use of org.nd4j.linalg.api.ops.impl.transforms.Sin in project deeplearning4j by deeplearning4j.
the class TestEarlyStopping method testMinImprovementNEpochsTermination.
@Test
public void testMinImprovementNEpochsTermination() {
//Idea: terminate training if score (test set loss) does not improve more than minImprovement for 5 consecutive epochs
//Simulate this by setting LR = 0.0
Random rng = new Random(123);
Nd4j.getRandom().setSeed(12345);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(123).iterations(10).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(0.0).updater(Updater.NESTEROVS).momentum(0.9).list().layer(0, new DenseLayer.Builder().nIn(1).nOut(20).weightInit(WeightInit.XAVIER).activation(Activation.TANH).build()).layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MSE).weightInit(WeightInit.XAVIER).activation(Activation.IDENTITY).weightInit(WeightInit.XAVIER).nIn(20).nOut(1).build()).pretrain(false).backprop(true).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.setListeners(new ScoreIterationListener(1));
int nSamples = 100;
//Generate the training data
INDArray x = Nd4j.linspace(-10, 10, nSamples).reshape(nSamples, 1);
INDArray y = Nd4j.getExecutioner().execAndReturn(new Sin(x.dup()));
DataSet allData = new DataSet(x, y);
List<DataSet> list = allData.asList();
Collections.shuffle(list, rng);
DataSetIterator training = new ListDataSetIterator(list, nSamples);
double minImprovement = 0.0009;
EarlyStoppingModelSaver<MultiLayerNetwork> saver = new InMemoryModelSaver<>();
EarlyStoppingConfiguration<MultiLayerNetwork> esConf = new EarlyStoppingConfiguration.Builder<MultiLayerNetwork>().epochTerminationConditions(new MaxEpochsTerminationCondition(1000), //Go on for max 5 epochs without any improvements that are greater than minImprovement
new ScoreImprovementEpochTerminationCondition(5, minImprovement)).iterationTerminationConditions(new MaxTimeIterationTerminationCondition(3, TimeUnit.MINUTES)).scoreCalculator(new DataSetLossCalculator(training, true)).modelSaver(saver).build();
IEarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConf, net, training);
EarlyStoppingResult result = trainer.fit();
assertEquals(6, result.getTotalEpochs());
assertEquals(EarlyStoppingResult.TerminationReason.EpochTerminationCondition, result.getTerminationReason());
String expDetails = new ScoreImprovementEpochTerminationCondition(5, minImprovement).toString();
assertEquals(expDetails, result.getTerminationDetails());
}
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