public ConvolutionalNetwork(ConvolutionalChromosome chromosome, int epoch, string networkName)
{
this.epoch = epoch;
indexNumber = chromosome.indexNumber;
this.networkName = networkName;
trainConstSpeed = chromosome.trainConstSpeed;
optimizer = chromosome.optimizer;
loss_function = chromosome.loss_function;
outputs = chromosome.nrp.networkOutputNumb;
batchSize = chromosome.nrp.batchSize;
networkLearningEpochs = chromosome.nrp.epochs;
slidingWindows = new List <int[]>();
convActivationIndexes = new List <int>();
filters = new List <int>();
denseActivationIndexes = new List <int>();
neurons = new List <int>();
denseDropoutIndexes = new List <int>();
denseDropoutRates = new List <int>();
convDropoutIndexes = new List <int>();
convDropoutRates = new List <int>();
for (int i = 0; i < chromosome.convPart.convLayers.Count; i++)
{
slidingWindows.Add(new int[2] {
chromosome.convPart.convLayers[i].slidingWindow[0], chromosome.convPart.convLayers[i].slidingWindow[1]
});
convActivationIndexes.Add(chromosome.convPart.convLayers[i].activationIndex);
filters.Add(chromosome.convPart.convLayers[i].filters);
if (chromosome.convPart.convLayers[i].dropoutExist)
{
convDropoutIndexes.Add(i);
convDropoutRates.Add(chromosome.convPart.convLayers[i].dropoutRate);
}
}
for (int i = 0; i < chromosome.densePart.denseLayers.Count; i++)
{
denseActivationIndexes.Add(chromosome.densePart.denseLayers[i].activationIndex);
neurons.Add(chromosome.densePart.denseLayers[i].neurons);
if (chromosome.densePart.denseLayers[i].dropoutExist)
{
denseDropoutIndexes.Add(i);
denseDropoutRates.Add(chromosome.densePart.denseLayers[i].dropoutRate);
}
}
}
///////////////////////////////////////////////////////
// new Process
public void UpdateAssessmentParams(ConvolutionalChromosome chromosome, GeneticProgramm gp, int epoch)
{
instance.Invoke(new Action(() => {
if (!instance.errDontClearChB.Checked)
{
instance.ErrorTB.Clear();
}
if (!instance.chrDontClearChB.Checked)
{
instance.chrOutTB.Clear();
}
}));
ConvolutionalNetwork tempNet = new ConvolutionalNetwork(chromosome, epoch, gp._nrp.networkName);
ConvolutionalNetwork.CreateNetworkScript(tempNet, chromosome.crp.convActivations, chromosome.drp.denseActivations);
ConvolutionalNetwork.CreateConvBatFile("temp_convolution.py", @"C:\keras\Directory\scripts\convolutional\genetic", gp._nrp.datasetPath,
gp._nrp._modelPath, gp._nrp._labelPath, gp._nrp._plotPath, gp._nrp.networkName, epoch, chromosome.indexNumber, 0);
CreateConvBatProcess();
chromosome.accuracy = instance.tempAccuracy;
chromosome.paramsCount = instance.tempParameters;
}
