neural_network.cpp added

samples/cpp/neural_network.cpp

+#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)
+        {
+            data(i, 0) = 1;
+            data(i, 1) = 0;
+        }
+        else
+        {
+            data(i, 0) = 0;
+            data(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;
+}