how to calculate the output of neural network manually using input data and weights.

9 次查看（过去 30 天）
prabakaran jayaraman
回答： Amir Qolami 2020-4-12
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?

请先登录，再回答此问题。

采纳的回答

Greg Heath
Greg Heath 2015-6-25
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 个评论
Greg Heath
Greg Heath 2015-6-28
编辑：Greg Heath 2015-6-28
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

请先登录，再进行评论。

更多回答（1 个）

Amir Qolami
Amir Qolami 2020-4-12
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

类别

在 Help Center 和 File Exchange 中查找有关 Deep Learning Toolbox 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by