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