Examples with INDArray org.nd4j.linalg.api.ndarray.INDArray used on opensource projects

Example 16 with INDArray

use of org.nd4j.linalg.api.ndarray.INDArray in project deeplearning4j by deeplearning4j.

the class RBMTests method testGradient.

@Test
public void testGradient() {
    float[][] data = new float[][] { { 1, 1, 1, 0, 0, 0 }, { 1, 0, 1, 0, 0, 0 }, { 1, 1, 1, 0, 0, 0 }, { 0, 0, 1, 1, 1, 0 }, { 0, 0, 1, 1, 0, 0 }, { 0, 0, 1, 1, 1, 0 }, { 0, 0, 1, 1, 1, 0 } };
    INDArray input = Nd4j.create(data);
    INDArray params = Nd4j.create(1, 6 * 4 + 6 + 4);
    RBM rbm = getRBMLayer(6, 4, HiddenUnit.BINARY, VisibleUnit.BINARY, params, true, false, 1, LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD);
    rbm.fit(input);
    double value = rbm.score();
    Gradient grad2 = rbm.gradient();
    assertEquals(24, grad2.getGradientFor("W").length());
}

Example 17 with INDArray

use of org.nd4j.linalg.api.ndarray.INDArray in project deeplearning4j by deeplearning4j.

the class BatchNormalizationTest method testCnnForwardPass.

@Test
public void testCnnForwardPass() {
    int nOut = 10;
    Layer l = getLayer(nOut, 0.0, false, -1, -1);
    //Gamma, beta, global mean, global var
    assertEquals(4 * nOut, l.numParams());
    int hw = 15;
    Nd4j.getRandom().setSeed(12345);
    INDArray randInput = Nd4j.rand(12345, 100, nOut, hw, hw);
    INDArray output = l.activate(randInput, true);
    assertEquals(4, output.rank());
    INDArray mean = output.mean(0, 2, 3);
    INDArray stdev = output.std(false, 0, 2, 3);
    assertArrayEquals(new float[nOut], mean.data().asFloat(), 1e-6f);
    assertArrayEquals(Nd4j.ones(1, nOut).data().asFloat(), stdev.data().asFloat(), 1e-6f);
    //If we fix gamma/beta: expect different mean and variance...
    double gamma = 2.0;
    double beta = 3.0;
    l = getLayer(nOut, 0.0, true, gamma, beta);
    //Should have only global mean/var parameters
    assertEquals(2 * nOut, l.numParams());
    output = l.activate(randInput, true);
    mean = output.mean(0, 2, 3);
    stdev = output.std(false, 0, 2, 3);
    assertEquals(Nd4j.valueArrayOf(mean.shape(), beta), mean);
    assertEquals(Nd4j.valueArrayOf(stdev.shape(), gamma), stdev);
}

Example 18 with INDArray

use of org.nd4j.linalg.api.ndarray.INDArray in project deeplearning4j by deeplearning4j.

the class BatchNormalizationTest method checkMeanVarianceEstimateCNN.

@Test
public void checkMeanVarianceEstimateCNN() throws Exception {
    Nd4j.getRandom().setSeed(12345);
    //Check that the internal global mean/variance estimate is approximately correct
    //First, Mnist data as 2d input (NOT taking into account convolution property)
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).updater(Updater.RMSPROP).seed(12345).list().layer(0, new BatchNormalization.Builder().nIn(3).nOut(3).eps(1e-5).decay(0.95).build()).layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MSE).weightInit(WeightInit.XAVIER).activation(Activation.IDENTITY).nOut(10).build()).backprop(true).pretrain(false).setInputType(InputType.convolutional(5, 5, 3)).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    int minibatch = 32;
    List<DataSet> list = new ArrayList<>();
    for (int i = 0; i < 100; i++) {
        list.add(new DataSet(Nd4j.rand(new int[] { minibatch, 3, 5, 5 }), Nd4j.rand(minibatch, 10)));
    }
    DataSetIterator iter = new ListDataSetIterator(list);
    INDArray expMean = Nd4j.valueArrayOf(new int[] { 1, 3 }, 0.5);
    //Expected variance of U(0,1) distribution: 1/12 * (1-0)^2 = 0.0833
    INDArray expVar = Nd4j.valueArrayOf(new int[] { 1, 3 }, 1 / 12.0);
    for (int i = 0; i < 10; i++) {
        iter.reset();
        net.fit(iter);
    }
    INDArray estMean = net.getLayer(0).getParam(BatchNormalizationParamInitializer.GLOBAL_MEAN);
    INDArray estVar = net.getLayer(0).getParam(BatchNormalizationParamInitializer.GLOBAL_VAR);
    float[] fMeanExp = expMean.data().asFloat();
    float[] fMeanAct = estMean.data().asFloat();
    float[] fVarExp = expVar.data().asFloat();
    float[] fVarAct = estVar.data().asFloat();
    //        System.out.println("Mean vs. estimated mean:");
    //        System.out.println(Arrays.toString(fMeanExp));
    //        System.out.println(Arrays.toString(fMeanAct));
    //
    //        System.out.println("Var vs. estimated var:");
    //        System.out.println(Arrays.toString(fVarExp));
    //        System.out.println(Arrays.toString(fVarAct));
    assertArrayEquals(fMeanExp, fMeanAct, 0.01f);
    assertArrayEquals(fVarExp, fVarAct, 0.01f);
}

Example 19 with INDArray

use of org.nd4j.linalg.api.ndarray.INDArray in project deeplearning4j by deeplearning4j.

the class BatchNormalizationTest method checkSerialization.

