Examples with CustomOutputLayerImpl org.deeplearning4j.nn.layers.custom.testclasses.CustomOutputLayerImpl used on opensource projects

Example 1 with CustomOutputLayerImpl

use of org.deeplearning4j.nn.layers.custom.testclasses.CustomOutputLayerImpl in project deeplearning4j by deeplearning4j.

the class TestCustomLayers method testCustomOutputLayerMLN.

@Test
public void testCustomOutputLayerMLN() {
    //First: Ensure that the CustomOutputLayer class is registered
    ObjectMapper mapper = NeuralNetConfiguration.mapper();
    AnnotatedClass ac = AnnotatedClass.construct(Layer.class, mapper.getSerializationConfig().getAnnotationIntrospector(), null);
    Collection<NamedType> types = mapper.getSubtypeResolver().collectAndResolveSubtypes(ac, mapper.getSerializationConfig(), mapper.getSerializationConfig().getAnnotationIntrospector());
    Set<Class<?>> registeredSubtypes = new HashSet<>();
    boolean found = false;
    for (NamedType nt : types) {
        System.out.println(nt);
        //            registeredSubtypes.add(nt.getType());
        if (nt.getType() == CustomOutputLayer.class)
            found = true;
    }
    assertTrue("CustomOutputLayer: not registered with NeuralNetConfiguration mapper", found);
    //Second: let's create a MultiLayerCofiguration with one, and check JSON and YAML config actually works...
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).learningRate(0.1).list().layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build()).layer(1, new CustomOutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(10).nOut(10).build()).pretrain(false).backprop(true).build();
    String json = conf.toJson();
    String yaml = conf.toYaml();
    System.out.println(json);
    MultiLayerConfiguration confFromJson = MultiLayerConfiguration.fromJson(json);
    assertEquals(conf, confFromJson);
    MultiLayerConfiguration confFromYaml = MultiLayerConfiguration.fromYaml(yaml);
    assertEquals(conf, confFromYaml);
    //Third: check initialization
    Nd4j.getRandom().setSeed(12345);
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    assertTrue(net.getLayer(1) instanceof CustomOutputLayerImpl);
    //Fourth: compare to an equivalent standard output layer (should be identical)
    MultiLayerConfiguration conf2 = new NeuralNetConfiguration.Builder().seed(12345).learningRate(0.1).weightInit(WeightInit.XAVIER).list().layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build()).layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(10).nOut(10).build()).pretrain(false).backprop(true).build();
    Nd4j.getRandom().setSeed(12345);
    MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
    net2.init();
    assertEquals(net2.params(), net.params());
    INDArray testFeatures = Nd4j.rand(1, 10);
    INDArray testLabels = Nd4j.zeros(1, 10);
    testLabels.putScalar(0, 3, 1.0);
    DataSet ds = new DataSet(testFeatures, testLabels);
    assertEquals(net2.output(testFeatures), net.output(testFeatures));
    assertEquals(net2.score(ds), net.score(ds), 1e-6);
}

Example 2 with CustomOutputLayerImpl

use of org.deeplearning4j.nn.layers.custom.testclasses.CustomOutputLayerImpl in project deeplearning4j by deeplearning4j.

the class TestCustomLayers method testCustomOutputLayerCG.

@Test
public void testCustomOutputLayerCG() {
    //Create a ComputationGraphConfiguration with custom output layer, and check JSON and YAML config actually works...
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).learningRate(0.1).graphBuilder().addInputs("in").addLayer("0", new DenseLayer.Builder().nIn(10).nOut(10).build(), "in").addLayer("1", new CustomOutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(10).nOut(10).build(), "0").setOutputs("1").pretrain(false).backprop(true).build();
    String json = conf.toJson();
    String yaml = conf.toYaml();
    System.out.println(json);
    ComputationGraphConfiguration confFromJson = ComputationGraphConfiguration.fromJson(json);
    assertEquals(conf, confFromJson);
    ComputationGraphConfiguration confFromYaml = ComputationGraphConfiguration.fromYaml(yaml);
    assertEquals(conf, confFromYaml);
    //Third: check initialization
    Nd4j.getRandom().setSeed(12345);
    ComputationGraph net = new ComputationGraph(conf);
    net.init();
    assertTrue(net.getLayer(1) instanceof CustomOutputLayerImpl);
    //Fourth: compare to an equivalent standard output layer (should be identical)
    ComputationGraphConfiguration conf2 = new NeuralNetConfiguration.Builder().seed(12345).learningRate(0.1).graphBuilder().addInputs("in").addLayer("0", new DenseLayer.Builder().nIn(10).nOut(10).build(), "in").addLayer("1", new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(10).nOut(10).build(), "0").setOutputs("1").pretrain(false).backprop(true).build();
    Nd4j.getRandom().setSeed(12345);
    ComputationGraph net2 = new ComputationGraph(conf2);
    net2.init();
    assertEquals(net2.params(), net.params());
    INDArray testFeatures = Nd4j.rand(1, 10);
    INDArray testLabels = Nd4j.zeros(1, 10);
    testLabels.putScalar(0, 3, 1.0);
    DataSet ds = new DataSet(testFeatures, testLabels);
    assertEquals(net2.output(testFeatures)[0], net.output(testFeatures)[0]);
    assertEquals(net2.score(ds), net.score(ds), 1e-6);
}