f4334277
 Hu Chunming
提交3rdparty
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#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;
}
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