Errors when predicting from a Neutral Network

asked 2016-04-22 17:57:54 -0600

valentine
66 ●2 ●8

updated 2016-04-23 06:13:47 -0600

Hello everyone,

I am trying to train a neutral network in opencv using one hidden layer network. Everything seems fine but the program crashes when the predict function for the model is called. Below is my code:

float sc[6] = { 0.3, 0.5, 1, 1, 0.2, 0.4 };
float sc1[3] = { 0.75, 0.81, 0.93 };
float sc2[2] = { 0.2, 1};
Mat traindata = Mat(3,2,CV_32FC1,&sc);
Mat trainlabel = Mat(3, 1,CV_32FC1,&sc1);
Mat testdata = Mat(1, 2,CV_32FC1,&sc2);
Ptr<ml::ANN_MLP> mlp = ml::ANN_MLP::create();
mlp->setActivationFunction(ml::ANN_MLP::SIGMOID_SYM);
mlp->setTrainMethod(ml::ANN_MLP::BACKPROP);
mlp->setBackpropMomentumScale(0.1);
mlp->setBackpropWeightScale(0.1);
mlp->setTermCriteria(TermCriteria(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 1000, 1e-6));

Mat layers = Mat(3, 1, CV_32SC1);
layers.row(0) = Scalar(2);
layers.row(1) = Scalar(3);
layers.row(2) = Scalar(1);
//layers.row(3) = Scalar(5);

mlp->setLayerSizes(layers);

mlp->train(traindata,ml::ROW_SAMPLE,trainlabel);

Mat responses = Mat(3,1,CV_32FC1);
mlp->predict(testdata,responses);
float result = responses.at<float>(0, 0);

The error message is given below:

Unhandled exception at 0x00007FFE0076855C (opencv_world310d.dll) in fetchvideoapp.exe: 0xC0000005: Access violation reading location 0x000001D3A69DF000.

errormessage.png

Your assistance will be duly appreciated.

Regards.

Comments

hi berak this only error message Unhandled exception at 0x00007FFE0076855C (opencv_world310d.dll) in fetchvideoapp.exe: 0xC0000005: Access violation reading location 0x000001D3A69DF000.

valentine ( 2016-04-23 06:35:56 -0600 )edit

weirdly, i can't reprocuce the crash, it just returns [-1.INF] (or [-nan] on linux) .

but i think, your traindata/labels are ~~schrott~~ not nice, for a classification you would need one output node per class (and a 2d, numFeatures x numClasses response Mat for training)

berak ( 2016-04-23 06:50:44 -0600 )edit

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answered 2016-04-27 10:15:00 -0600

valentine
66 ●2 ●8

Hi berak,

solved the problem using the same data. I basically removed the setMethod, setBackpropMomentumScale, setBackpropWeightscale, setTermCriteria and it worked. I dont know why but will like someone to explain. Here is the code below:

  int inputLayerSize = 2;
int outputLayerSize = 1;
int numSamples = 3;
int hiddenLayer = 3;
vector<int> layerSizes = { inputLayerSize,hiddenLayer, outputLayerSize };
Ptr<ml::ANN_MLP> nnPtr = ml::ANN_MLP::create();
nnPtr->setLayerSizes(layerSizes);
nnPtr->setActivationFunction(ANN_MLP::SIGMOID_SYM);
//nnPtr->setActivationFunction(ml::ANN_MLP::SIGMOID_SYM);
/*nnPtr->setTrainMethod(ml::ANN_MLP::BACKPROP);
nnPtr->setBackpropMomentumScale(0.1);
nnPtr->setBackpropWeightScale(0.1);
nnPtr->setTermCriteria(TermCriteria(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 100, 1e-4));*/

Mat samples(Size(inputLayerSize, numSamples), CV_32F);
samples.at<float>(Point(0, 0)) = 0.3f;
samples.at<float>(Point(1, 0)) = 1.0f;
samples.at<float>(Point(0, 1)) = 0.2f;
samples.at<float>(Point(1, 1)) = 0.2f;
samples.at<float>(Point(0, 2)) = 1.0f;
samples.at<float>(Point(1, 2)) = 0.4f;
Mat responses(Size(outputLayerSize, numSamples), CV_32F);
responses.at<float>(Point(0, 0)) = 0.75f;
//responses.at<float>(Point(0, 1)) = 0.64f;
responses.at<float>(Point(0, 1)) = 0.82f;
//responses.at<float>(Point(1, 1)) = 0.74f;
responses.at<float>(Point(0, 2)) = 0.93f;
//responses.at<float>(Point(2, 1)) = 0.84f;

cout << "samples:\n" << samples << endl;
cout << "\nresponses:\n" << responses << endl;

if (!nnPtr->train(samples, ml::ROW_SAMPLE, responses))
    return 1;
cout << "\nweights[0]:\n" << nnPtr->getWeights(0) << endl;
cout << "\nweights[1]:\n" << nnPtr->getWeights(1) << endl;
cout << "\nweights[2]:\n" << nnPtr->getWeights(2) << endl;
cout << "\nweights[3]:\n" << nnPtr->getWeights(3) << endl;

Mat output;
Mat samplestest(Size(inputLayerSize, 1), CV_32F);
samplestest.at<float>(Point(0, 0)) = 0.8f;
samplestest.at<float>(Point(1, 0)) = 0.6f;
//samplestest.at<float>(Point(0, 2)) = 0.3f;
nnPtr->predict(samplestest, output);
cout << "\noutput:\n" << output << endl;

and the output is given below:

samples: [0.30000001, 1; 0.2, 0.2; 1, 0.40000001]

responses: [0.75; 0.81999999; 0.93000001]

weights[0]: [2.809757458984826, -1.404878743448629, 2.941742042246543, -1.568929097965231]

weights[1]: [-0.2094534488630105, 0.5412198073856522, 1.658734440612611; -0.8916550488630105, -1.139978207385652, -0.7469871593873889; 0.7505247417458868, 0.7944425035100793, -0.1239852716789556]

weights[2]: [-0.3488449412441831; 0.8637626791966008; 1.178125967026814; -0.2092970630454225]

weights[3]: [0.09473684586976705, 0.8400000035762786]

output: [0.88864398]

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