PythonBigDLKeras

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1. final def !=(arg0: AnyRef): Boolean

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2. final def !=(arg0: Any): Boolean

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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5. final def ==(arg0: Any): Boolean

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6. def activityToJTensors(outputActivity: Activity): List[JTensor]

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   PythonBigDL

7. def addScheduler(seq: SequentialSchedule, scheduler: LearningRateSchedule, maxIteration: Int): SequentialSchedule

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   PythonBigDL

8. final def asInstanceOf[T0]: T0

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   Any

9. def batching(dataset: DataSet[dataset.Sample[T]], batchSize: Int): DataSet[MiniBatch[T]]

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   PythonBigDL

10. def clone(): AnyRef

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    protected[java.lang]

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    @throws( ... )

11. def compile(module: KerasModel[T], optimizer: OptimMethod[T], loss: Criterion[T], metrics: List[ValidationMethod[T]] = null): Unit

12. def createAbs(): Abs[T]

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    PythonBigDL

13. def createAbsCriterion(sizeAverage: Boolean = true): AbsCriterion[T]

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14. def createActivityRegularization(l1: Double, l2: Double): ActivityRegularization[T]

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15. def createAdadelta(decayRate: Double = 0.9, Epsilon: Double = 1e-10): Adadelta[T]

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16. def createAdagrad(learningRate: Double = 1e-3, learningRateDecay: Double = 0.0, weightDecay: Double = 0.0): Adagrad[T]

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    PythonBigDL

17. def createAdam(learningRate: Double = 1e-3, learningRateDecay: Double = 0.0, beta1: Double = 0.9, beta2: Double = 0.999, Epsilon: Double = 1e-8): Adam[T]

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    PythonBigDL

18. def createAdamax(learningRate: Double = 0.002, beta1: Double = 0.9, beta2: Double = 0.999, Epsilon: Double = 1e-38): Adamax[T]

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19. def createAdd(inputSize: Int): Add[T]

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20. def createAddConstant(constant_scalar: Double, inplace: Boolean = false): AddConstant[T]

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    PythonBigDL

21. def createAspectScale(scale: Int, scaleMultipleOf: Int, maxSize: Int, resizeMode: Int = 1, useScaleFactor: Boolean = true, minScale: Double = 1): FeatureTransformer

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    PythonBigDL

22. def createBCECriterion(weights: JTensor = null, sizeAverage: Boolean = true): BCECriterion[T]

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    PythonBigDL

23. def createBatchNormalization(nOutput: Int, eps: Double = 1e-5, momentum: Double = 0.1, affine: Boolean = true, initWeight: JTensor = null, initBias: JTensor = null, initGradWeight: JTensor = null, initGradBias: JTensor = null): BatchNormalization[T]

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    PythonBigDL

24. def createBiRecurrent(merge: AbstractModule[Table, Tensor[T], T] = null): BiRecurrent[T]

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    PythonBigDL

25. def createBifurcateSplitTable(dimension: Int): BifurcateSplitTable[T]

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    PythonBigDL

26. def createBilinear(inputSize1: Int, inputSize2: Int, outputSize: Int, biasRes: Boolean = true, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null): Bilinear[T]

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    PythonBigDL

27. def createBilinearFiller(): BilinearFiller.type

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    PythonBigDL

28. def createBinaryThreshold(th: Double, ip: Boolean): BinaryThreshold[T]

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    PythonBigDL

29. def createBinaryTreeLSTM(inputSize: Int, hiddenSize: Int, gateOutput: Boolean = true, withGraph: Boolean = true): BinaryTreeLSTM[T]

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    PythonBigDL

30. def createBottle(module: AbstractModule[Activity, Activity, T], nInputDim: Int = 2, nOutputDim1: Int = Int.MaxValue): Bottle[T]

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    PythonBigDL

31. def createBrightness(deltaLow: Double, deltaHigh: Double): Brightness

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    PythonBigDL

32. def createBytesToMat(byteKey: String): BytesToMat

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    PythonBigDL

33. def createCAdd(size: List[Int], bRegularizer: Regularizer[T] = null): CAdd[T]

