Package tv.floe.metronome.classification.neuralnetworks.networks

Examples of tv.floe.metronome.classification.neuralnetworks.networks.MultiLayerPerceptronNetwork

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    c.setConfValue("neuronType", Neuron.class);
    c.setConfValue("networkType", NeuralNetwork.NetworkType.MULTI_LAYER_PERCEPTRON);
    c.setConfValue("layerNeuronCounts", "2,3,1" );
    c.parse(null);
   
    NeuralNetwork nn = new MultiLayerPerceptronNetwork();
   
    try {
      nn.buildFromConf(c);
    } catch (Exception e) {
      // TODO Auto-generated catch block
      e.printStackTrace();
    }
    /*
 
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  }
 
 
  public static MultiLayerPerceptronNetwork loadModelFromDisk(String model_path) {
   
    MultiLayerPerceptronNetwork nnet = null;
   
    try {

      Path out = new Path(model_path.trim());
      FileSystem fs =
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  }
 
  public static void scoreNeuralNetworkModel(String modelFileLocation, String schema, String records_file) throws Exception {
   
    // load model
    MultiLayerPerceptronNetwork mlp = loadModelFromDisk(modelFileLocation);
   
    //Utils.PrintNeuralNetwork(  mlp );
   
    //System.out.println("Layers: " + mlp.getLayersCount());
   
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    c.setConfValue("neuronType", Neuron.class);
    c.setConfValue("networkType", NeuralNetwork.NetworkType.MULTI_LAYER_PERCEPTRON);
    c.setConfValue("layerNeuronCounts", this.layerNeuronCounts );
    c.parse(null);
   
    this.nn = new MultiLayerPerceptronNetwork();
    try {
      this.nn.buildFromConf(c);
    } catch (Exception e) {
      // TODO Auto-generated catch block
      e.printStackTrace();
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    if (null != c.getConfValue("networkType")) {
     
      if (c.getConfValue("networkType").equals(NetworkType.MULTI_LAYER_PERCEPTRON)) {
       
        MultiLayerPerceptronNetwork mlp_network = new MultiLayerPerceptronNetwork();
               
        mlp_network.buildFromConf(c);   
       
        return mlp_network;
       
      } else {
       
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    } else {
     
      if (null == this.master_nn) {
       
        System.out.println("Building base master MLP network");
        this.master_nn = new MultiLayerPerceptronNetwork();
            try {
          this.master_nn.buildFromConf(first.networkUpdate.network.getConfig());
        } catch (Exception e) {
          // TODO Auto-generated catch block
          e.printStackTrace();
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    int[] neurons = { 4, 16, 3 };
    c.setLayerNeuronCounts( neurons );
   
    // layers: 4, 16, 3
        MultiLayerPerceptronNetwork neuralNet = new MultiLayerPerceptronNetwork();
        neuralNet.buildFromConf(c);
   
       
       
        // now test the lms_algo
       
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Related Classes of tv.floe.metronome.classification.neuralnetworks.networks.MultiLayerPerceptronNetwork

  • tv.floe.metronome.classification.neuralnetworks.core.Layer
  • tv.floe.metronome.classification.neuralnetworks.core.neurons.BiasNeuron
  • tv.floe.metronome.classification.neuralnetworks.core.TestLayerToVectorSerde
  • tv.floe.metronome.classification.neuralnetworks.core.TestNeuralNetwork
  • tv.floe.metronome.classification.neuralnetworks.eval.ModelEvaluator
  • tv.floe.metronome.classification.neuralnetworks.iterativereduce.abalone.AbaloneModelTestingTools
  • tv.floe.metronome.classification.neuralnetworks.iterativereduce.iris.IrisDatasetUtils
  • tv.floe.metronome.classification.neuralnetworks.iterativereduce.MasterNode
  • tv.floe.metronome.classification.neuralnetworks.iterativereduce.NeuralNetworkUtil
  • tv.floe.metronome.classification.neuralnetworks.iterativereduce.TestNeuralNetworkUtil
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