OpenCV的錯誤當火車ANN_MLP

我想訓練神經網絡分類兩件式的圖像，但是當列車網絡發生這樣的錯誤：OpenCV的錯誤當火車ANN_MLP

OpenCV的錯誤：錯誤的參數（輸出訓練數據應該是一個浮點矩陣數的行等於訓練樣本的數量和等於最後（輸出）層的大小的列的數量）在cv :: ml :: ANN_MLPImpl :: prepare_to_train中，文件C：\ buildslave64 \ win64_amdocl \ master_PackSlave-win64-vc14 -shared \的OpenCV \模塊\毫升\ SRC \ ann_mlp.cpp，線675

我的代碼：

#include "opencv2\core.hpp" 
#include "opencv2\imgproc.hpp" 
#include "opencv2\imgcodecs.hpp" 
#include "opencv2\highgui.hpp" 
#include "opencv2\ml.hpp" 
#include <string> 
#include "lbp.h" 
using namespace cv; 
using namespace cv::ml; 
void LoadTrainingData(); 

Mat Data; 
Mat Lables; 
//const int numberOfClass1 = 2384; 
//const int numberOfClass2 = 2462; 
const int numberOfClass1 = 23; 
const int numberOfClass2 = 24; 
int Class1 = 1; 
int Class2 = -1; 
const int imageDimention = 22; 

std::string NumberToString(size_t Number) 
{ 
    std::stringstream ss; 
    ss << Number; 
    return ss.str(); 
} 



void main() { 
    LoadTrainingData(); 
    Ptr<ANN_MLP> annClassifier; 
    annClassifier = ANN_MLP::create(); 
    annClassifier->setActivationFunction(ANN_MLP::ActivationFunctions::SIGMOID_SYM); 
    Mat layers(1, 3, CV_32F); 
    layers.at<float>(0) = Data.cols; 
    layers.at<float>(1) = 100; 
    layers.at<float>(2) = 2; 
    annClassifier->setLayerSizes(layers); 
    annClassifier->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6)); 
    annClassifier->setTrainMethod(ANN_MLP::TrainingMethods::BACKPROP); 
    bool trained = annClassifier->train(Data,ROW_SAMPLE,Lables); 
    if (trained) 
     annClassifier->save("Ann_sigmoid_eye"); 

} 
void LoadTrainingData() { 

    Data = Mat(numberOfClass1 + numberOfClass2, imageDimention*imageDimention, CV_32FC1); 
    Lables = Mat(numberOfClass1 + numberOfClass2,1 , CV_32SC1); 
    // load openEye 
    Mat img; 
    Mat lbpImg; 
    Mat row; 
    std::string path; 
    for (size_t i = 1; i <= numberOfClass2; i++) 
    { 
     path = "class1 (" + NumberToString(i) + ").jpg"; 
     img = imread(path); 
     if (img.channels() > 1) 
      cvtColor(img, img, CV_BGR2GRAY); 
     lbp::ELBP(img,lbpImg, 1, 16);  
     row = lbpImg.reshape(0, 1); 
     row.convertTo(row, CV_32FC1); 
     Data.push_back(row); 
     Lables.push_back(Class1); 

    } 
    for (size_t i = 1; i <= numberOfClass1; i++) 
    { 
     path ="class2 (" + NumberToString(i) + ").jpg"; 
     img = imread(path); 
     if (img.channels() > 1) 
      cvtColor(img, img, CV_BGR2GRAY); 
     lbp::ELBP(img,lbpImg, 1, 16); 
     row = lbpImg.reshape(0, 1); 
     row.convertTo(row, CV_32FC1); 
     Data.push_back(row); 
     Lables.push_back(Class2); 
    } 
}

我不這樣做的原因！請幫助我，謝謝。

來源

2016-04-24 ali kiani

對於ann的火車響應與通常的opencv ml方法有點不同。

如果您的ann中有2個輸出神經元，那麼每個訓練特徵也需要2個輸出神經元，而不是單個「類標籤」（例如使用SVM）。

它應該是這樣的：

[train_data]    [train_responses] 

lbpfeature1     -1 1  // class A 
lbpfeature2     -1 1  // class A 
lbpfeature3     1 -1  // class B 
lbpfeature4     1 -1  // class B

因此，響應必須NUM_FEATURES行X 2周的cols。單程（有無盡..）將是：

// leave Labels empty (btw, your original code seems to leave the 1st element empty) 
    // Lables = Mat(numberOfClass1 + numberOfClass2,1 , CV_32SC1); 

    // instead of 
    //Lables.push_back(Class1); 
    Lables.push_back(1.0f); 
    Lables.push_back(-1.0f); 

    // and instead of 
    //Lables.push_back(Class2); 
    Lables.push_back(-1.0f); 
    Lables.push_back(1.0f); 

    // then, before training, reshape to N x 2: 
    Labels = Labels.reshape(1, Labels.rows/2); 
    Data.convertTo(Data, CV_32FC1); 
    Lables.convertTo(Lables, CV_32FC1);

來源

2016-04-24 15:55:19

OpenCV的錯誤當火車ANN_MLP

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