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    PythonBigDL

34. def createCAddTable(inplace: Boolean = false): CAddTable[T, T]

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    PythonBigDL

35. def createCAveTable(inplace: Boolean = false): CAveTable[T]

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36. def createCDivTable(): CDivTable[T]

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    PythonBigDL

37. def createCMaxTable(): CMaxTable[T]

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38. def createCMinTable(): CMinTable[T]

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39. def createCMul(size: List[Int], wRegularizer: Regularizer[T] = null): CMul[T]

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    PythonBigDL

40. def createCMulTable(): CMulTable[T]

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    PythonBigDL

41. def createCSubTable(): CSubTable[T]

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42. def createCategoricalCrossEntropy(): CategoricalCrossEntropy[T]

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    PythonBigDL

43. def createCenterCrop(cropWidth: Int, cropHeight: Int, isClip: Boolean): CenterCrop

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    PythonBigDL

44. def createChannelNormalize(meanR: Double, meanG: Double, meanB: Double, stdR: Double = 1, stdG: Double = 1, stdB: Double = 1): FeatureTransformer

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    PythonBigDL

45. def createChannelOrder(): ChannelOrder

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46. def createChannelScaledNormalizer(meanR: Int, meanG: Int, meanB: Int, scale: Double): ChannelScaledNormalizer

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    PythonBigDL

47. def createClamp(min: Int, max: Int): Clamp[T]

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    PythonBigDL

48. def createClassNLLCriterion(weights: JTensor = null, sizeAverage: Boolean = true, logProbAsInput: Boolean = true): ClassNLLCriterion[T]

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    PythonBigDL

49. def createClassSimplexCriterion(nClasses: Int): ClassSimplexCriterion[T]

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    PythonBigDL

50. def createColorJitter(brightnessProb: Double = 0.5, brightnessDelta: Double = 32, contrastProb: Double = 0.5, contrastLower: Double = 0.5, contrastUpper: Double = 1.5, hueProb: Double = 0.5, hueDelta: Double = 18, saturationProb: Double = 0.5, saturationLower: Double = 0.5, saturationUpper: Double = 1.5, randomOrderProb: Double = 0, shuffle: Boolean = false): ColorJitter

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    PythonBigDL

51. def createConcat(dimension: Int): Concat[T]

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    PythonBigDL

52. def createConcatTable(): ConcatTable[T]

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53. def createConstInitMethod(value: Double): ConstInitMethod

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    PythonBigDL

54. def createContiguous(): Contiguous[T]

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    PythonBigDL

55. def createContrast(deltaLow: Double, deltaHigh: Double): Contrast

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    PythonBigDL

56. def createConvLSTMPeephole(inputSize: Int, outputSize: Int, kernelI: Int, kernelC: Int, stride: Int = 1, padding: Int = 1, activation: TensorModule[T] = null, innerActivation: TensorModule[T] = null, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, cRegularizer: Regularizer[T] = null, withPeephole: Boolean = true): ConvLSTMPeephole[T]

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    PythonBigDL

57. def createConvLSTMPeephole3D(inputSize: Int, outputSize: Int, kernelI: Int, kernelC: Int, stride: Int = 1, padding: Int = 1, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, cRegularizer: Regularizer[T] = null, withPeephole: Boolean = true): ConvLSTMPeephole3D[T]

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    PythonBigDL

58. def createCosine(inputSize: Int, outputSize: Int): Cosine[T]

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    PythonBigDL

59. def createCosineDistance(): CosineDistance[T]

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60. def createCosineDistanceCriterion(sizeAverage: Boolean = true): CosineDistanceCriterion[T]

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61. def createCosineEmbeddingCriterion(margin: Double = 0.0, sizeAverage: Boolean = true): CosineEmbeddingCriterion[T]

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62. def createCosineProximityCriterion(): CosineProximityCriterion[T]

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    PythonBigDL

63. def createCropping2D(heightCrop: List[Int], widthCrop: List[Int], dataFormat: String = "NCHW"): Cropping2D[T]

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    PythonBigDL

64. def createCropping3D(dim1Crop: List[Int], dim2Crop: List[Int], dim3Crop: List[Int], dataFormat: String = Cropping3D.CHANNEL_FIRST): Cropping3D[T]

