MATLAB: How to calculate the output of neural network manually using input data and weights.

ann output

i am having ann program with 3 inputs and one output. i am using back propagation and feed forward network. the activation functions are tansig and purelin. no of layer is 2 and no of neuron in hidden layer is 20. i want to calculate the output of network manually using the input and weights(iw,lw,b) i need an equation to find the output. can you help me?

Best Answer

When I-dimensional "I"nput x and O-dimensional "O"utput target t are normalized via the default mapminmax (or mapstd),the relationship between the normalized input and output is

yn = repmat( b2, O, N ) + LW * tanh( repmat( b1 , I, N ) + IW * xn);

Thank you for formally accepting my answer

Greg

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MATLAB: Neural network (fitnet) and data decomposition

The training and test indices are given in tr. Type, without semicolon

 tr = tr

to see what info is in tr.

Hope this helps.

Thank you for formally accepting my answer

Greg

MATLAB: How to manually calculate a Neural Network output

you missed several normalization parameters, here I leave the solution :

[x, y] = crab_dataset;
size(x)    % 6 x 200
size(y)    % 2 x 200
setdemorandstream(491218382);
net = patternnet(10);
[net, tr_info] = train(net, x, y);
xoffset=net.inputs{1}.processSettings{1}.xoffset;
gain=net.inputs{1}.processSettings{1}.gain;
ymin=net.inputs{1}.processSettings{1}.ymin;
w1 = net.IW{1};   % (10x6)
w2 = net.LW{2};   % (2x10)
b1 = net.b{1};    % (10x1)
b2 = net.b{2};
% Input 1
y1 = bsxfun(@times,bsxfun(@minus,x,xoffset),gain);
y1 = bsxfun(@plus,y1,ymin);
% Layer 1
a1 = 2 ./ (1 + exp(-2*(repmat(b1,1,size(x,2)) + w1*y1))) - 1;
% output
n=repmat(b2,1,size(x,2)) + w2*a1;
nmax = max(n,[],1);
n = bsxfun(@minus,n,nmax);
num = exp(n);
den = sum(num,1);
den(den == 0) = 1;
y2 = bsxfun(@rdivide,num,den);%y2==outputnet == net(x)

Best Answer

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MATLAB: Neural network (fitnet) and data decomposition

MATLAB: How to manually calculate a Neural Network output

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