Examples with MultiLayerNetwork org.deeplearning4j.nn.multilayer.MultiLayerNetwork used on opensource projects

Example 96 with MultiLayerNetwork

use of org.deeplearning4j.nn.multilayer.MultiLayerNetwork in project deeplearning4j by deeplearning4j.

the class GradientCheckTests method testGradientCnnFfRnn.

@Test
public void testGradientCnnFfRnn() {
    //Test gradients with CNN -> FF -> LSTM -> RnnOutputLayer
    //time series input/output (i.e., video classification or similar)
    int nChannelsIn = 3;
    //10px x 10px x 3 channels
    int inputSize = 10 * 10 * nChannelsIn;
    int miniBatchSize = 4;
    int timeSeriesLength = 10;
    int nClasses = 3;
    //Generate
    Nd4j.getRandom().setSeed(12345);
    INDArray input = Nd4j.rand(new int[] { miniBatchSize, inputSize, timeSeriesLength });
    INDArray labels = Nd4j.zeros(miniBatchSize, nClasses, timeSeriesLength);
    Random r = new Random(12345);
    for (int i = 0; i < miniBatchSize; i++) {
        for (int j = 0; j < timeSeriesLength; j++) {
            int idx = r.nextInt(nClasses);
            labels.putScalar(new int[] { i, idx, j }, 1.0);
        }
    }
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().updater(Updater.NONE).seed(12345).weightInit(WeightInit.DISTRIBUTION).dist(new UniformDistribution(-2, 2)).list().layer(0, new ConvolutionLayer.Builder(5, 5).nIn(3).nOut(5).stride(1, 1).activation(Activation.TANH).build()).layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2).stride(1, 1).build()).layer(2, new DenseLayer.Builder().nIn(5 * 5 * 5).nOut(4).activation(Activation.TANH).build()).layer(3, new GravesLSTM.Builder().nIn(4).nOut(3).activation(Activation.TANH).build()).layer(4, new RnnOutputLayer.Builder().lossFunction(LossFunction.MCXENT).nIn(3).nOut(nClasses).activation(Activation.SOFTMAX).build()).setInputType(InputType.convolutional(10, 10, 3)).pretrain(false).backprop(true).build();
    //Here: ConvolutionLayerSetup in config builder doesn't know that we are expecting time series input, not standard FF input -> override it here
    conf.getInputPreProcessors().put(0, new RnnToCnnPreProcessor(10, 10, 3));
    MultiLayerNetwork mln = new MultiLayerNetwork(conf);
    mln.init();
    System.out.println("Params per layer:");
    for (int i = 0; i < mln.getnLayers(); i++) {
        System.out.println("layer " + i + "\t" + mln.getLayer(i).numParams());
    }
    boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
    assertTrue(gradOK);
}

Example 97 with MultiLayerNetwork

use of org.deeplearning4j.nn.multilayer.MultiLayerNetwork in project deeplearning4j by deeplearning4j.

the class GradientCheckTests method testGravesLSTMBasicMultiLayer.

@Test
public void testGravesLSTMBasicMultiLayer() {
    //Basic test of GravesLSTM layer
    Nd4j.getRandom().setSeed(12345L);
    int timeSeriesLength = 4;
    int nIn = 2;
    int layerSize = 2;
    int nOut = 2;
    int miniBatchSize = 5;
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().regularization(false).seed(12345L).list().layer(0, new GravesLSTM.Builder().nIn(nIn).nOut(layerSize).activation(Activation.SIGMOID).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1.0)).updater(Updater.NONE).build()).layer(1, new GravesLSTM.Builder().nIn(layerSize).nOut(layerSize).activation(Activation.SIGMOID).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1.0)).updater(Updater.NONE).build()).layer(2, new RnnOutputLayer.Builder(LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(layerSize).nOut(nOut).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1.0)).updater(Updater.NONE).build()).pretrain(false).backprop(true).build();
    MultiLayerNetwork mln = new MultiLayerNetwork(conf);
    mln.init();
    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);
            }
        }
    }
    INDArray labels = Nd4j.zeros(miniBatchSize, nOut, timeSeriesLength);
    for (int i = 0; i < miniBatchSize; i++) {
        for (int j = 0; j < timeSeriesLength; j++) {
            int idx = r.nextInt(nOut);
            labels.putScalar(new int[] { i, idx, j }, 1.0);
        }
    }
    if (PRINT_RESULTS) {
        System.out.println("testGravesLSTMBasic()");
        for (int j = 0; j < mln.getnLayers(); j++) System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
    }
    boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
    assertTrue(gradOK);
}

Example 98 with MultiLayerNetwork

use of org.deeplearning4j.nn.multilayer.MultiLayerNetwork in project deeplearning4j by deeplearning4j.

the class GradientCheckTests method testEmbeddingLayerSimple.