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65. def createCrossEntropyCriterion(weights: JTensor = null, sizeAverage: Boolean = true): CrossEntropyCriterion[T]

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    PythonBigDL

66. def createCrossProduct(numTensor: Int = 0, embeddingSize: Int = 0): CrossProduct[T]

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    PythonBigDL

67. def createDLClassifier(model: Module[T], criterion: Criterion[T], featureSize: ArrayList[Int], labelSize: ArrayList[Int]): DLClassifier[T]

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    PythonBigDL

68. def createDLClassifierModel(model: Module[T], featureSize: ArrayList[Int]): DLClassifierModel[T]

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    PythonBigDL

69. def createDLEstimator(model: Module[T], criterion: Criterion[T], featureSize: ArrayList[Int], labelSize: ArrayList[Int]): DLEstimator[T]

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    PythonBigDL

70. def createDLImageTransformer(transformer: FeatureTransformer): DLImageTransformer

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    PythonBigDL

71. def createDLModel(model: Module[T], featureSize: ArrayList[Int]): DLModel[T]

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    PythonBigDL

72. def createDatasetFromImageFrame(imageFrame: ImageFrame): DataSet[ImageFeature]

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73. def createDefault(): Default

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    PythonBigDL

74. def createDenseToSparse(): DenseToSparse[T]

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    PythonBigDL

75. def createDetectionCrop(roiKey: String, normalized: Boolean): DetectionCrop

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    PythonBigDL

76. def createDetectionOutputFrcnn(nmsThresh: Float = 0.3f, nClasses: Int, bboxVote: Boolean, maxPerImage: Int = 100, thresh: Double = 0.05): DetectionOutputFrcnn

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    PythonBigDL

77. def createDetectionOutputSSD(nClasses: Int, shareLocation: Boolean, bgLabel: Int, nmsThresh: Double, nmsTopk: Int, keepTopK: Int, confThresh: Double, varianceEncodedInTarget: Boolean, confPostProcess: Boolean): DetectionOutputSSD[T]

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78. def createDiceCoefficientCriterion(sizeAverage: Boolean = true, epsilon: Float = 1.0f): DiceCoefficientCriterion[T]

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    PythonBigDL

79. def createDistKLDivCriterion(sizeAverage: Boolean = true): DistKLDivCriterion[T]

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    PythonBigDL

80. def createDistriOptimizer(model: AbstractModule[Activity, Activity, T], trainingRdd: JavaRDD[Sample], criterion: Criterion[T], optimMethod: Map[String, OptimMethod[T]], endTrigger: Trigger, batchSize: Int): Optimizer[T, MiniBatch[T]]

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    PythonBigDL

81. def createDistriOptimizerFromDataSet(model: AbstractModule[Activity, Activity, T], trainDataSet: DataSet[ImageFeature], criterion: Criterion[T], optimMethod: Map[String, OptimMethod[T]], endTrigger: Trigger, batchSize: Int): Optimizer[T, MiniBatch[T]]

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    PythonBigDL

82. def createDistributedImageFrame(imageRdd: JavaRDD[JTensor], labelRdd: JavaRDD[JTensor]): DistributedImageFrame

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    PythonBigDL

83. def createDotProduct(): DotProduct[T]

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    PythonBigDL

84. def createDotProductCriterion(sizeAverage: Boolean = false): DotProductCriterion[T]

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85. def createDropout(initP: Double = 0.5, inplace: Boolean = false, scale: Boolean = true): Dropout[T]

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    PythonBigDL

86. def createELU(alpha: Double = 1.0, inplace: Boolean = false): ELU[T]

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    PythonBigDL

87. def createEcho(): Echo[T]

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    PythonBigDL

88. def createEuclidean(inputSize: Int, outputSize: Int, fastBackward: Boolean = true): Euclidean[T]

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    PythonBigDL

89. def createEveryEpoch(): Trigger

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    PythonBigDL

90. def createExp(): Exp[T]

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    PythonBigDL

91. def createExpand(meansR: Int = 123, meansG: Int = 117, meansB: Int = 104, minExpandRatio: Double = 1.0, maxExpandRatio: Double = 4.0): Expand

