/** * Simple helper class to redirect legacy JSON format to {@link LegacyIActivationDeserializer} via annotation * on {@link org.nd4j.linalg.activations.IActivation} */ @JsonDeserialize(using = LegacyIActivationDeserializer.class) public class LegacyIActivationDeserializerHelper { private LegacyIActivationDeserializerHelper(){ } }
/** * Simple helper class to redirect legacy JSON format to {@link LegacyILossFunctionDeserializer} via annotation * on {@link org.nd4j.linalg.lossfunctions.ILossFunction} */ @JsonDeserialize(using = LegacyILossFunctionDeserializer.class) public class LegacyILossFunctionDeserializerHelper { private LegacyILossFunctionDeserializerHelper(){ } }
@JsonSerialize(using = JsonSerializerAtomicDouble.class) @JsonDeserialize(using = JsonDeserializerAtomicDouble.class) public class AtomicDouble extends com.google.common.util.concurrent.AtomicDouble {
@JsonDeserialize(using = RowVectorDeserializer.class) private final INDArray weights;
@JsonDeserialize(using = RowVectorDeserializer.class) protected final INDArray weights;
@JsonDeserialize(using = RowVectorDeserializer.class) private final INDArray weights;
@JsonDeserialize(using = RowVectorDeserializer.class) protected final INDArray weights;
@JsonDeserialize(using = RowVectorDeserializer.class) private INDArray weights;
@JsonDeserialize(using = RowVectorDeserializer.class) private final INDArray weights;
/** * A dummy helper "distribution" for deserializing distributions in legacy/different JSON format. * Used in conjuction with {@link LegacyDistributionDeserializer} to provide backward compatability; * see that class for details. * * @author Alex Black */ @JsonDeserialize(using = LegacyDistributionDeserializer.class) public class LegacyDistributionHelper extends Distribution { private LegacyDistributionHelper() { } }
@JsonSerialize(using = JsonSerializerAtomicDouble.class) @JsonDeserialize(using = JsonDeserializerAtomicDouble.class) public class AtomicDouble extends com.google.common.util.concurrent.AtomicDouble {
public class FixedValue<T> implements ParameterSpace<T> { @JsonSerialize(using = GenericSerializer.class) @JsonDeserialize(using = GenericDeserializer.class) private Object value; private int index;
@JsonDeserialize(using = RealDistributionDeserializer.class) private RealDistribution distribution; private int index = -1;
public class FixedValue<T> implements ParameterSpace<T> { @JsonSerialize(using = GenericSerializer.class) @JsonDeserialize(using = GenericDeserializer.class) private Object value; private int index;
@JsonDeserialize(using = RowVectorDeserializer.class) protected final INDArray weights;
@JsonDeserialize(using = RowVectorDeserializer.class) protected final INDArray weights;
@JsonDeserialize(using = NDArrayDeSerializer.class) private INDArray rDiagBinPosCount; @JsonSerialize(using = NDArraySerializer.class) @JsonDeserialize(using = NDArrayDeSerializer.class) private INDArray rDiagBinTotalCount; @JsonSerialize(using = NDArraySerializer.class) @JsonDeserialize(using = NDArrayDeSerializer.class) private INDArray rDiagBinSumPredictions; @JsonDeserialize(using = RowVectorDeserializer.class) private INDArray labelCountsEachClass; @JsonSerialize(using = RowVectorSerializer.class) @JsonDeserialize(using = RowVectorDeserializer.class) private INDArray predictionCountsEachClass; @JsonDeserialize(using = RowVectorDeserializer.class) private INDArray residualPlotOverall; @JsonSerialize(using = NDArraySerializer.class) @JsonDeserialize(using = NDArrayDeSerializer.class) private INDArray residualPlotByLabelClass; @JsonDeserialize(using = RowVectorDeserializer.class) private INDArray probHistogramOverall; //Simple histogram over all probabilities @JsonSerialize(using = NDArraySerializer.class) @JsonDeserialize(using = NDArrayDeSerializer.class) private INDArray probHistogramByLabelClass; //Histogram - for each label class separately
private int precision; @JsonSerialize(using = RowVectorSerializer.class) @JsonDeserialize(using = RowVectorDeserializer.class) private INDArray exampleCountPerColumn; //Necessary to account for per-output masking @JsonSerialize(using = RowVectorSerializer.class) @JsonDeserialize(using = RowVectorDeserializer.class) private INDArray labelsSumPerColumn; //sum(actual) per column -> used to calculate mean @JsonSerialize(using = RowVectorSerializer.class) @JsonDeserialize(using = RowVectorDeserializer.class) private INDArray sumSquaredErrorsPerColumn; //(predicted - actual)^2 @JsonSerialize(using = RowVectorSerializer.class) @JsonDeserialize(using = RowVectorDeserializer.class) private INDArray sumAbsErrorsPerColumn; //abs(predicted-actial) @JsonSerialize(using = RowVectorSerializer.class) @JsonDeserialize(using = RowVectorDeserializer.class) private INDArray currentMean; @JsonSerialize(using = RowVectorSerializer.class) @JsonDeserialize(using = RowVectorDeserializer.class) private INDArray currentPredictionMean; @JsonSerialize(using = RowVectorSerializer.class) @JsonDeserialize(using = RowVectorDeserializer.class) private INDArray sumOfProducts; @JsonSerialize(using = RowVectorSerializer.class) @JsonDeserialize(using = RowVectorDeserializer.class) private INDArray sumSquaredLabels; @JsonSerialize(using = RowVectorSerializer.class) @JsonDeserialize(using = RowVectorDeserializer.class) private INDArray sumSquaredPredicted;
@JsonDeserialize(using = RowVectorDeserializer.class) private final INDArray weights;
@JsonDeserialize(using = RowVectorDeserializer.class) private final INDArray weights;