CODEWITHSUNDEEP
machine-learningneural-networkclassificationweka
Leo
Leo

Reputation: 496

why is there threshold in multilayer perceptron in weka

Hi I have trained multilayer perceptron on iris data set in weka tool. It gives me following model as a result.

    === Run information ===

    Scheme:weka.classifiers.functions.MultilayerPerceptron -L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a -G -R
    Relation:     iris
    Instances:    150
    Attributes:   5
                  sepallength
                  sepalwidth
                  petallength
                  petalwidth
                  class
    Test mode:split 66.0% train, remainder test

    === Classifier model (full training set) ===

    Sigmoid Node 0
        Inputs    Weights
        Threshold    -3.5015971588434014
        Node 3    -1.0058110853859945
        Node 4    9.07503844669134
        Node 5    -4.107780453339234
    Sigmoid Node 1
        Inputs    Weights
        Threshold    1.0692845992273177
        Node 3    3.8988736877894024
        Node 4    -9.768910360340264
        Node 5    -8.599134493151348
    Sigmoid Node 2
        Inputs    Weights
        Threshold    -1.007176238343649
        Node 3    -4.2184061338270356
        Node 4    -3.626059686321118
        Node 5    8.805122981737854
    Sigmoid Node 3
        Inputs    Weights
        Threshold    3.382485556685675
        Attrib sepallength    0.9099827458022276
        Attrib sepalwidth    1.5675138827531276
        Attrib petallength    -5.037338107319895
        Attrib petalwidth    -4.915469682506087
    Sigmoid Node 4
        Inputs    Weights
        Threshold    -3.330573592291832
        Attrib sepallength    -1.1116750023770083
        Attrib sepalwidth    3.125009686667653
        Attrib petallength    -4.133137022912305
        Attrib petalwidth    -4.079589727871456
    Sigmoid Node 5
        Inputs    Weights
        Threshold    -7.496091023618089
        Attrib sepallength    -1.2158878822058787
        Attrib sepalwidth    -3.5332821317534897
        Attrib petallength    8.401834252274096
        Attrib petalwidth    9.460215580472827
    Class Iris-setosa
        Input
        Node 0
    Class Iris-versicolor
        Input
        Node 1
    Class Iris-virginica
        Input
        Node 2


    Time taken to build model: 34.13 seconds

I'm new to weka I dont understand how are nodes numbered in this? and why is there need of threshold when we are using sigmoid. Can there be multiple attributes in output?

Upvotes: 1

Views: 2008

Answers (1)

lejlot
lejlot

Reputation: 66815

There are 3 output nodes (0, 1, 2) and 3 hidden units (3, 4, 5). You can differentiate by looking at what are they connected to, for example 

    Sigmoid Node 3
    Inputs    Weights
    Threshold    3.382485556685675
    Attrib sepallength    0.9099827458022276
    Attrib sepalwidth    1.5675138827531276
    Attrib petallength    -5.037338107319895
    Attrib petalwidth    -4.915469682506087

is clearly a hidden node as it is connected to input attributes. Thus node connected to this one is in next layer (0, 1, 2).

In general WEKA numebrs your nodes from the output layer to the input layer, thus you first get outptu nodes, then the ones connected to them, then previous layer, previous... and finally first hidden layer.

Why is there threshold? Because sigmoid is defined as

sigmoid(w,x,b) = 1/(1+exp(-(<w,x>-b)))

and b is the threshold. Without it, each node would answer the exact same output for x=0 no matter what are the weights.

Upvotes: 2

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