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    PythonBigDL

92. def createExponential(decayStep: Int, decayRate: Double, stairCase: Boolean = false): Exponential

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    PythonBigDL

93. def createFiller(startX: Double, startY: Double, endX: Double, endY: Double, value: Int = 255): Filler

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    PythonBigDL

94. def createFixExpand(eh: Int, ew: Int): FixExpand

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    PythonBigDL

95. def createFixedCrop(wStart: Double, hStart: Double, wEnd: Double, hEnd: Double, normalized: Boolean, isClip: Boolean): FixedCrop

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    PythonBigDL

96. def createFlattenTable(): FlattenTable[T]

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    PythonBigDL

97. def createFtrl(learningRate: Double = 1e-3, learningRatePower: Double = 0.5, initialAccumulatorValue: Double = 0.1, l1RegularizationStrength: Double = 0.0, l2RegularizationStrength: Double = 0.0, l2ShrinkageRegularizationStrength: Double = 0.0): Ftrl[T]

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    PythonBigDL

98. def createGRU(inputSize: Int, outputSize: Int, p: Double = 0, activation: TensorModule[T] = null, innerActivation: TensorModule[T] = null, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null): GRU[T]

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    PythonBigDL

99. def createGaussianCriterion(): GaussianCriterion[T]

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    PythonBigDL

100. def createGaussianDropout(rate: Double): GaussianDropout[T]

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     PythonBigDL

101. def createGaussianNoise(stddev: Double): GaussianNoise[T]

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102. def createGaussianSampler(): GaussianSampler[T]

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103. def createGradientReversal(lambda: Double = 1): GradientReversal[T]

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     PythonBigDL

104. def createHFlip(): HFlip

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     PythonBigDL

105. def createHardShrink(lambda: Double = 0.5): HardShrink[T]

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     PythonBigDL

106. def createHardSigmoid: HardSigmoid[T]

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     PythonBigDL

107. def createHardTanh(minValue: Double = 1, maxValue: Double = 1, inplace: Boolean = false): HardTanh[T]

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     PythonBigDL

108. def createHighway(size: Int, withBias: Boolean, activation: TensorModule[T] = null, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null): Graph[T]

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     PythonBigDL

109. def createHingeEmbeddingCriterion(margin: Double = 1, sizeAverage: Boolean = true): HingeEmbeddingCriterion[T]

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     PythonBigDL

110. def createHitRatio(k: Int = 10, negNum: Int = 100): ValidationMethod[T]

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     PythonBigDL

111. def createHue(deltaLow: Double, deltaHigh: Double): Hue

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     PythonBigDL

112. def createIdentity(): Identity[T]

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     PythonBigDL

113. def createImageFeature(data: JTensor = null, label: JTensor = null, uri: String = null): ImageFeature

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     PythonBigDL

114. def createImageFrameToSample(inputKeys: List[String], targetKeys: List[String], sampleKey: String): ImageFrameToSample[T]

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     PythonBigDL

115. def createIndex(dimension: Int): Index[T]

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     PythonBigDL

116. def createInferReshape(size: List[Int], batchMode: Boolean = false): InferReshape[T]

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     PythonBigDL

117. def createInput(): ModuleNode[T]

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     PythonBigDL

118. def createJoinTable(dimension: Int, nInputDims: Int): JoinTable[T]

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     PythonBigDL

119. def createKLDCriterion(sizeAverage: Boolean): KLDCriterion[T]

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     PythonBigDL

120. def createKerasActivation(activation: String, inputShape: List[Int] = null): Activation[T]

121. def createKerasAtrousConvolution1D(nbFilter: Int, filterLength: Int, init: String = "glorot_uniform", activation: String = null, subsampleLength: Int = 1, atrousRate: Int = 1, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, inputShape: List[Int] = null): AtrousConvolution1D[T]

122. def createKerasAtrousConvolution2D(nbFilter: Int, nbRow: Int, nbCol: Int, init: String = "glorot_uniform", activation: String = null, subsample: List[Int], atrousRate: List[Int], dimOrdering: String = "th", wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, inputShape: List[Int] = null): AtrousConvolution2D[T]

