Exemplo n.º 1
0
  @Test
  public void testSGDUpdater() {
    double lr = 0.05;

    NeuralNetConfiguration conf =
        new NeuralNetConfiguration.Builder()
            .learningRate(lr)
            .layer(
                new DenseLayer.Builder()
                    .nIn(nIn)
                    .nOut(nOut)
                    .updater(org.deeplearning4j.nn.conf.Updater.SGD)
                    .build())
            .build();

    int numParams = LayerFactories.getFactory(conf).initializer().numParams(conf, true);
    INDArray params = Nd4j.create(1, numParams);
    Layer layer = LayerFactories.getFactory(conf).create(conf, null, 0, params, true);
    Updater updater = UpdaterCreator.getUpdater(layer);

    updater.update(layer, gradient, -1, 1);

    Gradient gradientDup = new DefaultGradient();
    gradientDup.setGradientFor(DefaultParamInitializer.WEIGHT_KEY, weightGradient.dup());
    gradientDup.setGradientFor(DefaultParamInitializer.BIAS_KEY, biasGradient.dup());

    for (Map.Entry<String, INDArray> entry : gradientDup.gradientForVariable().entrySet()) {
      val = entry.getValue();
      gradExpected = val.mul(lr);
      assertEquals(gradExpected, gradient.getGradientFor(entry.getKey()));
    }
    assertEquals(lr, layer.conf().getLayer().getLearningRate(), 1e-4);
  }
Exemplo n.º 2
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  @Test
  public void testNoOpUpdater() {
    Random r = new Random(12345L);
    double lr = 0.5;

    NeuralNetConfiguration conf =
        new NeuralNetConfiguration.Builder()
            .learningRate(lr)
            .layer(
                new DenseLayer.Builder()
                    .nIn(nIn)
                    .nOut(nOut)
                    .updater(org.deeplearning4j.nn.conf.Updater.NONE)
                    .build())
            .build();

    int numParams = LayerFactories.getFactory(conf).initializer().numParams(conf, true);
    INDArray params = Nd4j.create(1, numParams);
    Layer layer = LayerFactories.getFactory(conf).create(conf, null, 0, params, true);
    Updater updater = UpdaterCreator.getUpdater(layer);

    for (int i = 0; i < weightGradient.length(); i++) weightGradient.putScalar(i, r.nextDouble());
    for (int i = 0; i < biasGradient.length(); i++) biasGradient.putScalar(i, r.nextDouble());

    gradient.gradientForVariable().put(DefaultParamInitializer.WEIGHT_KEY, weightGradient);
    gradient.gradientForVariable().put(DefaultParamInitializer.BIAS_KEY, biasGradient);

    updater.update(layer, gradient, -1, 1);

    INDArray weightGradActual = gradient.getGradientFor(DefaultParamInitializer.WEIGHT_KEY);
    INDArray biasGradActual = gradient.getGradientFor(DefaultParamInitializer.BIAS_KEY);

    assertEquals(weightGradient, weightGradActual);
    assertEquals(biasGradient, biasGradActual);
  }
Exemplo n.º 3
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  @Test
  public void testAdamUpdater() {
    INDArray m, v;
    double lr = 0.01;
    int iteration = 0;
    double beta1 = 0.8;
    double beta2 = 0.888;

    NeuralNetConfiguration conf =
        new NeuralNetConfiguration.Builder()
            .learningRate(lr)
            .iterations(iteration)
            .adamMeanDecay(beta1)
            .adamVarDecay(beta2)
            .layer(
                new DenseLayer.Builder()
                    .nIn(nIn)
                    .nOut(nOut)
                    .updater(org.deeplearning4j.nn.conf.Updater.ADAM)
                    .build())
            .build();

    int numParams = LayerFactories.getFactory(conf).initializer().numParams(conf, true);
    INDArray params = Nd4j.create(1, numParams);
    Layer layer = LayerFactories.getFactory(conf).create(conf, null, 0, params, true);
    Updater updater = UpdaterCreator.getUpdater(layer);
    int updaterStateSize = updater.stateSizeForLayer(layer);
    INDArray updaterState = Nd4j.create(1, updaterStateSize);
    updater.setStateViewArray(layer, updaterState, true);

    updater.update(layer, gradient, iteration, 1);

    double beta1t = FastMath.pow(beta1, iteration);
    double beta2t = FastMath.pow(beta2, iteration);
    double alphat = lr * FastMath.sqrt(1 - beta2t) / (1 - beta1t);
    if (Double.isNaN(alphat) || alphat == 0.0) alphat = epsilon;

    Gradient gradientDup = new DefaultGradient();
    gradientDup.setGradientFor(DefaultParamInitializer.WEIGHT_KEY, weightGradient);
    gradientDup.setGradientFor(DefaultParamInitializer.BIAS_KEY, biasGradient);

    for (Map.Entry<String, INDArray> entry : gradientDup.gradientForVariable().entrySet()) {
      val = entry.getValue();
      m = Nd4j.zeros(val.shape());
      v = Nd4j.zeros(val.shape());

      m.muli(beta1).addi(val.mul(1.0 - beta1));
      v.muli(beta2).addi(val.mul(val).mul(1.0 - beta2));
      gradExpected = m.mul(alphat).divi(Transforms.sqrt(v).addi(epsilon));
      if (!gradExpected.equals(gradient.getGradientFor(entry.getKey()))) {
        System.out.println(Arrays.toString(gradExpected.dup().data().asFloat()));
        System.out.println(
            Arrays.toString(gradient.getGradientFor(entry.getKey()).dup().data().asFloat()));
      }
      assertEquals(gradExpected, gradient.getGradientFor(entry.getKey()));
    }

    assertEquals(beta1, layer.conf().getLayer().getAdamMeanDecay(), 1e-4);
    assertEquals(beta2, layer.conf().getLayer().getAdamVarDecay(), 1e-4);
  }
Exemplo n.º 4
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  @Test
  public void testRMSPropUpdater() {
    double lr = 0.01;
    double rmsDecay = 0.25;
    Map<String, INDArray> lastG = new HashMap<>();

    NeuralNetConfiguration conf =
        new NeuralNetConfiguration.Builder()
            .learningRate(lr)
            .rmsDecay(rmsDecay)
            .layer(
                new DenseLayer.Builder()
                    .nIn(nIn)
                    .nOut(nOut)
                    .updater(org.deeplearning4j.nn.conf.Updater.RMSPROP)
                    .build())
            .build();

    int numParams = LayerFactories.getFactory(conf).initializer().numParams(conf, true);
    INDArray params = Nd4j.create(1, numParams);
    Layer layer = LayerFactories.getFactory(conf).create(conf, null, 0, params, true);
    Updater updater = UpdaterCreator.getUpdater(layer);
    int updaterStateSize = updater.stateSizeForLayer(layer);
    INDArray updaterState = Nd4j.create(1, updaterStateSize);
    updater.setStateViewArray(layer, updaterState, true);

    updater.update(layer, gradient, -1, 1);

    Gradient gradientDup = new DefaultGradient();
    gradientDup.setGradientFor(DefaultParamInitializer.WEIGHT_KEY, weightGradient.dup());
    gradientDup.setGradientFor(DefaultParamInitializer.BIAS_KEY, biasGradient.dup());

    for (Map.Entry<String, INDArray> entry : gradientDup.gradientForVariable().entrySet()) {
      key = entry.getKey();
      val = entry.getValue();
      INDArray lastGTmp = lastG.get(key);

      if (lastGTmp == null) lastGTmp = Nd4j.zeros(val.shape());

      lastGTmp.muli(rmsDecay).addi(val.mul(val).muli(1 - rmsDecay));
      gradExpected = val.mul(lr).div(Transforms.sqrt(lastGTmp.add(Nd4j.EPS_THRESHOLD)));

      assertEquals(gradExpected, gradient.getGradientFor(entry.getKey()));
      lastG.put(key, lastGTmp);
    }
    assertEquals(rmsDecay, layer.conf().getLayer().getRmsDecay(), 1e-4);
  }
Exemplo n.º 5
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  @Test
  public void testNestorovsUpdater() {
    double lr = 1e-2;
    double mu = 0.6;
    INDArray v, vPrev;

