Click or drag to resize
Accord.NET (logo) LevenbergMarquardtLearning Properties

The LevenbergMarquardtLearning type exposes the following members.

Properties
  NameDescription
Public propertyAdjustment
Learning rate adjustment. Default value is 10.
Public propertyAlpha
Gets or sets the importance of the squared sum of network weights in the cost function. Used by the regularization.
Public propertyBeta
Gets or sets the importance of the squared sum of network errors in the cost function. Used by the regularization.
Public propertyBlocks
Gets or sets the number of blocks to divide the Jacobian matrix in the Hessian calculation to preserve memory. Default is 1.
Public propertyEffectiveParameters
Gets the number of effective parameters being used by the network as determined by the Bayesian regularization.
Public propertyGradient
Gets the gradient vector computed in the last iteration.
Public propertyHessian
Gets the approximate Hessian matrix of second derivatives generated in the last algorithm iteration. The Hessian is stored in the upper triangular part of this matrix. See remarks for details.
Public propertyJacobian
Gets the Jacobian matrix created in the last iteration.
Public propertyLearningRate
Levenberg's damping factor (lambda). This value must be positive. Default is 0.1.
Public propertyNumberOfParameters
Gets the total number of parameters in the network being trained.
Public propertyParallelOptions
Gets or sets the parallelization options for this algorithm.
Public propertyUseRegularization
Gets or sets whether to use Bayesian Regularization.
Top
See Also