Examples of aima.core.learning.neural.Layer

aima.core.learning.neural.Layer
Artificial Intelligence A Modern Approach (3rd Edition): page 729

Feed-forward networks are usually arranged in layers, such that each unit receives input only from units in the immediately preceding layer. @author Ravi Mohan @author Mike Stampone


    Vector biasVector1 = new Vector(2);
    biasVector1.setValue(0, -0.48);
    biasVector1.setValue(1, -0.13);


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


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


    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);
    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();
    Vector result2 = layer2.feedForward(inputVector2);
    Assert.assertEquals(0.446, result2.getValue(0), 0.001);
  }

View Full Code Here


    Vector biasVector1 = new Vector(2);
    biasVector1.setValue(0, -0.48);
    biasVector1.setValue(1, -0.13);


    Layer layer1 = new Layer(weightMatrix1, biasVector1,
        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);


    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);

View Full Code Here


    Vector biasVector1 = new Vector(2);
    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);

View Full Code Here


    Vector biasVector1 = new Vector(2);
    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(
        layer2Sensitivity, layer1.getLastActivationValues(), 0.1);
    Assert.assertEquals(0.0809, weightUpdateMatrix2.get(0, 0), 0.001);
    Assert.assertEquals(0.0928, weightUpdateMatrix2.get(0, 1), 0.001);


    Matrix lastWeightUpdateMatrix2 = layer2.getLastWeightUpdateMatrix();
    Assert.assertEquals(0.0809, lastWeightUpdateMatrix2.get(0, 0), 0.001);
    Assert.assertEquals(0.0928, lastWeightUpdateMatrix2.get(0, 1), 0.001);


    Matrix penultimateWeightUpdatematrix2 = layer2
        .getPenultimateWeightUpdateMatrix();
    Assert.assertEquals(0.0, penultimateWeightUpdatematrix2.get(0, 0),
        0.001);
    Assert.assertEquals(0.0, penultimateWeightUpdatematrix2.get(0, 1),
        0.001);

View Full Code Here


    Vector biasVector1 = new Vector(2);
    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(
        layer2Sensitivity, 0.1);
    Assert.assertEquals(0.2522, biasUpdateVector2.getValue(0), 0.001);


    Vector lastBiasUpdateVector2 = layer2.getLastBiasUpdateVector();
    Assert.assertEquals(0.2522, lastBiasUpdateVector2.getValue(0), 0.001);


    Vector penultimateBiasUpdateVector2 = layer2
        .getPenultimateBiasUpdateVector();
    Assert.assertEquals(0.0, penultimateBiasUpdateVector2.getValue(0),
        0.001);


    Vector biasUpdateVector1 = BackPropLearning.calculateBiasUpdates(

View Full Code Here


    Vector biasVector1 = new Vector(2);
    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,
        layer1.getLastActivationValues(), 0.1);


    BackPropLearning.calculateBiasUpdates(layer2Sensitivity, 0.1);


    BackPropLearning.calculateWeightUpdates(layer1Sensitivity,
        inputVector1, 0.1);


    BackPropLearning.calculateBiasUpdates(layer1Sensitivity, 0.1);


    layer2.updateWeights();
    Matrix newWeightMatrix2 = layer2.getWeightMatrix();
    Assert.assertEquals(0.171, newWeightMatrix2.get(0, 0), 0.001);
    Assert.assertEquals(-0.0772, newWeightMatrix2.get(0, 1), 0.001);


    layer2.updateBiases();
    Vector newBiasVector2 = layer2.getBiasVector();
    Assert.assertEquals(0.7322, newBiasVector2.getValue(0), 0.00001);


    layer1.updateWeights();
    Matrix newWeightMatrix1 = layer1.getWeightMatrix();

View Full Code Here

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

aima.core.util.math.Matrix

aima.core.util.math.Vector

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

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