123. def createKerasAveragePooling1D(poolLength: Int = 2, stride: Int = 1, borderMode: String = "valid", inputShape: List[Int] = null): AveragePooling1D[T]

124. def createKerasAveragePooling2D(poolSize: List[Int], strides: List[Int], borderMode: String = "valid", dimOrdering: String = "th", inputShape: List[Int] = null): AveragePooling2D[T]

125. def createKerasAveragePooling3D(poolSize: List[Int], strides: List[Int], dimOrdering: String = "th", inputShape: List[Int] = null): AveragePooling3D[T]

126. def createKerasBatchNormalization(epsilon: Double = 0.001, momentum: Double = 0.99, betaInit: String = "zero", gammaInit: String = "one", dimOrdering: String = "th", inputShape: List[Int] = null): BatchNormalization[T]

127. def createKerasBidirectional(layer: Recurrent[T], mergeMode: String = "concat", inputShape: List[Int] = null): Bidirectional[T]

128. def createKerasConvLSTM2D(nbFilter: Int, nbKernel: Int, activation: String = "tanh", innerActivation: String = "hard_sigmoid", dimOrdering: String = "th", subsample: Int = 1, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, returnSequences: Boolean = false, goBackwards: Boolean = false, inputShape: List[Int] = null): ConvLSTM2D[T]

129. def createKerasConvolution1D(nbFilter: Int, filterLength: Int, init: String = "glorot_uniform", activation: String = null, borderMode: String = "valid", subsampleLength: Int = 1, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: List[Int] = null): Convolution1D[T]

130. def createKerasConvolution2D(nbFilter: Int, nbRow: Int, nbCol: Int, init: String = "glorot_uniform", activation: String = null, borderMode: String = "valid", subsample: List[Int], dimOrdering: String = "th", wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: List[Int] = null): Convolution2D[T]

131. def createKerasConvolution3D(nbFilter: Int, kernelDim1: Int, kernelDim2: Int, kernelDim3: Int, init: String = "glorot_uniform", activation: String = null, borderMode: String = "valid", subsample: List[Int], dimOrdering: String = "th", wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: List[Int] = null): Convolution3D[T]

132. def createKerasCropping1D(cropping: List[Int], inputShape: List[Int] = null): Cropping1D[T]

133. def createKerasCropping2D(heightCrop: List[Int], widthCrop: List[Int], dimOrdering: String = "th", inputShape: List[Int] = null): Cropping2D[T]

134. def createKerasCropping3D(dim1Crop: List[Int], dim2Crop: List[Int], dim3Crop: List[Int], dimOrdering: String = "th", inputShape: List[Int] = null): Cropping3D[T]

135. def createKerasDeconvolution2D(nbFilter: Int, nbRow: Int, nbCol: Int, init: String = "glorot_uniform", activation: String = null, subsample: List[Int], dimOrdering: String = "th", wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: List[Int] = null): Deconvolution2D[T]

136. def createKerasDense(outputDim: Int, init: String = "glorot_uniform", activation: String = null, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: List[Int] = null): Dense[T]

137. def createKerasDropout(p: Double, inputShape: List[Int] = null): Dropout[T]

138. def createKerasELU(alpha: Double = 1.0, inputShape: List[Int] = null): ELU[T]

139. def createKerasEmbedding(inputDim: Int, outputDim: Int, init: String = "uniform", wRegularizer: Regularizer[T] = null, inputShape: List[Int] = null): Embedding[T]

140. def createKerasFlatten(inputShape: List[Int] = null): Flatten[T]

141. def createKerasGRU(outputDim: Int, activation: String = "tanh", innerActivation: String = "hard_sigmoid", returnSequences: Boolean = false, goBackwards: Boolean = false, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, inputShape: List[Int] = null): GRU[T]

142. def createKerasGaussianDropout(p: Double, inputShape: List[Int] = null): GaussianDropout[T]

143. def createKerasGaussianNoise(sigma: Double, inputShape: List[Int] = null): GaussianNoise[T]

