/// <summary>
/// Place an <see cref="SigmaPanel" /> onto the grid. For granular control use
/// <see cref="AddElement" />.
/// </summary>
/// <param name="panel">The <see cref="SigmaPanel" /> that will be added.</param>
/// <param name="row">The row in which the <see cref="SigmaPanel" /> should be added.</param>
/// <param name="column">The column in which the <see cref="SigmaPanel" /> should be added.</param>
/// <param name="rowSpan">How many rows the <see cref="SigmaPanel" /> uses.</param>
/// <param name="columnSpan">How many columns the <see cref="SigmaPanel" /> uses.</param>
/// <param name="legend">Mark a panel to a special colour / legend. </param>
/// <exception cref="ArgumentOutOfRangeException">
/// If rowSpan is smaller or equal to zero or if columnSpan
/// is smaller or equal to zero.
/// </exception>
/// <exception cref="IndexOutOfRangeException">If there is no space for the new <see cref="SigmaPanel" />. </exception>
public void AddPanel(SigmaPanel panel, int row, int column, int rowSpan = 1, int columnSpan = 1,
StatusBarLegendInfo legend = null)
{
panel.Monitor = Monitor;
AddElement(panel, row, column, rowSpan, columnSpan);
ApplyLegend(panel, legend);
panel.Initialise(Monitor.Window);
}
/// <summary>
/// </summary>
/// <param name="app"></param>
/// <param name="window"></param>
/// <param name="parameters">One <see cref="StatusBarLegendInfo" /></param>
/// <returns></returns>
public UIElement CreateElement(Application app, Window window, params object[] parameters)
{
if (parameters.Length != 1)
{
throw new ArgumentException(@"Value has to be a single-value array.", nameof(parameters));
}
StatusBarLegendInfo info = parameters[0] as StatusBarLegendInfo;
if (info == null)
{
throw new ArgumentException($@"Value cannot be casted to {typeof(StatusBarLegendInfo)}", nameof(parameters));
}
return(info.Apply(new StatusBarLegend()));
}
/// <summary>
/// Apply the legend colour-coding to a passed panel.
/// </summary>
/// <param name="panel">The given panel. </param>
/// <param name="legend">The given panel. </param>
protected virtual void ApplyLegend(SigmaPanel panel, StatusBarLegendInfo legend)
{
if (legend != null)
{
panel.Header.Background = new SolidColorBrush(legend.LegendColor);
if (legend.ForegroundColor.HasValue)
{
SolidColorBrush brush = new SolidColorBrush(legend.ForegroundColor.Value);
foreach (object headerChild in panel.Header.Children)
{
Control control = headerChild as Control;
if (control != null)
{
control.Foreground = brush;
}
}
}
}
}
private static void Main()
{
SigmaEnvironment.EnableLogging();
SigmaEnvironment sigma = SigmaEnvironment.Create("sigma_demo");
// create a new mnist trainer
string name = DemoMode.Name;
ITrainer trainer = DemoMode.CreateTrainer(sigma);
trainer.AddLocalHook(new MetricProcessorHook <INDArray>("network.layers.*.weights", (a, h) => h.Divide(h.Sum(a), a.Length), "shared.network_weights_average"));
trainer.AddLocalHook(new MetricProcessorHook <INDArray>("network.layers.*.weights", (a, h) => h.StandardDeviation(a), "shared.network_weights_stddev"));
trainer.AddLocalHook(new MetricProcessorHook <INDArray>("network.layers.*.biases", (a, h) => h.Divide(h.Sum(a), a.Length), "shared.network_biases_average"));
trainer.AddLocalHook(new MetricProcessorHook <INDArray>("network.layers.*.biases", (a, h) => h.StandardDeviation(a), "shared.network_biases_stddev"));
trainer.AddLocalHook(new MetricProcessorHook <INDArray>("optimiser.updates", (a, h) => h.Divide(h.Sum(a), a.Length), "shared.optimiser_updates_average"));
trainer.AddLocalHook(new MetricProcessorHook <INDArray>("optimiser.updates", (a, h) => h.StandardDeviation(a), "shared.optimiser_updates_stddev"));
trainer.AddLocalHook(new MetricProcessorHook <INDArray>("network.layers.*<external_output>._outputs.default.activations", (a, h) => h.Divide(h.Sum(a), a.Length), "shared.network_activations_mean"));
