Neural network mlp trouble

Question

I am programming in C++ with Visual Studio 2012 and Opencv 2.4.6. I have a set of training images for which I have calculated the feature vectors. These feature vectors should become the input of my neural network, realized with the class CvANN_MLP. Every feature vector is composed of 60 attributes, 59 are the "inputs" of the neural network, and the last is the "output", that can be only 1 or 0. I have realized this neural network:

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";
}

The values that are returned are of this type:

 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

So I haven't results of 0 or 1. Is it correct? If not, what is the mistake in my code?

score 0 · Answer 1 · edited May 23 '17 at 10:26

I dont know much about about C interface but I had a similar problem with c++ interface here :

OpenCV Neural Network Sigmoid Output

Mlp's create function default parameters was not working and they should be given as :

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

But as i told, I dont know about c interface. Since C doesnt have default arguments, there maybe another function to give alpha and beta parameters of sigmoid.

And by the way, OpenCV does not have well known implementation of sigmoid which in range of 0 and 1. As it is stated in the docs, it is between -1 and 1.

Neural network mlp trouble

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