Examples with ScoreIterationListener org.deeplearning4j.optimize.listeners.ScoreIterationListener used on opensource projects

Example 41 with ScoreIterationListener

use of org.deeplearning4j.optimize.listeners.ScoreIterationListener in project deeplearning4j by deeplearning4j.

the class TestSerialization method testModelSerde.

@Test
public void testModelSerde() throws Exception {
    ObjectMapper mapper = getMapper();
    NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().momentum(0.9f).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1000).learningRate(1e-1f).layer(new org.deeplearning4j.nn.conf.layers.AutoEncoder.Builder().nIn(4).nOut(3).corruptionLevel(0.6).sparsity(0.5).lossFunction(LossFunctions.LossFunction.RECONSTRUCTION_CROSSENTROPY).build()).build();
    DataSet d2 = new IrisDataSetIterator(150, 150).next();
    INDArray input = d2.getFeatureMatrix();
    int numParams = conf.getLayer().initializer().numParams(conf);
    INDArray params = Nd4j.create(1, numParams);
    AutoEncoder da = (AutoEncoder) conf.getLayer().instantiate(conf, Arrays.asList(new ScoreIterationListener(1), new HistogramIterationListener(1)), 0, params, true);
    da.setInput(input);
    ModelAndGradient g = new ModelAndGradient(da);
    String json = mapper.writeValueAsString(g);
    ModelAndGradient read = mapper.readValue(json, ModelAndGradient.class);
    assertEquals(g, read);
}

Example 42 with ScoreIterationListener

use of org.deeplearning4j.optimize.listeners.ScoreIterationListener in project deeplearning4j by deeplearning4j.

the class TestFlowListener method testUI.

@Test
public void testUI() throws Exception {
    // Number of input channels
    int nChannels = 1;
    // The number of possible outcomes
    int outputNum = 10;
    // Test batch size
    int batchSize = 64;
    DataSetIterator mnistTrain = new MnistDataSetIterator(batchSize, true, 12345);
    MultiLayerConfiguration conf = // Training iterations as above
    new NeuralNetConfiguration.Builder().seed(12345).iterations(1).regularization(true).l2(0.0005).learningRate(0.01).weightInit(WeightInit.XAVIER).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(Updater.NESTEROVS).momentum(0.9).list().layer(0, new ConvolutionLayer.Builder(5, 5).nIn(nChannels).stride(1, 1).nOut(20).activation(Activation.IDENTITY).build()).layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2).stride(2, 2).build()).layer(2, new ConvolutionLayer.Builder(5, 5).stride(1, 1).nOut(50).activation(Activation.IDENTITY).build()).layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2).stride(2, 2).build()).layer(4, new DenseLayer.Builder().activation(Activation.RELU).nOut(500).build()).layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).nOut(outputNum).activation(Activation.SOFTMAX).build()).setInputType(//See note below
    InputType.convolutionalFlat(28, 28, 1)).backprop(true).pretrain(false).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    net.setListeners(new FlowIterationListener(1), new ScoreIterationListener(1));
    for (int i = 0; i < 50; i++) {
        net.fit(mnistTrain.next());
        Thread.sleep(1000);
    }
    Thread.sleep(100000);
}

Example 43 with ScoreIterationListener

use of org.deeplearning4j.optimize.listeners.ScoreIterationListener in project deeplearning4j by deeplearning4j.

the class ManualTests method testHistograms.

@Test
public void testHistograms() throws Exception {
    final int numRows = 28;
    final int numColumns = 28;
    int outputNum = 10;
    int numSamples = 60000;
    int batchSize = 100;
    int iterations = 10;
    int seed = 123;
    int listenerFreq = batchSize / 5;
    log.info("Load data....");
    DataSetIterator iter = new MnistDataSetIterator(batchSize, numSamples, true);
    log.info("Build model....");
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(seed).gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue).gradientNormalizationThreshold(1.0).iterations(iterations).momentum(0.5).momentumAfter(Collections.singletonMap(3, 0.9)).optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT).list().layer(0, new RBM.Builder().nIn(numRows * numColumns).nOut(500).weightInit(WeightInit.XAVIER).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).visibleUnit(RBM.VisibleUnit.BINARY).hiddenUnit(RBM.HiddenUnit.BINARY).build()).layer(1, new RBM.Builder().nIn(500).nOut(250).weightInit(WeightInit.XAVIER).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).visibleUnit(RBM.VisibleUnit.BINARY).hiddenUnit(RBM.HiddenUnit.BINARY).build()).layer(2, new RBM.Builder().nIn(250).nOut(200).weightInit(WeightInit.XAVIER).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).visibleUnit(RBM.VisibleUnit.BINARY).hiddenUnit(RBM.HiddenUnit.BINARY).build()).layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).activation(Activation.SOFTMAX).nIn(200).nOut(outputNum).build()).pretrain(true).backprop(false).build();
    //        UiServer server = UiServer.getInstance();
    //        UiConnectionInfo connectionInfo = server.getConnectionInfo();
    //        connectionInfo.setSessionId("my session here");
    MultiLayerNetwork model = new MultiLayerNetwork(conf);
    model.init();
    model.setListeners(Arrays.asList(new ScoreIterationListener(listenerFreq), new HistogramIterationListener(listenerFreq), new FlowIterationListener(listenerFreq)));
    log.info("Train model....");
    // achieves end to end pre-training
    model.fit(iter);
    log.info("Evaluate model....");
    Evaluation eval = new Evaluation(outputNum);
    DataSetIterator testIter = new MnistDataSetIterator(100, 10000);
    while (testIter.hasNext()) {
        DataSet testMnist = testIter.next();
        INDArray predict2 = model.output(testMnist.getFeatureMatrix());
        eval.eval(testMnist.getLabels(), predict2);
    }
    log.info(eval.stats());
    log.info("****************Example finished********************");
    fail("Not implemented");
}

Example 44 with ScoreIterationListener

use of org.deeplearning4j.optimize.listeners.ScoreIterationListener in project deeplearning4j by deeplearning4j.

the class ManualTests method testCNNActivationsVisualization.

