Examples with WeightInit org.deeplearning4j.nn.weights.WeightInit used on opensource projects

Example 1 with WeightInit

use of org.deeplearning4j.nn.weights.WeightInit in project deeplearning4j by deeplearning4j.

the class MultiNeuralNetConfLayerBuilderTest method testNeuralNetConfigAPI.

@Test
public void testNeuralNetConfigAPI() {
    LossFunction newLoss = LossFunction.SQUARED_LOSS;
    int newNumIn = numIn + 1;
    int newNumOut = numOut + 1;
    WeightInit newWeight = WeightInit.UNIFORM;
    double newDrop = 0.5;
    int[] newFS = new int[] { 3, 3 };
    int newFD = 7;
    int[] newStride = new int[] { 3, 3 };
    Convolution.Type newConvType = Convolution.Type.SAME;
    PoolingType newPoolType = PoolingType.AVG;
    double newCorrupt = 0.5;
    double newSparsity = 0.5;
    HiddenUnit newHidden = HiddenUnit.BINARY;
    VisibleUnit newVisible = VisibleUnit.BINARY;
    MultiLayerConfiguration multiConf1 = new NeuralNetConfiguration.Builder().list().layer(0, new DenseLayer.Builder().nIn(newNumIn).nOut(newNumOut).activation(act).build()).layer(1, new DenseLayer.Builder().nIn(newNumIn + 1).nOut(newNumOut + 1).activation(act).build()).build();
    NeuralNetConfiguration firstLayer = multiConf1.getConf(0);
    NeuralNetConfiguration secondLayer = multiConf1.getConf(1);
    assertFalse(firstLayer.equals(secondLayer));
}

Example 2 with WeightInit

use of org.deeplearning4j.nn.weights.WeightInit in project deeplearning4j by deeplearning4j.

the class KerasLayer method getWeightInitFromConfig.

/**
     * Get weight initialization from Keras layer configuration.
     *
     * @param layerConfig       dictionary containing Keras layer configuration
     * @param enforceTrainingConfig
     * @return
     * @throws InvalidKerasConfigurationException
     * @throws UnsupportedKerasConfigurationException
     */
protected WeightInit getWeightInitFromConfig(Map<String, Object> layerConfig, boolean enforceTrainingConfig) throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
    Map<String, Object> innerConfig = getInnerLayerConfigFromConfig(layerConfig);
    if (!innerConfig.containsKey(LAYER_FIELD_INIT))
        throw new InvalidKerasConfigurationException("Keras layer is missing " + LAYER_FIELD_INIT + " field");
    String kerasInit = (String) innerConfig.get(LAYER_FIELD_INIT);
    WeightInit init;
    try {
        init = mapWeightInitialization(kerasInit);
    } catch (UnsupportedKerasConfigurationException e) {
        if (enforceTrainingConfig)
            throw e;
        else {
            init = WeightInit.XAVIER;
            log.warn("Unknown weight initializer " + kerasInit + " (Using XAVIER instead).");
        }
    }
    return init;
}

Example 3 with WeightInit

use of org.deeplearning4j.nn.weights.WeightInit in project deeplearning4j by deeplearning4j.

the class KerasLstm method getRecurrentWeightInitFromConfig.

/**
     * Get LSTM recurrent weight initialization from Keras layer configuration.
     *
     * @param layerConfig       dictionary containing Keras layer configuration
     * @return                  epsilon
     * @throws InvalidKerasConfigurationException
     */
public static WeightInit getRecurrentWeightInitFromConfig(Map<String, Object> layerConfig, boolean train) throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
    Map<String, Object> innerConfig = getInnerLayerConfigFromConfig(layerConfig);
    if (!innerConfig.containsKey(LAYER_FIELD_INNER_INIT))
        throw new InvalidKerasConfigurationException("Keras LSTM layer config missing " + LAYER_FIELD_INNER_INIT + " field");
    String kerasInit = (String) innerConfig.get(LAYER_FIELD_INNER_INIT);
    WeightInit init;
    try {
        init = mapWeightInitialization(kerasInit);
    } catch (UnsupportedKerasConfigurationException e) {
        if (train)
            throw e;
        else {
            init = WeightInit.XAVIER;
            log.warn("Unknown weight initializer " + kerasInit + " (Using XAVIER instead).");
        }
    }
    return init;
}

Example 4 with WeightInit

use of org.deeplearning4j.nn.weights.WeightInit in project deeplearning4j by deeplearning4j.

the class TrainModule method getLayerInfoTable.

