Mbernoulli
Yes
Yes
No
Multinomial
Yes
Yes
No
Mentropy
Yes
Yes
No
RANDF=number
Specifies the degrees of freedom parameter for random numbers. See the following
Randomization Options and Default Parameters table.
RANDIST=name
Specifies the type of distribution to be used for random initial weights and perturbations. The
distributions and default parameter values are as follows:
Randomization Options and Default Parameters
RANDIST
RANLOC
RANSCALE
DF
NORMAL
mean=0
std=1
-
UNIFORM
mean=0
halfrange=1
-
CAUCHY
median=0
scale=1
-
CHIINV
-
scale=1
df=1
Default:
NORMAL
RANDOM=integer
Specifies the random number seed.
Default:
0
RANLOC=number
Specifies the location parameter for random numbers. See the above Randomization Options and
Default Parameters table.
Specifies the scale parameter for random numbers. See the above Randomization Options and
Default Parameters table.
Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
The NEURAL Procedure
NLOPTIONS Statement
Identifies the nonlinear optimization options to set.
Category Option Statement - does not directly affect the network, but sets options for use in
subsequent action statements. The options persist until reset at a later stage in the
processing.
NLOPTIONS <nonlinear-options>;
Nonlinear Options
ABSCONV= number
Specifies an absolute function convergence criterion. ABSCONV= is a function of the
log-likelihood for the intercept-only model.
Default:
The default value is the negative square root of the largest double precision
value.
Range:___number'>Range:
number > 0
ABSFCONV= number
Specifies an absolute function convergence criterion.
Default:
0
Range:
number > 0
ABSGCONV= number
Specifies the absolute gradient convergence criterion.
Default:
1E-5
Range:
number > 0
ABSXCONV= number
Specifies the absolute parameter convergence criterion.
Default:
0
Range:
number > 0
DAMPSTEP= number
Specifies that the initial step size value for each line search used by the QUANEW, CONGRA, or
NEWRAP optimization technique cannot be larger than the product of number and the step size
value used in the former iteration.
Default:
2
Range:
number > 0
DIAHES
Forces the optimization algorithm (TRUREG, NEWRAP, or NRRIDG) to take advantage of the
diagonality.
FCONV= number
Specifies a function convergence criterion.
Default:
1E-4
Range:
number > 0
FSIZE= number
Specifies the parameter of the relative function and relative gradient termination criteria.
Default:
Not applicable.
Range:
number 0
GCONV= number
Specifies the relative gradient convergence criterion.
Default:
1E-8
Range:
number > 0
HESCAL= 0 | 1 | 2 |3
Specifies the scaling version of the Hessian or cross-product Jacobian matrix used in NRRIDG,
TRUREG, LEVMAR, NEWRAP, or DBLDOG optimization.
Default:
1 - for LEVMAR minimization technique
0 - for all others
INHESSIAN= number
Specifies how to define the initial estimate of the approximate Hessian for the quasi-Newton
techniques QUANEW and DBLDOG.
Default:
The default is to use a Hessian based on the initial weights as the initial
estimate of the approximate Hessian. When r=0, the initial estimate of the
approximate Hessian is computed from the magnitude of the initial gradient.
Range:
number > 0
INSTEP= number
Specifies the initial radius of the trust region used in the TRUREG, DBLDOG, and LEVMAR
algorithms.
Default:
1
Range:
number > 0
LCEPS | LCEPSILON= number
Specifies the range for active constraints.
Range:
number > 0
LCSINGULAR= number
Specifies the tolerance for dependent constraints
Range:
number > 0
LINESEARCH= number
Specifies the line-search method for the CONGRA, QUANEW, and NEWRAP optimization
techniques.
Default:
2
Range:
1 number 8
LSPRECISION= number
Specifies the degree of accuracy that should be obtained by the second and third line-search
algorithms.