Examples with MnistDataSetIterator org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator used on opensource projects

Example 31 with MnistDataSetIterator

use of org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator in project deeplearning4j by deeplearning4j.

the class ConvolutionLayerTest method testTwdFirstLayer.

@Test
public void testTwdFirstLayer() throws Exception {
    MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().seed(123).iterations(5).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).l2(2e-4).regularization(true).momentum(0.9).updater(Updater.NESTEROVS).useDropConnect(true).dropOut(0.5).list().layer(0, //16 filters kernel size 8 stride 4
    new ConvolutionLayer.Builder(8, 8).stride(4, 4).nOut(16).dropOut(0.5).activation(Activation.RELU).weightInit(WeightInit.XAVIER).build()).layer(1, //32 filters kernel size 4 stride 2
    new ConvolutionLayer.Builder(4, 4).stride(2, 2).nOut(32).dropOut(0.5).activation(Activation.RELU).weightInit(WeightInit.XAVIER).build()).layer(2, //fully connected with 256 rectified units
    new DenseLayer.Builder().nOut(256).activation(Activation.RELU).weightInit(WeightInit.XAVIER).dropOut(0.5).build()).layer(3, //output layer
    new OutputLayer.Builder(LossFunctions.LossFunction.SQUARED_LOSS).nOut(10).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).build()).setInputType(InputType.convolutionalFlat(28, 28, 1)).backprop(true).pretrain(false);
    DataSetIterator iter = new MnistDataSetIterator(10, 10);
    MultiLayerConfiguration conf = builder.build();
    MultiLayerNetwork network = new MultiLayerNetwork(conf);
    network.init();
    network.fit(iter.next());
}

Example 32 with MnistDataSetIterator

use of org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator in project deeplearning4j by deeplearning4j.

the class ConvolutionLayerTest method testCNNMLNBackprop.

@Test
public void testCNNMLNBackprop() throws Exception {
    int numSamples = 10;
    int batchSize = 10;
    DataSetIterator mnistIter = new MnistDataSetIterator(batchSize, numSamples, true);
    MultiLayerNetwork model = getCNNMLNConfig(true, false);
    model.fit(mnistIter);
    MultiLayerNetwork model2 = getCNNMLNConfig(true, false);
    model2.fit(mnistIter);
    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 33 with MnistDataSetIterator

use of org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator in project deeplearning4j by deeplearning4j.

the class BatchNormalizationTest method testGradientAndUpdaters.

@Test
public void testGradientAndUpdaters() throws Exception {
    //Global mean/variance are part of the parameter vector. Expect 0 gradient, and no-op updater for these
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).updater(Updater.RMSPROP).seed(12345).list().layer(0, new ConvolutionLayer.Builder().nIn(1).nOut(6).weightInit(WeightInit.XAVIER).activation(Activation.IDENTITY).build()).layer(1, new BatchNormalization.Builder().build()).layer(2, new ActivationLayer.Builder().activation(Activation.LEAKYRELU).build()).layer(3, new DenseLayer.Builder().nOut(10).activation(Activation.LEAKYRELU).build()).layer(4, new BatchNormalization.Builder().build()).layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).nOut(10).build()).backprop(true).pretrain(false).setInputType(InputType.convolutionalFlat(28, 28, 1)).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    DataSetIterator iter = new MnistDataSetIterator(16, true, 12345);
    DataSet ds = iter.next();
    net.setInput(ds.getFeatures());
    net.setLabels(ds.getLabels());
    net.computeGradientAndScore();
    Gradient g = net.gradient();
    Map<String, INDArray> map = g.gradientForVariable();
    for (String s : map.keySet()) {
        INDArray grad = map.get(s);
        if (s.endsWith(BatchNormalizationParamInitializer.GLOBAL_MEAN) || s.endsWith(BatchNormalizationParamInitializer.GLOBAL_VAR)) {
            assertEquals(Nd4j.zeros(grad.shape()), grad);
        }
    }
    org.deeplearning4j.nn.api.Updater u = net.getUpdater();
    Field f = MultiLayerUpdater.class.getDeclaredField("layerUpdaters");
    f.setAccessible(true);
    org.deeplearning4j.nn.api.Updater[] updaters = (org.deeplearning4j.nn.api.Updater[]) f.get(u);
    assertNotNull(updaters);
    assertEquals(6, updaters.length);
    for (int i = 0; i <= 5; i++) {
        LayerUpdater lu = (LayerUpdater) updaters[i];
        Map<String, GradientUpdater> guMap = lu.getUpdaterForVariable();
        for (Map.Entry<String, GradientUpdater> entry : guMap.entrySet()) {
            if (i == 1 || i == 4) {
                String param = entry.getKey();
                if (BatchNormalizationParamInitializer.GLOBAL_MEAN.equals(param) || BatchNormalizationParamInitializer.GLOBAL_VAR.equals(param)) {
                    assertTrue(entry.getValue() instanceof NoOpUpdater);
                } else {
                    assertTrue(entry.getValue() instanceof RmsProp);
                }
            } else {
                assertTrue(entry.getValue() instanceof RmsProp);
            }
        }
    }
}

Example 34 with MnistDataSetIterator

use of org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator in project deeplearning4j by deeplearning4j.

the class BatchNormalizationTest method testCNNBNActivationCombo.

@Test
public void testCNNBNActivationCombo() throws Exception {
    DataSetIterator iter = new MnistDataSetIterator(2, 2);
    DataSet next = iter.next();
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(2).seed(123).list().layer(0, new ConvolutionLayer.Builder().nIn(1).nOut(6).weightInit(WeightInit.XAVIER).activation(Activation.IDENTITY).build()).layer(1, new BatchNormalization.Builder().build()).layer(2, new ActivationLayer.Builder().activation(Activation.RELU).build()).layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).nOut(10).build()).backprop(true).pretrain(false).setInputType(InputType.convolutionalFlat(28, 28, 1)).build();
    MultiLayerNetwork network = new MultiLayerNetwork(conf);
    network.init();
    network.fit(next);
    assertNotEquals(null, network.getLayer(0).getParam("W"));
    assertNotEquals(null, network.getLayer(0).getParam("b"));
}

Example 35 with MnistDataSetIterator

use of org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator in project deeplearning4j by deeplearning4j.

the class BatchNormalizationTest method testDBNBNMultiLayer.

@Test
public void testDBNBNMultiLayer() 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).list().layer(0, new DenseLayer.Builder().nIn(28 * 28).nOut(10).weightInit(WeightInit.XAVIER).activation(Activation.RELU).build()).layer(1, new BatchNormalization.Builder().nOut(10).build()).layer(2, new ActivationLayer.Builder().activation(Activation.RELU).build()).layer(3, new 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.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);
}