Training Error Log

[selfuser]

[Hai Users, Can anyone explain me the function of training error log as in the picture below. Kindly need your helps. Thank You.

[selfuser]

**Hai Users, Can anyone explain me the function of training error log as in the picture below. Kindly need your helps. Thank You.

**

[arunkumars]

shepri, your question doesnot seem to be understandable, please be more clear on your requirements, thanks, and also show what have you done.

[selfuser]

arunkumar thanks for your reply. the training error log contain this codes. I can't understdn this code

private void Train(object sender, EventArgs e)
{
btnTrain.Enabled = false;

        int cycles = 200;
        if (!int.TryParse(txtCycles.Text, out cycles)) { cycles = 200; }
        txtCycles.Text = cycles.ToString();

        int currentCombination = 0;
        int totalCombinations = Alphabet.LetterCount * (Alphabet.LetterCount - 1) / 2;

        for (int i = 0; i < Alphabet.LetterCount; i++)
        {
            for (int j = i + 1; j < Alphabet.LetterCount; j++)
            {
                ActivationLayer inputLayer = new LinearLayer(400);
                ActivationLayer hiddenLayer = new SigmoidLayer(4);
                ActivationLayer outputLayer = new SigmoidLayer(2);
                new BackpropagationConnector(inputLayer, hiddenLayer);
                new BackpropagationConnector(hiddenLayer, outputLayer);
                BackpropagationNetwork network = new BackpropagationNetwork(inputLayer, outputLayer);

                TrainingSet trainingSet = new TrainingSet(400, 2);
                Alphabet ithLetter = Alphabet.GetLetter(i);
                Alphabet jthLetter = Alphabet.GetLetter(j);
                foreach (Letter instance in ithLetter.Instances)
                {
                    trainingSet.Add(new TrainingSample(instance.GetEquivalentVector(20, 20), new double[] { 1d, 0d }));
                }
                foreach (Letter instance in jthLetter.Instances)
                {
                    trainingSet.Add(new TrainingSample(instance.GetEquivalentVector(20, 20), new double[] { 0d, 1d }));
                }

                progressTraining.Value = 100 * currentCombination / totalCombinations;

                Application.DoEvents();

                bool correct = false;

                int currentCycles = 35;
                int count = trainingSet.TrainingSampleCount;

                while (correct == false & currentCycles <= cycles)
                {
                    network.Initialize();
                    network.Learn(trainingSet, currentCycles);
                    correct = true;
                    for (int sampleIndex = 0; sampleIndex < count; sampleIndex++)
                    {
                        double[] op = network.Run(trainingSet[sampleIndex].InputVector);
                        if (((trainingSet[sampleIndex].OutputVector[0] > trainingSet[sampleIndex].OutputVector[1]) && op[0] - op[1] < 0.4) || ((trainingSet[sampleIndex].OutputVector[0] < trainingSet[sampleIndex].OutputVector[1]) && op[1] - op[0] < 0.4))
                        {
                            correct = false;
                            trainingSet.Add(trainingSet[sampleIndex]);
                        }
                    }
                    currentCycles *= 2;
                }

                lstLog.Items.Add(cboAplhabet.Items[i] + " & " + cboAplhabet.Items[j] + " = " + network.MeanSquaredError.ToString("0.0000"));
                lstLog.TopIndex = lstLog.Items.Count - (int)(lstLog.Height / lstLog.ItemHeight);
                try
                {
                    using (Stream stream = File.Open(Application.StartupPath + @"\Networks\" + i.ToString("00") + j.ToString("00") + ".ndn", FileMode.Create))
                    {
                        IFormatter formatter = new BinaryFormatter();
                        formatter.Serialize(stream, network);
                    }
                }
                catch (Exception)
                {
                    MessageBox.Show("Failed to save trained neural networks", "Critical Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
                    return;
                }
                currentCombination++;
            }
        }
        progressTraining.Value = 0;
        btnTrain.Enabled = false;
    }

[thines01]

It looks as if it is a visible representation of the similarity of the a suspected letter against other letters of the alphabet -- where the higher number represents a greater similarity.