private String[][] getLayerInfoTable(int layerIdx, TrainModuleUtils.GraphInfo gi, I18N i18N, boolean noData, StatsStorage ss, String wid) {
    List<String[]> layerInfoRows = new ArrayList<>();
    layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerName"), gi.getVertexNames().get(layerIdx) });
    layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerType"), "" });
    if (!noData) {
        Persistable p = ss.getStaticInfo(currentSessionID, StatsListener.TYPE_ID, wid);
        if (p != null) {
            StatsInitializationReport initReport = (StatsInitializationReport) p;
            String configJson = initReport.getModelConfigJson();
            String modelClass = initReport.getModelClassName();
            //TODO error handling...
            String layerType = "";
            Layer layer = null;
            NeuralNetConfiguration nnc = null;
            if (modelClass.endsWith("MultiLayerNetwork")) {
                MultiLayerConfiguration conf = MultiLayerConfiguration.fromJson(configJson);
                //-1 because of input
                int confIdx = layerIdx - 1;
                if (confIdx >= 0) {
                    nnc = conf.getConf(confIdx);
                    layer = nnc.getLayer();
                } else {
                    //Input layer
                    layerType = "Input";
                }
            } else if (modelClass.endsWith("ComputationGraph")) {
                ComputationGraphConfiguration conf = ComputationGraphConfiguration.fromJson(configJson);
                String vertexName = gi.getVertexNames().get(layerIdx);
                Map<String, GraphVertex> vertices = conf.getVertices();
                if (vertices.containsKey(vertexName) && vertices.get(vertexName) instanceof LayerVertex) {
                    LayerVertex lv = (LayerVertex) vertices.get(vertexName);
                    nnc = lv.getLayerConf();
                    layer = nnc.getLayer();
                } else if (conf.getNetworkInputs().contains(vertexName)) {
                    layerType = "Input";
                } else {
                    GraphVertex gv = conf.getVertices().get(vertexName);
                    if (gv != null) {
                        layerType = gv.getClass().getSimpleName();
                    }
                }
            } else if (modelClass.endsWith("VariationalAutoencoder")) {
                layerType = gi.getVertexTypes().get(layerIdx);
                Map<String, String> map = gi.getVertexInfo().get(layerIdx);
                for (Map.Entry<String, String> entry : map.entrySet()) {
                    layerInfoRows.add(new String[] { entry.getKey(), entry.getValue() });
                }
            }
            if (layer != null) {
                layerType = getLayerType(layer);
            }
            if (layer != null) {
                String activationFn = null;
                if (layer instanceof FeedForwardLayer) {
                    FeedForwardLayer ffl = (FeedForwardLayer) layer;
                    layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerNIn"), String.valueOf(ffl.getNIn()) });
                    layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerSize"), String.valueOf(ffl.getNOut()) });
                    activationFn = layer.getActivationFn().toString();
                }
                int nParams = layer.initializer().numParams(nnc);
                layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerNParams"), String.valueOf(nParams) });
                if (nParams > 0) {
                    WeightInit wi = layer.getWeightInit();
                    String str = wi.toString();
                    if (wi == WeightInit.DISTRIBUTION) {
                        str += layer.getDist();
                    }
                    layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerWeightInit"), str });
                    Updater u = layer.getUpdater();
                    String us = (u == null ? "" : u.toString());
                    layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerUpdater"), us });
                //TODO: Maybe L1/L2, dropout, updater-specific values etc
                }
                if (layer instanceof ConvolutionLayer || layer instanceof SubsamplingLayer) {
                    int[] kernel;
                    int[] stride;
                    int[] padding;
                    if (layer instanceof ConvolutionLayer) {
                        ConvolutionLayer cl = (ConvolutionLayer) layer;
                        kernel = cl.getKernelSize();
                        stride = cl.getStride();
                        padding = cl.getPadding();
                    } else {
                        SubsamplingLayer ssl = (SubsamplingLayer) layer;
                        kernel = ssl.getKernelSize();
                        stride = ssl.getStride();
                        padding = ssl.getPadding();
                        activationFn = null;
                        layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerSubsamplingPoolingType"), ssl.getPoolingType().toString() });
                    }
                    layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerCnnKernel"), Arrays.toString(kernel) });
                    layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerCnnStride"), Arrays.toString(stride) });
                    layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerCnnPadding"), Arrays.toString(padding) });
                }
                if (activationFn != null) {
                    layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerActivationFn"), activationFn });
                }
            }
            layerInfoRows.get(1)[1] = layerType;
        }
    }
    return layerInfoRows.toArray(new String[layerInfoRows.size()][0]);
}

Example 5 with WeightInit

use of org.deeplearning4j.nn.weights.WeightInit in project deeplearning4j by deeplearning4j.

the class FineTuneConfiguration method applyToNeuralNetConfiguration.

