# MATLAB: How to train and simulate a Newff or nntool

nntool newff neuralnetwork

Hi, I have been stuck with this problem, if someone could help please..
I have a input data of (512×240) and a target data of (512×1). This data is basically divided by two classes I want to classify. My target is this matrix consisted of 1 or 0. I am using 50% of data for training and other 50% (I mean 512×240) as my test input. Which will have the size output of (1×512). Of course the training and test input/output are different. I can train my network using the code bellow:
net = newff(input,target,10,{'tansig'},'trainrp','learngd','msereg'); net = train(net,input,target); simulacao = sim(net,test);
Is this code providing me a neural network which will have two different inputs? I mean the way I set up my data is going to provide my the output I need? I need the network to classify my inputs as 1 or 0. This code performance its been pretty poor as I added up more data and trained with a 50-50% rate.

 GEH0. Ending semicolons have been purposely omitted for display  purposes 1. Transpose so that    [ I0  N ] = size(input)    % [ 240 512 ]     [ O0   N ] = size(target0) % [ 1   512 ] 2. Transform target0 into a 2x512 matrix with {0,1} unit vector columns that correspond to class indices 1 and 2 target = full(ind2vec(target0+1)) trueclassindices = vec2ind(target) [ O N ] = size(target)  % [ 2 512 ] 3. Reduce the input size from I0 = 240 to something more practical. 512 points in 240 dimensions is averaging only ~2 points per dimension. At least 10 to 30 points per dimension would be preferable 4. I prefer PLSREGRESS over PCA(which ignores targets) for classifier dimensionality reduction. I don't think the linear coefficient STEPWISEFIT works well for unit vector target classifiers. help PLSREGRESS doc PLSREGRESS   [ I  N ]   = size(input)    %  [ ?  512 ]  5. If you don't have the current classifier PATTERNNET, use the obsolete NEWPR (NEWFF modified for classification) help NEWPR doc NEWPR 6. The first time through use as many defaults as possible (e.g., 0.7/0.15/0.15 ratios for training, validation and testing).
 Ntrn     = N -2*round(0.15*N) % 358 Ntrneq = Ntrn*O               % 716 Hub     = (Ntrn*O-O)/(I+O+1)  % 2.9 ==> NEED to reduce I 7. If the net is overfit MUST consider     a. VALIDATION STOPPING     b. BAYESIAN REGULARIZATION 8. For  regression, MSEREG and TRAINBR would be    considered w.r.t. 7b. However, I have never used either for classification.
 CONSIDER DOCUMENTATION EXAMPLE Class indices are 1 and 2 Remove ending semicolons to investigate details close all, clear all, clc help newpr [ x  t ]  = simpleclass_dataset; [ I  N ] = size(x)    % [ 2 1000 ] [O N ] = size(t)      % [ 4 1000 ] trueclassind = vec2ind(t); vart1   = mean(var(t',1))% 0.18723 MSE Reference H        = 2             % From x-plane plot net     = newpr( x, t, H); rng('default') [ net  tr  y  e ] = train( net, x , t); % y= net( x ); e = t - y; outclassind = vec2ind(y); err = outclassind~=trueclassind;  Nerr = sum(err)           % 0 PctErr = 100*Nerr/N       % 0 plotperform plotconfusion Hope this helps. *Thank you for formally accepting my answer*