LevenbergMarquardtLearning Properties

Learning rate adjustment. Default value is 10.

Gets or sets the importance of the squared sum of network weights in the cost function. Used by the regularization.

Gets or sets the importance of the squared sum of network errors in the cost function. Used by the regularization.

Gets or sets the number of blocks to divide the Jacobian matrix in the Hessian calculation to preserve memory. Default is 1.

Gets the number of effective parameters being used by the network as determined by the Bayesian regularization.

Gets the gradient vector computed in the last iteration.

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.

Gets the Jacobian matrix created in the last iteration.

Levenberg's damping factor (lambda). This value must be positive. Default is 0.1.

Gets the total number of parameters in the network being trained.

Gets or sets the parallelization options for this algorithm.

Gets or sets whether to use Bayesian Regularization.