private static TrainerChartPanel <TChart, TSeries, TChartValues, TData> CreateChartPanel <TChart, TSeries, TChartValues, TData>(string title, ITrainer trainer, string hookedValue, ITimeStep timestep, bool averageMode = false) where TChart : Chart, new() where TSeries : Series, new() where TChartValues : IList <TData>, IChartValues, new()
{
var panel = new TrainerChartPanel <TChart, TSeries, TChartValues, TData>(title, trainer, hookedValue, timestep, averageMode);
panel.Fast();
panel.MaxPoints = 50;
return(panel);
}
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();
}
