public void Stop()
{
if ((ExecutionState != ExecutionState.Started) && (ExecutionState != ExecutionState.Paused))
{
throw new InvalidOperationException(string.Format("Stop not allowed in execution state \"{0}\".", ExecutionState));
}
if (Optimizers.Count == 0)
{
return;
}
experimentStarted = false;
experimentStopped = true;
if (Optimizers.Any(x => (x.ExecutionState == ExecutionState.Started) || (x.ExecutionState == ExecutionState.Paused)))
{
foreach (var optimizer in Optimizers.Where(x => (x.ExecutionState == ExecutionState.Started) || (x.ExecutionState == ExecutionState.Paused)))
{
// a race-condition may occur when the optimizer has changed the state by itself in the meantime
try { optimizer.Stop(); } catch (InvalidOperationException) { }
}
}
else
{
OnStopped();
}
}
private void optimizer_Stopped(object sender, EventArgs e)
{
lock (locker) {
if (experimentStopped)
{
if (Optimizers.All(x => (x.ExecutionState == ExecutionState.Stopped) || (x.ExecutionState == ExecutionState.Prepared)))
{
OnStopped();
}
}
else
{
if (experimentStarted && Optimizers.Any(x => (x.ExecutionState == ExecutionState.Prepared) || (x.ExecutionState == ExecutionState.Paused)))
{
Optimizers.First(x => (x.ExecutionState == ExecutionState.Prepared) || (x.ExecutionState == ExecutionState.Paused)).Start();
}
else if (Optimizers.All(x => x.ExecutionState == ExecutionState.Stopped))
{
OnStopped();
}
else if (Optimizers.Any(x => (x.ExecutionState == ExecutionState.Prepared) || (x.ExecutionState == ExecutionState.Paused)) && Optimizers.All(o => o.ExecutionState != ExecutionState.Started))
{
OnPaused();
}
}
}
}
public void LogisticRegression()
{
var X = Matrix <double> .Build.DenseOfArray(new double[, ] {
{ 1 }, { 2 }
});
var Y = Matrix <double> .Build.DenseOfArray(new double[, ] {
{ 1 }
});
var model = new DeepCat.DeepCat();
model.Add(new Dense(1, Activations.Sigmoid(), weightInitializer: Initializations.Fixed()));
model.Compile(X.RowCount, LossFunctions.CrossEntropy(), Optimizers.GradientDescent(0.02));
model.Fit(X, Y, 1);
var a = model.Predict(X);
a[0, 0] = Math.Round(a[0, 0], 8);
var expectedResult = Matrix <double> .Build.DenseOfArray(new double[, ] {
{ 0.59859297 }
});
Assert.AreEqual(a, expectedResult);
}
public void Prepare(bool clearRuns)
{
if ((ExecutionState != ExecutionState.Prepared) && (ExecutionState != ExecutionState.Paused) && (ExecutionState != ExecutionState.Stopped))
{
throw new InvalidOperationException(string.Format("Prepare not allowed in execution state \"{0}\".", ExecutionState));
}
if (Optimizers.Count == 0)
{
return;
}
if (clearRuns)
{
runs.Clear();
}
experimentStarted = false;
experimentStopped = false;
foreach (IOptimizer optimizer in Optimizers.Where(x => x.ExecutionState != ExecutionState.Started))
{
// a race-condition may occur when the optimizer has changed the state by itself in the meantime
try { optimizer.Prepare(clearRuns); }
catch (InvalidOperationException) { }
}
}
private void optimizer_Prepared(object sender, EventArgs e)
{
lock (locker)
if (Optimizers.All(x => x.ExecutionState == ExecutionState.Prepared))
{
OnPrepared();
}
}
private void optimizer_Paused(object sender, EventArgs e)
{
lock (locker)
if (Optimizers.All(x => x.ExecutionState != ExecutionState.Started))
{
OnPaused();
}
}
private void UpdateExecutionState()
{
// Execution states of the Experiment are determined using the following _basic_ rules:
// if any Optimizer is Started => Experiment is Started (2. if)
// if any Optimizer is Paused => Experiment is Paused (3. if)
// if any Optimizer is Prepared => Experiment is Prepared (5. if)
// else (all Optimizer are Stopped) => Experiment is Stopped (6. if)
// Additional there are two extra rules:
// if the Experiment is running and there are still optimizers that can be started => keep the Experiment Running (1. if)
// if experiment-stop is pending: Stop Experiment even if there are still Prepared Optimizer (4. if)
lock (locker) {
// 1. experiment is running & further startable optimizers are available => continue executing
if (experimentStarted && StartableOptimizers.Any())
{
return;
}
// 2. any optimizer is running => continue executing
if (Optimizers.Any(x => x.ExecutionState == ExecutionState.Started))
{
return;
}
experimentStarted = false;
// 3. any optimizer is paused => experiment paused
if (Optimizers.Any(x => x.ExecutionState == ExecutionState.Paused))
{
OnPaused();
}
// 4. stop pending & all optimizers either stopped or prepared => experiment stopped
else if (experimentStopped)
{
OnStopped();
}
// 5. any optimizer prepared => experiment prepared
else if (Optimizers.Any(x => x.ExecutionState == ExecutionState.Prepared))
{
OnPrepared();
}
// 6. (else) all optimizers stopped
else
{
OnStopped();
}
}
}
private void optimizer_ExecutionTimeChanged(object sender, EventArgs e)
{
// only wait for maximally 100ms to acquire lock, otherwise return and don't update the execution time
var success = Monitor.TryEnter(locker, 100);
if (!success)
{
return;
}
try {
ExecutionTime = Optimizers.Aggregate(TimeSpan.Zero, (t, o) => t + o.ExecutionTime);
} finally {
Monitor.Exit(locker);
}
}
public void Start()
{
if ((ExecutionState != ExecutionState.Prepared) && (ExecutionState != ExecutionState.Paused))
{
throw new InvalidOperationException(string.Format("Start not allowed in execution state \"{0}\".", ExecutionState));
}
if (Optimizers.Count == 0)
{
return;
}
experimentStarted = true;
experimentStopped = false;
IOptimizer optimizer = Optimizers.FirstOrDefault(x => (x.ExecutionState == ExecutionState.Prepared) || (x.ExecutionState == ExecutionState.Paused));
if (optimizer != null)
{
// a race-condition may occur when the optimizer has changed the state by itself in the meantime
try { optimizer.Start(); }
catch (InvalidOperationException) { }
}
}
static void Main(string[] args)
{
var X = Matrix <double> .Build.Random(5, 100);
var Y = Matrix <double> .Build.Random(1, 100);
var test = Matrix <double> .Build.Random(5, 1);
var model = new DeepCat();
model.Add(new Dense(5, Activations.Relu(), weightInitializer: Initializations.RandomNormal()));
model.Add(new Dense(5, Activations.Relu(), weightInitializer: Initializations.RandomNormal()));
model.Add(new Dense(1, Activations.Sigmoid()));
model.Compile(X.RowCount, LossFunctions.CrossEntropy(), Optimizers.GradientDescent(0.002));
model.Fit(X, Y, 100);
model.Predict(test);
var x = 1;
}
public void Remove(Optimizer optimizer)
{
Optimizers.Remove(optimizer);
}
public void Add(Optimizer optimizer)
{
Optimizers.Add(optimizer);
}
