Examples of feedForward()

Examples of aima.core.learning.neural.Layer.feedForward()

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    Vector expected = new Vector(2);
    expected.setValue(0, 0.321);
    expected.setValue(1, 0.368);

    Vector result1 = layer1.feedForward(inputVector1);
    Assert.assertEquals(expected.getValue(0), result1.getValue(0), 0.001);
    Assert.assertEquals(expected.getValue(1), result1.getValue(1), 0.001);

    Matrix weightMatrix2 = new Matrix(1, 2);
    weightMatrix2.set(0, 0, 0.09);
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Examples of aima.core.learning.neural.Layer.feedForward()

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    biasVector2.setValue(0, 0.48);

    Layer layer2 = new Layer(weightMatrix2, biasVector2,
        new PureLinearActivationFunction());
    Vector inputVector2 = layer1.getLastActivationValues();
    Vector result2 = layer2.feedForward(inputVector2);
    Assert.assertEquals(0.446, result2.getValue(0), 0.001);
  }

  @Test
  public void testSensitivityMatrixCalculationFromErrorVector() {
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Examples of aima.core.learning.neural.Layer.feedForward()

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        new LogSigActivationFunction());

    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);
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Examples of aima.core.learning.neural.Layer.feedForward()

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    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);
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Examples of aima.core.learning.neural.Layer.feedForward()

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    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);
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Examples of aima.core.learning.neural.Layer.feedForward()

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    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);
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Examples of aima.core.learning.neural.Layer.feedForward()

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    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);
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Examples of aima.core.learning.neural.Layer.feedForward()

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    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);
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Examples of aima.core.learning.neural.Layer.feedForward()

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    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);
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Examples of aima.core.learning.neural.Layer.feedForward()

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    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);
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