Examples with MultiLayerConfiguration org.deeplearning4j.nn.conf.MultiLayerConfiguration used on opensource projects

Example 21 with MultiLayerConfiguration

use of org.deeplearning4j.nn.conf.MultiLayerConfiguration in project deeplearning4j by deeplearning4j.

the class RBMTests method getMultiLayerRBMNet.

private static MultiLayerNetwork getMultiLayerRBMNet(boolean backprop, boolean pretrain, INDArray input, int nOut1, int nOut2, int nOut3, WeightInit weightInit) {
    MultiLayerConfiguration rbm = new NeuralNetConfiguration.Builder().seed(0xDEADBEEF).iterations(1000).biasInit(0).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(Updater.NONE).epsilon(1).weightInit(weightInit).list(new org.deeplearning4j.nn.conf.layers.RBM.Builder().lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).nOut(nOut1).build(), new org.deeplearning4j.nn.conf.layers.RBM.Builder().lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).nOut(nOut2).build(), new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.RELU).nOut(nOut3).build()).pretrain(pretrain).backprop(backprop).setInputType(InputType.feedForward(input.columns())).build();
    MultiLayerNetwork network = new MultiLayerNetwork(rbm);
    network.init();
    return network;
}

Example 22 with MultiLayerConfiguration

use of org.deeplearning4j.nn.conf.MultiLayerConfiguration 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 23 with MultiLayerConfiguration

use of org.deeplearning4j.nn.conf.MultiLayerConfiguration 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 24 with MultiLayerConfiguration

use of org.deeplearning4j.nn.conf.MultiLayerConfiguration 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 25 with MultiLayerConfiguration

use of org.deeplearning4j.nn.conf.MultiLayerConfiguration in project deeplearning4j by deeplearning4j.

the class OutputLayerTest method testOutputLayersRnnForwardPass.

@Test
public void testOutputLayersRnnForwardPass() {
    //Test output layer with RNNs (
    //Expect all outputs etc. to be 2d
    int nIn = 2;
    int nOut = 5;
    int layerSize = 4;
    int timeSeriesLength = 6;
    int miniBatchSize = 3;
    Random r = new Random(12345L);
    INDArray input = Nd4j.zeros(miniBatchSize, nIn, timeSeriesLength);
    for (int i = 0; i < miniBatchSize; i++) {
        for (int j = 0; j < nIn; j++) {
            for (int k = 0; k < timeSeriesLength; k++) {
                input.putScalar(new int[] { i, j, k }, r.nextDouble() - 0.5);
            }
        }
    }
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345L).list().layer(0, new GravesLSTM.Builder().nIn(nIn).nOut(layerSize).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).activation(Activation.TANH).updater(Updater.NONE).build()).layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).updater(Updater.NONE).build()).inputPreProcessor(1, new RnnToFeedForwardPreProcessor()).build();
    MultiLayerNetwork mln = new MultiLayerNetwork(conf);
    mln.init();
    INDArray out2d = mln.feedForward(input).get(2);
    assertArrayEquals(out2d.shape(), new int[] { miniBatchSize * timeSeriesLength, nOut });
    INDArray out = mln.output(input);
    assertArrayEquals(out.shape(), new int[] { miniBatchSize * timeSeriesLength, nOut });
    INDArray act = mln.activate();
    assertArrayEquals(act.shape(), new int[] { miniBatchSize * timeSeriesLength, nOut });
    INDArray preout = mln.preOutput(input);
    assertArrayEquals(preout.shape(), new int[] { miniBatchSize * timeSeriesLength, nOut });
    //As above, but for RnnOutputLayer. Expect all activations etc. to be 3d
    MultiLayerConfiguration confRnn = new NeuralNetConfiguration.Builder().seed(12345L).list().layer(0, new GravesLSTM.Builder().nIn(nIn).nOut(layerSize).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).activation(Activation.TANH).updater(Updater.NONE).build()).layer(1, new org.deeplearning4j.nn.conf.layers.RnnOutputLayer.Builder(LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).updater(Updater.NONE).build()).build();
    MultiLayerNetwork mlnRnn = new MultiLayerNetwork(confRnn);
    mln.init();
    INDArray out3d = mlnRnn.feedForward(input).get(2);
    assertArrayEquals(out3d.shape(), new int[] { miniBatchSize, nOut, timeSeriesLength });
    INDArray outRnn = mlnRnn.output(input);
    assertArrayEquals(outRnn.shape(), new int[] { miniBatchSize, nOut, timeSeriesLength });
    INDArray actRnn = mlnRnn.activate();
    assertArrayEquals(actRnn.shape(), new int[] { miniBatchSize, nOut, timeSeriesLength });
    INDArray preoutRnn = mlnRnn.preOutput(input);
    assertArrayEquals(preoutRnn.shape(), new int[] { miniBatchSize, nOut, timeSeriesLength });
}