Package aima.core.learning.neural

Examples of aima.core.learning.neural.PureLinearActivationFunction

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    Vector biasVector2 = new Vector(1);
    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);
  }
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    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);
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    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);
View Full Code Here
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    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);
View Full Code Here
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    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);
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    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);
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Related Classes of aima.core.learning.neural.PureLinearActivationFunction

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