I am trying to classify arrays of integers of size 202 (which are physical curves) into either ‘Bad’ or ‘Good’ labels.
My observations consist of 1608 pre-labelled columns.

I have read the data from CSV file that I prepared in another application into Data-matrix (1608 over 202 2D array), and the labels into Labels_matrix (1608 1D array).
I have trained the net using Learn method, and even successfully predicted the observations of Test_matrix. Here is the working code:

string csvDataFilePath = @"C:gitGalTestingdsdDATA_for_classification_learner.csv";
string csvLabelsFilePath = @"C:gitGalTestingdsdlabels_numbers_for_classification_learner.csv";
string csvTestFilePath = @"C:gitGalTestingdsdTest_for_classification_learner.csv";



double[][] Data_matrix;
double[] Labels_matrix;
double[][] Test_matrix;

var lines = File.ReadAllLines(csvDataFilePath);
int numRows = lines.Length;
int numCols = lines[0].Split(',').Length;

Data_matrix = new double[numCols][];

for (int i = 0; i < numCols; i++)
{
    Data_matrix[i] = new double[numRows];
    for (int j = 0; j < numRows; j++)
    {
        var values1 = lines[j].Split(',');

        if (double.TryParse(values1[i], out double parsedValue))
            Data_matrix[i][j] = parsedValue;
        else
            Data_matrix[i][j] = 0; // Default value if parsing fails
    }
}


lines = File.ReadAllLines(csvLabelsFilePath);
numRows = 1;
numCols = lines[0].Split(',').Length;

Labels_matrix = new double[numCols];
var values = lines[0].Split(',');

for (int j = 0; j < numCols; j++)
{
    if (double.TryParse(values[j], out double parsedValue))
        Labels_matrix[j] = parsedValue;
    else
        Labels_matrix[j] = 0; // Default value if parsing fails
}

lines = File.ReadAllLines(csvTestFilePath);
numRows = lines.Length;
numCols = lines[0].Split(',').Length;

Test_matrix = new double[numCols][];

for (int i = 0; i < numCols; i++)
{
    Test_matrix[i] = new double[numRows];
    for (int j = 0; j < numRows; j++)
    {
        var values1 = lines[j].Split(',');

        if (double.TryParse(values1[i], out double parsedValue))
            Test_matrix[i][j] = parsedValue;
        else
            Test_matrix[i][j] = 0; // Default value if parsing fails
    }
}

var net = new NeuralNet();
net.Add(new InputLayer(202));
net.Add(new DenseLayer(25, Activation.Relu));
//net.Add(new DenseLayer(32, Activation.Relu));
net.Add(new SoftMaxLayer(2));
net.Initialize(1, new Random());


ClassificationNeuralNetLearner learner = new ClassificationNeuralNetLearner(net, new LogLoss());

ClassificationNeuralNetModel model =  learner.Learn(Data_matrix, Labels_matrix);
double[] final_predictions = new double[Test_matrix[0].Length];
final_predictions = model.Predict(Test_matrix);

Now I need not only to classify the data into the labels, but also to know the probability that each observation was given to tha label.
How can I do that?

Thanks,
Gal