Examples with ConvolutionLayerSetup org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup used on opensource projects

Example 1 with ConvolutionLayerSetup

use of org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup in project deeplearning4j by deeplearning4j.

the class ConvolutionLayerTest method testCNNZeroStride.

@Test(expected = Exception.class)
public void testCNNZeroStride() {
    int imageHeight = 20;
    int imageWidth = 23;
    int nChannels = 1;
    int classes = 2;
    int numSamples = 200;
    int kernelHeight = imageHeight;
    int kernelWidth = imageWidth;
    DataSet trainInput;
    MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().seed(123).iterations(1).list().layer(0, new ConvolutionLayer.Builder(kernelHeight, kernelWidth).stride(1, 0).nOut(2).activation(Activation.RELU).weightInit(WeightInit.XAVIER).build()).layer(1, new OutputLayer.Builder().nOut(classes).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).build()).backprop(true).pretrain(false);
    new ConvolutionLayerSetup(builder, imageHeight, imageWidth, nChannels);
    MultiLayerConfiguration conf = builder.build();
    MultiLayerNetwork model = new MultiLayerNetwork(conf);
    model.init();
    INDArray emptyFeatures = Nd4j.zeros(numSamples, imageWidth * imageHeight * nChannels);
    INDArray emptyLables = Nd4j.zeros(numSamples, classes);
    trainInput = new DataSet(emptyFeatures, emptyLables);
    model.fit(trainInput);
}

Example 2 with ConvolutionLayerSetup

use of org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup 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 3 with ConvolutionLayerSetup

use of org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup 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 4 with ConvolutionLayerSetup

use of org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup 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********************");
}

Example 5 with ConvolutionLayerSetup

use of org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup in project deeplearning4j by deeplearning4j.

the class ParallelWrapperTest method testParallelWrapperRun.

@Test
public void testParallelWrapperRun() throws Exception {
    int nChannels = 1;
    int outputNum = 10;
    // for GPU you usually want to have higher batchSize
    int batchSize = 128;
    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 will take care of load balancing between GPUs.
    ParallelWrapper wrapper = new ParallelWrapper.Builder(model).prefetchBuffer(24).workers(2).averagingFrequency(3).reportScoreAfterAveraging(true).useLegacyAveraging(true).build();
    log.info("Train model....");
    model.setListeners(new ScoreIterationListener(100));
    long timeX = System.currentTimeMillis();
    for (int i = 0; i < nEpochs; i++) {
        long time1 = System.currentTimeMillis();
        // Please note: we're feeding ParallelWrapper with iterator, not model directly
        //            wrapper.fit(mnistMultiEpochIterator);
        wrapper.fit(mnistTrain);
        long time2 = System.currentTimeMillis();
        log.info("*** Completed epoch {}, time: {} ***", i, (time2 - time1));
    }
    long timeY = System.currentTimeMillis();
    log.info("*** Training complete, time: {} ***", (timeY - timeX));
    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********************");
    wrapper.shutdown();
}