Iteration determine in Self-Organizing Map to Cluster Data

Question

ahmad karim 2017-6-29

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/346705-iteration-determine-in-self-organizing-map-to-cluster-data

评论： Qichen Deng 2022-4-6

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in the attach i share an example of Self-Organizing Map that provided by MATLAB my problem is that the iteration number is fixed in 200. change it with other value like 150, 100, 500..

Regards

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Qichen Deng 2022-4-6

Could it be net.trainParam.epochs?

"epochs is maximum number of training iterations before training is stopped."

net.trainParam.epochs = some number

will change the number of training iterations

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Answer 1

Greg Heath 2017-6-29

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https://ww2.mathworks.cn/matlabcentral/answers/346705-iteration-determine-in-self-organizing-map-to-cluster-data#answer_272332

在 MATLAB Online 中打开

Type, without ending semicolon

net = net

then all properties will be displayed.

Chose the one that corresponds to iteration number.

Then change it.

Something along the lines of

net.property.iteration number = 100.

to change 200 to 100.

Hope this helps.

Thank you for formally accepting my answer

Greg

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Answer 2

ahmad karim 2017-6-29

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/346705-iteration-determine-in-self-organizing-map-to-cluster-data#answer_272354

在 MATLAB Online 中打开

Thanks alot sir, but when i type net=net. the informations that listed below are displayed bu there is not any information about iterartion. net =

    Neural Network
              name: 'Self-Organizing Map'
          userdata: (your custom info)
    dimensions:
         numInputs: 1
         numLayers: 1
        numOutputs: 1
    numInputDelays: 0
    numLayerDelays: 0
 numFeedbackDelays: 0
 numWeightElements: 4800
        sampleTime: 1
    connections:
       biasConnect: false
      inputConnect: true
      layerConnect: false
     outputConnect: true
    subobjects:
             input: Equivalent to inputs{1}
            output: Equivalent to outputs{1}
            inputs: {1x1 cell array of 1 input}
            layers: {1x1 cell array of 1 layer}
           outputs: {1x1 cell array of 1 output}
            biases: {1x1 cell array of 0 biases}
      inputWeights: {1x1 cell array of 1 weight}
      layerWeights: {1x1 cell array of 0 weights}
    functions:
          adaptFcn: 'adaptwb'
        adaptParam: (none)
          derivFcn: 'defaultderiv'
         divideFcn: (none)
       divideParam: (none)
        divideMode: 'sample'
           initFcn: 'initlay'
        performFcn: 'mse'
      performParam: .regularization, .normalization
          plotFcns: {'plotsomtop', plotsomnc, plotsomnd,
                    plotsomplanes, plotsomhits, plotsompos}
        plotParams: {1x6 cell array of 6 params}
          trainFcn: 'trainbu'
        trainParam: .showWindow, .showCommandLine, .show, .epochs,
                    .time
    weight and bias values:
                IW: {1x1 cell} containing 1 input weight matrix
                LW: {1x1 cell} containing 0 layer weight matrices
                 b: {1x1 cell} containing 0 bias vectors
    methods:
             adapt: Learn while in continuous use
         configure: Configure inputs & outputs
            gensim: Generate Simulink model
              init: Initialize weights & biases
           perform: Calculate performance
               sim: Evaluate network outputs given inputs
             train: Train network with examples
              view: View diagram
       unconfigure: Unconfigure inputs & outputs