Need help on neural network train test and validation accuracy

My data set have 420 images(24 features each). there are 160 for train. 20 for validation. 240 for testing. my problem is, after writing this code am getting 100% accuracy which is absurd. plz help me in this matter as am not sure if my code is correct or not.

% Solve a Pattern Recognition Problem with a Neural Network

load('ftrmat420.mat'); %420 x 24
load('class_test.mat'); %1 x 420

x=transpose(ftrmat420);
t=class_test;

inputs = x
targets = t

% Create a Pattern Recognition Network
hiddenLayerSize = 10;
net = patternnet(hiddenLayerSize);

% Set up Division of Data for Training, Validation, Testing
%net.divideParam.trainRatio = 30/100;
%net.divideParam.valRatio = 13/100;
%net.divideParam.testRatio = 57/100;

%[trainInd,valInd,testInd] = divideind(420,241:420,1:10,1:240);

net.divideFcn = 'divideind';
 net.divideParam.trainInd = 261:420;
net.divideParam.valInd   = 241:260;
 net.divideParam.testInd  = 1:240;

% Train the Network
[net,tr] = train(net,inputs,targets);

% Test the Network
outputs = net(inputs);
errors = gsubtract(targets,outputs)
performance = perform(net,targets,outputs)

% View the Network
view(net)

% Plots
% Uncomment these lines to enable various plots.
 figure, plotperform(tr)
 figure, plottrainstate(tr)
 figure, plotconfusion(targets,outputs)

[c,cm] = confusion(targets,outputs)

fprintf('Percentage Correct Classification   : %f%%\n', 100*(1-c));
fprintf('Percentage Incorrect Classification : %f%%\n', 100*c);

figure, ploterrhist(errors)

trainTargets = targets .* tr.trainMask{1};
 valTargets   = targets .* tr.valMask{1};
 testTargets  = targets .* tr.testMask{1};

trainPerformance = perform(net,trainTargets,outputs)
 valPerformance   = perform(net,valTargets,outputs)
 testPerformance  = perform(net,testTargets,outputs)

Answer 1

[Greg Heath]

Your data division fractions don't make sense at all.

    160/20/240  ==> 0.38/0.05/0.57

You only have 160 training equations to estimate

 (240+1)*10+(10+1)*1 =  2421 unknown weights

Your net is severely overfit.(i.e.,useless)

Stick with something close to the defaults 0.7/0.15/0.15

Hope this helps.

Thank you for formally accepting my answer

Greg