The arboretum procedure



Yüklə 3.07 Mb.

səhifə98/148
tarix30.04.2018
ölçüsü3.07 Mb.
1   ...   94   95   96   97   98   99   100   101   ...   148

Default:

NOMINAL for character variables.

INTERVAL for numeric variables.

MESTA=number

Specifies the scale constant for M estimation.



Default:

Default value is computed from MESTCON to give consistent scale

estimates for normal noise.

MESTCON=number

Specifies the tuning constant for M estimation.



Default:

Huber: 1.5;

Biweight: 9;

Wave: 2.1*



SIGMA=number

Specifies the fixed value of the scale parameter.



Default:

By default, SIGMA is not used; but with OBJECT=LIKE, the scale

parameter is estimated.

STD=method

Specifies the standardization method, where method is

NONE|NO

Variables are not altered.



STD

Variables are rescaled to have a mean of 0 and a standard deviation of 1.

RANGE|RAN

Variables are rescaled to have a minimum of 0 and a range of 1.

MIDRANGE|MID

Variables are rescaled to have a midrange of 0 and a half-range of 1 (that is, a minimum of

-1 and a maximum of 1.

Default:

NO

Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.




The NEURAL Procedure

THAW Statement

Thaws frozen weights.

Category Action Statement - affects the network or the data sets. Options set in an action statement

affect only that statement.



THAW weight-list;

Required Argument

weight-list

List of weights to thaw.

Weight-list consists of 0 or more repetitions of:

wname --> wname-2 where:

wname

is the unit name, the layer ID, BIAS, or ALTITUDE



wname-2

is the unit name or the layer ID



Default:

All weights are thawed.

Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.



The NEURAL Procedure

TRAIN Statement

Trains the network.

Category Action Statement - affects the network or the data sets. Options set in an action statement

affect only that statement.



TRAIN OUT=SAS-data-set

OUTEST=SAS-data-set

OUTFIT=SAS-data-set

<ACCEL|ACCELERATE=number>

<DECEL|DECELERATE=number>

<DUMMIES | NODUMMIES>

<ESTITER=i>

<LEARN=number>

<MAX|MAXMOMENTUM=number>

<MAXITER=integer>

<MAXLEARN=number>

<MAXTIME =number>

<MINLEARN=number>

<MOM|MOMENTUM=number>

<TECHNIQUE=name>;

Options

ACCEL | ACCELERATE=number

Specifies the rate of increase of learning for the RPROP optimization technique.



Range:

number > 1

Default:

1.2


DECEL | DECELERATE=number

Specifies the rate of decrease of learning for the RPROP optimization technique.



Range:

0 < number < 1



Default:

0.5


DUMMIES | NODUMMIES


Specifies whether to write dummy variables to the OUT= data set.

Default:

NODUMMIES



ESTITER=i

i = 0

Writes only initial and final weights to the OUTEST=data set.



i > 0

Writes weights after every i iterations, as well as the final weights, to the OUTEST= data

set.

Default:

0

LEARN=number

Specifies the learning rate for BPROP or the initial learning rate for QPROP and RPROP.

Range:

number > 0

Default:

0.1


MAXITER=integer

Maximum number of iterations.



Default:

100 for TRUREG and LEVMAR

200 for QUANEW and DBLDOG

400 for CONGRA

1000 for BPROP, RPROP, and QPROP

MAXLEARN=number

Specifies the maximum learning rate for RPROP.



Range:

number > 0

Default:

1/MACSQRTEPS



MAXMOM | MAXMOMENTUM=number

Specifies the maximum momentum for BPROP.



Range:

number > 0

Default:

1.75


MAXTIME=number


Specifies the amount of time after which training stops.

Default:

7 days, that is, 604800 seconds



MINLEARN=number

Specifies the minimum learning rate for RPROP.



Range:

number > 0

Default:

MACSQRTEPS



MOM | MOMENTUM=number

Specifies the momentum for BPROP.



Range:

0  number < 1



Default:

For BPROP: 0.9

For RPROP: 0.1

OUT=SAS-data-set

Specifies the output data set that contains the outputs.



OUTEST=SAS-data-set

Specifies the output data set that contains the network weights.



OUTFIT=SAS-data-set

Specifies the output data set that contains the fit statistics.



TECHNIQUE=name

Specifies the optimization technique where name is one of the following:

TRUREG

Requests Trust region.



LEVMAR

Requests Levenberg-Marquardt.

DBLDOG

Requests Double dogleg.



QUANEW

Requests quasi-Newton.

CONGRA

Requests Conjugate gradient.



BPROP

Requests standard backprop (backpropagation), that is, a variation on an algorithm called






Dostları ilə paylaş:
1   ...   94   95   96   97   98   99   100   101   ...   148


Verilənlər bazası müəlliflik hüququ ilə müdafiə olunur ©genderi.org 2017
rəhbərliyinə müraciət

    Ana səhifə