@Test
public void testEmbeddingLayerSimple() {
    Random r = new Random(12345);
    int nExamples = 5;
    INDArray input = Nd4j.zeros(nExamples, 1);
    INDArray labels = Nd4j.zeros(nExamples, 3);
    for (int i = 0; i < nExamples; i++) {
        input.putScalar(i, r.nextInt(4));
        labels.putScalar(new int[] { i, r.nextInt(3) }, 1.0);
    }
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().regularization(true).l2(0.2).l1(0.1).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).seed(12345L).list().layer(0, new EmbeddingLayer.Builder().nIn(4).nOut(3).weightInit(WeightInit.XAVIER).dist(new NormalDistribution(0, 1)).updater(Updater.NONE).activation(Activation.TANH).build()).layer(1, new OutputLayer.Builder(LossFunction.MCXENT).nIn(3).nOut(3).weightInit(WeightInit.XAVIER).dist(new NormalDistribution(0, 1)).updater(Updater.NONE).activation(Activation.SOFTMAX).build()).pretrain(false).backprop(true).build();
    MultiLayerNetwork mln = new MultiLayerNetwork(conf);
    mln.init();
    if (PRINT_RESULTS) {
        System.out.println("testEmbeddingLayerSimple");
        for (int j = 0; j < mln.getnLayers(); j++) System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
    }
    boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
    String msg = "testEmbeddingLayerSimple";
    assertTrue(msg, gradOK);
}

Example 99 with MultiLayerNetwork

use of org.deeplearning4j.nn.multilayer.MultiLayerNetwork in project deeplearning4j by deeplearning4j.

the class TestEarlyStopping method testListeners.

@Test
public void testListeners() {
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).updater(Updater.SGD).weightInit(WeightInit.XAVIER).list().layer(0, new OutputLayer.Builder().nIn(4).nOut(3).lossFunction(LossFunctions.LossFunction.MCXENT).build()).pretrain(false).backprop(true).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.setListeners(new ScoreIterationListener(1));
    DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
    EarlyStoppingModelSaver<MultiLayerNetwork> saver = new InMemoryModelSaver<>();
    EarlyStoppingConfiguration<MultiLayerNetwork> esConf = new EarlyStoppingConfiguration.Builder<MultiLayerNetwork>().epochTerminationConditions(new MaxEpochsTerminationCondition(5)).iterationTerminationConditions(new MaxTimeIterationTerminationCondition(1, TimeUnit.MINUTES)).scoreCalculator(new DataSetLossCalculator(irisIter, true)).modelSaver(saver).build();
    LoggingEarlyStoppingListener listener = new LoggingEarlyStoppingListener();
    IEarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConf, net, irisIter, listener);
    trainer.fit();
    assertEquals(1, listener.onStartCallCount);
    assertEquals(5, listener.onEpochCallCount);
    assertEquals(1, listener.onCompletionCallCount);
}

Example 100 with MultiLayerNetwork

use of org.deeplearning4j.nn.multilayer.MultiLayerNetwork in project deeplearning4j by deeplearning4j.

the class TestEarlyStopping method testEarlyStoppingEveryNEpoch.

@Test
public void testEarlyStoppingEveryNEpoch() {
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).updater(Updater.SGD).weightInit(WeightInit.XAVIER).list().layer(0, new OutputLayer.Builder().nIn(4).nOut(3).lossFunction(LossFunctions.LossFunction.MCXENT).build()).pretrain(false).backprop(true).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.setListeners(new ScoreIterationListener(1));
    DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
    EarlyStoppingModelSaver<MultiLayerNetwork> saver = new InMemoryModelSaver<>();
    EarlyStoppingConfiguration<MultiLayerNetwork> esConf = new EarlyStoppingConfiguration.Builder<MultiLayerNetwork>().epochTerminationConditions(new MaxEpochsTerminationCondition(5)).scoreCalculator(new DataSetLossCalculator(irisIter, true)).evaluateEveryNEpochs(2).modelSaver(saver).build();
    IEarlyStoppingTrainer<MultiLayerNetwork> trainer = new EarlyStoppingTrainer(esConf, net, irisIter);
    EarlyStoppingResult<MultiLayerNetwork> result = trainer.fit();
    System.out.println(result);
    assertEquals(5, result.getTotalEpochs());
    assertEquals(EarlyStoppingResult.TerminationReason.EpochTerminationCondition, result.getTerminationReason());
}