/**
     * This test is for manual execution only, since it's here just to get working CNN and visualize it's layers
     *
     * @throws Exception
     */
@Test
public void testCNNActivationsVisualization() throws Exception {
    final int numRows = 40;
    final int numColumns = 40;
    int nChannels = 3;
    int outputNum = LFWLoader.NUM_LABELS;
    int numSamples = LFWLoader.NUM_IMAGES;
    boolean useSubset = false;
    // numSamples/10;
    int batchSize = 200;
    int iterations = 5;
    int splitTrainNum = (int) (batchSize * .8);
    int seed = 123;
    int listenerFreq = iterations / 5;
    DataSet lfwNext;
    SplitTestAndTrain trainTest;
    DataSet trainInput;
    List<INDArray> testInput = new ArrayList<>();
    List<INDArray> testLabels = new ArrayList<>();
    log.info("Load data....");
    DataSetIterator lfw = new LFWDataSetIterator(batchSize, numSamples, new int[] { numRows, numColumns, nChannels }, outputNum, useSubset, true, 1.0, new Random(seed));
    log.info("Build model....");
    MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().seed(seed).iterations(iterations).activation(Activation.RELU).weightInit(WeightInit.XAVIER).gradientNormalization(GradientNormalization.RenormalizeL2PerLayer).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(0.01).momentum(0.9).regularization(true).updater(Updater.ADAGRAD).useDropConnect(true).list().layer(0, new ConvolutionLayer.Builder(4, 4).name("cnn1").nIn(nChannels).stride(1, 1).nOut(20).build()).layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[] { 2, 2 }).name("pool1").build()).layer(2, new ConvolutionLayer.Builder(3, 3).name("cnn2").stride(1, 1).nOut(40).build()).layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[] { 2, 2 }).name("pool2").build()).layer(4, new ConvolutionLayer.Builder(3, 3).name("cnn3").stride(1, 1).nOut(60).build()).layer(5, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[] { 2, 2 }).name("pool3").build()).layer(6, new ConvolutionLayer.Builder(2, 2).name("cnn3").stride(1, 1).nOut(80).build()).layer(7, new DenseLayer.Builder().name("ffn1").nOut(160).dropOut(0.5).build()).layer(8, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).nOut(outputNum).activation(Activation.SOFTMAX).build()).backprop(true).pretrain(false);
    new ConvolutionLayerSetup(builder, numRows, numColumns, nChannels);
    MultiLayerNetwork model = new MultiLayerNetwork(builder.build());
    model.init();
    log.info("Train model....");
    model.setListeners(Arrays.asList(new ScoreIterationListener(listenerFreq), new ConvolutionalIterationListener(listenerFreq)));
    while (lfw.hasNext()) {
        lfwNext = lfw.next();
        lfwNext.scale();
        // train set that is the result
        trainTest = lfwNext.splitTestAndTrain(splitTrainNum, new Random(seed));
        // get feature matrix and labels for training
        trainInput = trainTest.getTrain();
        testInput.add(trainTest.getTest().getFeatureMatrix());
        testLabels.add(trainTest.getTest().getLabels());
        model.fit(trainInput);
    }
    log.info("Evaluate model....");
    Evaluation eval = new Evaluation(lfw.getLabels());
    for (int i = 0; i < testInput.size(); i++) {
        INDArray output = model.output(testInput.get(i));
        eval.eval(testLabels.get(i), output);
    }
    INDArray output = model.output(testInput.get(0));
    eval.eval(testLabels.get(0), output);
    log.info(eval.stats());
    log.info("****************Example finished********************");
}

Example 45 with ScoreIterationListener

use of org.deeplearning4j.optimize.listeners.ScoreIterationListener in project deeplearning4j by deeplearning4j.

the class TestPlayUI method testUICompGraph.

@Test
@Ignore
public void testUICompGraph() throws Exception {
    StatsStorage ss = new InMemoryStatsStorage();
    UIServer uiServer = UIServer.getInstance();
    uiServer.attach(ss);
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().graphBuilder().addInputs("in").addLayer("L0", new DenseLayer.Builder().activation(Activation.TANH).nIn(4).nOut(4).build(), "in").addLayer("L1", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(4).nOut(3).build(), "L0").pretrain(false).backprop(true).setOutputs("L1").build();
    ComputationGraph net = new ComputationGraph(conf);
    net.init();
    net.setListeners(new StatsListener(ss), new ScoreIterationListener(1));
    DataSetIterator iter = new IrisDataSetIterator(150, 150);
    for (int i = 0; i < 100; i++) {
        net.fit(iter);
        Thread.sleep(100);
    }
    Thread.sleep(100000);
}