Examples with DataSetIterator org.nd4j.linalg.dataset.api.iterator.DataSetIterator used on opensource projects

Example 6 with DataSetIterator

use of org.nd4j.linalg.dataset.api.iterator.DataSetIterator in project deeplearning4j by deeplearning4j.

the class ConvolutionLayerTest method testCNNMLNPretrain.

//////////////////////////////////////////////////////////////////////////////////
@Test
public void testCNNMLNPretrain() throws Exception {
    // Note CNN does not do pretrain
    int numSamples = 10;
    int batchSize = 10;
    DataSetIterator mnistIter = new MnistDataSetIterator(batchSize, numSamples, true);
    MultiLayerNetwork model = getCNNMLNConfig(false, true);
    model.fit(mnistIter);
    mnistIter.reset();
    MultiLayerNetwork model2 = getCNNMLNConfig(false, true);
    model2.fit(mnistIter);
    mnistIter.reset();
    DataSet test = mnistIter.next();
    Evaluation eval = new Evaluation();
    INDArray output = model.output(test.getFeatureMatrix());
    eval.eval(test.getLabels(), output);
    double f1Score = eval.f1();
    Evaluation eval2 = new Evaluation();
    INDArray output2 = model2.output(test.getFeatureMatrix());
    eval2.eval(test.getLabels(), output2);
    double f1Score2 = eval2.f1();
    assertEquals(f1Score, f1Score2, 1e-4);
}

Example 7 with DataSetIterator

use of org.nd4j.linalg.dataset.api.iterator.DataSetIterator in project deeplearning4j by deeplearning4j.

the class ActivationLayerTest method testDenseActivationLayer.

@Test
public void testDenseActivationLayer() throws Exception {
    DataSetIterator iter = new MnistDataSetIterator(2, 2);
    DataSet next = iter.next();
    // Run without separate activation layer
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).seed(123).list().layer(0, new DenseLayer.Builder().nIn(28 * 28 * 1).nOut(10).activation(Activation.RELU).weightInit(WeightInit.XAVIER).build()).layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).nIn(10).nOut(10).build()).backprop(true).pretrain(false).build();
    MultiLayerNetwork network = new MultiLayerNetwork(conf);
    network.init();
    network.fit(next);
    // Run with separate activation layer
    MultiLayerConfiguration conf2 = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).seed(123).list().layer(0, new DenseLayer.Builder().nIn(28 * 28 * 1).nOut(10).activation(Activation.IDENTITY).weightInit(WeightInit.XAVIER).build()).layer(1, new org.deeplearning4j.nn.conf.layers.ActivationLayer.Builder().activation(Activation.RELU).build()).layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).nIn(10).nOut(10).build()).backprop(true).pretrain(false).build();
    MultiLayerNetwork network2 = new MultiLayerNetwork(conf2);
    network2.init();
    network2.fit(next);
    // check parameters
    assertEquals(network.getLayer(0).getParam("W"), network2.getLayer(0).getParam("W"));
    assertEquals(network.getLayer(1).getParam("W"), network2.getLayer(2).getParam("W"));
    assertEquals(network.getLayer(0).getParam("b"), network2.getLayer(0).getParam("b"));
    assertEquals(network.getLayer(1).getParam("b"), network2.getLayer(2).getParam("b"));
    // check activations
    network.init();
    network.setInput(next.getFeatureMatrix());
    List<INDArray> activations = network.feedForward(true);
    network2.init();
    network2.setInput(next.getFeatureMatrix());
    List<INDArray> activations2 = network2.feedForward(true);
    assertEquals(activations.get(1).reshape(activations2.get(2).shape()), activations2.get(2));
    assertEquals(activations.get(2), activations2.get(3));
}

Example 8 with DataSetIterator

use of org.nd4j.linalg.dataset.api.iterator.DataSetIterator in project deeplearning4j by deeplearning4j.

the class SubsamplingLayerTest method getData.

public INDArray getData() throws Exception {
    DataSetIterator data = new MnistDataSetIterator(5, 5);
    DataSet mnist = data.next();
    nExamples = mnist.numExamples();
    return mnist.getFeatureMatrix().reshape(nExamples, nChannelsIn, inputWidth, inputHeight);
}

Example 9 with DataSetIterator

use of org.nd4j.linalg.dataset.api.iterator.DataSetIterator 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 10 with DataSetIterator

use of org.nd4j.linalg.dataset.api.iterator.DataSetIterator 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);
}