public static void HandleEpochEnded(BackpropAlgorithm alg,
MultiRegressionSample <double[][, ]> test,
MultiRegressionSample <double[][, ]> train,
string outputPath)
{
Console.WriteLine("\r------------------------------------------- Epoch #{0} ({1}) ", alg.Epoch, DateTime.Now);
Console.WriteLine("L:\t{0}", alg.LossValue);
Console.WriteLine("DW:\t{0}", alg.Step2);
Console.WriteLine("LR:\t{0}", alg.LearningRate);
double?pct = null;
if (test == null || !test.Any())
{
Console.WriteLine("Test: none");
}
else
{
var terrors = alg.GetErrors(test, 0, true);
var tec = terrors.Count();
var tdc = test.Count;
var tpct = Math.Round(100.0F * tec / tdc, 2);
Console.WriteLine("Test: {0} of {1} ({2}%)", tec, tdc, tpct);
pct = tpct;
}
if (train == null || !train.Any())
{
Console.WriteLine("Train: none");
}
else
{
var verrors = alg.GetErrors(train, 0, true);
var vec = verrors.Count();
var vdc = train.Count;
var vpct = Math.Round(100.0F * vec / vdc, 2);
Console.WriteLine("Train: {0} of {1} ({2}%)", vec, vdc, vpct);
if (!pct.HasValue)
{
pct = vpct;
}
}
var ofileName = string.Format("cn_e{0}_p{1}.mld", alg.Epoch, Math.Round(pct.Value, 2));
var ofilePath = Path.Combine(outputPath, ofileName);
using (var stream = File.Open(ofilePath, FileMode.Create))
{
alg.Net.Serialize(stream);
}
}
public static void HandleEpochEnded(BackpropAlgorithm alg, ClassifiedSample <double[][, ]> test, string outputPath)
{
Console.WriteLine("---------------- Epoch #: {0} ({1})", alg.Epoch, DateTime.Now);
Console.WriteLine("L:\t{0}", alg.LossValue);
Console.WriteLine("DW:\t{0}", alg.Step2);
Console.WriteLine("LR:\t{0}", alg.LearningRate);
Console.WriteLine("Errors:");
var errors = alg.GetErrors(test);
var ec = errors.Count();
var dc = test.Count;
var pct = Math.Round(100.0F * ec / dc, 2);
Console.WriteLine("{0} of {1} ({2}%)", ec, dc, pct);
var ofileName = string.Format("cn_e{0}_p{1}.mld", alg.Epoch, Math.Round(pct, 2));
var ofilePath = Path.Combine(outputPath, ofileName);
using (var stream = File.Open(ofilePath, FileMode.Create))
{
alg.Net.Serialize(stream);
}
}
private void onTestButtonClick(object sender, RoutedEventArgs e)
{
var path = @"F:\Work\science\Machine learning\data\cat-dog\train\kaggle";
var errors1 = 0;
var errors1C = 0;
var errors1D = 0;
var errors2 = 0;
var errors2C = 0;
var errors2D = 0;
var errorsC1 = 0;
var errorsC2 = 0;
var errorsR = 0;
var pct1 = 0;
var pct1C = 0;
var pct1D = 0;
var pct2 = 0;
var pct2C = 0;
var pct2D = 0;
var pctC = 0;
var pctC1 = 0;
var pctC2 = 0;
var pctR = 0;
var alp1 = 0.95D;
var alp2 = 0.05D;
var dir = new DirectoryInfo(path);
var total = dir.GetFiles().Length;
var sample = new MultiRegressionSample <double[][, ]>();
var cat = new double[] { 1.0D, 0.0D };
var dog = new double[] { 0.0D, 1.0D };
int cnt = 0;
foreach (var file in dir.EnumerateFiles().Shuffle(0).Skip(10000).Take(500))
{
var fname = Path.GetFileNameWithoutExtension(file.Name);
var expected = fname.StartsWith("cat.") ? 0 : 1;
var data1 = getNetData(file.FullName);
double[][,] data2;
using (var image = (Bitmap)System.Drawing.Image.FromFile(file.FullName))
data2 = getNetFData(image);
sample.Add(data2, expected == 0 ? cat : dog);
var result1 = m_Network.Calculate(data1).Select(d => d[0, 0]).ToArray();
var actual1 = ML.Core.Mathematics.MathUtils.ArgMax(result1);
if (expected != actual1)
{
if (expected == 0)
{
errors1C++;
}
else
{
errors1D++;
}
errors1++;
}
var result2 = m_NetworkF.Calculate(data2).Select(d => d[0, 0]).ToArray();
var actual2 = ML.Core.Mathematics.MathUtils.ArgMax(result2);
if (expected != actual2)
{
if (expected == 0)
{
errors2C++;
}
else
{
errors2D++;
}
errors2++;
}
var resultR = new double[result1.Length];
resultR[0] = alp1 * result1[0] + (1 - alp1) * result2[0];
resultR[1] = alp2 * result1[1] + (1 - alp2) * result2[1];
var actualR = ML.Core.Mathematics.MathUtils.ArgMax(resultR);
if (expected != actualR)
{
errorsR++;
}
if ((expected != actual1) && (expected != actual2))
{
if (expected == 0)
{
errorsC1++;
}
else
{
errorsC2++;
}
}
cnt++;
pct1 = errors1 * 100 / cnt;
pct2 = errors2 * 100 / cnt;
pctC1 = errorsC1 * 100 / cnt;
pctC2 = errorsC2 * 100 / cnt;
pctC = (errorsC1 + errorsC2) * 100 / cnt;
pctR = errorsR * 100 / cnt;
}
var alg = new BackpropAlgorithm(m_NetworkF);
var err = alg.GetErrors(sample, 0, true);
var message = "Errors1: {0}%, Errors2: {1}%, ErrorsC: {2}%, ErrorR: {3}%";
MessageBox.Show(string.Format(message, pct1, pct2, pctC, pctR));
}
