SharpLearning neural network classification with probabilities

Hi,
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:

Code:

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using SharpLearning.Containers.Matrices;
using SharpLearning.InputOutput.Csv;
using SharpLearning.Neural;
using SharpLearning.Neural.Activations;
using SharpLearning.Neural.Layers;
using SharpLearning.Neural.Learners;
using SharpLearning.Neural.Loss;
using System;
using System.Globalization;
using System.IO;
using System.Windows.Forms;
using SharpLearning.Metrics.Classification;
using SharpLearning.Neural.Models;
using SharpLearning.Neural.Optimizers;
using System.Collections.Generic;
using SharpLearning.Neural.TargetEncoders;

 string csvDataFilePath = @"C:\git\GalTesting\dsd\DATA_for_classification_learner.csv";
 string csvLabelsFilePath = @"C:\git\GalTesting\dsd\labels_numbers_for_classification_learner.csv";
 string csvTestFilePath = @"C:\git\GalTesting\dsd\Test_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);

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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