Java FeatureUtil.toOutcomeMatrix方法代码示例

import org.nd4j.linalg.util.FeatureUtil; //导入方法依赖的package包/类
@Test
public void testSplitTestAndTrain() throws Exception {
    INDArray labels = FeatureUtil.toOutcomeMatrix(new int[] {0, 0, 0, 0, 0, 0, 0, 0}, 1);
    DataSet data = new DataSet(Nd4j.rand(8, 1), labels);

    SplitTestAndTrain train = data.splitTestAndTrain(6, new Random(1));
    assertEquals(train.getTrain().getLabels().length(), 6);

    SplitTestAndTrain train2 = data.splitTestAndTrain(6, new Random(1));
    assertEquals(getFailureMessage(), train.getTrain().getFeatureMatrix(), train2.getTrain().getFeatureMatrix());

    DataSet x0 = new IrisDataSetIterator(150, 150).next();
    SplitTestAndTrain testAndTrain = x0.splitTestAndTrain(10);
    assertArrayEquals(new int[] {10, 4}, testAndTrain.getTrain().getFeatureMatrix().shape());
    assertEquals(x0.getFeatureMatrix().getRows(ArrayUtil.range(0, 10)), testAndTrain.getTrain().getFeatureMatrix());
    assertEquals(x0.getLabels().getRows(ArrayUtil.range(0, 10)), testAndTrain.getTrain().getLabels());


}
 

import org.nd4j.linalg.util.FeatureUtil; //导入方法依赖的package包/类
/**
 * Fit the model
 *
 * @param examples the examples to classify (one example in each row)
 * @param labels   the labels for each example (the number of labels must match
 */
@Override
public void fit(INDArray examples, int[] labels) {
    INDArray outcomeMatrix = FeatureUtil.toOutcomeMatrix(labels, numLabels());
    fit(examples, outcomeMatrix);

}
 

import org.nd4j.linalg.util.FeatureUtil; //导入方法依赖的package包/类
@Test
public void testDenseToOutputLayer() {
    final int numRows = 76;
    final int numColumns = 76;
    int nChannels = 3;
    int outputNum = 6;
    int seed = 123;

    //setup the network
    MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().seed(seed)
                    .l1(1e-1).l2(2e-4).dropOut(0.5).miniBatch(true)
                    .optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT).list()
                    .layer(0, new ConvolutionLayer.Builder(5, 5).nOut(5).dropOut(0.5).weightInit(WeightInit.XAVIER)
                                    .activation(Activation.RELU).build())
                    .layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[] {2, 2})
                                    .build())
                    .layer(2, new ConvolutionLayer.Builder(3, 3).nOut(10).dropOut(0.5).weightInit(WeightInit.XAVIER)
                                    .activation(Activation.RELU).build())
                    .layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[] {2, 2})
                                    .build())
                    .layer(4, new DenseLayer.Builder().nOut(100).activation(Activation.RELU).build())
                    .layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                                    .nOut(outputNum).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX)
                                    .build())
                    .backprop(true).pretrain(false)
                    .setInputType(InputType.convolutional(numRows, numColumns, nChannels));

    DataSet d = new DataSet(Nd4j.rand(12345, 10, nChannels, numRows, numColumns),
                    FeatureUtil.toOutcomeMatrix(new int[] {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}, 6));
    MultiLayerNetwork network = new MultiLayerNetwork(builder.build());
    network.init();
    network.fit(d);

}
 

import org.nd4j.linalg.util.FeatureUtil; //导入方法依赖的package包/类
private static INDArray getData() {
    Random r = new Random(1);
    int[] result = new int[window];
    for (int i = 0; i < window; i++) {
        result[i] = r.nextInt(nIn);
    }
    return FeatureUtil.toOutcomeMatrix(result, nIn);
}
 

import org.nd4j.linalg.util.FeatureUtil; //导入方法依赖的package包/类
@Test
public void testFilterAndStrip() {
    INDArray labels = FeatureUtil.toOutcomeMatrix(new int[]{0,1,2,1,2,2,0,1,2,1},3);

    DataSet  d = new org.nd4j.linalg.dataset.DataSet(Nd4j.ones(10, 2),labels);

    //strip the dataset down to just these labels. Rearrange them such that each label is in the specified position.
    d.filterAndStrip(new int[]{1,2});

    for(int i = 0; i < d.numExamples(); i++) {
        int outcome = d.get(i).outcome();
        assertTrue(outcome == 0 || outcome == 1);
    }



}