private static void SampleXor()
{
SigmaEnvironment sigma = SigmaEnvironment.Create("logical");
sigma.SetRandomSeed(0);
sigma.Prepare();
RawDataset dataset = new RawDataset("xor");
dataset.AddRecords("inputs", new[] { 0, 0 }, new[] { 0, 1 }, new[] { 1, 0 }, new[] { 1, 1 });
dataset.AddRecords("targets", new[] { 0 }, new[] { 0 }, new[] { 0 }, new[] { 1 });
ITrainer trainer = sigma.CreateTrainer("xor-trainer");
trainer.Network.Architecture = InputLayer.Construct(2) + FullyConnectedLayer.Construct(2) + FullyConnectedLayer.Construct(1) + OutputLayer.Construct(1) + SquaredDifferenceCostLayer.Construct();
trainer.TrainingDataIterator = new MinibatchIterator(1, dataset);
trainer.AddNamedDataIterator("validation", new UndividedIterator(dataset));
trainer.Optimiser = new GradientDescentOptimiser(learningRate: 0.1);
trainer.Operator = new CudaSinglethreadedOperator();
trainer.AddInitialiser("*.*", new GaussianInitialiser(standardDeviation: 0.05));
trainer.AddLocalHook(new StopTrainingHook(atEpoch: 10000));
trainer.AddLocalHook(new AccumulatedValueReporter("optimiser.cost_total", TimeStep.Every(1, TimeScale.Epoch), averageValues: true));
trainer.AddLocalHook(new AccumulatedValueReporter("optimiser.cost_total", TimeStep.Every(1, TimeScale.Stop), averageValues: true));
trainer.AddLocalHook(new ValueReporter("network.layers.*<external_output>._outputs.default.activations", TimeStep.Every(1, TimeScale.Stop)));
trainer.AddLocalHook(new ValueReporter("network.layers.*-fullyconnected.weights", TimeStep.Every(1, TimeScale.Stop)));
trainer.AddLocalHook(new ValueReporter("network.layers.*-fullyconnected.biases", TimeStep.Every(1, TimeScale.Stop)));
sigma.Run();
}
private static void Main()
{
SigmaEnvironment.EnableLogging();
SigmaEnvironment sigma = SigmaEnvironment.Create("Sigma-MNIST");
// create a new mnist trainer
ITrainer trainer = CreateMnistTrainer(sigma);
// for the UI we have to activate more features
if (UI)
{
// create and attach a new UI framework
WPFMonitor gui = sigma.AddMonitor(new WPFMonitor("MNIST"));
// create a tab
gui.AddTabs("Overview");
// access the window inside the ui thread
gui.WindowDispatcher(window =>
{
// enable initialisation
window.IsInitializing = true;
// add a panel that controls the learning process
window.TabControl["Overview"].AddCumulativePanel(new ControlPanel("Control", trainer));
// create an accuracy cost that updates every iteration
var cost = new TrainerChartPanel <CartesianChart, LineSeries, TickChartValues <double>, double>("Cost", trainer, "optimiser.cost_total", TimeStep.Every(1, TimeScale.Iteration));
// improve the chart performance
cost.Fast();
// add the newly created panel
window.TabControl["Overview"].AddCumulativePanel(cost);
// finish initialisation
window.IsInitializing = false;
});
// the operators should not run instantly but when the user clicks play
sigma.StartOperatorsOnRun = false;
}
sigma.Prepare();
sigma.Run();
}
