Java Code Examples for org.deeplearning4j.arbiter.optimize.api.ParameterSpace#setIndices()

The following examples show how to use org.deeplearning4j.arbiter.optimize.api.ParameterSpace#setIndices() . You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example 1
Source File: BaseCandidateGenerator.java    From deeplearning4j with Apache License 2.0 6 votes vote down vote up 
protected void initialize() {
    if(!initDone) {
        //First: collect leaf parameter spaces objects and remove duplicates
        List<ParameterSpace> noDuplicatesList = LeafUtils.getUniqueObjects(parameterSpace.collectLeaves());

        //Second: assign each a number
        int i = 0;
        for (ParameterSpace ps : noDuplicatesList) {
            int np = ps.numParameters();
            if (np == 1) {
                ps.setIndices(i++);
            } else {
                int[] values = new int[np];
                for (int j = 0; j < np; j++)
                    values[j] = i++;
                ps.setIndices(values);
            }
        }
        initDone = true;
    }
}
Example 2
Source File: BaseUpdaterSpace.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up 
@Override
public void setIndices(int... indices){
    int soFar = 0;
    for(ParameterSpace p : collectLeaves()){
        int numParams = p.numParameters();
        if(numParams <= 0){
            continue;
        }
        int[] subset = Arrays.copyOfRange(indices, soFar, soFar + numParams);
        p.setIndices(subset);
    }
}
Example 3
Source File: TestMultiLayerSpace.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up 
@Test
public void testDropout(){

    MultiLayerSpace mls = new MultiLayerSpace.Builder().updater(new Sgd(0.005)).seed(12345)
            .addLayer(new ConvolutionLayerSpace.Builder().nOut(2)
                    .dropOut(new ContinuousParameterSpace(0.4,0.6))
                    .build())
            .addLayer(new GlobalPoolingLayerSpace.Builder().dropOut(new ContinuousParameterSpace(0.4,0.6)).build())
            .addLayer(new OutputLayerSpace.Builder().activation(Activation.SOFTMAX).nIn(10).nOut(5).build())
            .setInputType(InputType.convolutional(28, 28, 1))
            .build();

    int nParams = mls.numParameters();
    List<ParameterSpace> l = LeafUtils.getUniqueObjects(mls.collectLeaves());
    int x=0;
    for( ParameterSpace p : l){
        int n = p.numParameters();
        int[] arr = new int[n];
        for(int i=0; i<arr.length; i++ ){
            arr[i] = x++;
        }
        p.setIndices(arr);
    }


    MultiLayerConfiguration conf = mls.getValue(new double[nParams]).getMultiLayerConfiguration();
}
Example 4
Source File: TestMultiLayerSpace.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up 
@Test
public void testDropout2(){

    MultiLayerSpace mls = new MultiLayerSpace.Builder().updater(new Sgd(0.005)).seed(12345)
            .addLayer(new ConvolutionLayerSpace.Builder().nOut(2)
                    .dropOut(new ContinuousParameterSpace(0.4,0.6))
                    .build())
            .addLayer(new DropoutLayerSpace.Builder().dropOut(new ContinuousParameterSpace(0.4,0.6)).build())
            .addLayer(new OutputLayerSpace.Builder().activation(Activation.SOFTMAX).nIn(10).nOut(5).build())
            .setInputType(InputType.convolutional(28, 28, 1))
            .build();

    int nParams = mls.numParameters();
    List<ParameterSpace> l = LeafUtils.getUniqueObjects(mls.collectLeaves());
    int x=0;
    for( ParameterSpace p : l){
        int n = p.numParameters();
        int[] arr = new int[n];
        for(int i=0; i<arr.length; i++ ){
            arr[i] = x++;
        }
        p.setIndices(arr);
    }


