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samples/cpp/neural_network.cpp
65 строк
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LaurentBerger
Issue 6780
07 июл 2016, 10:29
07 июл 2016, 10:29
62a87ce
Код
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О чём код?
#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; }