// create and attach a new UI framework
WPFMonitor gui = sigma.AddMonitor(new WPFMonitor(name, DemoMode.Language));
gui.ColourManager.Dark = DemoMode.Dark;
gui.ColourManager.PrimaryColor = DemoMode.PrimarySwatch;
StatusBarLegendInfo iris = new StatusBarLegendInfo(name, MaterialColour.Blue);
StatusBarLegendInfo general = new StatusBarLegendInfo("General", MaterialColour.Grey);
gui.AddLegend(iris);
gui.AddLegend(general);
// create a tab
gui.AddTabs("Overview", "Metrics", "Validation", "Maximisation", "Reproduction", "Update");
// access the window inside the ui thread
gui.WindowDispatcher(window =>
{
// enable initialisation
window.IsInitializing = true;
window.TabControl["Metrics"].GridSize = new GridSize(2, 4);
window.TabControl["Validation"].GridSize = new GridSize(2, 5);
window.TabControl["Maximisation"].GridSize = new GridSize(2, 5);
window.TabControl["Reproduction"].GridSize = new GridSize(2, 5);
window.TabControl["Update"].GridSize = new GridSize(1, 1);
window.TabControl["Overview"].GridSize.Rows -= 1;
window.TabControl["Overview"].GridSize.Columns -= 1;
// add a panel that controls the learning process
window.TabControl["Overview"].AddCumulativePanel(new ControlPanel("Control", trainer), legend: iris);
ITimeStep reportTimeStep = DemoMode.Slow ? TimeStep.Every(1, TimeScale.Iteration) : TimeStep.Every(10, TimeScale.Epoch);
var cost1 = CreateChartPanel <CartesianChart, GLineSeries, GearedValues <double>, double>("Cost / Epoch", trainer, "optimiser.cost_total", TimeStep.Every(1, TimeScale.Epoch)).Linearify();
var cost2 = CreateChartPanel <CartesianChart, GLineSeries, GearedValues <double>, double>("Cost / Epoch", trainer, "optimiser.cost_total", reportTimeStep);
var weightAverage = CreateChartPanel <CartesianChart, GLineSeries, GearedValues <double>, double>("Mean of Weights / Epoch", trainer, "shared.network_weights_average", reportTimeStep, averageMode: true).Linearify();
var weightStddev = CreateChartPanel <CartesianChart, GLineSeries, GearedValues <double>, double>("Standard Deviation of Weights / Epoch", trainer, "shared.network_weights_stddev", reportTimeStep, averageMode: true).Linearify();
var biasesAverage = CreateChartPanel <CartesianChart, GLineSeries, GearedValues <double>, double>("Mean of Biases / Epoch", trainer, "shared.network_biases_average", reportTimeStep, averageMode: true).Linearify();
var biasesStddev = CreateChartPanel <CartesianChart, GLineSeries, GearedValues <double>, double>("Standard Deviation of Biases / Epoch", trainer, "shared.network_biases_stddev", reportTimeStep, averageMode: true).Linearify();
var updateAverage = CreateChartPanel <CartesianChart, GLineSeries, GearedValues <double>, double>("Mean of Parameter Updates / Epoch", trainer, "shared.optimiser_updates_average", reportTimeStep, averageMode: true).Linearify();
var updateStddev = CreateChartPanel <CartesianChart, GLineSeries, GearedValues <double>, double>("Standard Deviation of Parameter Updates / Epoch", trainer, "shared.optimiser_updates_stddev", reportTimeStep, averageMode: true).Linearify();
var outputActivationsMean = CreateChartPanel <CartesianChart, GLineSeries, GearedValues <double>, double>("Mean of Output Activations", trainer, "shared.network_activations_mean", reportTimeStep, averageMode: true).Linearify();
AccuracyPanel accuracy1 = null, accuracy2 = null;
if (DemoMode != DemoType.Wdbc && DemoMode != DemoType.Parkinsons)
{
accuracy1 = new AccuracyPanel("Validation Accuracy", trainer, DemoMode.Slow ? TimeStep.Every(1, TimeScale.Epoch) : reportTimeStep, null, 1, 2);
accuracy1.Fast().Linearify();
accuracy2 = new AccuracyPanel("Validation Accuracy", trainer, DemoMode.Slow ? TimeStep.Every(1, TimeScale.Epoch) : reportTimeStep, null, 1, 2);
accuracy2.Fast().Linearify();
}
