Neural Networks - Function approximation

Problem is to suggest weights of a multi-layered neural network computing the function f(x1, x2) = 3 − x1 − x2, where x1, x2 are input bits (of value 0 or 1 each). The neurons of the network should use the sigmoidal transfer function with the slope 1 and they have biases. The topology of the network must be the following:

(a) two input neurons – inputs are bits (with value 0 or 1),

(b) two neurons in a single hidden layer, and

(c) two neurons in the output layer

Outputs of the network will be interpreted as two-bit binary number in the following way:

• output greater or equal to 0.5 will be considered as logical 1,

• output greater less than 0.5 will be considered as logical 0.

List weights and biases of all neurons and also a table with the actual outputs of the network (before rounding) for all four combinations of the input bits...

What is wrong with this code? :

clc;
in=[1 0 1 0
     0 1 1 0];
out=[0.85 0.85 0.2 0.85
      0.2 0.2 0.85 0.85];
net=feedforwardnet(2);
net = configure(net,in,out);
net.layers{1}.transferFcn='logsig';
net.layers{2}.transferFcn='logsig';
net.trainParam.epochs = 100;
net.trainParam.goal = 1e-6;
net = init(net);                
[net,tr] = train(net, in, out)      
weights = getwb(net)
weight=net.IW{1}
weight1=net.LW{2}
b1=net.b{1}
b2=net.b{2}