Package tv.floe.metronome.classification.neuralnetworks.conf

Examples of tv.floe.metronome.classification.neuralnetworks.conf.Config

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    Vector v3_out = new DenseVector(1);
    v3_out.set(0, 0);
    //xor_recs.add(v3);

   
    Config c = new Config();
    c.parse(null); // default layer: 2-3-2
        c.setConfValue("inputFunction", WeightedSum.class);
    c.setConfValue("transferFunction", Tanh.class);
    c.setConfValue("neuronType", Neuron.class);
    c.setConfValue("networkType", NeuralNetwork.NetworkType.MULTI_LAYER_PERCEPTRON);
    c.setConfValue("layerNeuronCounts", "2,3,1" );
    c.parse(null);
   
    MultiLayerPerceptronNetwork mlp_network = new MultiLayerPerceptronNetwork();
   
   
   
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    Vector vec = new DenseVector(2);
    vec.set(0, 1);
    vec.set(1,  2);
   
    Config c = new Config();
    c.parse(null); // default layer: 2-3-2
   
    MultiLayerPerceptronNetwork mlp_network = new MultiLayerPerceptronNetwork();
   
    mlp_network.buildFromConf(c);
   
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  }
 
  @Test
  public void testCreateLayerViaConfig() throws Exception {
   
    Config c = new Config();
    c.parse(null);
   
    Layer input_layer = Layer.createLayer(c, 0);
   
    Layer middle_layer = Layer.createLayer(c, 1);
   
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  @Test
  public void testAddNeurons() throws Exception {
   
    //System.out.println("> testAddNeurons() ");
   
    Config c = new Config();
   
    c.parse(null);
   
    // adds 2 input neurons
    Layer input_layer = Layer.createLayer(c, 0);
   
    Neuron n1 = new InputNeuron();
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    Vector v3_out = new DenseVector(1);
    v3_out.set(0, 0);
    //xor_recs.add(v3);

   
    Config c = new Config();
    c.parse(null); // default layer: 2-3-2
        c.setConfValue("inputFunction", WeightedSum.class);
    c.setConfValue("transferFunction", Tanh.class);
    c.setConfValue("neuronType", Neuron.class);
    c.setConfValue("networkType", NeuralNetwork.NetworkType.MULTI_LAYER_PERCEPTRON);
    c.setConfValue("layerNeuronCounts", "2,3,1" );
   
   
    MultiLayerPerceptronNetwork mlp_network = new MultiLayerPerceptronNetwork();
   
   
   

//    mlp_network.setInputVector(vec);
   
    int[] neurons = { 2, 3, 1 };
    c.setLayerNeuronCounts( neurons );
   
    mlp_network.buildFromConf(c);
       
   
    for ( int x = 0; x < 40000; x++ ) {
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    Vector v3_out = new DenseVector(1);
    v3_out.set(0, 0);
    //xor_recs.add(v3);

   
    Config c = new Config();
    //c.parse(null); // default layer: 2-3-2
        c.setConfValue("inputFunction", WeightedSum.class);
    c.setConfValue("transferFunction", Tanh.class);
    c.setConfValue("neuronType", Neuron.class);
    c.setConfValue("networkType", NeuralNetwork.NetworkType.MULTI_LAYER_PERCEPTRON);
    c.setConfValue("layerNeuronCounts", "2,3,1" );
    c.parse(null);
   
    MultiLayerPerceptronNetwork mlp_network = new MultiLayerPerceptronNetwork();
   
   
   
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    Vector v3_out = new DenseVector(1);
    v3_out.set(0, 0);
    //xor_recs.add(v3);

   
    Config c = new Config();
    //c.parse(null); // default layer: 2-3-2
        c.setConfValue("inputFunction", WeightedSum.class);
    c.setConfValue("transferFunction", Tanh.class);
    c.setConfValue("neuronType", Neuron.class);
    c.setConfValue("networkType", NeuralNetwork.NetworkType.MULTI_LAYER_PERCEPTRON);
    c.setConfValue("layerNeuronCounts", "2,3,1" );
    c.parse(null);
   
    MultiLayerPerceptronNetwork mlp_network = new MultiLayerPerceptronNetwork();
   
   
   
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  @Test
  public void testBaseJavaObjectSerialization() {

    NetworkWeightsUpdateable nwu = new NetworkWeightsUpdateable();
   
    Config c = new Config();
    c.parse(null); // default layer: 2-3-2
        c.setConfValue("inputFunction", WeightedSum.class);
    c.setConfValue("transferFunction", Tanh.class);
    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);
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    Vector vec = new DenseVector(2);
    vec.set(0, 1);
    vec.set(1,  2);
   
    Config c = new Config();
    c.parse(null);
   
    NeuralNetwork n1 = new NeuralNetwork();
   
    Layer l0 = Layer.createLayer(c, 0);
    Layer l1 = Layer.createLayer(c, 1);
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  @Test
  public void testMasterNodeObjectSerialization() {

    NetworkWeightsUpdateable nwu = new NetworkWeightsUpdateable();
   
    Config c = new Config();
    c.parse(null); // default layer: 2-3-2
        c.setConfValue("inputFunction", WeightedSum.class);
    c.setConfValue("transferFunction", Tanh.class);
    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);
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Related Classes of tv.floe.metronome.classification.neuralnetworks.conf.Config

  • tv.floe.metronome.classification.neuralnetworks.core.TestLayer
  • tv.floe.metronome.classification.neuralnetworks.core.TestLayerToVectorSerde
  • tv.floe.metronome.classification.neuralnetworks.core.TestNeuralNetwork
  • tv.floe.metronome.classification.neuralnetworks.iterativereduce.TestNeuralNetworkUtil
  • tv.floe.metronome.classification.neuralnetworks.iterativereduce.WorkerNode
  • tv.floe.metronome.classification.neuralnetworks.learning.TestLeastMeanSquaresLearningAlgorithm
  • tv.floe.metronome.classification.neuralnetworks.network.TestLearnXORFunctionWithMLPN
  • tv.floe.metronome.classification.neuralnetworks.network.TestMultiLayerPerceptronNetwork
  • tv.floe.metronome.classification.neuralnetworks.serde.TestNetworkSerdeMechanics
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