The arboretum procedure



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INITIAL INEST=SAS-data-set

OUTEST=SAS-data-set

<BIADJUST=adjustment-value>

<INFAN=number>

<RANDBIAS|NORANDBIAS >

<RANDOUT|NORANDOUT >

<RANDOM=integer>

<RANDSCALE | NORANDSCALE>;

INPUT variable-list /

ID=name

<LEVEL=value>

<STD=method>;

NETOPTIONS network-option(s);

NLOPTIONS <nonlinear-options>;

PERTURB weight-list /

OUTEST=SAS-data-set

DF=number

<RANDIST=name>

<RANDOM=integer>

<RANLOC=number>

<RANSCALE=number>;

PRELIM integer

INEST=SAS-data-set

OUTEST=SAS-data-set

<ACCELERATE=number>

<DECELERATE=number>

<LEARN=number>

<MAXLEARN=number>

<MAX | MAXMOMENTUM=number>

<MINLEARN=number >

<MOM | MOMENTUM=number>

<PREITER=integer>

<PRETECH=name>

<PRETIME=number>

<RANDBIAS|NORANDBIAS >

<RANDOUT|NORANDOUT>

<RANDOM=integer>;

QUIT;


RANOPTIONSconnection-list /

<RANDIST=name>

<RANDOM=integer>

<RANLOC=number>

<RANSCALE=number>;

SAVE OUTEST=SAS-data-set

NETWORK=screen-specification;

SCORE DATA=SAS-data-set

OUT=SAS-data-set

OUTFIT=SAS-data-set

<DUMMIES|NODUMMIES>

<ROLE=role-option>;

SET weight-list number;

SHOW weights;

TARGET variable-list /

<ACT=keyword>

<BIAS|NOBIAS>

<COMBINE=keyword>

<ERROR=keyword>

<ID=name>

<LEVEL=value>

<MESTA=number>

<MESTCON=number>

<SIGMA=number>

<STD=method>

<WEIGHT=number>;

THAW weight-list;


TRAIN OUT=SAS-data-set

OUTEST=SAS-data-set

OUTFIT= SAS-data-set

<ACCEL|ACCELERATE=number>

<DECEL|DECELERATE=number>

<DECAY=number>

<DUMMIES|NODUMMIES>

<ESTITER=i>

<LEARN=number>

<MAX|MAXMOMENTUM=number>

<MAXITER=integer>

<MAXLEARN=number>

<MAXTIME =number>

<MINLEARN=number>

<MOM|MOMENTUM=number >

<TECHNIQUE=name>;

USE SAS-data-set;

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




The NEURAL Procedure

PROC NEURAL Statement

Invokes the NEURAL procedure.

PROC NEURAL <option-list>;

Required Arguments

DATA=SAS-data-set

Specifies the DMDB-encoded input SAS data set containing the training data.



DMDBCAT=SAS-catalog

Specifies the DMDB catalog.



Options

GRAPH

Plots the objective function, validation error, and test error during training.



NETWORK=screen-specification

Constructs a network according to a description that was saved by using a SAVE statement during

a previous execution of the NEURAL procedure. screen-specification is the catalog entry that was

specified in the SAVE statement.



Default:

None


RANDOM=integer

Specifies the random number seed used in network weight initialization.



Default:

12345


CAUTION:

The weights and predicted outputs from the network cannot be reproduced when a 0

or negative RANDOM= value is specified. When a 0 or negative value is specified, the

system clock is used to generate the seed. The actual value of this seed will be

unavailable, and you lose control over the initialization of weights. Different

initializations will result in different final weights and predicted outputs for repeated

runs of the same set of NEURAL statements and same input data sets.  

 

STOPFILE='file path name'

This option enables you to stop the NEURAL training when you are running a large job. Before



you invoke the NEURAL procedure, specify the file path name in the STOPFILE option. For

example, STOPFILE= "c:\mydir\haltneural". Initially, this file should not exist.The NEURAL

procedure checks for the existence of this file between iterations in the training process. When you

want to stop the job, create the specified file, and the NEURAL procedure will halt the training at

the current iteration. The file does not have to contain any contents.

TESTDATA=SAS-data-set

Specifies a data set used to compute the test average error during training. At selected iterations

(controlled by ESTITER=), each observation in the TESTDATA= data set is read in, scored using

the current network weight values, and the error computed. The average test error is then output to

the OUTEST= data set.

Note:   This requires the TESTDATA= data set to contain the inputs and target variables.  

VALIDATA=SAS-data-set

Specifies a data set used to compute the validation average error during training. At selected

iterations (controlled by ESTITER=), each observation in the VALIDATA= data set is read in,

scored using the current network weight values, and the error computed. The average validation

error is then output to the OUTEST= data set.

Note:   This requires the VALIDATA= data set to contain the inputs and target variables.  

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





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