internal ConfigViewModel(string type, string groupDesc, StackPanel container, IEnumerable selections)
{
Container = container;
var consumerGroupPanel = Container.AddGroupStackPanel(type, groupDesc);
ComboBox = new ComboBox {
ItemsSource = selections, Tag = this
};
consumerGroupPanel.AddLabeledRow(type, ComboBox);
ParamPanel = new ParameterPanel {
CanCollapse = false, Tag = this
};
Container.Children.Add(ParamPanel);
}
private void RebuildGui()
{
TableLayoutPanel g = new TableLayoutPanel();
foreach (Parameters t in subSelectionControl.parameters)
{
t.SetValuesFromControl();
}
for (int i = 0; i < subSelectionControl.parameters.Count; i++)
{
g.RowStyles.Add(new RowStyle(SizeType.AutoSize, 100));
}
for (int i = 0; i < subSelectionControl.parameters.Count; i++)
{
ParameterPanel tb = new ParameterPanel();
tb.Init(subSelectionControl.parameters[i]);
g.Controls.Add(tb, 0, i);
}
Controls.Add(g);
}
public void LoadParameters(List <Parameter> parameters)
{
SuspendLayout();
for (int i = Controls.Count - 1; i >= 0; i--)
{
if (Controls[i] is ParameterPanel)
{
Controls.RemoveAt(i);
}
}
Parameters = parameters;
paramPanels = new List <ParameterPanel>();
for (int i = parameters.Count - 1; i >= 0; i--)
{
ParameterPanel panel = new ParameterPanel(parameters[i]);
panel.Dock = DockStyle.Top;
Controls.Add(panel);
paramPanels.Add(panel);
}
ResumeLayout();
}
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);
}
