ANN_MLP output training value of 1 causes error

edit

It appears that the problem only occurs when UPDATE_WEIGHTS is used.

I used the UPDATE_WEIGHTS | NO_INPUT_SCALE | NO_OUTPUT_SCALE parameter set and it works with values -1 and 1 now; 100% success rate. I could have sworn that I already tried this, without success. Anyway, thanks @LBerger!

Here is my final code:

#include <opencv2/opencv.hpp>
using namespace cv;
#pragma comment(lib, "opencv_world331.lib")

#include <iostream>
#include <iomanip>

using namespace cv;
using namespace ml;
using namespace std;

float round_float(const float input)
{
    return floor(input + 0.5f);
}

void add_noise(Mat &mat, float scale)
{
    for (int j = 0; j < mat.rows; j++)
    {
        for (int i = 0; i < mat.cols; i++)
        {
            float noise = static_cast<float>(rand() % 256);
            noise /= 255.0f;

            mat.at<float>(j, i) = (mat.at<float>(j, i) + noise*scale) / (1.0f + scale);

            if (mat.at<float>(j, i) < 0)
                mat.at<float>(j, i) = 0;
            else if (mat.at<float>(j, i) > 1)
                mat.at<float>(j, i) = 1;
        }
    }
}

int main(void)
{
    const int image_width = 64;
    const int image_height = 64;

    // Read in 64 row x 64 column images
    Mat dove = imread("dove.png", IMREAD_GRAYSCALE);
    Mat flowers = imread("flowers.png", IMREAD_GRAYSCALE);
    Mat peacock = imread("peacock.png", IMREAD_GRAYSCALE);
    Mat statue = imread("statue.png", IMREAD_GRAYSCALE);

    // Reshape from 64 rows x 64 columns image to 1 row x (64*64) columns
    dove = dove.reshape(0, 1);
    flowers = flowers.reshape(0, 1);
    peacock = peacock.reshape(0, 1);
    statue = statue.reshape(0, 1);

    // Convert CV_8UC1 to CV_32FC1
    Mat flt_dove(dove.rows, dove.cols, CV_32FC1);

    for (int j = 0; j < dove.rows; j++)
        for (int i = 0; i < dove.cols; i++)
            flt_dove.at<float>(j, i) = dove.at<unsigned char>(j, i) / 255.0f;

    Mat flt_flowers(flowers.rows, flowers.cols, CV_32FC1);

    for (int j = 0; j < flowers.rows; j++)
        for (int i = 0; i < flowers.cols; i++)
            flt_flowers.at<float>(j, i) = flowers.at<unsigned char>(j, i) / 255.0f;

    Mat flt_peacock(peacock.rows, peacock.cols, CV_32FC1);

    for (int j = 0; j < peacock.rows; j++)
        for (int i = 0; i < peacock.cols; i++)
            flt_peacock.at<float>(j, i) = peacock.at<unsigned char>(j, i) / 255.0f;

    Mat flt_statue = Mat(statue.rows, statue.cols, CV_32FC1);

    for (int j = 0; j < statue.rows; j++)
        for (int i = 0; i < statue.cols; i++)
            flt_statue.at<float>(j, i) = statue.at<unsigned char>(j, i) / 255.0f;

    Ptr<ANN_MLP> mlp = ANN_MLP::create();

    // Slow the learning process
    //mlp->setBackpropMomentumScale(0.1);

    // Neural network elements
    const int num_input_neurons = dove.cols; // One input neuron per grayscale pixel
    const int num_output_neurons = 2; // 4 images to classify, so number of bits needed is ceiling(ln(n)/ln(2))
    const int num_hidden_neurons = static_cast<int>(sqrtf(image_width*image_height*num_output_neurons));
    Mat layersSize = Mat(3, 1, CV_16UC1);
    layersSize.row(0) = Scalar(num_input_neurons);
    layersSize.row(1) = Scalar(num_hidden_neurons);
    layersSize.row(2) = Scalar(num_output_neurons);
    mlp->setLayerSizes(layersSize);

    // Set various parameters
    mlp->setActivationFunction(ANN_MLP::ActivationFunctions::SIGMOID_SYM);
    TermCriteria termCrit = TermCriteria(TermCriteria::Type::COUNT + TermCriteria::Type::EPS, 1, 0.000001);
    mlp->setTermCriteria(termCrit);
    mlp->setTrainMethod(ANN_MLP::TrainingMethods::BACKPROP, 0.0001);

    Mat output_training_data = Mat(1, num_output_neurons, CV_32FC1).clone();

    // Train the network once
    output_training_data.at<float>(0, 0) = -1 ...