my data set consest of 25500 letter image to 34 diffrent
letter each letter has 750 image stored in
one folder as training dataset k and 6800 letter image 200
for any letter as testing dataset
25500 training images 30x30pixel ,350 features

the input = 350 feature

classes = 34 ( the number of letters)

the classifeir is backpropagation neural network
load

d= data.X;

t=data.y’;

net=newff(minmax(data.X),[350,34],{’tansig’,’purelin’});
net.trainParam.show =600;
net.trainParam.lr = 0.05;
net.trainParam.epochs = 300;
%net = init(net);
%training
[net,tr]=train(net,data.X,data.y);
my problem

i want the 34 output node ( the number of classes )
but the matlab error message is ( the matrix must have 34 rows)

dataset , data.X= (350 25500)

labels target , data.y = ( 25500 1)

i want code with backpropagation using matlab
to classify this data set and test iT.

no one know my problem i want my code doing with 34 output nodes
34 class to 25500 images in one matrice with matlab software

hoooo my friend

are you dont want me with your forum

why no one replyyyyyyyyyyyyyy?????????????????????

Hi

> net=newff(minmax(data.X),[350,34],{’tansig’,’purelin’});

This actually inits a 3-layer network, with TWO hidden layers (350 and 34). You also do not need 350 neurons in the first hidden layer. Try something like this:

net=newff(minmax(data.X),[8],{’tansig’});

thank marty
but net=newff(minmax(data.X),[8],{’tansig’});
8 is output node
my problem still

my fault, it should be:

net=newff(minmax(data.X),minmax(data.y’),[8],{’tansig’});

my teacher
no solution
any matrix of image in dataset has a label
many matrices of images to one class
output node in last layer must be equal to
the classes number
the label to my dataset is data.y=(25500 1)
25500 matrix represrnt 25500 image
to 34 class any class has 750 image matrix

You have 25500 objects with 350 features each and you want to map it into 34 classes, right?

You need a network with 350 input units, some number of hidden units and 34 output units. To create such network you have to issue the following command:

net=newff(minmax(data.X),minmax(data.y’),[8],{’tansig’});

The number of input and output units will be determined automatically from minmax(data.X) and minmax(data.y’). You only need to provide the number of hidden units, 8 in this case.

Now, your problem is that data.y = (25500 1), it should be (25500 34) - you have to convert it from class label (1..34) into a binary target vector, otherwise it will not work.

Check MATLAB documentation, it’s all there.

helo my friend Marcinbu
oh how i can do label (25500 34)into binary target vector?
my code to convert images pattern to vector :
any class images=750 sample store in one folder
and i have 34 folder
code like this
for allFolders=1:34

SubIm=reshape(SubIm,1,[]);

?d reshape image to image matrix

X_vector = [ X_vector ;SubIm ];

?d reshape image’s class to classes matrix

y_vector = [ y_vector; ones(1,1)*(allFolders-1)];
how i can do classes 34 in this code

my friends
how wwwwwwwwwwwwwwwwwwwwwwwwww
i can convert large dataset image contains
34 class each class has 700 image
to matrix
SubIm=reshape(SubIm,1,[]);

?d reshape image to image matrix

X_vector = [ X_vector ;SubIm ];

?d reshape image’s class to classes matrix

y_vector = [ y_vector; ones(1,1)*(allFolders-1)];

hep meeeeeeeeeeeee