Obtaining neural network formula

2017 3 6
2 个回答
更新时间：2017 3 7
3 次查看（30 天）

0 个投票

Hi, I'm trying to obtain the formula from neural network using a linear simple case:
F = [-9 1 2 3 4 5 6 7 8 9 10 30 60];%neural network input
T = F*3+1; %target
net = newff(F, T,[],{},'traingdx');
net1 = train(net,F,T,[],[]);
x = 5; %new input
y = sim(net1,x); %ann output ~= 16;
%now i'm trying to obtain the formula from NN:
IW = net1.IW{1};
b = net1.b{1};
y2 = b+IW*x;
%But y2 = 5 whereas y = 3*x+1 = 16!!!
I understand that mapminmax function is involved for normalization..
Do you know how to renormilize "y2" in order to obtain the "y" target ?
Any help will be really appriciated

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回答（2 个）

Greg Heath
Greg Heath 2017-3-7

0 个投票

What are b, IW and LW?
Given those values and the fact that x and t are both scaled to [-1 1 ] before training and y is rescaled with the t inverse transformation after training , should lead you to the answer.
Thank you for formally accepting my answer
Greg
Gino Massafra
Gino Massafra 2017-3-7
编辑：Gino Massafra 2017-3-7

0 个投票

Dear Greg, I've found the answer to my question for this simple case:
F = [-9 1 2 3 4 5 6 7 8 9 10 30 60];%neural network input
T = F*3+1; %target
net = newff(F, T,[],{},'traingdx');
net.trainParam.epochs = 1000;
net.trainParam.goal = 2e-32;
net.trainParam.min_grad = 1e-16;
net.trainParam.max_fail = 15000;
net.performFcn='mae';
net.trainParam.showWindow=1;
net1 = train(net,F,T,[],[]);
x = 5; %new input
y = sim(net1,x); %ann output = 16; this is the goal I want to reach
%now i'm trying to obtain the formula from NN:
IW = net1.IW{1};
b = net1.b{1};
y2 = b+IW*x;
Tmin = cell2mat(net1.outputs.range(1));
Tmax = cell2mat(net1.outputs.range(2));
Fmin = cell2mat(net1.inputs.range(1));
Fmax = cell2mat(net1.inputs.range(2));
%y3 = (max(T)-min(T))*(y2-min(F))/(max(F)-min(F)) + min(T);
y3 = (Tmax-Tmin)*(y2-Fmin)/(Fmax-Fmin) + Tmin;
y3 is = 16 !!! That's it y = y3
Next step is how to find y3 if the input is not a single row vector:
F = [-9 1 2 3 4 5 6 7 8 9 10 30 60; 21 10 22 3 4 -10 7 8 9 33 22 0 -13];%neural network input
T = sum(F)*5+4; %target
net = newff(F, T,[],{},'traingdx');
net.trainParam.epochs = 1000;
net.trainParam.goal = 2e-32;
net.trainParam.min_grad = 1e-16;
net.trainParam.max_fail = 15000;
net.performFcn='mae';
net.trainParam.showWindow=1;
net1 = train(net,F,T,[],[]);
x = [5; 6]; %new input
y = sim(net1,x); % y = 59;
%now i'm trying to obtain the formula from NN:
IW = net1.IW{1};
b = net1.b{1};
y2 = b+IW*x;
Tmin = cell2mat(net1.outputs.range(1));
Tmax = cell2mat(net1.outputs.range(2));
Fmin = cell2mat(net1.inputs.range(1));
Fmax = cell2mat(net1.inputs.range(2));
y3 = (Tmax-Tmin)*(y2-Fmin)/(Fmax-Fmin) + Tmin;
But now y3 is different from y
This doesn't work because Fmin and Fmax are wrong:
A = net1.inputs.range;
>> A{1,1}
ans =
-9 60
-13 33
Which is now Fmin and Fmax?
Please help me

3 个评论
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Steven Lord
Steven Lord 2017-3-7
See this documentation page for information about pre- and post-processing functions.
Gino Massafra
Gino Massafra 2017-3-7
I red it, but I can't find the solution in network inputs are not a simple row vector
Gino Massafra
Gino Massafra 2017-3-7
编辑：Gino Massafra 2017-3-7
Ok I SOLVED it: F = [-9 1 2 3 4 5 6 7 8 9 10 30 60; 21 10 22 3 4 -10 7 8 9 33 22 0 -13];%neural network input
T = sum(F)*5+4; %target
net = newff(F, T,[],{},'traingdx');
net.trainParam.epochs = 1000;
net.trainParam.goal = 2e-32;
net.trainParam.min_grad = 1e-16;
net.trainParam.max_fail = 15000;
net.performFcn='mae';
net.trainParam.showWindow=1;
net1 = train(net,F,T,[],[]);
x = [5; 6]; %new input
y = sim(net1,x); %ann output = 16;
%now i'm trying to obtain the formula from NN:
IW = net1.IW{1};
b = net1.b{1};
x2 = -1+2*(x-[min(F')]')./([max(F')]'-[min(F')]') %I normalize inputs in [-1 +1] range
y2 = b+IW*x2;
y3 = (y2+1)*(max(T)-min(T))/2+min(T);
Now y3 = y
Cheers

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