我创建了一个神经网络。 原文:
load dati.mat;
inputs=dati(:,1:8) ;
targets=dati(:,9) ;
hiddenLayerSize = 10;
net = patternnet(hiddenLayerSize);
net.inputs{1}.processFcns = { removeconstantrows , mapminmax , mapstd , processpca };
net.outputs{2}.processFcns = { removeconstantrows , mapminmax , mapstd , processpca };
net = struct(net);
net.inputs{1}.processParams{2}.ymin = 0;
net.inputs{1}.processParams{4}.maxfrac = 0.02;
net.outputs{2}.processParams{4}.maxfrac = 0.02;
net.outputs{2}.processParams{2}.ymin = 0;
net = network(net);
net.divideFcn = divideind ;
net.divideMode = sample ; % Divide up every sample
net.divideParam.trainInd = 1:428;
net.divideParam.valInd = 429:520;
net.divideParam.testInd = 521:612;
net.trainFcn = trainscg ; % Scaled conjugate gradient backpropagation
net.performFcn = mse ; % Mean squared error
net.plotFcns = { plotperform , plottrainstate , ploterrhist , plotregression , plotconfusion , plotroc };
net=init(net);
net.trainParam.max_fail=20;
[net,tr] = train(net,inputs,targets);
outputs = net(inputs);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs)
Now I want to save the weights and biases of the network and write the equation. I had saved the weights and biases:
W1=net.IW{1,1};
W2=net.LW{2,1};
b1=net.b{1,1};
b2=net.b{2,1};
So, I ve done the data preprocessing and I wrote the following equation
max_range=0;
[y,ps]=removeconstantrows(input, max_range);
ymin=0;
ymax=1;
[y,ps2]=mapminmax(y,ymin,ymax);
ymean=0;
ystd=1;
y=mapstd(x,ymean,ystd);
maxfrac=0.02;
y=processpca(y,maxfrac);
in=y ;
uscita=tansig(W2*(tansig(W1*in+b1))+b2);
But with the same input input=[1:8] I get different results. why? What s wrong? Help me please! It s important!
I use Matlab R2010B