Revision history [back]

Here is code to train the neural network to solve the XOR problem:

// https://stackoverflow.com/questions/37500713/opencv-image-recognition-setting-up-ann-mlp

#include <opencv2/opencv.hpp>
using namespace cv;
#pragma comment(lib, "opencv_world331.lib")

#include <iostream>
#include <iomanip>

using namespace cv;
using namespace ml;
using namespace std;

void print(Mat& mat, int prec)
{
    for (int i = 0; i<mat.size().height; i++)
    {
        cout << "[";
        for (int j = 0; j<mat.size().width; j++)
        {
            cout << fixed << setw(2) << setprecision(prec) << mat.at<float>(i, j);
            if (j != mat.size().width - 1)
                cout << ", ";
            else
                cout << "]" << endl;
        }
    }
}

int main(void)
{
    const int hiddenLayerSize = 4;
    float inputTrainingDataArray[4][2] = {
        { 0.0, 0.0 },
        { 0.0, 1.0 },
        { 1.0, 0.0 },
        { 1.0, 1.0 }
    };
    Mat inputTrainingData = Mat(4, 2, CV_32F, inputTrainingDataArray);

    float outputTrainingDataArray[4][1] = {
        { 0.0 },
        { 1.0 },
        { 1.0 },
        { 0.0 }
    };
    Mat outputTrainingData = Mat(4, 1, CV_32F, outputTrainingDataArray);

    Ptr<ANN_MLP> mlp = ANN_MLP::create();

    Mat layersSize = Mat(3, 1, CV_16U);
    layersSize.row(0) = Scalar(inputTrainingData.cols);
    layersSize.row(1) = Scalar(hiddenLayerSize);
    layersSize.row(2) = Scalar(outputTrainingData.cols);
    mlp->setLayerSizes(layersSize);

    mlp->setActivationFunction(ANN_MLP::ActivationFunctions::SIGMOID_SYM);

    TermCriteria termCrit = TermCriteria(TermCriteria::Type::COUNT + TermCriteria::Type::EPS, 1, 0.000001);
    mlp->setTermCriteria(termCrit);

    mlp->setTrainMethod(ANN_MLP::TrainingMethods::BACKPROP);

    Ptr<TrainData> trainingData = TrainData::create(inputTrainingData, SampleTypes::ROW_SAMPLE, outputTrainingData);

    mlp->train(trainingData);

    for(int i = 0; i < 10000; i++)
    mlp->train(trainingData, ANN_MLP::TrainFlags::UPDATE_WEIGHTS);

    for (int i = 0; i < inputTrainingData.rows; i++) 
    {
        Mat sample = Mat(1, inputTrainingData.cols, CV_32F, inputTrainingDataArray[i]);
        Mat result;
        mlp->predict(sample, result);
        cout << sample << " -> ";// << result << endl;
        print(result, 0);
        cout << endl;
    }
    return 0;

}

Here Below is code to train the neural network to solve the XOR problem:problem. Changing it to the OR problem is a trivial matter.

// https://stackoverflow.com/questions/37500713/opencv-image-recognition-setting-up-ann-mlp

#include <opencv2/opencv.hpp>
using namespace cv;
#pragma comment(lib, "opencv_world331.lib")

#include <iostream>
#include <iomanip>

using namespace cv;
using namespace ml;
using namespace std;

void print(Mat& mat, int prec)
{
    for (int i = 0; i<mat.size().height; i++)
    {
        cout << "[";
        for (int j = 0; j<mat.size().width; j++)
        {
            cout << fixed << setw(2) << setprecision(prec) << mat.at<float>(i, j);
            if (j != mat.size().width - 1)
                cout << ", ";
            else
                cout << "]" << endl;
        }
    }
}

int main(void)
{
    const int hiddenLayerSize = 4;
    float inputTrainingDataArray[4][2] = {
        { 0.0, 0.0 },
        { 0.0, 1.0 },
        { 1.0, 0.0 },
        { 1.0, 1.0 }
    };
    Mat inputTrainingData = Mat(4, 2, CV_32F, inputTrainingDataArray);

    float outputTrainingDataArray[4][1] = {
        { 0.0 },
        { 1.0 },
        { 1.0 },
        { 0.0 }
    };
    Mat outputTrainingData = Mat(4, 1, CV_32F, outputTrainingDataArray);

    Ptr<ANN_MLP> mlp = ANN_MLP::create();

