Examples of aima.core.learning.neural.LayerSensitivity

aima.core.learning.neural.LayerSensitivity
@author Ravi Mohan

    Vector inputVector2 = layer1.getLastActivationValues();
    layer2.feedForward(inputVector2);


    Vector errorVector = new Vector(1);
    errorVector.setValue(0, 1.261);
    LayerSensitivity layer2Sensitivity = new LayerSensitivity(layer2);
    layer2Sensitivity.sensitivityMatrixFromErrorMatrix(errorVector);


    Matrix sensitivityMatrix = layer2Sensitivity.getSensitivityMatrix();
    Assert.assertEquals(-2.522, sensitivityMatrix.get(0, 0), 0.0001);
  }

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    biasVector1.setValue(0, -0.48);
    biasVector1.setValue(1, -0.13);


    Layer layer1 = new Layer(weightMatrix1, biasVector1,
        new LogSigActivationFunction());
    LayerSensitivity layer1Sensitivity = new LayerSensitivity(layer1);


    Vector inputVector1 = new Vector(1);
    inputVector1.setValue(0, 1);


    layer1.feedForward(inputVector1);


    Matrix weightMatrix2 = new Matrix(1, 2);
    weightMatrix2.set(0, 0, 0.09);
    weightMatrix2.set(0, 1, -0.17);


    Vector biasVector2 = new Vector(1);
    biasVector2.setValue(0, 0.48);


    Layer layer2 = new Layer(weightMatrix2, biasVector2,
        new PureLinearActivationFunction());
    Vector inputVector2 = layer1.getLastActivationValues();
    layer2.feedForward(inputVector2);


    Vector errorVector = new Vector(1);
    errorVector.setValue(0, 1.261);
    LayerSensitivity layer2Sensitivity = new LayerSensitivity(layer2);
    layer2Sensitivity.sensitivityMatrixFromErrorMatrix(errorVector);


    layer1Sensitivity
        .sensitivityMatrixFromSucceedingLayer(layer2Sensitivity);
    Matrix sensitivityMatrix = layer1Sensitivity.getSensitivityMatrix();

View Full Code Here

    biasVector1.setValue(0, -0.48);
    biasVector1.setValue(1, -0.13);


    Layer layer1 = new Layer(weightMatrix1, biasVector1,
        new LogSigActivationFunction());
    LayerSensitivity layer1Sensitivity = new LayerSensitivity(layer1);


    Vector inputVector1 = new Vector(1);
    inputVector1.setValue(0, 1);


    layer1.feedForward(inputVector1);


    Matrix weightMatrix2 = new Matrix(1, 2);
    weightMatrix2.set(0, 0, 0.09);
    weightMatrix2.set(0, 1, -0.17);


    Vector biasVector2 = new Vector(1);
    biasVector2.setValue(0, 0.48);


    Layer layer2 = new Layer(weightMatrix2, biasVector2,
        new PureLinearActivationFunction());
    Vector inputVector2 = layer1.getLastActivationValues();
    layer2.feedForward(inputVector2);


    Vector errorVector = new Vector(1);
    errorVector.setValue(0, 1.261);
    LayerSensitivity layer2Sensitivity = new LayerSensitivity(layer2);
    layer2Sensitivity.sensitivityMatrixFromErrorMatrix(errorVector);


    layer1Sensitivity
        .sensitivityMatrixFromSucceedingLayer(layer2Sensitivity);


    Matrix weightUpdateMatrix2 = BackPropLearning.calculateWeightUpdates(

View Full Code Here

    biasVector1.setValue(0, -0.48);
    biasVector1.setValue(1, -0.13);


    Layer layer1 = new Layer(weightMatrix1, biasVector1,
        new LogSigActivationFunction());
    LayerSensitivity layer1Sensitivity = new LayerSensitivity(layer1);


    Vector inputVector1 = new Vector(1);
    inputVector1.setValue(0, 1);


    layer1.feedForward(inputVector1);


    Matrix weightMatrix2 = new Matrix(1, 2);
    weightMatrix2.set(0, 0, 0.09);
    weightMatrix2.set(0, 1, -0.17);


    Vector biasVector2 = new Vector(1);
    biasVector2.setValue(0, 0.48);


    Layer layer2 = new Layer(weightMatrix2, biasVector2,
        new PureLinearActivationFunction());
    LayerSensitivity layer2Sensitivity = new LayerSensitivity(layer2);
    Vector inputVector2 = layer1.getLastActivationValues();
    layer2.feedForward(inputVector2);


    Vector errorVector = new Vector(1);
    errorVector.setValue(0, 1.261);
    layer2Sensitivity.sensitivityMatrixFromErrorMatrix(errorVector);


    layer1Sensitivity
        .sensitivityMatrixFromSucceedingLayer(layer2Sensitivity);


    Vector biasUpdateVector2 = BackPropLearning.calculateBiasUpdates(

View Full Code Here

    biasVector1.setValue(0, -0.48);
    biasVector1.setValue(1, -0.13);


    Layer layer1 = new Layer(weightMatrix1, biasVector1,
        new LogSigActivationFunction());
    LayerSensitivity layer1Sensitivity = new LayerSensitivity(layer1);


    Vector inputVector1 = new Vector(1);
    inputVector1.setValue(0, 1);


    layer1.feedForward(inputVector1);


    Matrix weightMatrix2 = new Matrix(1, 2);
    weightMatrix2.set(0, 0, 0.09);
    weightMatrix2.set(0, 1, -0.17);


    Vector biasVector2 = new Vector(1);
    biasVector2.setValue(0, 0.48);


    Layer layer2 = new Layer(weightMatrix2, biasVector2,
        new PureLinearActivationFunction());
    Vector inputVector2 = layer1.getLastActivationValues();
    layer2.feedForward(inputVector2);


    Vector errorVector = new Vector(1);
    errorVector.setValue(0, 1.261);
    LayerSensitivity layer2Sensitivity = new LayerSensitivity(layer2);
    layer2Sensitivity.sensitivityMatrixFromErrorMatrix(errorVector);


    layer1Sensitivity
        .sensitivityMatrixFromSucceedingLayer(layer2Sensitivity);


    BackPropLearning.calculateWeightUpdates(layer2Sensitivity,

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Related Classes of aima.core.learning.neural.LayerSensitivity

aima.core.util.math.Matrix

aima.test.core.unit.learning.neural.LayerTests

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