神經網絡mlp麻煩

我用Visual Studio 2012和Opencv 2.4.6在C++編程。我有一套訓練圖像，我已經計算了特徵向量。這些特徵向量應該成爲我的神經網絡的輸入，用CvANN_MLP類來實現。每個特徵向量由60個屬性，59是神經網絡的「輸入」，最後是「輸出」，即只能是1或0。我已經意識到這個神經網絡：神經網絡mlp麻煩

CvANN_MLP machineBrain; 

double td[NUMERO_ESEMPI_TOTALE][60]; 

CvMat* trainData = cvCreateMat(NUMERO_ESEMPI_TOTALE, 59, CV_32FC1); 

CvMat* trainClasses = cvCreateMat(NUMERO_ESEMPI_TOTALE, 1, CV_32FC1); 

CvMat* sampleWts = cvCreateMat(NUMERO_ESEMPI_TOTALE, 1, CV_32FC1); 
//The matrix representation of our ANN. We'll have four layers. 
CvMat* neuralLayers = cvCreateMat(4, 1, CV_32SC1); 
CvMat trainData1, trainClasses1, neuralLayers1, sampleWts1; 

cvGetRows(trainData, &trainData1, 0, NUMERO_ESEMPI_TOTALE); 
cvGetRows(trainClasses, &trainClasses1, 0, NUMERO_ESEMPI_TOTALE); 
cvGetRows(trainClasses, &trainClasses1, 0, NUMERO_ESEMPI_TOTALE); 
cvGetRows(sampleWts, &sampleWts1, 0, NUMERO_ESEMPI_TOTALE); 
cvGetRows(neuralLayers, &neuralLayers1, 0, 4); 



cvSet1D(&neuralLayers1, 0, cvScalar(59)); 
cvSet1D(&neuralLayers1, 1, cvScalar(3)); 
cvSet1D(&neuralLayers1, 2, cvScalar(3)); 
cvSet1D(&neuralLayers1, 3, cvScalar(1)); 



for(int i=0;i<NUMERO_ESEMPI_TOTALE;i++){ 
    for(int j=0;j<59;j++){ 
     td[i][j] = featureVect[i][j]; 
    } 
    if(i<45){ 
     td[i][59] = 0; //è una bocca! 
    }else{ 
     td[i][59] = 1; //non è una bocca! 
    } 
} 

//Mettiamo insieme i training data 
for (int i=0; i<NUMERO_ESEMPI_TOTALE; i++){ 
    //I 59 input 
    for(int j=0;j<59;j++){ 
     cvSetReal2D(&trainData1, i, 0, td[i][j]); 
    } 
    //Output 
    cvSet1D(&trainClasses1, i, cvScalar(td[i][59])); 
    //I pesi (vengono tutti settati a 1) 
    cvSet1D(&sampleWts1, i, cvScalar(1)); 
} 


machineBrain.create(neuralLayers); 
cout<<"Rete creata"<<endl; 

//Train it with our data. 
machineBrain.train(trainData,trainClasses,sampleWts,0,CvANN_MLP_TrainParams(cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,100000,/*1.0*/0.01/*riprovare 0.01*/),CvANN_MLP_TrainParams::BACKPROP,0.001,0.05)); 
cout<<"Rete addestrata"<<endl; 

Mat pred(num_test_sample, 1, CV_32FC1); 
Mat pred1(num_test_sample, 1, CV_32FC1); 
for(int i=0;i<NUMERO_ESEMPI_TEST; i++){ 
    float _sample[59]; 
    CvMat sample = cvMat(1, 59, CV_32FC1, _sample); 
    float _predout[1]; 
    CvMat predout = cvMat(1, 1, CV_32FC1, _predout); 
    for(int j=0;j<59;j++){ 
     sample.data.fl[j] = featureVectTest[i][j]; 
    } 
    machineBrain.predict(&sample, &predout); 
    cout<<endl<<predout.data.fl[i]<<endl;//risultato predizione! 
    pred.at<float>(i,0)=predout.data.fl[i]; 
    pred1.at<float>(i,0)=predout.data.fl[i]; 
    file<<"Value Image "<<i<<": "<<predout.data.fl[i]<<"\n"; 
}

返回的值是這種類型的：

Value Image 0: 0.475639 
Value Image 1: 0 
Value Image 2: 4.2039e-044 
Value Image 3: 1.4013e-045 
Value Image 4: -7.88636e-016 
Value Image 5: 1.31722e-043 
Value Image 6: 4.2039e-044 
Value Image 7: 1.4013e-045 
Value Image 8: 0.0154511 
Value Image 9: 0.00100189 
Value Image 10: 0.00161414 
Value Image 11: 0.0449422 
Value Image 12: 7.5433 
Value Image 13: 65.8052 
Value Image 14: 24.301 
Value Image 15: 19.7311 
Value Image 16: 0.985553 
Value Image 17: 0.965309 
Value Image 18: 0.971295

所以我還沒有結果0或1。對不對？如果不是，我的代碼中有什麼錯誤？

來源

2013-10-02 user140888

我不知道很多關於關於C接口，但我曾與C++接口這裏有類似的問題：

OpenCV Neural Network Sigmoid Output

MLP的創建功能默認參數不工作，應給予他們爲：

mlp.create（層，CvANN_MLP :: SIGMOID_SYM，1,1）

但正如我說，我不知道關於C接口。由於C沒有默認參數，因此可能有另一個函數可以給出sigmoid的alpha和beta參數。

順便說一下，OpenCV的不具有公知的執行，其在0和1範圍作爲它在docs陳述乙狀結腸的，它是-1和1

之間

來源

2014-02-28 07:59:04 yutasrobot

神經網絡mlp麻煩

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