// ReSharper disable once InconsistentNaming
public static void TrainConvolutionalNN()
{
Support.pointParamsList.Clear();
Support.pointAssesList.Clear();
instance.currentTask++;
instance.Invoke(new Action((() =>
{
instance.curTaskLabel.Text = instance.currentTask.ToString();
instance.AssesGenZedGraph.Invalidate();
instance.ParamsZedGraph.Invalidate();
instance.accZG.Invalidate();
instance.currentTaskPB.Minimum = 0;
instance.currentTaskPB.Maximum = (int)instance.geneticEpochsNUD.Value + 1;
instance.currentTaskPB.Value = 0;
instance.currentTaskPB.Step = 1;
})));
string dateDirectory = Support.CreateDateTimeDirectory(@"C:\keras\Directory\reports", 0);
string timeDirectory = Support.CreateDateTimeDirectory(dateDirectory, 1);
File.Create(timeDirectory + @"\report.txt").Close();
int maxParams = 0;
GeneticProgramm gp = instance.geneticProgramms.Dequeue();
if (!instance.genDontClearChB.Checked)
{
instance.Invoke(new Action(instance.textBox9.Clear));
}
instance.population = new List <ConvolutionalChromosome>(gp._populationSize);
while (instance.population.Count != instance.population.Capacity)
{
instance.population.Add(new ConvolutionalChromosome(gp, random));
instance.population[instance.population.Count - 1].name = "Chromosome " + instance.population.Count.ToString();
instance.population[instance.population.Count - 1].indexNumber = instance.population.Count;
}
for (int i = 0; i < instance.population.Count; i++)
{
instance.tempAccuracy = instance.tempParameters = 0;
instance.WriteAdds(0, i);
instance.UpdateAssessmentParams(instance.population[i], gp, 0);
File.AppendAllText(@"C:\keras\Directory\Assessment_Statistic.txt",
instance.population[i].accuracy.ToString() + "_" + instance.population[i].paramsCount + Environment.NewLine);
if (instance.population[i].paramsCount > maxParams)
{
maxParams = instance.population[i].paramsCount;
}
Support.DrawParamsGraph(instance.ParamsZedGraph, instance.population[i].accuracy, instance.population[i].paramsCount, maxParams);
Support.DrawAccGraph(instance.accZG, 0, instance.population[0]._gp._genEpochs, instance.population[i].accuracy);
}
Support.UpdateAssesment(instance.population);
for (int z = 0; z < instance.population.Count; z++)
{
Support.DrawAssesGraph(instance.AssesGenZedGraph, 0, instance.population[z]._gp._genEpochs, instance.population[z].assessment, z);
}
instance.SortChromosome(instance.population, gp._assessmentIndex, gp._percent);
//instance.population = SortChromosome(instance.population, gp._assessmentIndex, gp._percent);
//instance.population.Sort(new ChromosomeComparer4((int)instance.percentNUD.Value));
//instance.population.Sort(new ChromosomeComparer2());
//instance.eliteChromosomes.Add(new ConvolutionalNetwork(instance.population[0]));
instance.WriteConvGeneticOutput(0);
File.WriteAllLines(timeDirectory + @"\report.txt", instance.textBox9.Lines);
instance.Invoke(new Action(instance.currentTaskPB.PerformStep));
// Evolve
for (int i = 1; i <= gp._genEpochs; i++)
{
for (int m = 1; m < instance.population.Count; m++)
{
// ReSharper disable once CompareOfFloatsByEqualityOperator
if (instance.population[m].assessment == 0)
{
string buff = instance.population[m].name;
instance.population[m] = new ConvolutionalChromosome(gp, random)
{
name = buff
};
}
else
{
if (new Random().Next() < 5)
{
string buff = instance.population[m].name;
instance.population[m] = new ConvolutionalChromosome(gp, random)
{
name = buff
};
}
else
{
instance.population[m].MutateConvolutional(gp._crp, gp._mutateRate);
instance.population[m].MutateDense(gp._drp, gp._mutateRate);
}
}
}
for (int n = 1; n < instance.population.Count; n++)
{
instance.population[n].assessment = instance.population[n].accuracy = instance.population[n].paramsCount = 0;
instance.tempAccuracy = instance.tempParameters = 0;
instance.WriteAdds(i, n);
instance.UpdateAssessmentParams(instance.population[n], gp, i);
File.AppendAllText(@"C:\keras\Directory\Assessment_Statistic.txt",
instance.population[n].accuracy.ToString() + "_" + instance.population[n].paramsCount + Environment.NewLine);
if (instance.population[n].paramsCount > maxParams)
{
maxParams = instance.population[n].paramsCount;
}
Support.DrawParamsGraph(instance.ParamsZedGraph, instance.population[n].accuracy, instance.population[n].paramsCount, maxParams);
}
Support.UpdateAssesment(instance.population);
for (int z = 0; z < instance.population.Count; z++)
{
Support.DrawAssesGraph(instance.AssesGenZedGraph, i, instance.population[z]._gp._genEpochs, instance.population[z].assessment, z);
}
instance.SortChromosome(instance.population, gp._assessmentIndex, gp._percent);
//instance.population.Sort(new ChromosomeComparer4((int)instance.percentNUD.Value));
//population.Sort(new ChromosomeComparer((float)memPriorityNUD.Value, (float)accPriorityNUD.Value));
//instance.population.Sort(new ChromosomeComparer2());
//instance.eliteChromosomes.Add(new ConvolutionalNetwork(instance.population[0]));
instance.WriteConvGeneticOutput(i);
File.WriteAllLines(timeDirectory + @"\report.txt", instance.textBox9.Lines);
instance.Invoke(new Action(instance.currentTaskPB.PerformStep));
}
Image image = new Bitmap(instance.AssesGenZedGraph.GetImage());
image.Save(timeDirectory + @"\asses.png");
image = new Bitmap(instance.ParamsZedGraph.GetImage());
image.Save(timeDirectory + @"\params.png");
image = new Bitmap(instance.accZG.GetImage());
image.Save(timeDirectory + @"\acc.png");
}
