Training feedforward neural network

I have two gaussian distribution samples, one guassian contains 10,000 samples and the other gaussian also contains 10,000 samples, I would like to train a feed-forward neural network with these samples but I dont know how many samples I have to take in order to get an optimal decision boundary. Here is the code but I dont know exactly the solution and the output are weirds.

    x1 = -49:1:50;
    x2 = -49:1:50;
    [X1, X2] = meshgrid(x1, x2);
    Gaussian1 = mvnpdf([X1(:) X2(:)], mean1, var1);// for class A
    Gaussian2 = mvnpdf([X1(:) X2(:)], mean2, var2);// for Class B
    net = feedforwardnet(10);
    G1 = reshape(Gaussian1, 10000,1);
    G2 = reshape(Gaussian2, 10000,1);
    input = [G1, G2];
    output = [0, 1];
    net = train(net, input, output);

When I ran the code it give me weird results. If the code is not correct, can someone please suggest me so that I can get a decision boundary for these two distributions.

Answer 1

[Greg Heath]

For N I-dimensional input vectors from c classes, the corresponding N target vectors are {0,1} c-dimensional unit vectors:

 [ I N ] = size(input)
 [ c N ] = size(target)

where

 target = ind2vec(trueclassindices)

and

 trueclassindices  = vec2ind(target)

Hope this helps.

Thank you for formally accepting my answer

Greg