neural network input .

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

Answer 1

[Mo al]

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?????????????????????

Answer 2

[Greg Heath]

See comments, examples and sample code by searching tne newsgroup using

heath newff close clear

heath newff Neq Nw Ntrials

Hope this helps.

Greg

[Mo al]

thank you sir , but couldn't find my answer any way thank you

Answer 3

[Mo al]

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?????????????????????

Answer 4

[Greg Heath]

>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?

It is hard to answer your question without knowing the sizes of the pi (i=1:13).

For an I-H-O node topology and Ntrn I/O training pairs,

[I Ntrn] = size(p)

[O Ntrn] = size(t)

The I/O vectors are columns of p and t.

>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 ];

Size(p)? size(t1)?

Normalization of p and t1?

H = 15, O = 4?

Why 15? Better to search for the smallest good value satisfying

H <= floor( (Ntrn*O-O)/(I+O+1) )

Add an outer loop over candidate H values.

> RandStream.setDefaultStream(RandStream('mt19937ar','seed',1));

Only initialize once BEFORE the loops!

%rand('state',sum(100*clock)) % initialize the random net = init(net);

If you ever do this, replace 100 by 1e9. Don't need INIT because NEWFF is self-initializing.

>for i=1:1:10

>>net=newff(minmax(p),t,[H , O],{'tansig','purelin'},'trainrp');

>net.performFcn='msereg'; net.performParam.ratio=0.5;

Why MSEREG?? Why not use as many defaults as possible?

>net.trainParam.show = 5;

>net.trainParam.lr = 0.5; % learning rate

>net.trainParam.epochs = 1000;

>net.trainParam.goal = 1e-5;

Why 1e-5??

>[net,tr]=train(net,p,t); > a = sim(net,p); e = a-t;

Why not [net, tr, a, e] = ... ?

>rmse = sqrt(mse(e)); >acc=1-rmse

Useless performance index. Use R^2.

Hope this helps.

Greg

 %simpleclusterOutputs = sim(net,p);
%plotroc(t,simpleclusterOutputs);
end

Answer 5

[Mo al]

Greg , thank you very much , about your wondering

1-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?

It is hard to answer your question without knowing the sizes of the pi (i=1:13). the p1, p2 ...p12 and p13 each one has 255 by 255 the total p=[ p1; p2; ...;p12 and p13] contain all of them my question is how does the NN will take the all p1which 255 by 255 and after that p2 , p3... p13 or the NN will take the first element in p1 and the first one in p2..... p13 (one by one) ? .==================================================== 2- >>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 ];

Size(p)? size(t1)? the size of p is (13 x255)=3315 elements.

the size of (t1) is 255

Normalization of p and t1? all the elements value for (p and t1) between 0 and 1.

H = 15, O = 4? one hidden lair has 15 neuron based on previous researches . out put in the target has two values either 1 or 0 so once i put the output 4 i achieved much better regression and accuracy values . ================================================================== 3-net.performFcn='msereg'; net.performParam.ratio=0.5;

Why MSEREG?? Why not use as many defaults as possible? if i keep the default my accuracy and regression will R=0.333 and the accuracy 90 but if i use net.performFcn='msereg' the R =.6999 and the accuracy 96% =================================== 4-net.trainParam.goal = 1e-5;

Why 1e-5?? its the best value i can get better performance result through . ====================================================== 5->[net,tr]=train(net,p,t); > a = sim(net,p); e = a-t;

Why not [net, tr, a, e] = ... ? what is the wrong with my code why yours is better ? =================================================== 6->rmse = sqrt(mse(e)); >acc=1-rmse

Useless performance index. Use R^2 what is R^2? ============================ thank you in advance

Answer 6

[Greg Heath]

>1-i have these code with these input to my neural network , my question is how does the NN take the input ?

Once the matrix p is created, the net takes the first column of p, then the second column of p, etc. If

[ I N ] = size(p),

then p is interpreted as containing N I-dimensional column vectors. Since

size(pi) = [ 255 255] % i = 1:13

you need to columnize your pi matrices and form

p = [ p1(:), p2(:), ..., p13(:)];

THEN

size(p) = [ 255^2 13] = [ 65025 133 ]

AND

you need to investigate 2-D dimensionality reduction via averaging pixels.

====================================================

>2. p=[p1;p2;p3;p4;p5;p6;p7;p8;p9;p10;p11;p12;p13];

Incorrect. See above

Size(p)? size(t1)?

> the size of p is (13 x255)=3315 elements.

Incorrect. See above.

>the size of (t1) is 255

Incorrect. Size has two dimensions. See the documentation

help size

doc size

The correct answer is

size(t1) = [ O N ]

and is interpreted as N O-dimensional output column vectors.

H = 15, O = 4?

>one hidden lair (sp: layer) has 15 neuron based on previous researches . out put in the target has two values either 1 or 0 so once i put the output 4 i achieved much better regression and accuracy values .

I don't understand. What do the 4 outputs represent?

Oh! Oh! Is this classification and you are using a binary coded output?

For classification use

size(t1) = [ 13 N]

where each column is a column of the 13 dimensional unit matrix eye(13). The input image vector is then assigned to the class corresponding to the maximum output.

==================================================================

Answer 7

[Greg Heath]

...continued

>3-net.performFcn='msereg'; net.performParam.ratio=0.5;

Why MSEREG?? Why not use as many defaults as possible?

>if i keep the default my accuracy and regression will R=0.333 and the accuracy 90 but if i use net.performFcn='msereg' the R =.6999 and the accuracy 96%

Something is wrong somewhere.

How is R defined? Square root of the coefficient of determination (See Wikipedia)?

Try the default again with the columnized image inputs and unit matrix column outputs

===================================

 4-net.trainParam.goal = 1e-5;

Why 1e-5??

> its the best value i can get better performance result through .

No need to search for a suitable value. Just choose a goal for R. Then work backwords to find the corresponding goal for MSE.

======================================================

5->[net,tr]=train(net,p,t); > a = sim(net,p); e = a-t;

Why not [net, tr, a, e] = ... ?

what is the wrong with my code why yours is better ?

Nothing is wrong with your code. Just letting you know that the additional code and computations are unnecessary.

 ===================================================

6->rmse = sqrt(mse(e)); acc=1-rmse

Useless scale-dependent performance index. Use R^2.

> what is R^2?

A common statistical measure of fit. Look up coefficient of determination in Wikipedia.

============================

Hope this helps

Greg

[Mo al]

thank u sir it helped me allot

Answer 8

[Mo al]

hi sir , is thiers any way i can make the NN take the input (p) as a row instead of column? thank u sir

[Walter Roberson]

No. If your variable is the wrong way around, transpose it as you pass it in, using p.' or transpose(p)

[Mo al]

thanx