}\text{r}\text{w}\text{i}\text{s}\text{e}& \end{array}$

where $v$ denotes the weighted sum of a node. Each of the input nodes $(1$ and $2)$ can only receive

binary values $($either $0$ or $1).$ Calculate the output of the network $(y5$ and $y6)$ for each of the input

patterns: