@Test
public void testDbnFaces() {
DataSetIterator iter = new LFWDataSetIterator(28, 28);
DataSet next = iter.next();
next.normalizeZeroMeanZeroUnitVariance();
MultiLayerConfiguration conf =
new NeuralNetConfiguration.Builder()
.nIn(next.numInputs())
.nOut(next.numOutcomes())
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
.constrainGradientToUnitNorm(true)
.weightInit(WeightInit.DISTRIBUTION)
.dist(new NormalDistribution(0, 1e-5))
.iterations(10)
.learningRate(1e-3)
.lossFunction(LossFunctions.LossFunction.RMSE_XENT)
.visibleUnit(RBM.VisibleUnit.GAUSSIAN)
.hiddenUnit(RBM.HiddenUnit.RECTIFIED)
.layer(new RBM())
.list(4)
.hiddenLayerSizes(600, 250, 100)
.override(3, new ClassifierOverride())
.build();
MultiLayerNetwork network = new MultiLayerNetwork(conf);
network.init();
network.setListeners(
Arrays.<IterationListener>asList(
new ScoreIterationListener(10), new NeuralNetPlotterIterationListener(1)));
network.fit(next);
}
@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());
}