public void applyToNeuralNetConfiguration(NeuralNetConfiguration nnc) {
    Layer l = nnc.getLayer();
    Updater originalUpdater = null;
    WeightInit origWeightInit = null;
    if (l != null) {
        originalUpdater = l.getUpdater();
        origWeightInit = l.getWeightInit();
        if (activationFn != null)
            l.setActivationFn(activationFn);
        if (weightInit != null)
            l.setWeightInit(weightInit);
        if (biasInit != null)
            l.setBiasInit(biasInit);
        if (dist != null)
            l.setDist(dist);
        if (learningRate != null) {
            //usually the same learning rate is applied to both bias and weights
            //so always overwrite the learning rate to both?
            l.setLearningRate(learningRate);
            l.setBiasLearningRate(learningRate);
        }
        if (biasLearningRate != null)
            l.setBiasLearningRate(biasLearningRate);
        if (learningRateSchedule != null)
            l.setLearningRateSchedule(learningRateSchedule);
        //        if(lrScoreBasedDecay != null)
        if (l1 != null)
            l.setL1(l1);
        if (l2 != null)
            l.setL2(l2);
        if (l1Bias != null)
            l.setL1Bias(l1Bias);
        if (l2Bias != null)
            l.setL2Bias(l2Bias);
        if (dropOut != null)
            l.setDropOut(dropOut);
        if (updater != null)
            l.setUpdater(updater);
        if (momentum != null)
            l.setMomentum(momentum);
        if (momentumSchedule != null)
            l.setMomentum(momentum);
        if (epsilon != null)
            l.setEpsilon(epsilon);
        if (rho != null)
            l.setRho(rho);
        if (rmsDecay != null)
            l.setRmsDecay(rmsDecay);
        if (adamMeanDecay != null)
            l.setAdamMeanDecay(adamMeanDecay);
        if (adamVarDecay != null)
            l.setAdamVarDecay(adamVarDecay);
    }
    if (miniBatch != null)
        nnc.setMiniBatch(miniBatch);
    if (numIterations != null)
        nnc.setNumIterations(numIterations);
    if (maxNumLineSearchIterations != null)
        nnc.setMaxNumLineSearchIterations(maxNumLineSearchIterations);
    if (seed != null)
        nnc.setSeed(seed);
    if (useRegularization != null)
        nnc.setUseRegularization(useRegularization);
    if (optimizationAlgo != null)
        nnc.setOptimizationAlgo(optimizationAlgo);
    if (stepFunction != null)
        nnc.setStepFunction(stepFunction);
    if (useDropConnect != null)
        nnc.setUseDropConnect(useDropConnect);
    if (minimize != null)
        nnc.setMinimize(minimize);
    if (gradientNormalization != null)
        l.setGradientNormalization(gradientNormalization);
    if (gradientNormalizationThreshold != null)
        l.setGradientNormalizationThreshold(gradientNormalizationThreshold);
    if (learningRatePolicy != null)
        nnc.setLearningRatePolicy(learningRatePolicy);
    if (lrPolicySteps != null)
        nnc.setLrPolicySteps(lrPolicySteps);
    if (lrPolicyPower != null)
        nnc.setLrPolicyPower(lrPolicyPower);
    if (convolutionMode != null && l instanceof ConvolutionLayer) {
        ((ConvolutionLayer) l).setConvolutionMode(convolutionMode);
    }
    if (convolutionMode != null && l instanceof SubsamplingLayer) {
        ((SubsamplingLayer) l).setConvolutionMode(convolutionMode);
    }
    //Check the updater config. If we change updaters, we want to remove the old config to avoid warnings
    if (l != null && updater != null && originalUpdater != null && updater != originalUpdater) {
        switch(originalUpdater) {
            case ADAM:
                if (adamMeanDecay == null)
                    l.setAdamMeanDecay(Double.NaN);
                if (adamVarDecay == null)
                    l.setAdamVarDecay(Double.NaN);
                break;
            case ADADELTA:
                if (rho == null)
                    l.setRho(Double.NaN);
                if (epsilon == null)
                    l.setEpsilon(Double.NaN);
                break;
            case NESTEROVS:
                if (momentum == null)
                    l.setMomentum(Double.NaN);
                if (momentumSchedule == null)
                    l.setMomentumSchedule(null);
                if (epsilon == null)
                    l.setEpsilon(Double.NaN);
                break;
            case ADAGRAD:
                if (epsilon == null)
                    l.setEpsilon(Double.NaN);
                break;
            case RMSPROP:
                if (rmsDecay == null)
                    l.setRmsDecay(Double.NaN);
                if (epsilon == null)
                    l.setEpsilon(Double.NaN);
                break;
        }
    }
    //Check weight init. Remove dist if originally was DISTRIBUTION, and isn't now -> remove no longer needed distribution
    if (l != null && origWeightInit == WeightInit.DISTRIBUTION && weightInit != null && weightInit != WeightInit.DISTRIBUTION) {
        l.setDist(null);
    }
    //Perform validation. This also sets the defaults for updaters. For example, Updater.RMSProp -> set rmsDecay
    if (l != null) {
        LayerValidation.updaterValidation(l.getLayerName(), l, momentum, momentumSchedule, adamMeanDecay, adamVarDecay, rho, rmsDecay, epsilon);
        boolean useDropCon = (useDropConnect == null ? nnc.isUseDropConnect() : useDropConnect);
        LayerValidation.generalValidation(l.getLayerName(), l, nnc.isUseRegularization(), useDropCon, dropOut, l2, l2Bias, l1, l1Bias, dist);
    }
    //Also: update the LR, L1 and L2 maps, based on current config (which might be different to original config)
    if (nnc.variables(false) != null) {
        for (String s : nnc.variables(false)) {
            nnc.setLayerParamLR(s);
        }
    }
}