public void Run()
{
// Prepare data
var baseDataDirectoryPath = @"E:\DataSets\Mnist";
var trainFilePath = Path.Combine(baseDataDirectoryPath, "Train-28x28_cntk_text.txt");
// Define data type and device for the model.
var dataType = DataType.Float;
var device = DeviceDescriptor.UseDefaultDevice();
// Setup initializers
var random = new Random(232);
Func <CNTKDictionary> weightInit = () => Initializers.Xavier(random.Next(), scale: 0.02);
var biasInit = Initializers.Zero();
// Ensure reproducible results with CNTK.
CNTKLib.SetFixedRandomSeed((uint)random.Next());
CNTKLib.ForceDeterministicAlgorithms();
// Setup generator
var ganeratorInputShape = NDShape.CreateNDShape(new int[] { 100 });
var generatorInput = Variable.InputVariable(ganeratorInputShape, dataType);
var generatorNetwork = Generator(generatorInput, weightInit, biasInit, device, dataType);
// Setup discriminator
var discriminatorInputShape = NDShape.CreateNDShape(new int[] { 784 }); // 28 * 28 * 1.
var discriminatorInput = Variable.InputVariable(discriminatorInputShape, dataType);
// scale image input between -1.0 and 1.0.
var discriminatorInputScaled = CNTKLib.Minus(
CNTKLib.ElementTimes(Constant.Scalar(2 * 0.00390625f, device), discriminatorInput),
Constant.Scalar(1.0f, device));
var discriminatorNetwork = Discriminator(discriminatorInputScaled, weightInit, biasInit, device, dataType);
// The discriminator must be used on both the real MNIST images and fake images generated by the generator function.
// One way to represent this in the computational graph is to create a clone of the output of the discriminator function,
// but with substituted inputs. Setting method = share in the clone function ensures that both paths through the discriminator model
// use the same set of parameters.
var discriminatorNetworkFake = discriminatorNetwork
.Clone(ParameterCloningMethod.Share, replacements:
new Dictionary <Variable, Variable>
{
{ discriminatorInputScaled.Output, generatorNetwork.Output },
});
// Create minibatch source for providing the real images.
var imageNameToVariable = new Dictionary <string, Variable> {
{ "features", discriminatorInput }
};
var imageMinibatchSource = CreateMinibatchSource(trainFilePath, imageNameToVariable, randomize: true);
// Create minibatch source for providing the noise.
var noiseNameToVariable = new Dictionary <string, Variable> {
{ "noise", generatorInput }
};
var noiseMinibatchSource = new UniformNoiseMinibatchSource(noiseNameToVariable, min: -1.0f, max: 1.0f, seed: random.Next());
// Combine both sources in the composite minibatch source.
var compositeMinibatchSource = new CompositeMinibatchSource(imageMinibatchSource, noiseMinibatchSource);
// Setup generator loss: 1.0 - C.log(D_fake)
var generatorLossFunc = CNTKLib.Minus(Constant.Scalar(1.0f, device),
CNTKLib.Log(discriminatorNetworkFake));
// Setup discriminator loss: -(C.log(D_real) + C.log(1.0 - D_fake))
var discriminatorLossFunc = CNTKLib.Negate(CNTKLib.Plus(CNTKLib.Log(discriminatorNetwork),
CNTKLib.Log(CNTKLib.Minus(Constant.Scalar(1.0f, device), discriminatorNetworkFake))));
// Create fitters for the training loop.
// Generator uses Adam and discriminator SGD.
// Advice from: https://github.com/soumith/ganhacks
var generatorLearner = Learners.Adam(generatorNetwork.Parameters(),
learningRate: 0.0002, momentum: 0.5, gradientClippingThresholdPerSample: 1.0);
var generatorFitter = CreateFitter(generatorLearner, generatorNetwork, generatorLossFunc, device);
var discriminatorLearner = Learners.SGD(discriminatorNetwork.Parameters(),
learningRate: 0.0002, gradientClippingThresholdPerSample: 1.0);
var discriminatorFitter = CreateFitter(discriminatorLearner, discriminatorNetwork, discriminatorLossFunc, device);
int epochs = 30;
int batchSize = 128;
// Controls how many steps the discriminator takes,
// each time the generator takes 1 step.
// Default from the original paper is 1.
int discriminatorSteps = 1;
var isSweepEnd = false;
for (int epoch = 0; epoch < epochs;)
{
for (int step = 0; step < discriminatorSteps; step++)
{
// Discriminator needs both real images and noise,
// so uses the composite minibatch source.
var minibatchItems = compositeMinibatchSource.GetNextMinibatch(batchSize, device);
isSweepEnd = minibatchItems.isSweepEnd;
discriminatorFitter.FitNextStep(minibatchItems.minibatch, batchSize);
DisposeValues(minibatchItems.minibatch);
}
// Generator only needs noise images,
// so uses the noise minibatch source separately.
var noiseMinibatchItems = noiseMinibatchSource.GetNextMinibatch(batchSize, device);
generatorFitter.FitNextStep(noiseMinibatchItems.minibatch, batchSize);
DisposeValues(noiseMinibatchItems.minibatch);
if (isSweepEnd)
{
var generatorCurrentLoss = generatorFitter.CurrentLoss;
generatorFitter.ResetLossAndMetricAccumulators();
var discriminatorCurrentLoss = discriminatorFitter.CurrentLoss;
discriminatorFitter.ResetLossAndMetricAccumulators();
var traceOutput = $"Epoch: {epoch + 1:000} Generator Loss = {generatorCurrentLoss:F8}, Discriminator Loss = {discriminatorCurrentLoss:F8}";
Trace.WriteLine(traceOutput);
++epoch;
}
}
// Sample 6x6 images from generator.
var samples = 6 * 6;
var batch = noiseMinibatchSource.GetNextMinibatch(samples, device);
var noise = batch.minibatch;
var predictor = new Predictor(generatorNetwork, device);
var images = predictor.PredictNextStep(noise);
var imagesData = images.SelectMany(t => t).ToArray();
// Show examples
var app = new Application();
var window = new PlotWindowBitMap("Generated Images", imagesData, 28, 28, 1, true);
window.Show();
app.Run(window);
}
public void SGD_LearningRate_Below_Zero_Throws()
{
Learners.SGD(new List <Parameter>(), learningRate: -0.1);
}
public void SGD_L2Regularization_Below_Zero_Throws()
{
Learners.SGD(new List <Parameter>(), l2Regularization: -0.1);
}
public void SGD_Parameters_Is_Null_Throws()
{
Learners.SGD(null);
}
