I have to approximate nonlinear function with neural network. The number of layers and number of neurons in layers are chosen by me. Suppose i have a certain dataset (attached).
Dataset contains 7 variables. T1-T5 are used to create function (see code) and X1 and X2 are inputs.
Now my code just does not work as intended. I am not even sure this is correct so a confirmation would be nice ☺.
But now the real problem. After i train the network, the performance graph shows three lines, one for training, one for validation and one for test. How can i change it. I only used train command for training set and it divided the training set into validation, training and test.
Lets suppose I join those three together into the big one. How does the algorithm decide how to divide them? Does it divide the data evenly? Can i decide how it will be divided?
load 'Data_Problem1_regression.mat';
Tnew = (8*T1 + 5*T2 + 2*T3 + 1*T4 + 0*T5)/(8+5+2+1+0);
training = Tnew(1:1000)';
validation = Tnew(1001:2000)';
test = Tnew(2001:3000)';
x = {X1(1:1000)';X2(1:1000)'};
net1=feedforwardnet(12,'trainlm');
net1.numinputs = 2;
view(net1)
net1 = train(net1,x,training);
