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

Example 1 with CustomLayer

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

the class TestCustomLayers method checkInitializationFF.

@Test
public void checkInitializationFF() {
    //Actually create a network with a custom layer; check initialization and forward pass
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().learningRate(0.1).list().layer(0, new DenseLayer.Builder().nIn(9).nOut(10).build()).layer(1, //hard-coded nIn/nOut of 10
    new CustomLayer(3.14159)).layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(10).nOut(11).build()).pretrain(false).backprop(true).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    assertEquals(9 * 10 + 10, net.getLayer(0).numParams());
    assertEquals(10 * 10 + 10, net.getLayer(1).numParams());
    assertEquals(10 * 11 + 11, net.getLayer(2).numParams());
    //Check for exceptions...
    net.output(Nd4j.rand(1, 9));
    net.fit(new DataSet(Nd4j.rand(1, 9), Nd4j.rand(1, 11)));
}

Example 2 with CustomLayer

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

the class TestCustomLayers method testJsonMultiLayerNetwork.

@Test
public void testJsonMultiLayerNetwork() {
    //First: Ensure that the CustomLayer 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() == CustomLayer.class)
            found = true;
    }
    assertTrue("CustomLayer: 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().learningRate(0.1).list().layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build()).layer(1, new CustomLayer(3.14159)).layer(2, new OutputLayer.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);
}

Example 3 with CustomLayer

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

the class TestCustomLayers method testJsonComputationGraph.

@Test
public void testJsonComputationGraph() {
    //ComputationGraph with a custom layer; check JSON and YAML config actually works...
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().learningRate(0.1).graphBuilder().addInputs("in").addLayer("0", new DenseLayer.Builder().nIn(10).nOut(10).build(), "in").addLayer("1", new CustomLayer(3.14159), "0").addLayer("2", new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(10).nOut(10).build(), "1").setOutputs("2").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);
}