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ParameterDifferentiableCostMinimizer
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How to use
ParameterDifferentiableCostMinimizer
in
gov.sandia.cognition.learning.algorithm.regression

Best Java code snippets using gov.sandia.cognition.learning.algorithm.regression.ParameterDifferentiableCostMinimizer (Showing top 5 results out of 315)

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}
origin: gov.sandia.foundry/gov-sandia-cognition-learning-core

@Override
public ParameterCostEvaluatorDerivativeBased createInternalFunction()
{
  return new ParameterCostEvaluatorDerivativeBased( 
    this.getResult(),(DifferentiableCostFunction) this.getCostFunction() );
}

origin: openimaj/openimaj

final ParameterDifferentiableCostMinimizer conjugateGradient = new ParameterDifferentiableCostMinimizer(
    new FunctionMinimizerLiuStorey());
conjugateGradient.setObjectToOptimize(nn);
conjugateGradient.addIterativeAlgorithmListener(this);
conjugateGradient.setMaxIterations(50);
neuralNet = conjugateGradient.learn(this.dataCollection);
origin: org.openimaj/sandbox

final ParameterDifferentiableCostMinimizer conjugateGradient = new ParameterDifferentiableCostMinimizer(
    new FunctionMinimizerLiuStorey());
conjugateGradient.setObjectToOptimize(nn);
conjugateGradient.addIterativeAlgorithmListener(this);
conjugateGradient.setMaxIterations(50);
neuralNet = conjugateGradient.learn(this.dataCollection);
origin: algorithmfoundry/Foundry

@Override
public ParameterCostEvaluatorDerivativeBased createInternalFunction()
{
  return new ParameterCostEvaluatorDerivativeBased( 
    this.getResult(),(DifferentiableCostFunction) this.getCostFunction() );
}

origin: algorithmfoundry/Foundry

@Override
public ParameterCostEvaluatorDerivativeBased createInternalFunction()
{
  return new ParameterCostEvaluatorDerivativeBased( 
    this.getResult(),(DifferentiableCostFunction) this.getCostFunction() );
}

gov.sandia.cognition.learning.algorithm.regressionParameterDifferentiableCostMinimizer

Javadoc

This class adapts the unconstrained nonlinear minimization algorithms in the "minimization" package to the task of estimating locally optimal (minimum-cost) parameter sets. This allows us to use algorithms like BFGS FunctionMinimizerBFGS to find locally optimal parameters of, for example, a DifferentiableFeedforwardNeuralNetwork. Any first-order derivative FunctionMinimizer may be dropped into this class.

My current preference is for using BFGS ( FunctionMinimizerBFGS) to solve virtually all problems. However, when there are too many parameters, then Liu-Storey conjugate gradient ( FunctionMinimizerLiuStorey) is another good choice.

When first-order derivative information is not available, then you may use either automatic differentiation ( GradientDescendableApproximator) or the derivative-free minimization routines, such as those used by ParameterDerivativeFreeCostMinimizer.

Most used methods

  • getCostFunction
  • getResult
  • <init>
    Creates a new instance of ParameterDerivativeFreeCostMinimizer
  • addIterativeAlgorithmListener
  • learn
  • setMaxIterations
  • setObjectToOptimize

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