Examples with LossMSE org.nd4j.linalg.lossfunctions.impl.LossMSE used on opensource projects

Example 6 with LossMSE

use of org.nd4j.linalg.lossfunctions.impl.LossMSE in project deeplearning4j by deeplearning4j.

the class RegressionTest050 method regressionTestMLP2.

@Test
public void regressionTestMLP2() throws Exception {
    File f = new ClassPathResource("regression_testing/050/050_ModelSerializer_Regression_MLP_2.zip").getTempFileFromArchive();
    MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(f, true);
    MultiLayerConfiguration conf = net.getLayerWiseConfigurations();
    assertEquals(2, conf.getConfs().size());
    assertTrue(conf.isBackprop());
    assertFalse(conf.isPretrain());
    DenseLayer l0 = (DenseLayer) conf.getConf(0).getLayer();
    assertTrue(l0.getActivationFn() instanceof ActivationLReLU);
    assertEquals(3, l0.getNIn());
    assertEquals(4, l0.getNOut());
    assertEquals(WeightInit.DISTRIBUTION, l0.getWeightInit());
    assertEquals(new NormalDistribution(0.1, 1.2), l0.getDist());
    assertEquals(Updater.RMSPROP, l0.getUpdater());
    assertEquals(0.96, l0.getRmsDecay(), 1e-6);
    assertEquals(0.15, l0.getLearningRate(), 1e-6);
    assertEquals(0.6, l0.getDropOut(), 1e-6);
    assertEquals(0.1, l0.getL1(), 1e-6);
    assertEquals(0.2, l0.getL2(), 1e-6);
    OutputLayer l1 = (OutputLayer) conf.getConf(1).getLayer();
    assertEquals("identity", l1.getActivationFn().toString());
    assertEquals(LossFunctions.LossFunction.MSE, l1.getLossFunction());
    assertTrue(l1.getLossFn() instanceof LossMSE);
    assertEquals(4, l1.getNIn());
    assertEquals(5, l1.getNOut());
    assertEquals(WeightInit.DISTRIBUTION, l0.getWeightInit());
    assertEquals(new NormalDistribution(0.1, 1.2), l0.getDist());
    assertEquals(Updater.RMSPROP, l0.getUpdater());
    assertEquals(0.96, l1.getRmsDecay(), 1e-6);
    assertEquals(0.15, l1.getLearningRate(), 1e-6);
    assertEquals(0.6, l1.getDropOut(), 1e-6);
    assertEquals(0.1, l1.getL1(), 1e-6);
    assertEquals(0.2, l1.getL2(), 1e-6);
    int numParams = net.numParams();
    assertEquals(Nd4j.linspace(1, numParams, numParams), net.params());
    int updaterSize = net.getUpdater().stateSizeForLayer(net);
    assertEquals(Nd4j.linspace(1, updaterSize, updaterSize), net.getUpdater().getStateViewArray());
}

Example 7 with LossMSE

use of org.nd4j.linalg.lossfunctions.impl.LossMSE in project deeplearning4j by deeplearning4j.

the class TestMiscFunctions method testVaeReconstructionErrorWithKey.

@Test
public void testVaeReconstructionErrorWithKey() {
    //Simple test. We CAN do a direct comparison here vs. local, as reconstruction error is deterministic
    int nIn = 10;
    MultiLayerConfiguration mlc = new NeuralNetConfiguration.Builder().list().layer(0, new org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder().reconstructionDistribution(new LossFunctionWrapper(Activation.IDENTITY, new LossMSE())).nIn(nIn).nOut(5).encoderLayerSizes(12).decoderLayerSizes(13).build()).build();
    MultiLayerNetwork net = new MultiLayerNetwork(mlc);
    net.init();
    VariationalAutoencoder vae = (VariationalAutoencoder) net.getLayer(0);
    List<Tuple2<Integer, INDArray>> toScore = new ArrayList<>();
    for (int i = 0; i < 100; i++) {
        INDArray arr = Nd4j.rand(1, nIn);
        toScore.add(new Tuple2<Integer, INDArray>(i, arr));
    }
    JavaPairRDD<Integer, INDArray> rdd = sc.parallelizePairs(toScore);
    JavaPairRDD<Integer, Double> reconstrErrors = rdd.mapPartitionsToPair(new VaeReconstructionErrorWithKeyFunction<Integer>(sc.broadcast(net.params()), sc.broadcast(mlc.toJson()), 16));
    Map<Integer, Double> l = reconstrErrors.collectAsMap();
    assertEquals(100, l.size());
    for (int i = 0; i < 100; i++) {
        assertTrue(l.containsKey(i));
        INDArray localToScore = toScore.get(i)._2();
        double localScore = vae.reconstructionError(localToScore).data().asDouble()[0];
        assertEquals(localScore, l.get(i), 1e-6);
    }
}