how to calculate the output of neural network manually using input data and weights.
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prabakaran jayaraman
le 19 Juin 2015
Réponse apportée : Amir Qolami
le 12 Avr 2020
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?
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Greg Heath
le 25 Juin 2015
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
2 commentaires
Greg Heath
le 28 Juin 2015
Modifié(e) : Greg Heath
le 28 Juin 2015
IW does not act on the original weights. It acts on the normalized weights. The default normalization documentation is
help mapminmax
doc mapminmax.
Search for examples using a subset of
greg xsettings tsettings
Greg
Plus de réponses (1)
Amir Qolami
le 12 Avr 2020
This works for any number of hidden layers and neurons;
function output = NET(net,inputs)
w = cellfun(@transpose,[net.IW{1},net.LW(2:size(net.LW,1)+1:end)],'UniformOutput',false);
b = cellfun(@transpose,net.b','UniformOutput',false);
tf = cellfun(@(x)x.transferFcn,net.layers','UniformOutput',false);
%%mapminmax on inputs
if strcmp(net.Inputs{1}.processFcns{:},'mapminmax')
xoffset = net.Inputs{1}.processSettings{1}.xoffset;
gain = net.Inputs{1}.processSettings{1}.gain;
ymin = net.Inputs{1}.processSettings{1}.ymin;
In0 = bsxfun(@plus,bsxfun(@times,bsxfun(@minus,inputs,xoffset),gain),ymin);
else
In0 = inputs;
end
In = cell(1,length(w)); Out = In;
In{1} = In0'*w{1}+b{1};
Out{1} = eval([tf{1},'(In{1})']);
for i=2:length(w)
In{i} = Out{i-1}*w{i}+b{i};
Out{i} = eval([tf{i},'(In{',num2str(i),'})']);
end
%%reverse mapminmax on outputs
if strcmp(net.Outputs{end}.processFcns{:},'mapminmax')
gain = net.outputs{end}.processSettings{:}.gain;
ymin = net.outputs{end}.processSettings{:}.ymin;
xoffset = net.outputs{end}.processSettings{:}.xoffset;
output = bsxfun(@plus,bsxfun(@rdivide,bsxfun(@minus,Out{end},ymin),gain),xoffset);
else
output = Out{end};
end
end
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