Examples with NormalDistribution org.deeplearning4j.nn.conf.distribution.NormalDistribution used on opensource projects

Example 6 with NormalDistribution

use of org.deeplearning4j.nn.conf.distribution.NormalDistribution in project deeplearning4j by deeplearning4j.

the class GradientCheckTests method testEmbeddingLayerSimple.

@Test
public void testEmbeddingLayerSimple() {
    Random r = new Random(12345);
    int nExamples = 5;
    INDArray input = Nd4j.zeros(nExamples, 1);
    INDArray labels = Nd4j.zeros(nExamples, 3);
    for (int i = 0; i < nExamples; i++) {
        input.putScalar(i, r.nextInt(4));
        labels.putScalar(new int[] { i, r.nextInt(3) }, 1.0);
    }
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().regularization(true).l2(0.2).l1(0.1).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).seed(12345L).list().layer(0, new EmbeddingLayer.Builder().nIn(4).nOut(3).weightInit(WeightInit.XAVIER).dist(new NormalDistribution(0, 1)).updater(Updater.NONE).activation(Activation.TANH).build()).layer(1, new OutputLayer.Builder(LossFunction.MCXENT).nIn(3).nOut(3).weightInit(WeightInit.XAVIER).dist(new NormalDistribution(0, 1)).updater(Updater.NONE).activation(Activation.SOFTMAX).build()).pretrain(false).backprop(true).build();
    MultiLayerNetwork mln = new MultiLayerNetwork(conf);
    mln.init();
    if (PRINT_RESULTS) {
        System.out.println("testEmbeddingLayerSimple");
        for (int j = 0; j < mln.getnLayers(); j++) System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
    }
    boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
    String msg = "testEmbeddingLayerSimple";
    assertTrue(msg, gradOK);
}

Example 7 with NormalDistribution

use of org.deeplearning4j.nn.conf.distribution.NormalDistribution in project deeplearning4j by deeplearning4j.

the class BNGradientCheckTest method testGradient2dSimple.

@Test
public void testGradient2dSimple() {
    DataNormalization scaler = new NormalizerMinMaxScaler();
    DataSetIterator iter = new IrisDataSetIterator(150, 150);
    scaler.fit(iter);
    iter.setPreProcessor(scaler);
    DataSet ds = iter.next();
    INDArray input = ds.getFeatureMatrix();
    INDArray labels = ds.getLabels();
    MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().learningRate(1.0).regularization(false).updater(Updater.NONE).seed(12345L).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).list().layer(0, new DenseLayer.Builder().nIn(4).nOut(3).activation(Activation.IDENTITY).build()).layer(1, new BatchNormalization.Builder().nOut(3).build()).layer(2, new ActivationLayer.Builder().activation(Activation.TANH).build()).layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(3).nOut(3).build()).pretrain(false).backprop(true);
    MultiLayerNetwork mln = new MultiLayerNetwork(builder.build());
    mln.init();
    if (PRINT_RESULTS) {
        for (int j = 0; j < mln.getnLayers(); j++) System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
    }
    boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
    assertTrue(gradOK);
}

Example 8 with NormalDistribution

use of org.deeplearning4j.nn.conf.distribution.NormalDistribution in project deeplearning4j by deeplearning4j.

the class BNGradientCheckTest method testGradient2dFixedGammaBeta.

@Test
public void testGradient2dFixedGammaBeta() {
    DataNormalization scaler = new NormalizerMinMaxScaler();
    DataSetIterator iter = new IrisDataSetIterator(150, 150);
    scaler.fit(iter);
    iter.setPreProcessor(scaler);
    DataSet ds = iter.next();
    INDArray input = ds.getFeatureMatrix();
    INDArray labels = ds.getLabels();
    MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().learningRate(1.0).regularization(false).updater(Updater.NONE).seed(12345L).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).list().layer(0, new DenseLayer.Builder().nIn(4).nOut(3).activation(Activation.IDENTITY).build()).layer(1, new BatchNormalization.Builder().lockGammaBeta(true).gamma(2.0).beta(0.5).nOut(3).build()).layer(2, new ActivationLayer.Builder().activation(Activation.TANH).build()).layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(3).nOut(3).build()).pretrain(false).backprop(true);
    MultiLayerNetwork mln = new MultiLayerNetwork(builder.build());
    mln.init();
    if (PRINT_RESULTS) {
        for (int j = 0; j < mln.getnLayers(); j++) System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
    }
    boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
    assertTrue(gradOK);
}

Example 9 with NormalDistribution

use of org.deeplearning4j.nn.conf.distribution.NormalDistribution in project deeplearning4j by deeplearning4j.

the class BNGradientCheckTest method testGradientCnnSimple.

@Test
public void testGradientCnnSimple() {
    Nd4j.getRandom().setSeed(12345);
    int minibatch = 10;
    int depth = 1;
    int hw = 4;
    int nOut = 4;
    INDArray input = Nd4j.rand(new int[] { minibatch, depth, hw, hw });
    INDArray labels = Nd4j.zeros(minibatch, nOut);
    Random r = new Random(12345);
    for (int i = 0; i < minibatch; i++) {
        labels.putScalar(i, r.nextInt(nOut), 1.0);
    }
    MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().learningRate(1.0).regularization(false).updater(Updater.NONE).seed(12345L).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 2)).list().layer(0, new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).nIn(depth).nOut(2).activation(Activation.IDENTITY).build()).layer(1, new BatchNormalization.Builder().build()).layer(2, new ActivationLayer.Builder().activation(Activation.TANH).build()).layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nOut(nOut).build()).setInputType(InputType.convolutional(hw, hw, depth)).pretrain(false).backprop(true);
    MultiLayerNetwork mln = new MultiLayerNetwork(builder.build());
    mln.init();
    if (PRINT_RESULTS) {
        for (int j = 0; j < mln.getnLayers(); j++) System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
    }
    boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
    assertTrue(gradOK);
}

Example 10 with NormalDistribution

use of org.deeplearning4j.nn.conf.distribution.NormalDistribution in project deeplearning4j by deeplearning4j.

the class BNGradientCheckTest method testGradientCnnFixedGammaBeta.

@Test
public void testGradientCnnFixedGammaBeta() {
    Nd4j.getRandom().setSeed(12345);
    int minibatch = 10;
    int depth = 1;
    int hw = 4;
    int nOut = 4;
    INDArray input = Nd4j.rand(new int[] { minibatch, depth, hw, hw });
    INDArray labels = Nd4j.zeros(minibatch, nOut);
    Random r = new Random(12345);
    for (int i = 0; i < minibatch; i++) {
        labels.putScalar(i, r.nextInt(nOut), 1.0);
    }
    MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().learningRate(1.0).regularization(false).updater(Updater.NONE).seed(12345L).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 2)).list().layer(0, new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).nIn(depth).nOut(2).activation(Activation.IDENTITY).build()).layer(1, new BatchNormalization.Builder().lockGammaBeta(true).gamma(2.0).beta(0.5).build()).layer(2, new ActivationLayer.Builder().activation(Activation.TANH).build()).layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nOut(nOut).build()).setInputType(InputType.convolutional(hw, hw, depth)).pretrain(false).backprop(true);
    MultiLayerNetwork mln = new MultiLayerNetwork(builder.build());
    mln.init();
    if (PRINT_RESULTS) {
        for (int j = 0; j < mln.getnLayers(); j++) System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
    }
    boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
    assertTrue(gradOK);
}