    NeuralNetConfiguration conf =
        new NeuralNetConfiguration.Builder()
            .learningRate(lr)
            .momentum(mu)
            .layer(
                new DenseLayer.Builder()
                    .nIn(nIn)
                    .nOut(nOut)
                    .updater(org.deeplearning4j.nn.conf.Updater.NESTEROVS)
                    .build())
            .build();

    int numParams = LayerFactories.getFactory(conf).initializer().numParams(conf, true);
    INDArray params = Nd4j.create(1, numParams);
    Layer layer = LayerFactories.getFactory(conf).create(conf, null, 0, params, true);
    Updater updater = UpdaterCreator.getUpdater(layer);
    int updaterStateSize = updater.stateSizeForLayer(layer);
    INDArray updaterState = Nd4j.create(1, updaterStateSize);
    updater.setStateViewArray(layer, updaterState, true);

    updater.update(layer, gradient, -1, 1);

    Gradient gradientDup = new DefaultGradient();
    gradientDup.setGradientFor(DefaultParamInitializer.WEIGHT_KEY, weightGradient.dup());
    gradientDup.setGradientFor(DefaultParamInitializer.BIAS_KEY, biasGradient.dup());

    for (Map.Entry<String, INDArray> entry : gradientDup.gradientForVariable().entrySet()) {
      val = entry.getValue();
      v = Nd4j.zeros(val.shape());
      vPrev = v;
      v = vPrev.mul(mu).subi(val.mul(lr));
      gradExpected = vPrev.muli(mu).addi(v.mul(-mu - 1));

      assertEquals(gradExpected, gradient.getGradientFor(entry.getKey()));
    }

    assertEquals(mu, layer.conf().getLayer().getMomentum(), 1e-4);
  }
  private static Layer getCNNConfig(
      int nIn, int nOut, int[] kernelSize, int[] stride, int[] padding) {
    ConvolutionLayer layer =
        new ConvolutionLayer.Builder(kernelSize, stride, padding).nIn(nIn).nOut(nOut).build();

    NeuralNetConfiguration conf =
        new NeuralNetConfiguration.Builder()
            .activationFunction("sigmoid")
            .iterations(1)
            .layer(layer)
            .build();
    return LayerFactories.getFactory(conf).create(conf);
  }
Exemplo n.º 7
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  @Test
  public void testAdaGradUpdater() {
    double lr = 1e-2;

    NeuralNetConfiguration conf =
        new NeuralNetConfiguration.Builder()
            .learningRate(lr)
            .layer(
                new DenseLayer.Builder()
                    .nIn(nIn)
                    .nOut(nOut)
                    .updater(org.deeplearning4j.nn.conf.Updater.ADAGRAD)
                    .build())
            .build();

    int numParams = LayerFactories.getFactory(conf).initializer().numParams(conf, true);
    INDArray params = Nd4j.create(1, numParams);
    Layer layer = LayerFactories.getFactory(conf).create(conf, null, 0, params, true);
    Updater updater = UpdaterCreator.getUpdater(layer);
    int updaterStateSize = updater.stateSizeForLayer(layer);
    INDArray updaterState = Nd4j.create(1, updaterStateSize);
    updater.setStateViewArray(layer, updaterState, true);

    updater.update(layer, gradient, -1, 1);

    Gradient gradientDup = new DefaultGradient();
    gradientDup.setGradientFor(DefaultParamInitializer.WEIGHT_KEY, weightGradient);
    gradientDup.setGradientFor(DefaultParamInitializer.BIAS_KEY, biasGradient);

    for (Map.Entry<String, INDArray> entry : gradientDup.gradientForVariable().entrySet()) {
      val = entry.getValue();
      gradExpected = Transforms.sqrt(val.mul(val).add(epsilon)).rdiv(lr).mul(val);
      assertEquals(gradExpected, gradient.getGradientFor(entry.getKey()));
    }
    assertEquals(lr, layer.conf().getLayer().getLearningRate(), 1e-4);
  }
  public ModelAndGradient() {
    NeuralNetConfiguration conf =
        new NeuralNetConfiguration.Builder()
            .lossFunction(LossFunctions.LossFunction.MCXENT)
            .optimizationAlgo(OptimizationAlgorithm.ITERATION_GRADIENT_DESCENT)
            .activationFunction("softmax")
            .iterations(10)
            .weightInit(WeightInit.XAVIER)
            .learningRate(1e-1)
            .nIn(4)
            .nOut(3)
            .layer(new org.deeplearning4j.nn.conf.layers.OutputLayer())
            .build();

    OutputLayer l =
        LayerFactories.getFactory(conf.getLayer())
            .create(conf, Arrays.<IterationListener>asList(new ScoreIterationListener(1)));
    this.model = l;
    l.setInput(Nd4j.ones(4));
    l.setLabels(Nd4j.ones(3));
    this.gradient = l.gradient();
  }
  @Test
  public void testModelSerde() throws Exception {
    ObjectMapper mapper = getMapper();
    NeuralNetConfiguration conf =
        new NeuralNetConfiguration.Builder()
            .momentum(0.9f)
            .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
            .iterations(1000)
            .constrainGradientToUnitNorm(true)
            .learningRate(1e-1f)
            .layer(
                new org.deeplearning4j.nn.conf.layers.AutoEncoder.Builder()
                    .nIn(4)
                    .nOut(3)
                    .corruptionLevel(0.6)
                    .sparsity(0.5)
                    .lossFunction(LossFunctions.LossFunction.RECONSTRUCTION_CROSSENTROPY)
                    .build())
            .build();

    DataSet d2 = new IrisDataSetIterator(150, 150).next();

    INDArray input = d2.getFeatureMatrix();
    AutoEncoder da =
        LayerFactories.getFactory(conf.getLayer())
            .create(
                conf,
                Arrays.<IterationListener>asList(
                    new ScoreIterationListener(1), new HistogramIterationListener(1)),
                0);
    da.setInput(input);
    ModelAndGradient g = new ModelAndGradient(da);
    String json = mapper.writeValueAsString(g);
    ModelAndGradient read = mapper.readValue(json, ModelAndGradient.class);
    assertEquals(g, read);
  }
Exemplo n.º 10
0
  @Test
  public void testAdaDeltaUpdate() {
    INDArray dxSquared;
    Map<String, INDArray> msg = new HashMap<>();
    Map<String, INDArray> msdx = new HashMap<>();

    double rho = 0.85;

    NeuralNetConfiguration conf =
        new NeuralNetConfiguration.Builder()
            .rho(rho)
            .layer(
                new DenseLayer.Builder()
                    .nIn(nIn)
                    .nOut(nOut)
                    .updater(org.deeplearning4j.nn.conf.Updater.ADADELTA)
                    .build())
            .build();

    int numParams = LayerFactories.getFactory(conf).initializer().numParams(conf, true);
    INDArray params = Nd4j.create(1, numParams);
    Layer layer = LayerFactories.getFactory(conf).create(conf, null, 0, params, true);
    Updater updater = UpdaterCreator.getUpdater(layer);
    int updaterStateSize = updater.stateSizeForLayer(layer);
    INDArray updaterState = Nd4j.create(1, updaterStateSize);
    updater.setStateViewArray(layer, updaterState, true);

