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

Example 26 with MultiLayerConfiguration

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

the class ConvolutionLayerSetupTest method testMnistLenet.

@Test
public void testMnistLenet() throws Exception {
    MultiLayerConfiguration.Builder incomplete = incompleteMnistLenet();
    incomplete.setInputType(InputType.convolutionalFlat(28, 28, 1));
    MultiLayerConfiguration testConf = incomplete.build();
    assertEquals(800, ((FeedForwardLayer) testConf.getConf(4).getLayer()).getNIn());
    assertEquals(500, ((FeedForwardLayer) testConf.getConf(5).getLayer()).getNIn());
    //test instantiation
    DataSetIterator iter = new MnistDataSetIterator(10, 10);
    MultiLayerNetwork network = new MultiLayerNetwork(testConf);
    network.init();
    network.fit(iter.next());
}

Example 27 with MultiLayerConfiguration

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

the class ConvolutionLayerSetupTest method testConvolutionLayerSetup.

@Test
public void testConvolutionLayerSetup() {
    MultiLayerConfiguration.Builder builder = inComplete();
    new ConvolutionLayerSetup(builder, 28, 28, 1);
    MultiLayerConfiguration completed = complete().build();
    MultiLayerConfiguration test = builder.build();
    assertEquals(completed, test);
}

Example 28 with MultiLayerConfiguration

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

the class ConvolutionLayerSetupTest method testCNNDBNMultiLayer.

@Test
public void testCNNDBNMultiLayer() throws Exception {
    DataSetIterator iter = new MnistDataSetIterator(2, 2);
    DataSet next = iter.next();
    // Run with separate activation layer
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(2).seed(123).weightInit(WeightInit.XAVIER).list().layer(0, new ConvolutionLayer.Builder(new int[] { 1, 1 }, new int[] { 1, 1 }).nIn(1).nOut(6).activation(Activation.IDENTITY).build()).layer(1, new BatchNormalization.Builder().build()).layer(2, new ActivationLayer.Builder().activation(Activation.RELU).build()).layer(3, new DenseLayer.Builder().nIn(28 * 28 * 6).nOut(10).activation(Activation.IDENTITY).build()).layer(4, new BatchNormalization.Builder().nOut(10).build()).layer(5, new ActivationLayer.Builder().activation(Activation.RELU).build()).layer(6, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nOut(10).build()).backprop(true).pretrain(false).cnnInputSize(28, 28, 1).build();
    MultiLayerNetwork network = new MultiLayerNetwork(conf);
    network.init();
    network.setInput(next.getFeatureMatrix());
    INDArray activationsActual = network.preOutput(next.getFeatureMatrix());
    assertEquals(10, activationsActual.shape()[1], 1e-2);
    network.fit(next);
    INDArray actualGammaParam = network.getLayer(1).getParam(BatchNormalizationParamInitializer.GAMMA);
    INDArray actualBetaParam = network.getLayer(1).getParam(BatchNormalizationParamInitializer.BETA);
    assertTrue(actualGammaParam != null);
    assertTrue(actualBetaParam != null);
}

Example 29 with MultiLayerConfiguration

use of org.deeplearning4j.nn.conf.MultiLayerConfiguration 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 30 with MultiLayerConfiguration

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

the class ManualTests method testCNNActivations2.

@Test
public void testCNNActivations2() 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();
    /*
        ParallelWrapper wrapper = new ParallelWrapper.Builder(model)
            .averagingFrequency(1)
            .prefetchBuffer(12)
            .workers(2)
            .reportScoreAfterAveraging(false)
            .useLegacyAveraging(false)
            .build();
        */
    log.info("Train model....");
    model.setListeners(new ConvolutionalIterationListener(1));
    //((NativeOpExecutioner) Nd4j.getExecutioner()).getLoop().setOmpNumThreads(8);
    long timeX = System.currentTimeMillis();
    //        nEpochs = 2;
    for (int i = 0; i < nEpochs; i++) {
        long time1 = System.currentTimeMillis();
        model.fit(mnistTrain);
        //wrapper.fit(mnistTrain);
        long time2 = System.currentTimeMillis();
        log.info("*** Completed epoch {}, Time elapsed: {} ***", i, (time2 - time1));
    }
    long timeY = System.currentTimeMillis();
    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());
    mnistTest.reset();
    log.info("****************Example finished********************");
}