Normalize Inputs and Targets of neural network

Question

afef 2017-6-13

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/344639-normalize-inputs-and-targets-of-neural-network

评论： afef 2017-6-15

Hi, i'm trying to create neural network using nprtool , i have input matrix with 9*1012 and output matrix with 2*1012 so i normalize my data using mapminmax as you can see in the code. But my data some input take a very high value numerically compared to other input so i want to know can the mapminmax can solve this problem or i should do something else to solve this because i still have a bad accuracy ?

 rng('default');
 x = patientInputs;
 t = patientTargets ;
inputs=mapminmax(x);
targets=t;
size(inputs);
 trainFcn = 'trainscg';  % Scaled conjugate gradient backpropagation.
 % Create a Pattern Recognition Network
hiddenLayerSize =10;
net = patternnet(hiddenLayerSize);
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};
net.divideFcn = 'dividerand';  % Divide data randomly
net.divideMode = 'sample';  % Divide up every sample
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
net.performFcn = 'mse';  % Cross-Entropy
 % Choose Plot Functions
% For a list of all plot functions type: help nnplot
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
    'plotconfusion', 'plotroc'};
     net.trainParam.max_fail =55;
     net.trainParam.min_grad=1e-10;
     net.trainParam.show=10;
     net.trainParam.lr=0.01;
     net.trainParam.epochs=90;
     net.trainParam.goal=0.001;
 % Train the Network
[net,tr] = train(net,inputs,targets);
 y = net(inputs);
 e = gsubtract(targets,y);
 performance = perform(net,targets,y)
tind = vec2ind(targets);
yind = vec2ind(y);
percentErrors = sum(tind ~= yind)/numel(tind);
 % Recalculate Training, Validation and Test Performance
trainTargets = t .* tr.trainMask{1};
valTargets = t .* tr.valMask{1};
testTargets = t .* tr.testMask{1};
trainPerformance = perform(net,trainTargets,y)
valPerformance = perform(net,valTargets,y)
testPerformance = perform(net,testTargets,y)
 % View the Network
view(net)