%% Create a custom network

% Define one sample of input-output
close all, clear all, clc, format compact
inputs = [1:6]' % input vector (6-dimensional pattern)
outputs = [1 2]' % corresponding target output vector

%% Create the network

net = network( ...
1, ... % numInputs, number of inputs,
2, ... % numLayers, number of layers
[1; 0], ... % biasConnect, numLayers-by-1 Boolean vector,
[1; 0], ... % inputConnect, numLayers-by-numInputs Boolean matrix,
[0 0; 1 0], ... % layerConnect, numLayers-by-numLayers Boolean matrix
[0 1] ... % outputConnect, 1-by-numLayers Boolean vector
);

% If you want the previous command as a single line:
%net=network(1,2,[1;0],[1;0],[0 0;1,0],[0 1]);

% View network structure
view(net);

%% Define more parameters:

% number of hidden layer neurons
net.layers{1}.size = 5;
% hidden layer transfer function
net.layers{1}.transferFcn = 'logsig';
view(net)

%% Configure the network (inputs and outputs)
net = configure(net,inputs,outputs);
view(net);

%% Training and output:

% initial network response without training
initial_output = net(inputs)
% network training
net.trainFcn = 'trainlm';
net.performFcn = 'mse';
net = train(net,inputs,outputs);
% network response after training
final_output = net(inputs)