Hello everyone!!
I am trying to develop an Artificial Neural Network in C++ using OpenCV 3.4.1 with the aim of being able to recognise 33 different characters, including both numbers and letters, but the results I am obtaining are always wrong.
I have tested my code with different parameters' values like the alpha and beta of the sigmoid function that I am using for training, the backpropagation parameters, or the number of hidden nodes but, although the result varies sometimes, it normally tends to be a vector of the following shape:
Classification result: [20.855789, -0.033862107, -0.0053131776, 0.026316155, -0.0032050854, 0.036046479, -0.025410429, -0.017537225, 0.015429396, -0.023276867, 0.013653283, -0.025660357, -0.051959664, -0.0032470606, 0.032143779, -0.011631044, 0.022339549, 0.041757714, 0.04414707, -0.044756029, 0.042280547, 0.012204648, 0.026924053, 0.016814215, -0.028257577, 0.05190875, -0.0070033628, -0.0084492415, -0.040644459, 0.00022287761, -0.0376678, -0.0021550131, -0.015310903]
That is, independently of which character I test, it is always predicting that the analysed character is the one in the first position of the characters vector, which corresponds to number '1'.
The training data is obtained from an .XML I have created, which contains 474 samples (rows) with 265 attributes each (cols). As for the training classes, following some advice I found in a previous question in this forum, it is obtained from another .XML file that contains 474 rows, one for each training sample, and 33 columns, one for each character/class.
I attach the code below so that you can perhaps kindly guess what I am doing wrong and I am so thankful in advance for any help you can offer! :)
//Create the Neural Network
Mat_<int> layerSizes(1, 3);
layerSizes(0, 0) = numFeaturesPerSample;
layerSizes(0, 1) = nlayers;
layerSizes(0, 2) = numClasses;
//Set ANN params
Ptr<ANN_MLP> network = ANN_MLP::create();
network->setLayerSizes(layerSizes);
network->setActivationFunction(ANN_MLP::SIGMOID_SYM, 0.6, 1);
network->setTrainMethod(ANN_MLP::BACKPROP, 0.1, 0.1);
Ptr<TrainData> trainData = TrainData::create(TrainingData, ROW_SAMPLE, classes);
network->train(trainData);
//Predict
if (network->isTrained())
{
trained = true;
Mat results;
cout << "Predict:" << endl;
network->predict(features, results);
cout << "Prediction done!" << endl;
cout << endl << "Classification result: " << endl << results << endl;
//We need to know where in output is the max val, the x (cols) is the class.
Point maxLoc;
double maxVal;
minMaxLoc(results, 0, &maxVal, 0, &maxLoc);
return maxLoc.x;
}