Examples with NeuralNetConfiguration org.deeplearning4j.nn.conf.NeuralNetConfiguration used on opensource projects

Example 71 with NeuralNetConfiguration

use of org.deeplearning4j.nn.conf.NeuralNetConfiguration in project deeplearning4j by deeplearning4j.

the class TestDecayPolicies method testLearningRatePolyDecaySingleLayer.

@Test
public void testLearningRatePolyDecaySingleLayer() {
    int iterations = 2;
    double lr = 1e-2;
    double power = 3;
    NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().learningRate(lr).learningRateDecayPolicy(LearningRatePolicy.Poly).lrPolicyPower(power).iterations(iterations).layer(new DenseLayer.Builder().nIn(nIn).nOut(nOut).updater(org.deeplearning4j.nn.conf.Updater.SGD).build()).build();
    int numParams = conf.getLayer().initializer().numParams(conf);
    INDArray params = Nd4j.create(1, numParams);
    Layer layer = conf.getLayer().instantiate(conf, null, 0, params, true);
    Updater updater = UpdaterCreator.getUpdater(layer);
    Gradient gradientActual = new DefaultGradient();
    gradientActual.setGradientFor(DefaultParamInitializer.WEIGHT_KEY, weightGradient);
    gradientActual.setGradientFor(DefaultParamInitializer.BIAS_KEY, biasGradient);
    for (int i = 0; i < iterations; i++) {
        updater.update(layer, gradientActual, i, 1);
        double expectedLr = calcPolyDecay(lr, i, power, iterations);
        assertEquals(expectedLr, layer.conf().getLearningRateByParam("W"), 1e-4);
        assertEquals(expectedLr, layer.conf().getLearningRateByParam("b"), 1e-4);
    }
}

Example 72 with NeuralNetConfiguration

use of org.deeplearning4j.nn.conf.NeuralNetConfiguration in project deeplearning4j by deeplearning4j.

the class TestGradientNormalization method testL2ClippingPerParamType.

@Test
public void testL2ClippingPerParamType() {
    Nd4j.getRandom().setSeed(12345);
    double threshold = 3;
    NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().layer(new DenseLayer.Builder().nIn(10).nOut(20).updater(org.deeplearning4j.nn.conf.Updater.NONE).gradientNormalization(GradientNormalization.ClipL2PerParamType).gradientNormalizationThreshold(threshold).build()).build();
    int numParams = conf.getLayer().initializer().numParams(conf);
    INDArray params = Nd4j.create(1, numParams);
    Layer layer = conf.getLayer().instantiate(conf, null, 0, params, true);
    Updater updater = UpdaterCreator.getUpdater(layer);
    INDArray weightGrad = Nd4j.rand(10, 20).muli(0.05);
    INDArray biasGrad = Nd4j.rand(1, 10).muli(10);
    INDArray weightGradCopy = weightGrad.dup();
    INDArray biasGradCopy = biasGrad.dup();
    Gradient gradient = new DefaultGradient();
    gradient.setGradientFor(DefaultParamInitializer.WEIGHT_KEY, weightGrad);
    gradient.setGradientFor(DefaultParamInitializer.BIAS_KEY, biasGrad);
    double weightL2 = weightGrad.norm2Number().doubleValue();
    double biasL2 = biasGrad.norm2Number().doubleValue();
    assertTrue(weightL2 < threshold);
    assertTrue(biasL2 > threshold);
    updater.update(layer, gradient, 0, 1);
    //weight norm2 < threshold -> no change
    assertEquals(weightGradCopy, weightGrad);
    //bias norm2 > threshold -> rescale
    assertNotEquals(biasGradCopy, biasGrad);
    double biasScalingFactor = threshold / biasL2;
    INDArray expectedBiasGrad = biasGradCopy.mul(biasScalingFactor);
    assertEquals(expectedBiasGrad, gradient.getGradientFor(DefaultParamInitializer.BIAS_KEY));
}

Example 73 with NeuralNetConfiguration

use of org.deeplearning4j.nn.conf.NeuralNetConfiguration in project deeplearning4j by deeplearning4j.

the class TestGradientNormalization method testAbsValueClippingPerElement.

@Test
public void testAbsValueClippingPerElement() {
    Nd4j.getRandom().setSeed(12345);
    double threshold = 3;
    NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().layer(new DenseLayer.Builder().nIn(10).nOut(20).updater(org.deeplearning4j.nn.conf.Updater.NONE).gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue).gradientNormalizationThreshold(threshold).build()).build();
    int numParams = conf.getLayer().initializer().numParams(conf);
    INDArray params = Nd4j.create(1, numParams);
    Layer layer = conf.getLayer().instantiate(conf, null, 0, params, true);
    Updater updater = UpdaterCreator.getUpdater(layer);
    INDArray weightGrad = Nd4j.rand(10, 20).muli(10).subi(5);
    INDArray biasGrad = Nd4j.rand(1, 10).muli(10).subi(5);
    INDArray weightGradCopy = weightGrad.dup();
    INDArray biasGradCopy = biasGrad.dup();
    Gradient gradient = new DefaultGradient();
    gradient.setGradientFor(DefaultParamInitializer.WEIGHT_KEY, weightGrad);
    gradient.setGradientFor(DefaultParamInitializer.BIAS_KEY, biasGrad);
    updater.update(layer, gradient, 0, 1);
    assertNotEquals(weightGradCopy, weightGrad);
    assertNotEquals(biasGradCopy, biasGrad);
    INDArray expectedWeightGrad = weightGradCopy.dup();
    for (int i = 0; i < expectedWeightGrad.length(); i++) {
        double d = expectedWeightGrad.getDouble(i);
        if (d > threshold)
            expectedWeightGrad.putScalar(i, threshold);
        else if (d < -threshold)
            expectedWeightGrad.putScalar(i, -threshold);
    }
    INDArray expectedBiasGrad = biasGradCopy.dup();
    for (int i = 0; i < expectedBiasGrad.length(); i++) {
        double d = expectedBiasGrad.getDouble(i);
        if (d > threshold)
            expectedBiasGrad.putScalar(i, threshold);
        else if (d < -threshold)
            expectedBiasGrad.putScalar(i, -threshold);
    }
    assertEquals(expectedWeightGrad, gradient.getGradientFor(DefaultParamInitializer.WEIGHT_KEY));
    assertEquals(expectedBiasGrad, gradient.getGradientFor(DefaultParamInitializer.BIAS_KEY));
}

