MATLAB: Neural network input .

neural networktraining

i have these code with these input to my neural network , my question is how does the NN take the input ? to be more clear is NN going to take p1[1,1], p2[1,1],p3[1,1]…..p13[1,1]; or it will take the whole p1 and after that the whole p2 , p3 …,p13?
my code is
clear;
clc;
load asp65 ;
load curv65;
load dfd65;
load dffl65;
load dfr65;
load gl65;
load lc65;
load prc65;
load plc65;
load pfc65;
load slop65;
load sot65;
load vec65;
load ele65;
load hlo65;
p1=transpose(asp65);
p2=transpose(curv65);
p3=transpose(dfd65);
p4=transpose(dffl65);
p5=transpose(dfr65);
p6=transpose(gl65);
p7=transpose(lc65);
p8=transpose(prc65);
p9=transpose(plc65);
p10=transpose(pfc65);
p11=transpose(slop65);
p12=transpose(vec65);
p13=transpose(ele65);
t1=transpose(hlo65);
p=[p1;p2;p3;p4;p5;p6;p7;p8;p9;p10;p11;p12;p13];
%p=[p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11,p12,p13]
%p15=[p7;p8;p9;p10;p11;12;13];
t=[t1];
%p = [-1 -1 2 2 ;0 5 0 5 ]
%t = [-1 -1 1 1 ];
for i=1:1:10
net=newff(minmax(p),t,[15 , 4],{'tansig','purelin'},'trainrp');
net.performFcn='msereg';
net.performParam.ratio=0.5; 
net.trainParam.show = 5;
net.trainParam.lr = 0.5; % learning rate 
net.trainParam.epochs = 1000;
net.trainParam.goal = 1e-5;
RandStream.setDefaultStream(RandStream('mt19937ar','seed',1));  
%rand('state',sum(100*clock))  % initialize therandom
 net = init(net);
[net,tr]=train(net,p,t);
a = sim(net,p);
   e = a-t;
   rmse = sqrt(mse(e));
   acc=1-rmse
   %simpleclusterOutputs = sim(net,p);
  %plotroc(t,simpleclusterOutputs);
  end

Best Answer

helpppppppppppppppppppppppppppp, any help i have these code with these input to my neural network , my question is how does the NN take the input ? to be more clear is NN going to take p1[1,1], p2[1,1],p3[1,1].....p13[1,1]; or it will take the whole p1 and after that the whole p2 , p3 ...,p13? my code is clear; clc; load asp65 ; load curv65; load dfd65; load dffl65; load dfr65; load gl65; load lc65; load prc65; load plc65; load pfc65; load slop65; load sot65; load vec65; load ele65; load hlo65;
p1=transpose(asp65); p2=transpose(curv65); p3=transpose(dfd65); p4=transpose(dffl65); p5=transpose(dfr65); p6=transpose(gl65); p7=transpose(lc65); p8=transpose(prc65); p9=transpose(plc65); p10=transpose(pfc65); p11=transpose(slop65); p12=transpose(vec65); p13=transpose(ele65); t1=transpose(hlo65);
p=[p1;p2;p3;p4;p5;p6;p7;p8;p9;p10;p11;p12;p13]; %p=[p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11,p12,p13] %p15=[p7;p8;p9;p10;p11;12;13]; t=[t1]; %p = [-1 -1 2 2 ;0 5 0 5 ] %t = [-1 -1 1 1 ]; for i=1:1:10 net=newff(minmax(p),t,[15 , 4],{'tansig','purelin'},'trainrp'); net.performFcn='msereg'; net.performParam.ratio=0.5;
net.trainParam.show = 5; net.trainParam.lr = 0.5; % learning rate net.trainParam.epochs = 1000; net.trainParam.goal = 1e-5; RandStream.setDefaultStream(RandStream('mt19937ar','seed',1)); %rand('state',sum(100*clock)) % initialize therandom net = init(net); [net,tr]=train(net,p,t); a = sim(net,p);
 e = a-t;
 rmse = sqrt(mse(e));
 acc=1-rmse
 %simpleclusterOutputs = sim(net,p);
%plotroc(t,simpleclusterOutputs);
end
any one can help?????????????????????
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