    Mat layersSize = Mat(3, 1, CV_16U);
    layersSize.row(0) = Scalar(inputTrainingData.cols);
    layersSize.row(1) = Scalar(hiddenLayerSize);
    layersSize.row(2) = Scalar(outputTrainingData.cols);
    mlp->setLayerSizes(layersSize);

    mlp->setActivationFunction(ANN_MLP::ActivationFunctions::SIGMOID_SYM);

    TermCriteria termCrit = TermCriteria(TermCriteria::Type::COUNT + TermCriteria::Type::EPS, 1, 0.000001);
    mlp->setTermCriteria(termCrit);

    mlp->setTrainMethod(ANN_MLP::TrainingMethods::BACKPROP);

    Ptr<TrainData> trainingData = TrainData::create(inputTrainingData, SampleTypes::ROW_SAMPLE, outputTrainingData);

    mlp->train(trainingData);

    for(int i = 0; i < 10000; i++)
    mlp->train(trainingData, ANN_MLP::TrainFlags::UPDATE_WEIGHTS);

    for (int i = 0; i < inputTrainingData.rows; i++) 
    {
        Mat sample = Mat(1, inputTrainingData.cols, CV_32F, inputTrainingDataArray[i]);
        Mat result;
        mlp->predict(sample, result);
        cout << sample << " -> ";// << result << endl;
        print(result, 0);
        cout << endl;
    }
    return 0;

}

Below is code to train the neural network to solve the XOR problem. problem using OpenCV 3.x. Changing it to the OR problem is a trivial matter.

// https://stackoverflow.com/questions/37500713/opencv-image-recognition-setting-up-ann-mlp

#include <opencv2/opencv.hpp>
using namespace cv;
#pragma comment(lib, "opencv_world331.lib")

#include <iostream>
#include <iomanip>

using namespace cv;
using namespace ml;
using namespace std;

void print(Mat& mat, int prec)
{
    for (int i = 0; i<mat.size().height; i++)
    {
        cout << "[";
        for (int j = 0; j<mat.size().width; j++)
        {
            cout << fixed << setw(2) << setprecision(prec) << mat.at<float>(i, j);
            if (j != mat.size().width - 1)
                cout << ", ";
            else
                cout << "]" << endl;
        }
    }
}

int main(void)
{
    const int hiddenLayerSize = 4;
    float inputTrainingDataArray[4][2] = {
        { 0.0, 0.0 },
        { 0.0, 1.0 },
        { 1.0, 0.0 },
        { 1.0, 1.0 }
    };
    Mat inputTrainingData = Mat(4, 2, CV_32F, inputTrainingDataArray);

    float outputTrainingDataArray[4][1] = {
        { 0.0 },
        { 1.0 },
        { 1.0 },
        { 0.0 }
    };
    Mat outputTrainingData = Mat(4, 1, CV_32F, outputTrainingDataArray);

    Ptr<ANN_MLP> mlp = ANN_MLP::create();

    Mat layersSize = Mat(3, 1, CV_16U);
    layersSize.row(0) = Scalar(inputTrainingData.cols);
    layersSize.row(1) = Scalar(hiddenLayerSize);
    layersSize.row(2) = Scalar(outputTrainingData.cols);
    mlp->setLayerSizes(layersSize);

    mlp->setActivationFunction(ANN_MLP::ActivationFunctions::SIGMOID_SYM);

    TermCriteria termCrit = TermCriteria(TermCriteria::Type::COUNT + TermCriteria::Type::EPS, 1, 0.000001);
    mlp->setTermCriteria(termCrit);

    mlp->setTrainMethod(ANN_MLP::TrainingMethods::BACKPROP);

    Ptr<TrainData> trainingData = TrainData::create(inputTrainingData, SampleTypes::ROW_SAMPLE, outputTrainingData);

    mlp->train(trainingData);

    for(int i = 0; i < 10000; i++)
    mlp->train(trainingData, ANN_MLP::TrainFlags::UPDATE_WEIGHTS);

    for (int i = 0; i < inputTrainingData.rows; i++) 
    {
        Mat sample = Mat(1, inputTrainingData.cols, CV_32F, inputTrainingDataArray[i]);
        Mat result;
        mlp->predict(sample, result);
        cout << sample << " -> ";// << result << endl;
        print(result, 0);
        cout << endl;
    }
    return 0;

}