Examples with MyCustomPreprocessor org.deeplearning4j.nn.conf.preprocessor.custom.MyCustomPreprocessor used on opensource projects

Example 1 with MyCustomPreprocessor

use of org.deeplearning4j.nn.conf.preprocessor.custom.MyCustomPreprocessor in project deeplearning4j by deeplearning4j.

the class CustomPreprocessorTest method testCustomPreprocessor.

@Test
public void testCustomPreprocessor() {
    //First: Ensure that the CustomLayer class is registered
    ObjectMapper mapper = NeuralNetConfiguration.mapper();
    AnnotatedClass ac = AnnotatedClass.construct(InputPreProcessor.class, mapper.getSerializationConfig().getAnnotationIntrospector(), null);
    Collection<NamedType> types = mapper.getSubtypeResolver().collectAndResolveSubtypes(ac, mapper.getSerializationConfig(), mapper.getSerializationConfig().getAnnotationIntrospector());
    boolean found = false;
    for (NamedType nt : types) {
        //            System.out.println(nt);
        if (nt.getType() == MyCustomPreprocessor.class) {
            found = true;
            break;
        }
    }
    assertTrue("MyCustomPreprocessor: 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 OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(10).nOut(10).build()).inputPreProcessor(0, new MyCustomPreprocessor()).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);
    assertTrue(confFromJson.getInputPreProcess(0) instanceof MyCustomPreprocessor);
}