在Matlab中计算神经网络的方程式 [英] Equation that compute a Neural Network in Matlab

问题描述

我创建了一个神经网络matlab。这是脚本：

 
 
 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'; ％分割每个样本
 net.divideParam.trainInd = 1：428; 
 net.divideParam.valInd = 429：520; 
 net.divideParam.testInd = 521：612; 
 net.trainFcn ='trainscg'; ％Scaled共轭梯度反向传播
 net.performFcn ='mse'; ％均方误差
 net.plotFcns = {'plotperform'，'plottrainstate'，'ploterrhist'，'plotregression'，'plotconfusion'，'plotroc'}; 
 net = init（net）; 
 net.trainParam.max_fail = 20; 
 
 [net，tr] =火车（净，投入，目标）; 
 
 outputs = net（inputs）; 
 errors = gsubtract（targets，outputs）; 
 performance = perform（net，targets，outputs）
 
 

现在我想保存权重和偏差网络和写方程。
我保存了权重和偏差：

 
 
 W1 = net.IW {1,1}; 
 W2 = net.LW {2,1}; 
 b1 = net.b {1,1}; 
 b2 = net.b {2,1}; 
 
 

所以，我已经完成了数据预处理，我写了以下公式。

 
 
 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）; 
 
 

但使用相同的输入输入= [1：8]我得到不同的结果。为什么？怎么了？
请帮帮我！这很重要！

我使用Matlab R2010B

解决方案

看起来像您是预处理输入，而不是后处理输出。后处理使用反向处理形式。 （目标预处理，因此输出被反向处理）。

I created a neural network matlab. This is the script:

    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

解决方案

It looks like you are pre-processing the inputs but not post-processing the outputs. Post processing uses the "reverse" processing form. (Targets are pre-processed, so outputs are reverse processed).

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