Examples with BackTrackLineSearch org.deeplearning4j.optimize.solvers.BackTrackLineSearch used on opensource projects

Example 1 with BackTrackLineSearch

use of org.deeplearning4j.optimize.solvers.BackTrackLineSearch in project deeplearning4j by deeplearning4j.

the class BackTrackLineSearchTest method testSingleMaxLineSearch.

@Test
public void testSingleMaxLineSearch() throws Exception {
    double score1, score2;
    OutputLayer layer = getIrisLogisticLayerConfig(Activation.SOFTMAX, 100, LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD);
    int nParams = layer.numParams();
    layer.setBackpropGradientsViewArray(Nd4j.create(1, nParams));
    layer.setInput(irisData.getFeatureMatrix());
    layer.setLabels(irisData.getLabels());
    layer.computeGradientAndScore();
    score1 = layer.score();
    BackTrackLineSearch lineSearch = new BackTrackLineSearch(layer, new NegativeDefaultStepFunction(), layer.getOptimizer());
    double step = lineSearch.optimize(layer.params(), layer.gradient().gradient(), layer.gradient().gradient());
    assertEquals(1.0, step, 1e-3);
}

Example 2 with BackTrackLineSearch

use of org.deeplearning4j.optimize.solvers.BackTrackLineSearch in project deeplearning4j by deeplearning4j.

the class BackTrackLineSearchTest method testMultMinLineSearch.

@Test
public void testMultMinLineSearch() throws Exception {
    double score1, score2;
    OutputLayer layer = getIrisLogisticLayerConfig(Activation.SOFTMAX, 100, LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD);
    int nParams = layer.numParams();
    layer.setBackpropGradientsViewArray(Nd4j.create(1, nParams));
    layer.setInput(irisData.getFeatureMatrix());
    layer.setLabels(irisData.getLabels());
    layer.computeGradientAndScore();
    score1 = layer.score();
    INDArray origGradient = layer.gradient().gradient().dup();
    NegativeDefaultStepFunction sf = new NegativeDefaultStepFunction();
    BackTrackLineSearch lineSearch = new BackTrackLineSearch(layer, sf, layer.getOptimizer());
    double step = lineSearch.optimize(layer.params(), layer.gradient().gradient(), layer.gradient().gradient());
    INDArray currParams = layer.params();
    sf.step(currParams, origGradient, step);
    layer.setParams(currParams);
    layer.computeGradientAndScore();
    score2 = layer.score();
    assertTrue("score1=" + score1 + ", score2=" + score2, score1 > score2);
}

Example 3 with BackTrackLineSearch

use of org.deeplearning4j.optimize.solvers.BackTrackLineSearch in project deeplearning4j by deeplearning4j.

the class BackTrackLineSearchTest method testSingleMinLineSearch.

@Test
public void testSingleMinLineSearch() throws Exception {
    OutputLayer layer = getIrisLogisticLayerConfig(Activation.SOFTMAX, 100, LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD);
    int nParams = layer.numParams();
    layer.setBackpropGradientsViewArray(Nd4j.create(1, nParams));
    layer.setInput(irisData.getFeatureMatrix());
    layer.setLabels(irisData.getLabels());
    layer.computeGradientAndScore();
    BackTrackLineSearch lineSearch = new BackTrackLineSearch(layer, layer.getOptimizer());
    double step = lineSearch.optimize(layer.params(), layer.gradient().gradient(), layer.gradient().gradient());
    assertEquals(1.0, step, 1e-3);
}

Example 4 with BackTrackLineSearch

use of org.deeplearning4j.optimize.solvers.BackTrackLineSearch in project deeplearning4j by deeplearning4j.

the class BackTrackLineSearchTest method testMultMaxLineSearch.

@Test
public void testMultMaxLineSearch() throws Exception {
    double score1, score2;
    irisData.normalizeZeroMeanZeroUnitVariance();
    OutputLayer layer = getIrisLogisticLayerConfig(Activation.SOFTMAX, 100, LossFunctions.LossFunction.MCXENT);
    int nParams = layer.numParams();
    layer.setBackpropGradientsViewArray(Nd4j.create(1, nParams));
    layer.setInput(irisData.getFeatureMatrix());
    layer.setLabels(irisData.getLabels());
    layer.computeGradientAndScore();
    score1 = layer.score();
    INDArray origGradient = layer.gradient().gradient().dup();
    DefaultStepFunction sf = new DefaultStepFunction();
    BackTrackLineSearch lineSearch = new BackTrackLineSearch(layer, sf, layer.getOptimizer());
    double step = lineSearch.optimize(layer.params().dup(), layer.gradient().gradient().dup(), layer.gradient().gradient().dup());
    INDArray currParams = layer.params();
    sf.step(currParams, origGradient, step);
    layer.setParams(currParams);
    layer.computeGradientAndScore();
    score2 = layer.score();
    assertTrue("score1 = " + score1 + ", score2 = " + score2, score1 < score2);
}