65 lines
1.7 KiB
C++
65 lines
1.7 KiB
C++
#include <opencv2/ml/ml.hpp>
|
|
|
|
using namespace std;
|
|
using namespace cv;
|
|
using namespace cv::ml;
|
|
|
|
int main()
|
|
{
|
|
//create random training data
|
|
Mat_<float> data(100, 100);
|
|
randn(data, Mat::zeros(1, 1, data.type()), Mat::ones(1, 1, data.type()));
|
|
|
|
//half of the samples for each class
|
|
Mat_<float> responses(data.rows, 2);
|
|
for (int i = 0; i<data.rows; ++i)
|
|
{
|
|
if (i < data.rows/2)
|
|
{
|
|
responses(i, 0) = 1;
|
|
responses(i, 1) = 0;
|
|
}
|
|
else
|
|
{
|
|
responses(i, 0) = 0;
|
|
responses(i, 1) = 1;
|
|
}
|
|
}
|
|
|
|
/*
|
|
//example code for just a single response (regression)
|
|
Mat_<float> responses(data.rows, 1);
|
|
for (int i=0; i<responses.rows; ++i)
|
|
responses(i, 0) = i < responses.rows / 2 ? 0 : 1;
|
|
*/
|
|
|
|
//create the neural network
|
|
Mat_<int> layerSizes(1, 3);
|
|
layerSizes(0, 0) = data.cols;
|
|
layerSizes(0, 1) = 20;
|
|
layerSizes(0, 2) = responses.cols;
|
|
|
|
Ptr<ANN_MLP> network = ANN_MLP::create();
|
|
network->setLayerSizes(layerSizes);
|
|
network->setActivationFunction(ANN_MLP::SIGMOID_SYM, 0.1, 0.1);
|
|
network->setTrainMethod(ANN_MLP::BACKPROP, 0.1, 0.1);
|
|
Ptr<TrainData> trainData = TrainData::create(data, ROW_SAMPLE, responses);
|
|
|
|
network->train(trainData);
|
|
if (network->isTrained())
|
|
{
|
|
printf("Predict one-vector:\n");
|
|
Mat result;
|
|
network->predict(Mat::ones(1, data.cols, data.type()), result);
|
|
cout << result << endl;
|
|
|
|
printf("Predict training data:\n");
|
|
for (int i=0; i<data.rows; ++i)
|
|
{
|
|
network->predict(data.row(i), result);
|
|
cout << result << endl;
|
|
}
|
|
}
|
|
|
|
return 0;
|
|
}
|