Question

以下代码显示了使用伪造值训练和测试OpenCV 3.0神经网络的最小示例：

#include <opencv2/opencv.hpp>
#include <iostream>
#include <vector>

int main()
{
    using namespace std;
    using namespace cv;

    int inputLayerSize = 1;
    int outputLayerSize = 1;
    int numSamples = 2;
    vector<int> layerSizes = { inputLayerSize, outputLayerSize };
    Ptr<ml::ANN_MLP> nnPtr = ml::ANN_MLP::create();
    nnPtr->setLayerSizes( layerSizes );

    Mat samples( Size( inputLayerSize, numSamples ), CV_32F );
    samples.at<float>( Point( 0, 0 ) ) = 0.1f;
    samples.at<float>( Point( 0, 1 ) ) = 0.2f;
    Mat responses( Size( outputLayerSize, numSamples ), CV_32F );
    responses.at<float>( Point( 0, 0 ) ) = 0.2f;
    responses.at<float>( Point( 0, 1 ) ) = 0.4f;

    cout << "samples:\n" << samples << endl;
    cout << "\nresponses:\n" << responses << endl;

    if ( !nnPtr->train( samples, ml::ROW_SAMPLE, responses ) )
        return 1;
    cout << "\nweights[0]:\n" << nnPtr->getWeights( 0 ) << endl;
    cout << "\nweights[1]:\n" << nnPtr->getWeights( 1 ) << endl;
    cout << "\nweights[2]:\n" << nnPtr->getWeights( 2 ) << endl;
    cout << "\nweights[3]:\n" << nnPtr->getWeights( 3 ) << endl;

    Mat output;
    nnPtr->predict( samples, output );
    cout << "\noutput:\n" << output << endl;
}

但预测只返回NaN而不是实际值。这是输出：

samples:
[0.1;
 0.2]

responses:
[0.2;
 0.40000001]

weights[0]:
[19.99999970197678, -3]

weights[1]:
[0.05355758607590463;
 0.01063728662926916]

weights[2]:
[inf, -nan(ind)]

weights[3]:
[0, 0]

output:
[-nan(ind);
 -nan(ind)]

我做错了什么？

Answer 1

好的，解决了。需要明确设置激活功能。因此，在调用setLayerSizes之后的第一行之后，问题就消失了：

nnPtr->setActivationFunction( cv::ml::ANN_MLP::SIGMOID_SYM );

输出：

samples:
[0.1;
 0.2]

responses:
[0.2;
 0.40000001]

weights[0]:
[19.99999970197678, -3]

weights[1]:
[1.811227207835904;
 -0.0006127133707308392]

weights[2]:
[0.1052631594632801, 0.3000000044703484]

weights[3]:
[9.49999985843897, -2.85]

output:
[0.20249137;
 0.39745635]

神经网络仅返回OpenCV 3.0中的NaN

1 个答案: