How to calculate output of this neural network? 

I am sure this is a pretty easy question for someone well versed with neural networks but it has had me running round the bends.
While I understand how this is to be done in principle, i.e. readjustment of weights according to the thresholds, but a solution or atleast a partial solution will be highly helpful.
Thank you
 A: Ok my best guess based on http://neuralnetworksanddeeplearning.com/chap1.html.
The input is a linear activation $f(x)=x$ function which means that the input value will be in the first layer. So this means that :


*

*Node 1 = 1

*Node 2 = 0

*Node 3 = 1

*Node 4 = 0


In order to calculate the hidden nodes, we use the simple perceptron rule $\sum\limits_{j}{w_j x_j}$ where $w$ are the weights written next to the links and $x$ are the values from the nodes in the previous layer and $j$ the amount of nodes in the previous layer
Which results in node 5:
$(-1*5) + (0*3)+ (1*2) +(0*4) = -5+2= -3$ (so multiply weights with the input)
Now we have a binary activation rule, which is just check if it is bigger than 0 or not. 
$-3<0$ so this means that this will be a 0.
Node 6:
$(1*6)+(0*-1)+(1*-2)+(0*5)= 6-2 = 4$ again we apply the binary threshold, and $4>0$ so this means this will become a 1.
Node 7: We take the output we just calculated and multiply it with the weight to get the output:
$(0*-1)+(2*1)=2$ which is larger than 0 so the output of the network is 1
I am not sure this is correct. But I hope it will at least help you to see someone else's interpretation of the exercise.
