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我試圖做一個簡單的ANN網絡OpenCV的QT間期和更晚發展它, 我用簡單的數據試過,我得到一個錯誤說:OpenCV Error : asserion failed ((unsigned)(i1 *datatype<_tp>::channels)) < unsigned(size.p[1]* channels())) in cv::mat::at
ANN OPENCV錯誤斷言失敗
這裏是我的代碼寫
#include <iostream>
#include <opencv2/ml.hpp>
#include <opencv/cv.h>
#include <opencv2/core.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui/highgui.hpp>
#include "nnet.h"
using namespace std;
using namespace cv;
int main()
{
string filename="data.csv";
Ptr<cv::ml::TrainData> tdata = cv::ml::TrainData::loadFromCSV(filename,0,-1,-1);
Mat trainData = tdata->getTrainSamples();
Mat trainLabels = tdata->getTrainResponses();
int numClasses = 3;
Mat hot(trainLabels.rows, numClasses, CV_32F, 0.0f);
for (int i=0; i<trainLabels.rows; i++) {
int id = (int)trainLabels.at<float>(i);
hot.at<float>(i, id) = 1.0f;
}
int input_neurons = 5;
int hidden_neurons = 5;
int output_neurons = 3;
Mat layerSizes = Mat(3, 1, CV_32SC1);
layerSizes.row(0) = Scalar(input_neurons);
layerSizes.row(1) = Scalar(hidden_neurons);
layerSizes.row(2) = Scalar(output_neurons);
Ptr<cv::ml::ANN_MLP> myNetwork = cv::ml::ANN_MLP::create();
myNetwork->setLayerSizes(layerSizes);
myNetwork->setTrainMethod(ml::ANN_MLP::SIGMOID_SYM);
myNetwork->setTermCriteria(TermCriteria(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 1000, 0.00001f));
myNetwork->setTrainMethod(ml::ANN_MLP::BACKPROP,0.1f,0.1f);
myNetwork->setActivationFunction(ml::ANN_MLP::SIGMOID_SYM, 1, 1);
myNetwork->train(trainData, 0, hot);
string testfilename="test-data.csv";
Ptr<cv::ml::TrainData> testdata = cv::ml::TrainData::loadFromCSV(testfilename, 0,0,-1);
Mat testData = testdata->getTrainSamples();
Mat testLabels = testdata->getTrainResponses();
Mat testResults;
myNetwork->predict(testData, testResults);
float accuracy = float(countNonZero(testResults == testLabels))/testLabels.rows;
printf("%f",accuracy);
return 0;
}
,併爲數據集,我有
data.csv包含
1,2,3,7,2
7,1,7,7,5
9,7,5,3,2
12,21,32,71,8
和數據test.csv包含:
1,2,1,1,2,
9,1,2,12,5,
11,28,14,50,8,
3,1,2,12,5,
11,28,24,20,8,
預先感謝您的幫助。
我建議你修改你的問題,並補充說,你已經發現,接近尾聲的解決方案。 – Samer