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How to insert 2D-matrix to a backpropagation neural network?

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nada fady
nada fady 2016년 5월 7일
댓글: nada fady 2016년 5월 8일
I am working on speech restoration, I used MFCC to extract the features. now I have 12*57 input matrix and 12*35 target matrix for each audio clip. My question is how can I insert this 2D-matrix to a backpropagation neural network?

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Greg Heath
Greg Heath 2016년 5월 8일
N I-dimensional "I"nput column vectors
[I N ] = size(input)
N O-dimensional "O"utput target vectors
[O N ] = size(target)
help fitnet % Regression and Curvefitting
help patternnet % Classification and Target Regression
%H = number of hidden nodes in I-H-O node topology
net = fitnet(input,target,H);
% You have a problem with I = 57, O = 35, N = 12 because even if you used all 12 images for training you would only have
Ntrn = N % 12 input/target pairs
Ntrneq = N*O % 420 training equations
whereas there are
Nw = (I+1)*H+(H+1)*O
unknown weights. Therefore, not to have fewer equations than unknowns (Ntrneq >= Nw), the number of hidden nodes H cannot be larger than the upper bound
Hub = (Ntrneq-O)/(I+O+1) = 4.1
It is more likely that you need more than H=4 hidden nodes. It is obvious from the above equation that decreasing I and/or increasing N is desired.
Alternatives are
1. Use MSEREG as the training goal
2. Use TRAINBR as the training function.
Personally, I would try input-variable reduction and/or obtain more input/target pairs.
The best inputs to keep are usually indicated via the linear coefficient model STEPWISEFIT.
Hope this helps.
Thank you for formally accepting my answer
Greg
  댓글 수: 1
nada fady
nada fady 2016년 5월 8일
Thank you very much for your clear answer. I think I wrote some thing wrong in my question, that is: I have 12*35 matrix input and 12*57 target output, so can input matrix size be smaller than target output size?

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