IRegistry regTest = new Registry();
regTest.Add("test", DateTime.Now);
var parameter = new ParameterPanel("Parameters", sigma, window);
parameter.Add("Time", typeof(DateTime), regTest, "test");
ValueSourceReporter valueHook = new ValueSourceReporter(TimeStep.Every(1, TimeScale.Epoch), "optimiser.cost_total");
trainer.AddGlobalHook(valueHook);
sigma.SynchronisationHandler.AddSynchronisationSource(valueHook);
var costBlock = (UserControlParameterVisualiser)parameter.Content.Add("Cost", typeof(double), trainer.Operator.Registry, "optimiser.cost_total");
costBlock.AutoPollValues(trainer, TimeStep.Every(1, TimeScale.Epoch));
var learningBlock = (UserControlParameterVisualiser)parameter.Content.Add("Learning rate", typeof(double), trainer.Operator.Registry, "optimiser.learning_rate");
learningBlock.AutoPollValues(trainer, TimeStep.Every(1, TimeScale.Epoch));
var paramCount = (UserControlParameterVisualiser)parameter.Content.Add("Parameter count", typeof(long), trainer.Operator.Registry, "network.parameter_count");
paramCount.AutoPollValues(trainer, TimeStep.Every(1, TimeScale.Start));
window.TabControl["Overview"].AddCumulativePanel(cost1, 1, 2, legend: iris);
window.TabControl["Overview"].AddCumulativePanel(parameter);
//window.TabControl["Overview"].AddCumulativePanel(accuracy1, 1, 2, legend: iris);
//window.TabControl["Metrics"].AddCumulativePanel(cost2, legend: iris);
//window.TabControl["Metrics"].AddCumulativePanel(weightAverage, legend: iris);
//window.TabControl["Metrics"].AddCumulativePanel(biasesAverage, legend: iris);
window.TabControl["Update"].AddCumulativePanel(updateAverage, legend: iris);
if (accuracy2 != null)
{
window.TabControl["Metrics"].AddCumulativePanel(accuracy2, legend: iris);
}
window.TabControl["Metrics"].AddCumulativePanel(weightStddev, legend: iris);
window.TabControl["Metrics"].AddCumulativePanel(biasesStddev, legend: iris);
window.TabControl["Metrics"].AddCumulativePanel(updateStddev, legend: iris);
window.TabControl["Metrics"].AddCumulativePanel(outputActivationsMean, legend: iris);
if (DemoMode == DemoType.Mnist)
{
NumberPanel outputpanel = new NumberPanel("Numbers", trainer);
DrawPanel drawPanel = new DrawPanel("Draw", trainer, 560, 560, 20, outputpanel);
window.TabControl["Validation"].AddCumulativePanel(drawPanel, 2, 3);
window.TabControl["Validation"].AddCumulativePanel(outputpanel, 2);
window.TabControl["Validation"].AddCumulativePanel(weightAverage);
window.TabControl["Validation"].AddCumulativePanel(biasesAverage);
for (int i = 0; i < 10; i++)
{
window.TabControl["Maximisation"].AddCumulativePanel(new MnistBitmapHookPanel($"Target Maximisation {i}", i, trainer, TimeStep.Every(1, TimeScale.Epoch)));
}
}
if (DemoMode == DemoType.TicTacToe)
{
window.TabControl["Overview"].AddCumulativePanel(new TicTacToePanel("Play TicTacToe!", trainer));
}
//for (int i = 0; i < 10; i++)
//{
// window.TabControl["Reproduction"].AddCumulativePanel(new MnistBitmapHookPanel($"Target Maximisation 7-{i}", 8, 28, 28, trainer, TimeStep.Every(1, TimeScale.Start)));
//}
});
if (DemoMode == DemoType.Mnist)
{
sigma.AddMonitor(new HttpMonitor("http://+:8080/sigma/"));
}
// the operators should not run instantly but when the user clicks play
sigma.StartOperatorsOnRun = false;
sigma.Prepare();
sigma.RunAsync();
gui.WindowDispatcher(window => window.IsInitializing = false);
}
/// <summary>
/// Add a <see cref="StatusBarLegendInfo"/> to the UI. It will be (with a normal configuration)
/// displayed in the lower right corner - Panels can be marked with an info.
/// </summary>
/// <param name="legend">The info that will be added.</param>
/// <returns>The passed legend for chaining / storing in a variable. </returns>
public StatusBarLegendInfo AddLegend(StatusBarLegendInfo legend)
{
AddLegends(legend);
return(GetLegendInfo(legend.Name));
}