    MultiLayerConfiguration conf = mls.getValue(new double[nParams]).getMultiLayerConfiguration();
}
Example 5
Source File: TestParameterSpaces.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up 
@Test
public void testDiscreteParameterSpace() {
    ParameterSpace<Integer> dps = new DiscreteParameterSpace<>(0, 1, 2, 3, 4);
    dps.setIndices(0);

    for (int i = 0; i < 5; i++) {
        double d = i / 5.0 + 0.1; //Center
        double dEdgeLower = i / 5.0 + 1e-8; //Edge case: just above split threshold
        double dEdgeUpper = (i + 1) / 5.0 - 1e-8; //Edge case: just below split threshold
        assertEquals(i, (int) dps.getValue(new double[]{d}));
        assertEquals(i, (int) dps.getValue(new double[]{dEdgeLower}));
        assertEquals(i, (int) dps.getValue(new double[]{dEdgeUpper}));
    }
}
Example 6
Source File: TestParameterSpaces.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up 
@Test
public void testIntegerParameterSpace() {
    ParameterSpace<Integer> ips = new IntegerParameterSpace(0, 4);
    ips.setIndices(0);

    for (int i = 0; i < 5; i++) {
        double d = i / 5.0 + 0.1; //Center
        double dEdgeLower = i / 5.0 + 1e-8; //Edge case: just above split threshold
        double dEdgeUpper = (i + 1) / 5.0 - 1e-8; //Edge case: just below split threshold
        assertEquals(i, (int) ips.getValue(new double[]{d}));
        assertEquals(i, (int) ips.getValue(new double[]{dEdgeLower}));
        assertEquals(i, (int) ips.getValue(new double[]{dEdgeUpper}));
    }
}
Example 7
Source File: TestParameterSpaces.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up 
@Test
public void testBooleanSpace() {
    ParameterSpace<Boolean> bSpace = new BooleanSpace();
    bSpace.setIndices(1); //randomly setting to non zero

    assertEquals(true, (boolean) bSpace.getValue(new double[]{0.0, 0.0}));
    assertEquals(true, (boolean) bSpace.getValue(new double[]{0.1, 0.5}));
    assertEquals(false, (boolean) bSpace.getValue(new double[]{0.2, 0.7}));
    assertEquals(false, (boolean) bSpace.getValue(new double[]{0.3, 1.0}));
}
Example 8
Source File: TestMultiLayerSpace.java    From deeplearning4j with Apache License 2.0 4 votes vote down vote up 
@Test
public void testBasic0() {
    MultiLayerConfiguration expected =
            new NeuralNetConfiguration.Builder()
                    .l1Bias(0.4)
                    .l2Bias(0.5)
                    .constrainBias(new NonNegativeConstraint())
                    .updater(new Sgd(0.005)).seed(12345).list()
                    .layer(0, new DenseLayer.Builder().l1Bias(0.6).nIn(10).nOut(10).build())
                    .layer(1, new DenseLayer.Builder().l2Bias(0.7).constrainBias(new UnitNormConstraint()).nIn(10).nOut(10).build()).layer(2,
                    new OutputLayer.Builder().lossFunction(LossFunction.MCXENT).activation(Activation.SOFTMAX)
                            .nIn(10).nOut(5).build())
                    .build();

    MultiLayerSpace mls =
            new MultiLayerSpace.Builder()
                    .l1Bias(0.4)
                    .l2Bias(0.5)
                    .constrainBias(new NonNegativeConstraint())
                    .updater(new Sgd(0.005)).seed(12345)
                    .addLayer(new DenseLayerSpace.Builder().l1Bias(new ContinuousParameterSpace(0,1)).nIn(10).nOut(10).build())
                    .addLayer(new DenseLayerSpace.Builder().l2Bias(0.7).constrainBias(new UnitNormConstraint()).nIn(10).nOut(10).build())
                    .addLayer(new OutputLayerSpace.Builder().lossFunction(LossFunction.MCXENT).activation(Activation.SOFTMAX)
                            .nIn(10).nOut(5).build())
                    .build();

    int nParams = mls.numParameters();
    assertEquals(1, nParams);

