Package tv.floe.metronome.classification.neuralnetworks.core.neurons

Examples of tv.floe.metronome.classification.neuralnetworks.core.neurons.Neuron

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  @Test
  public void testCalculate() throws Exception {
   
   
    Neuron input_layer_neuron_0 = new InputNeuron();
    Neuron input_layer_neuron_1 = new InputNeuron();
    Neuron input_layer_neuron_2 = new InputNeuron();
   
   
    Neuron middle_layer_neuron_0 = new Neuron();
    Neuron middle_layer_neuron_1 = new Neuron();
    Neuron middle_layer_neuron_2 = new Neuron();
   

    middle_layer_neuron_0.addInConnection(input_layer_neuron_0, 1.0);
   
    middle_layer_neuron_1.addInConnection(input_layer_neuron_0, 1.0);
    middle_layer_neuron_1.addInConnection(input_layer_neuron_1, 1.0);
   
    middle_layer_neuron_2.addInConnection(input_layer_neuron_0, 1.0);
    middle_layer_neuron_2.addInConnection(input_layer_neuron_1, 1.0);
    middle_layer_neuron_2.addInConnection(input_layer_neuron_2, 1.0);
   
   
   
   
    input_layer_neuron_0.setInput(0.2d);
    input_layer_neuron_1.setInput(0.2d);
   
    input_layer_neuron_2.setInput(0.3d);
   
   
/*   
   
    assertEquals( 0.2f, input_layer_neuron_0.getNetInput(), 0.0001f );
   
    input_layer_neuron_0.calcOutput();
    double n0_out = input_layer_neuron_0.getOutput();
   
    assertEquals(0.2f, n0_out, 0.000001f);
*/
    // ------ test: Middle Layer > Neuron 1 ----------
   
    input_layer_neuron_0.calcOutput();
    input_layer_neuron_1.calcOutput();
   
//    System.out.println("\n\n\n> CalcOutput --------");
    middle_layer_neuron_1.calcOutput();
    double n1_1_out = middle_layer_neuron_1.getOutput();
   
//    System.out.println("> CalcOutput --------\n\n");
//    assertEquals(0.4f, n1_1_out, 0.000001f);
   
    System.out.println("out: " + n1_1_out );

    // ------ test: Middle Layer > Neuron 2 ----------
   
    input_layer_neuron_2.calcOutput();

    middle_layer_neuron_2.calcOutput();
    double n2_1_out = middle_layer_neuron_2.getOutput();
   
//    System.out.println("> CalcOutput --------\n\n");
//    assertEquals(0.7d, n2_1_out, 0.000001f);
   
    System.out.println("out: " + n2_1_out );
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  @Test
  public void testCreateLayer() throws Exception {
   
    Layer l0 = new Layer(1);
   
    l0.addNeuron(new Neuron());
    l0.addNeuron(new Neuron());
    l0.addNeuron(new Neuron());
   
    assertEquals(3, l0.getNeuronsCount() );
   
  }
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    c.parse(null);
   
    // adds 2 input neurons
    Layer input_layer = Layer.createLayer(c, 0);
   
    Neuron n1 = new InputNeuron();
   
    input_layer.addNeuron(0, n1);
   
    assertEquals(3, input_layer.getNeuronsCount());
   
    Neuron n2 = new Neuron();
   
    try {
   
      input_layer.addNeuron(0, n2);
     
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  }
 
  @Test
  public void testConstructors() {
   
    Neuron n = new Neuron();
   
    Neuron n2 = new InputNeuron();
   
  }
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  }
 
  @Test
  public void testRemoveAllConnections() throws Exception {
   
    Neuron l_input_layer_neuron_0 = new InputNeuron();
    Neuron l_input_layer_neuron_1 = new InputNeuron();
    Neuron l_input_layer_neuron_2 = new InputNeuron();
   
    Neuron l_middle_layer_neuron_0 = new Neuron();
    Neuron l_middle_layer_neuron_1 = new Neuron();
    Neuron l_middle_layer_neuron_2 = new Neuron();
   
   
   
    l_middle_layer_neuron_0.addInConnection(l_input_layer_neuron_0, 1.0);
    l_middle_layer_neuron_0.addInConnection(l_input_layer_neuron_1, 1.0);

    l_middle_layer_neuron_1.addInConnection(l_input_layer_neuron_0, 1.0);
    l_middle_layer_neuron_1.addInConnection(l_input_layer_neuron_1, 1.0);
   
    l_middle_layer_neuron_2.addInConnection(l_input_layer_neuron_0, 1.0);
    l_middle_layer_neuron_2.addInConnection(l_input_layer_neuron_1, 1.0);
    l_middle_layer_neuron_2.addInConnection(l_input_layer_neuron_2, 1.0);
   
    assertEquals( 2, l_middle_layer_neuron_0.getInConnections().size() );
   
    // INPUT LAYER
    l_input_layer_neuron_0.removeAllConnections();
   
    assertEquals( 1, l_middle_layer_neuron_0.getInConnections().size() );
   
    assertEquals( 0, l_input_layer_neuron_0.getOutConnections().size() );
   
    System.out.println("in conns: " + l_middle_layer_neuron_2.getInConnections().size() );
   
    l_middle_layer_neuron_2.removeInputConnectionFrom(l_input_layer_neuron_2);
   
    assertEquals( 1, l_middle_layer_neuron_2.getInConnections().size() );
   
  }
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    String out = "";
   
    if (this.adagradLearningOn) {
     
      Neuron neuron = this.nn.getLayerByIndex(1).getNeurons().get(1);
       

       
      Connection c = neuron.getInConnections().get(0);
               
      AdagradLearningRate alr = (AdagradLearningRate)c.getWeight().trainingMetaData.get("adagrad");
                //lrTemp = alr.compute();
       
      out += "[Ada: " + alr.compute() +" ]";

      c = neuron.getInConnections().get(1);
       
      alr = (AdagradLearningRate)c.getWeight().trainingMetaData.get("adagrad");

      out += "[Ada: " + alr.compute() +" ]";
     
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    //System.out.println("Layer " + layerIndex + ": neurons: " + neuronCount );
   
    for (int x = 0; x < neuronCount; x++ ) {
     
      // create neuron
      Neuron n = Neuron.createNeuron(c, layerIndex);
      // add neuron
      layer.addNeuron(n);
     
    }
   
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Related Classes of tv.floe.metronome.classification.neuralnetworks.core.neurons.Neuron

  • tv.floe.metronome.classification.neuralnetworks.activation.ActivationFunction
  • tv.floe.metronome.classification.neuralnetworks.activation.Sigmoid
  • tv.floe.metronome.classification.neuralnetworks.activation.Step
  • tv.floe.metronome.classification.neuralnetworks.core.Connection
  • tv.floe.metronome.classification.neuralnetworks.core.Layer
  • tv.floe.metronome.classification.neuralnetworks.core.TestLayer
  • tv.floe.metronome.classification.neuralnetworks.core.TestNeuron
  • tv.floe.metronome.classification.neuralnetworks.input.TestWeightedSum
  • tv.floe.metronome.classification.neuralnetworks.input.WeightedSum
  • tv.floe.metronome.classification.neuralnetworks.learning.BackPropogationLearningAlgorithm
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