/// <summary>
/// Invoke this hook with a certain parameter registry if optional conditional criteria are satisfied.
/// </summary>
/// <param name="registry">The registry containing the required values for this hook's execution.</param>
/// <param name="resolver">A helper resolver for complex registry entries (automatically cached).</param>
public override void SubInvoke(IRegistry registry, IRegistryResolver resolver)
{
// we need copies of network and optimiser as to not affect the current internal state
INetwork network = (INetwork)resolver.ResolveGetSingle <INetwork>("network.self").DeepCopy();
BaseGradientOptimiser optimiser = (BaseGradientOptimiser)resolver.ResolveGetSingle <BaseGradientOptimiser>("optimiser.self").ShallowCopy();
INDArray desiredTargets = ParameterRegistry.Get <INDArray>("desired_targets");
IComputationHandler handler = new DebugHandler(Operator.Handler);
long[] inputShape = network.YieldExternalInputsLayerBuffers().First().Parameters.Get <long[]>("shape");
IDictionary <string, INDArray> block = DataUtils.MakeBlock("targets", desiredTargets); // desired targets don't change during execution
double desiredCost = ParameterRegistry.Get <double>("desired_cost"), currentCost = Double.MaxValue;
int maxOptimisationAttempts = ParameterRegistry.Get <int>("max_optimisation_attempts");
int maxOptimisationSteps = ParameterRegistry.Get <int>("max_optimisation_steps");
int optimisationSteps = 0;
INDArray maximisedInputs = CreateRandomisedInput(handler, inputShape);
for (int i = 0; i < maxOptimisationAttempts; i++)
{
optimisationSteps = 0;
do
{
// trace current inputs and run network as normal
uint traceTag = handler.BeginTrace();
block["inputs"] = handler.Trace(maximisedInputs.Reshape(ArrayUtils.Concatenate(new[] { 1L, 1L }, inputShape)), traceTag);
handler.BeginSession();
DataUtils.ProvideExternalInputData(network, block);
network.Run(handler, trainingPass: false);
// fetch current outputs and optimise against them (towards desired targets)
INDArray currentTargets = network.YieldExternalOutputsLayerBuffers().First(b => b.ExternalOutputs.Contains("external_default"))
.Outputs["external_default"].Get <INDArray>("activations");
INumber squaredDifference = handler.Sum(handler.Pow(handler.Subtract(handler.FlattenTimeAndFeatures(currentTargets), desiredTargets), 2));
handler.ComputeDerivativesTo(squaredDifference);
handler.EndSession();
INDArray gradient = handler.GetDerivative(block["inputs"]);
maximisedInputs = handler.ClearTrace(optimiser.Optimise("inputs", block["inputs"], gradient, handler));
currentCost = squaredDifference.GetValueAs <double>();
if (currentCost <= desiredCost)
{
goto Validation;
}
} while (++optimisationSteps < maxOptimisationSteps);
maximisedInputs = CreateRandomisedInput(handler, inputShape); // reset input
}
Validation:
maximisedInputs.ReshapeSelf(inputShape);
string sharedResultInput = ParameterRegistry.Get <string>("shared_result_input_key");
string sharedResultSuccess = ParameterRegistry.Get <string>("shared_result_success_key");
if (optimisationSteps >= maxOptimisationSteps)
{
_logger.Debug($"Aborted target maximisation for {desiredTargets}, failed after {maxOptimisationSteps} optimisation steps in {maxOptimisationAttempts} attempts (exceeded limit, current cost {currentCost} but desired {desiredCost}).");
resolver.ResolveSet(sharedResultSuccess, false, addIdentifierIfNotExists: true);
}
else
{
_logger.Debug($"Successfully finished target optimisation for {desiredTargets} after {optimiser} optimisation steps.");
resolver.ResolveSet(sharedResultSuccess, true, addIdentifierIfNotExists: true);
resolver.ResolveSet(sharedResultInput, maximisedInputs, addIdentifierIfNotExists: true);
}
}