public ConvolutionalChromosome(ConvolutionalNetwork cNet, ConvRandomParams crp, DenseRandomParams drp)
{
this.crp = crp;
this.drp = drp;
//добавить рандомные параметры
convPart = new ConvStructure(cNet.slidingWindows, cNet.convActivationIndexes, cNet.filters, cNet.convDropoutIndexes, cNet.convDropoutRates);
densePart = new DenseStructure(cNet.denseActivationIndexes, cNet.neurons, cNet.denseDropoutIndexes, cNet.denseDropoutRates);
} // конструктор для создания хромосомы из полей описательного класса ConvolutionalNetwork для последующего использования в генетических алгоритмах (например, для поиска лучшего варианта чем текущий)
public void MutateDense(DenseRandomParams drp, int mutateRate)
{
if (random.Next(100) < mutateRate)
{
if (random.Next(100) < 5)
{
densePart = new DenseStructure(nrp, drp, random);
}
else
{
densePart.MutateLayersNumb(drp, mutateRate);
densePart.MutateActivation(drp, mutateRate);
densePart.MutateNeurons(drp, mutateRate);
densePart.MutateDropouts(drp, mutateRate);
}
}
}
public ConvolutionalChromosome(GeneticProgramm gp, Random random)
{
_gp = gp;
this.random = random;
nrp = gp._nrp;
crp = gp._crp;
drp = gp._drp;
convPart = new ConvStructure(crp, random);
densePart = new DenseStructure(nrp, drp, random);
if (nrp.notRandomSpeed)
{
trainConstSpeed = nrp.trainConstSpeedRange[0];
}
else
{
trainConstSpeed = Convert.ToSingle(random.Next((int)(nrp.trainConstSpeedRange[0] * 1000), (int)(nrp.trainConstSpeedRange[1] * 1000))) / 1000;
}
optimizer = nrp.optimizers[random.Next(nrp.optimizers.Count)];
loss_function = nrp.loss_functions[random.Next(nrp.loss_functions.Count)];
}