@Test
public void checkSerialization() throws Exception {
    //Serialize the batch norm network (after training), and make sure we get same activations out as before
    // i.e., make sure state is properly stored
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(2).seed(12345).list().layer(0, new ConvolutionLayer.Builder().nIn(1).nOut(6).weightInit(WeightInit.XAVIER).activation(Activation.IDENTITY).build()).layer(1, new BatchNormalization.Builder().build()).layer(2, new ActivationLayer.Builder().activation(Activation.LEAKYRELU).build()).layer(3, new DenseLayer.Builder().nOut(10).activation(Activation.LEAKYRELU).build()).layer(4, new BatchNormalization.Builder().build()).layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).nOut(10).build()).backprop(true).pretrain(false).setInputType(InputType.convolutionalFlat(28, 28, 1)).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    DataSetIterator iter = new MnistDataSetIterator(16, true, 12345);
    for (int i = 0; i < 20; i++) {
        net.fit(iter.next());
    }
    INDArray in = iter.next().getFeatureMatrix();
    INDArray out = net.output(in, false);
    INDArray out2 = net.output(in, false);
    assertEquals(out, out2);
    ByteArrayOutputStream baos = new ByteArrayOutputStream();
    ModelSerializer.writeModel(net, baos, true);
    baos.close();
    byte[] bArr = baos.toByteArray();
    ByteArrayInputStream bais = new ByteArrayInputStream(bArr);
    MultiLayerNetwork net2 = ModelSerializer.restoreMultiLayerNetwork(bais, true);
    INDArray outDeser = net2.output(in, false);
    assertEquals(out, outDeser);
}

Example 20 with INDArray

use of org.nd4j.linalg.api.ndarray.INDArray in project deeplearning4j by deeplearning4j.

the class BatchNormalizationTest method checkMeanVarianceEstimate.

@Test
public void checkMeanVarianceEstimate() throws Exception {
    Nd4j.getRandom().setSeed(12345);
    //Check that the internal global mean/variance estimate is approximately correct
    //First, Mnist data as 2d input (NOT taking into account convolution property)
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).updater(Updater.RMSPROP).seed(12345).list().layer(0, new BatchNormalization.Builder().nIn(10).nOut(10).eps(1e-5).decay(0.95).build()).layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MSE).weightInit(WeightInit.XAVIER).activation(Activation.IDENTITY).nIn(10).nOut(10).build()).backprop(true).pretrain(false).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    int minibatch = 32;
    List<DataSet> list = new ArrayList<>();
    for (int i = 0; i < 200; i++) {
        list.add(new DataSet(Nd4j.rand(minibatch, 10), Nd4j.rand(minibatch, 10)));
    }
    DataSetIterator iter = new ListDataSetIterator(list);
    INDArray expMean = Nd4j.valueArrayOf(new int[] { 1, 10 }, 0.5);
    //Expected variance of U(0,1) distribution: 1/12 * (1-0)^2 = 0.0833
    INDArray expVar = Nd4j.valueArrayOf(new int[] { 1, 10 }, 1 / 12.0);
    for (int i = 0; i < 10; i++) {
        iter.reset();
        net.fit(iter);
    }
    INDArray estMean = net.getLayer(0).getParam(BatchNormalizationParamInitializer.GLOBAL_MEAN);
    INDArray estVar = net.getLayer(0).getParam(BatchNormalizationParamInitializer.GLOBAL_VAR);
    float[] fMeanExp = expMean.data().asFloat();
    float[] fMeanAct = estMean.data().asFloat();
    float[] fVarExp = expVar.data().asFloat();
    float[] fVarAct = estVar.data().asFloat();
    //        System.out.println("Mean vs. estimated mean:");
    //        System.out.println(Arrays.toString(fMeanExp));
    //        System.out.println(Arrays.toString(fMeanAct));
    //
    //        System.out.println("Var vs. estimated var:");
    //        System.out.println(Arrays.toString(fVarExp));
    //        System.out.println(Arrays.toString(fVarAct));
    assertArrayEquals(fMeanExp, fMeanAct, 0.02f);
    assertArrayEquals(fVarExp, fVarAct, 0.02f);
}