static void Train(Sequential generator, Sequential discriminator, Sequential gan, NDarray dataset, int latentDim, int epochs = 200, int batchSize = 128)
{
var batchPerEpoch = dataset.shape[0] / batchSize;
var halfBatch = batchSize / 2;
for (var i = 0; i < epochs; i++)
{
for (var j = 0; j < batchPerEpoch; j++)
{
var real = GenerateRealSamples(dataset, halfBatch);
var dLoss1 = discriminator.TrainOnBatch(real.Item1, real.Item2);
var fake = GenerateFakeGeneratorSamples(generator, latentDim, halfBatch);
var dLoss2 = discriminator.TrainOnBatch(fake.Item1, fake.Item2);
var xGan = GenerateLatentPoints(latentDim, batchSize);
var yGan = np.ones(new int[] { batchSize, 1 });
var gLoss = gan.TrainOnBatch(xGan, yGan);
Console.WriteLine("> EPOCH: " + i + " \t" + j + "/" + batchPerEpoch + " \td1=" + dLoss1.First() + " \td2=" + dLoss2.First() + " \tg=" + gLoss.Last() + "");
}
if (i % 10 == 0 || i < 10)
{
SummarizePerformance(i, generator, discriminator, dataset, latentDim);
}
}
}
static void TrainDiscriminator(Sequential model, NDarray dataset, int iterations = 20, int batchSize = 128)
{
var halfBatch = batchSize / 2;
for (int i = 0; i < iterations; i++)
{
var real = GenerateRealSamples(dataset, halfBatch);
var realAcc = model.TrainOnBatch(real.Item1, real.Item2);
var fake = GenerateFakeSamples(halfBatch);
var fakeAcc = model.TrainOnBatch(fake.Item1, fake.Item2);
Console.WriteLine(">" + i + "\t real=" + realAcc.Last() * 100 + "%\t fake=" + fakeAcc.Last() * 100 + "%");
}
}
static void TrainGan(Sequential ganModel, int latentDim, int epochs, int batchSize)
{
for (int i = 0; i < epochs; i++)
{
var xGan = GenerateLatentPoints(latentDim, batchSize);
var yGan = np.ones(new int[] { batchSize, 1 });
ganModel.TrainOnBatch(xGan, yGan);
}
}
