Default:
NORANDSCALE, which sets each scale estimate to the standard deviation
of the corresponding target variable.
Note: NORANDSCALE overrides whatever is specified in the
RANOPTIONS statement.
Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
The NEURAL Procedure
INPUT Statement
The INPUT statement allows you to group together input variables having common levels and
standardizations. You can specify as many INPUT statements as you want up to the limits imposed
by computer memory, time, and disk space. The input layers can be connected to hidden or output
layers using CONNECT statements.
Category Action Statement - affects the network or the data sets. Options set in an action statement
affect only that statement.
INPUT variable-list /
ID=
name
<
LEVEL=
value>
<
STD=
method>;
Required Arguments
variable-list
Specifies the input variables.
ID=name
Specifies the identifier for the layer.
Options
LEVEL=value
Specifies the measurement level, where value can be:
NOMINAL|NOM
Nominal
ORDINAL|ORD
Ordinal
INTERVAL|INT
Interval
Default:
Interval (for variables specified by a VAR statement in the DMDB
procedure) or nominal (for variables specified by a CLASS statement in the
DMDB procedure).
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. This standardization is not
recommended for input variables.
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:
STD
Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
The NEURAL Procedure
NETOPTIONS Statement
Identifies the network 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.
Alias: NETOPTS |NETOPT
NETOPTIONS network-option(s);
Network Options
DECAY=number
Specifies the weight decay.
Range:
number 0
Default:
For the QPROP optimization technique: .0001; for all others: 0
INVALIDTARGET=action
Specifies the action taken during training if an out-of-range target value is found, where action
can be:
OMITCASE | OMIT If INVALIDTARGET = OMITCASE is specified, and an invalid target
value is found in the training data set, a warning is given, the observation
is not used, but the training will continue.
STOP
If INVALIDTARGET = STOP is specified, an error is issued, and
training is terminated.
Example:
If ERROR = GAMMA is specified in a target statement, the target values
should be positive. If a zero or negative value is found for the variable(s)
listed in the target statement, the observation containing that value cannot be
used for training. The training either continue with the remaining valid
observations or stops depending on the INVALIDTARGET= specification.
Note, however, if the {\it mean}, over the training data set, of the target
variable(s) is zero or negative, an error is issued and the training is stopped,
regardless of the INVALIDTARGET= specification.
Default:
The default is INVALIDTARGET = STOP. INVALIDETARGET can be
abbreviated as INVALIDTARG or INVTARG.
OBJECT=objective-function
Specifies the objective function where objective-function can be one of the following:
DEV
Requests deviance (for ERROR=NORMAL, this is least squares).
LIKE
Requests negative log-likelihood.
MEST
Requests M estimation.
Default:
Depends on the error functions that the network uses. To determine the
default, examine the table below named Errors by Objective Functions.
Scan the table from left to right. The default is the first column that
contains a "yes" in every row corresponding to an error function used
in the network. If no such column exists in the table, an error message
is issued and the network cannot be trained.
Errors by Objective Functions
ERRORS
DEV
LIKE
MEST
Normal
Yes
Yes
No
Cauchy
No
Yes
No
Logistic
No
Yes
No
Huber
No
No
Yes
Biweight
No
No
Yes
Wave
No
No
Yes
Gamma
Yes
Yes
No
Poisson
Yes
Yes
No
Bernoulli
Yes
Yes
No
Binomial
Yes
Yes
No
Entropy
Yes
Yes
No