    //Assign numbers to each leaf ParameterSpace object (normally done by candidate generator - manual here for testing)
    List<ParameterSpace> noDuplicatesList = LeafUtils.getUniqueObjects(mls.collectLeaves());

    //Second: assign each a number
    int c = 0;
    for (ParameterSpace ps : noDuplicatesList) {
        int np = ps.numParameters();
        if (np == 1) {
            ps.setIndices(c++);
        } else {
            int[] values = new int[np];
            for (int j = 0; j < np; j++)
                values[c++] = j;
            ps.setIndices(values);
        }
    }
    MultiLayerConfiguration conf = mls.getValue(new double[] {0.6}).getMultiLayerConfiguration();

    assertEquals(expected, conf);
}
Example 9
Source File: TestMultiLayerSpace.java    From deeplearning4j with Apache License 2.0 4 votes vote down vote up 
@Test
public void testVariationalAutoencoderLayerSpaceBasic() {
    MultiLayerSpace mls =
                    new MultiLayerSpace.Builder()
                                    .updater(new Sgd(0.005)).seed(
                                                    12345)
                                    .addLayer(new VariationalAutoencoderLayerSpace.Builder()
                                                    .nIn(new IntegerParameterSpace(50, 75)).nOut(200)
                                                    .encoderLayerSizes(234, 567).decoderLayerSizes(123, 456)
                                                    .reconstructionDistribution(
                                                                    new DiscreteParameterSpace<ReconstructionDistribution>(
                                                                                    new GaussianReconstructionDistribution(),
                                                                                    new BernoulliReconstructionDistribution()))
                                                    .build())
                                    .build();

    int numParams = mls.numParameters();

    //Assign numbers to each leaf ParameterSpace object (normally done by candidate generator - manual here for testing)
    List<ParameterSpace> noDuplicatesList = LeafUtils.getUniqueObjects(mls.collectLeaves());

    //Second: assign each a number
    int c = 0;
    for (ParameterSpace ps : noDuplicatesList) {
        int np = ps.numParameters();
        if (np == 1) {
            ps.setIndices(c++);
        } else {
            int[] values = new int[np];
            for (int j = 0; j < np; j++)
                values[c++] = j;
            ps.setIndices(values);
        }
    }

    double[] zeros = new double[numParams];

    DL4JConfiguration configuration = mls.getValue(zeros);

    MultiLayerConfiguration conf = configuration.getMultiLayerConfiguration();
    assertEquals(1, conf.getConfs().size());

    NeuralNetConfiguration nnc = conf.getConf(0);
    VariationalAutoencoder vae = (VariationalAutoencoder) nnc.getLayer();

    assertEquals(50, vae.getNIn());
    assertEquals(200, vae.getNOut());

    assertArrayEquals(new int[] {234, 567}, vae.getEncoderLayerSizes());
    assertArrayEquals(new int[] {123, 456}, vae.getDecoderLayerSizes());

    assertTrue(vae.getOutputDistribution() instanceof GaussianReconstructionDistribution);



    double[] ones = new double[numParams];
    for (int i = 0; i < ones.length; i++)
        ones[i] = 1.0;

    configuration = mls.getValue(ones);

    conf = configuration.getMultiLayerConfiguration();
    assertEquals(1, conf.getConfs().size());

    nnc = conf.getConf(0);
    vae = (VariationalAutoencoder) nnc.getLayer();

    assertEquals(75, vae.getNIn());
    assertEquals(200, vae.getNOut());

    assertArrayEquals(new int[] {234, 567}, vae.getEncoderLayerSizes());
    assertArrayEquals(new int[] {123, 456}, vae.getDecoderLayerSizes());

    assertTrue(vae.getOutputDistribution() instanceof BernoulliReconstructionDistribution);
}