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

Example 21 with INDArray

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

the class BatchNormalizationTest method test2dVs4d.

@Test
public void test2dVs4d() {
    //Idea: 2d and 4d should be the same...
    Nd4j.getRandom().setSeed(12345);
    int m = 2;
    int h = 3;
    int w = 3;
    int nOut = 2;
    INDArray in = Nd4j.rand('c', m * h * w, nOut);
    INDArray in4 = in.dup();
    in4 = Shape.newShapeNoCopy(in4, new int[] { m, h, w, nOut }, false);
    assertNotNull(in4);
    in4 = in4.permute(0, 3, 1, 2).dup();
    INDArray arr = Nd4j.rand(1, m * h * w * nOut).reshape('f', h, w, m, nOut).permute(2, 3, 1, 0);
    in4 = arr.assign(in4);
    Layer l1 = getLayer(nOut);
    Layer l2 = getLayer(nOut);
    INDArray out2d = l1.activate(in.dup(), true);
    INDArray out4d = l2.activate(in4.dup(), true);
    INDArray out4dAs2 = out4d.permute(0, 2, 3, 1).dup('c');
    out4dAs2 = Shape.newShapeNoCopy(out4dAs2, new int[] { m * h * w, nOut }, false);
    assertEquals(out2d, out4dAs2);
    //Test backprop:
    INDArray epsilons2d = Nd4j.rand('c', m * h * w, nOut);
    INDArray epsilons4d = epsilons2d.dup();
    epsilons4d = Shape.newShapeNoCopy(epsilons4d, new int[] { m, h, w, nOut }, false);
    assertNotNull(epsilons4d);
    epsilons4d = epsilons4d.permute(0, 3, 1, 2).dup();
    Pair<Gradient, INDArray> b2d = l1.backpropGradient(epsilons2d);
    Pair<Gradient, INDArray> b4d = l2.backpropGradient(epsilons4d);
    INDArray e4dAs2d = b4d.getSecond().permute(0, 2, 3, 1).dup('c');
    e4dAs2d = Shape.newShapeNoCopy(e4dAs2d, new int[] { m * h * w, nOut }, false);
    assertEquals(b2d.getSecond(), e4dAs2d);
}

Example 22 with INDArray

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

the class LocalResponseTest method testLrnManual.

@Test
public void testLrnManual() {
    int wh = 5;
    int depth = 6;
    int minibatch = 3;
    int n = 4;
    double k = 2.0;
    double alpha = 1e-4;
    double beta = 0.75;
    INDArray in = Nd4j.rand(new int[] { minibatch, depth, wh, wh });
    INDArray outExp = Nd4j.zeros(minibatch, depth, wh, wh);
    for (int m = 0; m < minibatch; m++) {
        for (int x = 0; x < wh; x++) {
            for (int y = 0; y < wh; y++) {
                for (int i = 0; i < depth; i++) {
                    int jFrom = Math.max(0, i - n / 2);
                    int jTo = Math.min(depth - 1, i + n / 2);
                    double sum = 0.0;
                    for (int j = jFrom; j <= jTo; j++) {
                        double d = in.getDouble(m, j, x, y);
                        sum += d * d;
                    }
                    double out = in.getDouble(m, i, x, y) / Math.pow(k + alpha * sum, beta);
                    outExp.putScalar(m, i, x, y, out);
                }
            }
        }
    }
    LocalResponseNormalization lrn = new LocalResponseNormalization.Builder().build();
    NeuralNetConfiguration nnc = new NeuralNetConfiguration.Builder().layer(lrn).build();
    org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization layer = (org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization) lrn.instantiate(nnc, null, 0, null, false);
    INDArray outAct = layer.activate(in, true);
    assertEquals(outExp, outAct);
}

Example 23 with INDArray

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

the class ConvolutionLayerTest method testCNNTooLargeKernel.

@Test(expected = DL4JException.class)
public void testCNNTooLargeKernel() {
    int imageHeight = 20;
    int imageWidth = 23;
    int nChannels = 1;
    int classes = 2;
    int numSamples = 200;
    int kernelHeight = imageHeight;
    int kernelWidth = imageWidth + 1;
    DataSet trainInput;
    MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().seed(123).iterations(1).list().layer(0, //(img-kernel+2*padding)/stride + 1: must be >= 1. Therefore: with p=0, kernel <= img size
    new ConvolutionLayer.Builder(kernelHeight, kernelWidth).stride(1, 1).nOut(2).activation(Activation.RELU).weightInit(WeightInit.XAVIER).build()).layer(1, new OutputLayer.Builder().nOut(classes).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).build()).setInputType(InputType.convolutionalFlat(imageHeight, imageWidth, nChannels)).backprop(true).pretrain(false);
    MultiLayerConfiguration conf = builder.build();
    MultiLayerNetwork model = new MultiLayerNetwork(conf);
    model.init();
    INDArray emptyFeatures = Nd4j.zeros(numSamples, imageWidth * imageHeight * nChannels);
    INDArray emptyLables = Nd4j.zeros(numSamples, classes);
    trainInput = new DataSet(emptyFeatures, emptyLables);
    model.fit(trainInput);
}

Example 24 with INDArray

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

the class ConvolutionLayerTest method getCNNConfig.

//////////////////////////////////////////////////////////////////////////////////
private static Layer getCNNConfig(int nIn, int nOut, int[] kernelSize, int[] stride, int[] padding) {
    ConvolutionLayer layer = new ConvolutionLayer.Builder(kernelSize, stride, padding).nIn(nIn).nOut(nOut).activation(Activation.SIGMOID).build();
    NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().iterations(1).layer(layer).build();
    int numParams = conf.getLayer().initializer().numParams(conf);
    INDArray params = Nd4j.create(1, numParams);
    return conf.getLayer().instantiate(conf, null, 0, params, true);
}

Example 25 with INDArray

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

the class ConvolutionLayerTest method testCNNMLNPretrain.

//////////////////////////////////////////////////////////////////////////////////
@Test
public void testCNNMLNPretrain() throws Exception {
    // Note CNN does not do pretrain
    int numSamples = 10;
    int batchSize = 10;
    DataSetIterator mnistIter = new MnistDataSetIterator(batchSize, numSamples, true);
    MultiLayerNetwork model = getCNNMLNConfig(false, true);
    model.fit(mnistIter);
    mnistIter.reset();
    MultiLayerNetwork model2 = getCNNMLNConfig(false, true);
    model2.fit(mnistIter);
    mnistIter.reset();
    DataSet test = mnistIter.next();
    Evaluation eval = new Evaluation();
    INDArray output = model.output(test.getFeatureMatrix());
    eval.eval(test.getLabels(), output);
    double f1Score = eval.f1();
    Evaluation eval2 = new Evaluation();
    INDArray output2 = model2.output(test.getFeatureMatrix());
    eval2.eval(test.getLabels(), output2);
    double f1Score2 = eval2.f1();
    assertEquals(f1Score, f1Score2, 1e-4);
}