Training feedforward neural network
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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.
回答（1 个）
Greg Heath
2016-4-19
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
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