Examples with CnnToFeedForwardPreProcessor org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor used on opensource projects

Example 6 with CnnToFeedForwardPreProcessor

use of org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor in project deeplearning4j by deeplearning4j.

the class TestRenders method testHistogramComputationGraph.

@Test
public void testHistogramComputationGraph() throws Exception {
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).graphBuilder().addInputs("input").addLayer("cnn1", new ConvolutionLayer.Builder(2, 2).stride(2, 2).nIn(1).nOut(3).build(), "input").addLayer("cnn2", new ConvolutionLayer.Builder(4, 4).stride(2, 2).padding(1, 1).nIn(1).nOut(3).build(), "input").addLayer("max1", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2).build(), "cnn1", "cnn2").addLayer("output", new OutputLayer.Builder().nIn(7 * 7 * 6).nOut(10).build(), "max1").setOutputs("output").inputPreProcessor("cnn1", new FeedForwardToCnnPreProcessor(28, 28, 1)).inputPreProcessor("cnn2", new FeedForwardToCnnPreProcessor(28, 28, 1)).inputPreProcessor("output", new CnnToFeedForwardPreProcessor(7, 7, 6)).pretrain(false).backprop(true).build();
    ComputationGraph graph = new ComputationGraph(conf);
    graph.init();
    graph.setListeners(new HistogramIterationListener(1), new ScoreIterationListener(1));
    DataSetIterator mnist = new MnistDataSetIterator(32, 640, false, true, false, 12345);
    graph.fit(mnist);
}

Example 7 with CnnToFeedForwardPreProcessor

use of org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor in project deeplearning4j by deeplearning4j.

the class MultipleEpochsIteratorTest method testCifarDataSetIteratorReset.

// use when checking cifar dataset iterator
@Ignore
@Test
public void testCifarDataSetIteratorReset() {
    int epochs = 2;
    Nd4j.getRandom().setSeed(12345);
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().regularization(false).learningRate(1.0).weightInit(WeightInit.XAVIER).seed(12345L).list().layer(0, new DenseLayer.Builder().nIn(400).nOut(50).activation(Activation.RELU).build()).layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(50).nOut(10).build()).pretrain(false).backprop(true).inputPreProcessor(0, new CnnToFeedForwardPreProcessor(20, 20, 1)).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    net.setListeners(new ScoreIterationListener(1));
    MultipleEpochsIterator ds = new MultipleEpochsIterator(epochs, new CifarDataSetIterator(10, 20, new int[] { 20, 20, 1 }));
    net.fit(ds);
    assertEquals(epochs, ds.epochs);
    assertEquals(2, ds.batch);
}

Example 8 with CnnToFeedForwardPreProcessor

use of org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor in project deeplearning4j by deeplearning4j.

the class ComputationGraphConfigurationTest method testJSONBasic2.

@Test
public void testJSONBasic2() {
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).graphBuilder().addInputs("input").addLayer("cnn1", new ConvolutionLayer.Builder(2, 2).stride(2, 2).nIn(1).nOut(5).build(), "input").addLayer("cnn2", new ConvolutionLayer.Builder(2, 2).stride(2, 2).nIn(1).nOut(5).build(), "input").addLayer("max1", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2).build(), "cnn1", "cnn2").addLayer("dnn1", new DenseLayer.Builder().nOut(7).build(), "max1").addLayer("max2", new SubsamplingLayer.Builder().build(), "max1").addLayer("output", new OutputLayer.Builder().nIn(7).nOut(10).build(), "dnn1", "max2").setOutputs("output").inputPreProcessor("cnn1", new FeedForwardToCnnPreProcessor(32, 32, 3)).inputPreProcessor("cnn2", new FeedForwardToCnnPreProcessor(32, 32, 3)).inputPreProcessor("dnn1", new CnnToFeedForwardPreProcessor(8, 8, 5)).pretrain(false).backprop(true).build();
    String json = conf.toJson();
    ComputationGraphConfiguration conf2 = ComputationGraphConfiguration.fromJson(json);
    assertEquals(json, conf2.toJson());
    assertEquals(conf, conf2);
}

Example 9 with CnnToFeedForwardPreProcessor

use of org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor in project deeplearning4j by deeplearning4j.

the class TestInvalidConfigurations method testCnnInvalidConfigOrInput_BadStrides.

@Test
public void testCnnInvalidConfigOrInput_BadStrides() {
    //Idea: same as testCnnInvalidConfigPaddingStridesHeight() but network is fed incorrect sized data
    // or equivalently, network is set up without using InputType functionality (hence missing validation there)
    int depthIn = 3;
    int hIn = 10;
    int wIn = 10;
    //Invalid: (10-3+0)/2+1 = 4.5
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().convolutionMode(ConvolutionMode.Strict).list().layer(0, new ConvolutionLayer.Builder().kernelSize(3, 3).stride(2, 2).padding(0, 0).nIn(depthIn).nOut(5).build()).layer(1, new OutputLayer.Builder().nIn(5 * 4 * 4).nOut(10).build()).inputPreProcessor(1, new CnnToFeedForwardPreProcessor()).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    try {
        net.feedForward(Nd4j.create(3, depthIn, hIn, wIn));
        fail("Expected exception");
    } catch (DL4JException e) {
        System.out.println("testCnnInvalidConfigOrInput_BadStrides(): " + e.getMessage());
    } catch (Exception e) {
        e.printStackTrace();
        fail();
    }
}

Example 10 with CnnToFeedForwardPreProcessor

use of org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor in project deeplearning4j by deeplearning4j.

the class ConvolutionLayerSetupTest method testSubSamplingWithPadding.

@Test
public void testSubSamplingWithPadding() {
    MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().list().layer(0, //(28-2+0)/2+1 = 14
    new ConvolutionLayer.Builder(2, 2).padding(0, 0).stride(2, 2).nIn(1).nOut(3).build()).layer(1, //(14-2+2)/2+1 = 8 -> 8x8x3
    new SubsamplingLayer.Builder().kernelSize(2, 2).padding(1, 1).stride(2, 2).build()).layer(2, new OutputLayer.Builder().nOut(3).build());
    new ConvolutionLayerSetup(builder, 28, 28, 1);
    MultiLayerConfiguration conf = builder.build();
    assertNotNull(conf.getInputPreProcess(2));
    assertTrue(conf.getInputPreProcess(2) instanceof CnnToFeedForwardPreProcessor);
    CnnToFeedForwardPreProcessor proc = (CnnToFeedForwardPreProcessor) conf.getInputPreProcess(2);
    assertEquals(8, proc.getInputHeight());
    assertEquals(8, proc.getInputWidth());
    assertEquals(3, proc.getNumChannels());
    assertEquals(8 * 8 * 3, ((FeedForwardLayer) conf.getConf(2).getLayer()).getNIn());
}