scoreopt.control: Tuning parameters for score optimisation by Stochastic Gradient Descent
Description
Function to set tuning parameters for stochastic gradient descent used to
find a reconciliation matrix that optimises total score. The defaults are
those of adam;textualProbReco and more details on the tuning
parameters can be found therein.
Usage
scoreopt.control(
eta = 0.001,
beta1 = 0.9,
beta2 = 0.999,
maxIter = 500,
tol = 1e-04,
epsilon = 1e-08
)
Arguments
eta
Learning rate. Deafult is 0.001
beta1
Forgetting rate for mean. Default is 0.9.
beta2
Forgetting rate for variance. Default is 0.999.
maxIter
Maximum number of iterations. Default is 500
tol
Tolerance for stopping criterion. Algorithm stops when the change in all parameter values is less than this amount. Default is 0.0001.
epsilon
Small constant added to denominator of step size. Default is 1e-8
References
See Also
Other ProbReco functions:
inscoreopt(),
scoreopt(),
total_score()