    Gradient gradientDup = new DefaultGradient();
    gradientDup.setGradientFor(DefaultParamInitializer.WEIGHT_KEY, weightGradient.dup());
    gradientDup.setGradientFor(DefaultParamInitializer.BIAS_KEY, biasGradient.dup());

    for (int i = 0; i < 2; i++) {
      updater.update(layer, gradient, i, 1);

      // calculations for one iteration / update

      for (Map.Entry<String, INDArray> entry : gradientDup.gradientForVariable().entrySet()) {
        key = entry.getKey();
        val = entry.getValue();
        INDArray msgTmp = msg.get(key);
        INDArray msdxTmp = msdx.get(key);

        if (msgTmp == null) {
          msgTmp = Nd4j.zeros(val.shape());
          msdxTmp = Nd4j.zeros(val.shape());
        }

        msgTmp.muli(rho);
        msgTmp.addi(1 - rho).muli(val.mul(val));

        gradExpected =
            Transforms.sqrt(msdxTmp.add(Nd4j.EPS_THRESHOLD))
                .divi(Transforms.sqrt(msgTmp.add(Nd4j.EPS_THRESHOLD)))
                .muli(val);
        gradientDup.setGradientFor(key, gradExpected);
        assertEquals(gradExpected, gradient.getGradientFor(entry.getKey()));

        msdxTmp.muli(rho);
        dxSquared = gradExpected.mul(gradExpected);
        msdxTmp.addi(dxSquared.muli(1 - rho));

        msg.put(key, msgTmp);
        msdx.put(key, msdxTmp);
      }
      assertEquals(rho, layer.conf().getLayer().getRho(), 1e-4);
    }
  }
  public static void main(String... args) throws Exception {
    int numFeatures = 40;
    int iterations = 5;
    int seed = 123;
    int listenerFreq = iterations / 5;
    Nd4j.getRandom().setSeed(seed);

    log.info("Load dat....");
    INDArray input =
        Nd4j.create(
            2,
            numFeatures); // have to be at least two or else output layer gradient is a scalar and
    // cause exception
    INDArray labels = Nd4j.create(2, 2);

    INDArray row0 = Nd4j.create(1, numFeatures);
    row0.assign(0.1);
    input.putRow(0, row0);
    labels.put(0, 1, 1); // set the 4th column

    INDArray row1 = Nd4j.create(1, numFeatures);
    row1.assign(0.2);

    input.putRow(1, row1);
    labels.put(1, 0, 1); // set the 2nd column

    DataSet trainingSet = new DataSet(input, labels);

    log.info("Build model....");
    NeuralNetConfiguration conf =
        new NeuralNetConfiguration.Builder()
            .layer(new RBM())
            .nIn(trainingSet.numInputs())
            .nOut(trainingSet.numOutcomes())
            .seed(seed)
            .weightInit(WeightInit.SIZE)
            .constrainGradientToUnitNorm(true)
            .iterations(iterations)
            .activationFunction("tanh")
            .visibleUnit(RBM.VisibleUnit.GAUSSIAN)
            .hiddenUnit(RBM.HiddenUnit.RECTIFIED)
            .lossFunction(LossFunctions.LossFunction.RMSE_XENT)
            .learningRate(1e-1f)
            .optimizationAlgo(OptimizationAlgorithm.ITERATION_GRADIENT_DESCENT)
            .build();
    Layer model = LayerFactories.getFactory(conf).create(conf);
    model.setIterationListeners(
        Collections.singletonList((IterationListener) new ScoreIterationListener(listenerFreq)));

    log.info("Evaluate weights....");
    INDArray w = model.getParam(DefaultParamInitializer.WEIGHT_KEY);
    log.info("Weights: " + w);

    log.info("Train model....");
    model.fit(trainingSet.getFeatureMatrix());

    log.info("Visualize training results....");
    // Work in progress to get NeuralNetPlotter functioning
    NeuralNetPlotter plotter = new NeuralNetPlotter();
    plotter.plotNetworkGradient(model, model.gradient(), 10);
  }
Exemplo n.º 12
0
  @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());
  }