Notice that the MLP network with 3 hidden units correctly classifies all cases in the training data set.
MLP with 3 Hidden Units
Fits Statistics for the Training Data Set
Train: Train: Train: Train: Root
Akaike's Average Maximum Final
Information Squared Absolute Prediction
Criterion. Error. Error. Error.
34.0000 3.7039E-14 .0000030981 .00000020177
Train: Train: Number
Misclassification of Wrong
Rate. Classifications.
0 0
PROC FREQ Misclassification Table for the Training Data
MLP with 3 Hidden Units
Misclassification Table
TABLE OF F_C BY I_C
F_C(From: C) I_C(Into: C)
Frequency |
Percent |
Row Pct |
Col Pct | 1| 2| 3| Total
| | | |
-------------+--------+--------+--------+
1 | 8 | 0 | 0 | 8
| 4.44 | 0.00 | 0.00 | 4.44
| 100.00 | 0.00 | 0.00 |
| 100.00 | 0.00 | 0.00 |
-------------+--------+--------+--------+
2 | 0 | 59 | 0 | 59
| 0.00 | 32.78 | 0.00 | 32.78
| 0.00 | 100.00 | 0.00 |
| 0.00 | 100.00 | 0.00 |
-------------+--------+--------+--------+
3 | 0 | 0 | 113 | 113
| 0.00 | 0.00 | 62.78 | 62.78
| 0.00 | 0.00 | 100.00 |
| 0.00 | 0.00 | 100.00 |
-------------+--------+--------+--------+
Total 8 59 113 180
4.44 32.78 62.78 100.00
PROC GPLOT Plot of the Classification Results
PROC GGONTOUR Plots of the Posterior Probabilities
The legend at the bottom of the chart identifies the target level.
Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
PROC GPLOT creates a scatter plot of the Rings training data.
proc gplot data=sampsio.dmdring;
plot y*x=c /haxis=axis1 vaxis=axis2;
symbol c=black i=none v=dot;
symbol2 c=red i=none v=square;
symbol3 c=green i=none v=triangle;
axis1 c=black width=2.5 order=(0 to 30 by 5);
axis2 c=black width=2.5 minor=none order=(0 to 20 by 2);
title 'Plot of the Rings Training Data';
run;
The PROC NEURAL statement invokes the procedure. The DATA= option identifies
the training data set that is used to fit the model. The DMDBCAT= option identifies
the DMDB catalog. The RANDOM= option specifies the random number seed.
proc neural data=sampsio.dmdring
dmdbcat=sampsio.dmdring
random=789;
The INPUT statement specifies an interval input layer. The LEVEL= option
specifies the measurement level. The ID= option specifies an identifier for
the interval input layer.
input x y / level=interval id=i;
The TARGET statement defines an output layer. The output layer computes
predicted values and compares those predicted values with the value of the
target variable. The ID= option specifies an identifier for the output layer.
The LEVEL= option specifies the target measurement level. By default, for
nominal targets the combination function is set to linear, the activation
function is set to mlogistic, and the error function is set to mbernoulli.
target c / id=o level=nominal;
The HIDDEN statement defines the number of hidden units that are used
to perform the internal computations. By default, the input units are connected
to each hidden unit and each hidden unit is connected to the output unit.
The ID= option specifies an identifier for the hidden unit.
hidden 3 / id=h;
The PRELIM statement causes the procedure to search for the best starting
weights for subsequent training. The integer value of 5 specifies to use
5 preliminary runs. The weights from the seed with the smallest objective
function among all runs is chosen. Preliminary training may help prevent the
network from converging in a local minima.
prelim 5;
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