Example 74 with NeuralNetConfiguration

use of org.deeplearning4j.nn.conf.NeuralNetConfiguration in project deeplearning4j by deeplearning4j.

the class TestGradientNormalization method testRenormalizatonPerLayer.

@Test
public void testRenormalizatonPerLayer() {
    Nd4j.getRandom().setSeed(12345);
    NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().layer(new DenseLayer.Builder().nIn(10).nOut(20).updater(org.deeplearning4j.nn.conf.Updater.NONE).gradientNormalization(GradientNormalization.RenormalizeL2PerLayer).build()).build();
    int numParams = conf.getLayer().initializer().numParams(conf);
    INDArray params = Nd4j.create(1, numParams);
    Layer layer = conf.getLayer().instantiate(conf, null, 0, params, true);
    Updater updater = UpdaterCreator.getUpdater(layer);
    INDArray weightGrad = Nd4j.rand(10, 20);
    INDArray biasGrad = Nd4j.rand(1, 10);
    INDArray weightGradCopy = weightGrad.dup();
    INDArray biasGradCopy = biasGrad.dup();
    Gradient gradient = new DefaultGradient();
    gradient.setGradientFor(DefaultParamInitializer.WEIGHT_KEY, weightGrad);
    gradient.setGradientFor(DefaultParamInitializer.BIAS_KEY, biasGrad);
    updater.update(layer, gradient, 0, 1);
    assertNotEquals(weightGradCopy, weightGrad);
    assertNotEquals(biasGradCopy, biasGrad);
    double sumSquaresWeight = weightGradCopy.mul(weightGradCopy).sumNumber().doubleValue();
    double sumSquaresBias = biasGradCopy.mul(biasGradCopy).sumNumber().doubleValue();
    double sumSquares = sumSquaresWeight + sumSquaresBias;
    double l2Layer = Math.sqrt(sumSquares);
    INDArray normWeightsExpected = weightGradCopy.div(l2Layer);
    INDArray normBiasExpected = biasGradCopy.div(l2Layer);
    double l2Weight = gradient.getGradientFor(DefaultParamInitializer.WEIGHT_KEY).norm2Number().doubleValue();
    double l2Bias = gradient.getGradientFor(DefaultParamInitializer.BIAS_KEY).norm2Number().doubleValue();
    assertTrue(!Double.isNaN(l2Weight) && l2Weight > 0.0);
    assertTrue(!Double.isNaN(l2Bias) && l2Bias > 0.0);
    assertEquals(normWeightsExpected, gradient.getGradientFor(DefaultParamInitializer.WEIGHT_KEY));
    assertEquals(normBiasExpected, gradient.getGradientFor(DefaultParamInitializer.BIAS_KEY));
}

Example 75 with NeuralNetConfiguration

use of org.deeplearning4j.nn.conf.NeuralNetConfiguration in project deeplearning4j by deeplearning4j.

the class TestGradientNormalization method testRenormalizationPerParamType.

@Test
public void testRenormalizationPerParamType() {
    Nd4j.getRandom().setSeed(12345);
    NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().layer(new DenseLayer.Builder().nIn(10).nOut(20).updater(org.deeplearning4j.nn.conf.Updater.NONE).gradientNormalization(GradientNormalization.RenormalizeL2PerParamType).build()).build();
    int numParams = conf.getLayer().initializer().numParams(conf);
    INDArray params = Nd4j.create(1, numParams);
    Layer layer = conf.getLayer().instantiate(conf, null, 0, params, true);
    Updater updater = UpdaterCreator.getUpdater(layer);
    INDArray weightGrad = Nd4j.rand(10, 20);
    INDArray biasGrad = Nd4j.rand(1, 10);
    INDArray weightGradCopy = weightGrad.dup();
    INDArray biasGradCopy = biasGrad.dup();
    Gradient gradient = new DefaultGradient();
    gradient.setGradientFor(DefaultParamInitializer.WEIGHT_KEY, weightGrad);
    gradient.setGradientFor(DefaultParamInitializer.BIAS_KEY, biasGrad);
    updater.update(layer, gradient, 0, 1);
    INDArray normWeightsExpected = weightGradCopy.div(weightGradCopy.norm2Number());
    INDArray normBiasExpected = biasGradCopy.div(biasGradCopy.norm2Number());
    assertEquals(normWeightsExpected, gradient.getGradientFor(DefaultParamInitializer.WEIGHT_KEY));
    assertEquals(normBiasExpected, gradient.getGradientFor(DefaultParamInitializer.BIAS_KEY));
}