Examples with FlowIterationListener org.deeplearning4j.ui.flow.FlowIterationListener used on opensource projects

Example 1 with FlowIterationListener

use of org.deeplearning4j.ui.flow.FlowIterationListener in project deeplearning4j by deeplearning4j.

the class ManualTests method testFlowActivationsMLN1.

@Test
public void testFlowActivationsMLN1() throws Exception {
    int nChannels = 1;
    int outputNum = 10;
    int batchSize = 64;
    int nEpochs = 10;
    int iterations = 1;
    int seed = 123;
    log.info("Load data....");
    DataSetIterator mnistTrain = new MnistDataSetIterator(batchSize, true, 12345);
    DataSetIterator mnistTest = new MnistDataSetIterator(batchSize, false, 12345);
    log.info("Build model....");
    MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().seed(seed).iterations(iterations).regularization(true).l2(0.0005).learningRate(//.biasLearningRate(0.02)
    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()).backprop(true).pretrain(false);
    // The builder needs the dimensions of the image along with the number of channels. these are 28x28 images in one channel
    new ConvolutionLayerSetup(builder, 28, 28, 1);
    MultiLayerConfiguration conf = builder.build();
    MultiLayerNetwork model = new MultiLayerNetwork(conf);
    model.init();
    log.info("Train model....");
    model.setListeners(new FlowIterationListener(1));
    for (int i = 0; i < nEpochs; i++) {
        model.fit(mnistTrain);
        log.info("*** Completed epoch {} ***", i);
        mnistTest.reset();
    }
    log.info("Evaluate model....");
    Evaluation eval = new Evaluation(outputNum);
    while (mnistTest.hasNext()) {
        DataSet ds = mnistTest.next();
        INDArray output = model.output(ds.getFeatureMatrix(), false);
        eval.eval(ds.getLabels(), output);
    }
    log.info(eval.stats());
    log.info("****************Example finished********************");
}

Example 2 with FlowIterationListener

use of org.deeplearning4j.ui.flow.FlowIterationListener 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");
}