@Test
public void testBackProp() {
Nd4j.getRandom().setSeed(123);
MultiLayerConfiguration conf =
new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
.iterations(10)
.weightInit(WeightInit.XAVIER)
.dist(new UniformDistribution(0, 1))
.activationFunction("tanh")
.nIn(4)
.nOut(3)
.layer(new org.deeplearning4j.nn.conf.layers.OutputLayer())
.list(3)
.backward(true)
.pretrain(false)
.hiddenLayerSizes(new int[] {3, 2})
.override(
2,
new ConfOverride() {
@Override
public void overrideLayer(int i, NeuralNetConfiguration.Builder builder) {
builder.activationFunction("softmax");
builder.layer(new org.deeplearning4j.nn.conf.layers.OutputLayer());
builder.lossFunction(LossFunctions.LossFunction.MCXENT);
}
})
.build();
MultiLayerNetwork network = new MultiLayerNetwork(conf);
network.init();
network.setListeners(Lists.<IterationListener>newArrayList(new ScoreIterationListener(1)));
DataSetIterator iter = new IrisDataSetIterator(150, 150);
DataSet next = iter.next();
next.normalizeZeroMeanZeroUnitVariance();
SplitTestAndTrain trainTest = next.splitTestAndTrain(110);
network.setInput(trainTest.getTrain().getFeatureMatrix());
network.setLabels(trainTest.getTrain().getLabels());
network.init();
network.fit(trainTest.getTrain());
DataSet test = trainTest.getTest();
Evaluation eval = new Evaluation();
INDArray output = network.output(test.getFeatureMatrix());
eval.eval(test.getLabels(), output);
log.info("Score " + eval.stats());
}
@Test
public void testDbn() throws Exception {
Nd4j.MAX_SLICES_TO_PRINT = -1;
Nd4j.MAX_ELEMENTS_PER_SLICE = -1;
MultiLayerConfiguration conf =
new NeuralNetConfiguration.Builder()
.iterations(100)
.layer(new org.deeplearning4j.nn.conf.layers.RBM())
.weightInit(WeightInit.DISTRIBUTION)
.dist(new UniformDistribution(0, 1))
.activationFunction("tanh")
.momentum(0.9)
.optimizationAlgo(OptimizationAlgorithm.LBFGS)
.constrainGradientToUnitNorm(true)
.k(1)
.regularization(true)
.l2(2e-4)
.visibleUnit(org.deeplearning4j.nn.conf.layers.RBM.VisibleUnit.GAUSSIAN)
.hiddenUnit(org.deeplearning4j.nn.conf.layers.RBM.HiddenUnit.RECTIFIED)
.lossFunction(LossFunctions.LossFunction.RMSE_XENT)
.nIn(4)
.nOut(3)
.list(2)
.hiddenLayerSizes(3)
.override(1, new ClassifierOverride(1))
.build();
NeuralNetConfiguration conf2 =
new NeuralNetConfiguration.Builder()
.layer(new org.deeplearning4j.nn.conf.layers.RBM())
.nIn(784)
.nOut(600)
.applySparsity(true)
.sparsity(0.1)
.build();
Layer l =
LayerFactories.getFactory(conf2)
.create(conf2, Arrays.<IterationListener>asList(new ScoreIterationListener(2)), 0);
MultiLayerNetwork d = new MultiLayerNetwork(conf);
DataSetIterator iter = new IrisDataSetIterator(150, 150);
DataSet next = iter.next();
Nd4j.writeTxt(next.getFeatureMatrix(), "iris.txt", "\t");
next.normalizeZeroMeanZeroUnitVariance();
SplitTestAndTrain testAndTrain = next.splitTestAndTrain(110);
DataSet train = testAndTrain.getTrain();
d.fit(train);
DataSet test = testAndTrain.getTest();
Evaluation eval = new Evaluation();
INDArray output = d.output(test.getFeatureMatrix());
eval.eval(test.getLabels(), output);
log.info("Score " + eval.stats());
}