144. def createKerasGlobalAveragePooling1D(inputShape: List[Int] = null): GlobalAveragePooling1D[T]

145. def createKerasGlobalAveragePooling2D(dimOrdering: String = "th", inputShape: List[Int] = null): GlobalAveragePooling2D[T]

146. def createKerasGlobalAveragePooling3D(dimOrdering: String = "th", inputShape: List[Int] = null): GlobalAveragePooling3D[T]

147. def createKerasGlobalMaxPooling1D(inputShape: List[Int] = null): GlobalMaxPooling1D[T]

148. def createKerasGlobalMaxPooling2D(dimOrdering: String = "th", inputShape: List[Int] = null): GlobalMaxPooling2D[T]

149. def createKerasGlobalMaxPooling3D(dimOrdering: String = "th", inputShape: List[Int] = null): GlobalMaxPooling3D[T]

150. def createKerasHighway(activation: String = null, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: List[Int] = null): Highway[T]

151. def createKerasInput(name: String = null, inputShape: List[Int] = null): ModuleNode[T]

152. def createKerasInputLayer(inputShape: List[Int] = null): KerasLayer[Activity, Activity, T]

153. def createKerasLSTM(outputDim: Int, activation: String = "tanh", innerActivation: String = "hard_sigmoid", returnSequences: Boolean = false, goBackwards: Boolean = false, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, inputShape: List[Int] = null): LSTM[T]

154. def createKerasLeakyReLU(alpha: Double = 0.01, inputShape: List[Int] = null): LeakyReLU[T]

155. def createKerasLocallyConnected1D(nbFilter: Int, filterLength: Int, activation: String = null, subsampleLength: Int = 1, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: List[Int] = null): LocallyConnected1D[T]

156. def createKerasLocallyConnected2D(nbFilter: Int, nbRow: Int, nbCol: Int, activation: String = null, borderMode: String = "valid", subsample: List[Int], dimOrdering: String = "th", wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: List[Int] = null): LocallyConnected2D[T]

157. def createKerasMasking(maskValue: Double = 0.0, inputShape: List[Int] = null): Masking[T]

158. def createKerasMaxPooling1D(poolLength: Int = 2, stride: Int = 1, borderMode: String = "valid", inputShape: List[Int] = null): MaxPooling1D[T]

159. def createKerasMaxPooling2D(poolSize: List[Int], strides: List[Int], borderMode: String = "valid", dimOrdering: String = "th", inputShape: List[Int] = null): MaxPooling2D[T]

160. def createKerasMaxPooling3D(poolSize: List[Int], strides: List[Int], dimOrdering: String = "th", inputShape: List[Int] = null): MaxPooling3D[T]

161. def createKerasMaxoutDense(outputDim: Int, nbFeature: Int = 4, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: List[Int] = null): MaxoutDense[T]

162. def createKerasMerge(layers: List[AbstractModule[Activity, Activity, T]] = null, mode: String = "sum", concatAxis: Int = 1, inputShape: List[List[Int]]): Merge[T]

163. def createKerasModel(input: List[ModuleNode[T]], output: List[ModuleNode[T]]): Model[T]

164. def createKerasPermute(dims: List[Int], inputShape: List[Int] = null): Permute[T]

165. def createKerasRepeatVector(n: Int, inputShape: List[Int] = null): RepeatVector[T]

166. def createKerasReshape(targetShape: List[Int], inputShape: List[Int] = null): Reshape[T]

167. def createKerasSReLU(tLeftInit: String = "zero", aLeftInit: String = "glorot_uniform", tRightInit: String = "glorot_uniform", aRightInit: String = "one", sharedAxes: List[Int] = null, inputShape: List[Int] = null): SReLU[T]

168. def createKerasSeparableConvolution2D(nbFilter: Int, nbRow: Int, nbCol: Int, init: String = "glorot_uniform", activation: String = null, borderMode: String = "valid", subsample: List[Int], depthMultiplier: Int = 1, dimOrdering: String = "th", depthwiseRegularizer: Regularizer[T] = null, pointwiseRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: List[Int] = null): SeparableConvolution2D[T]

169. def createKerasSequential(): Sequential[T]

170. def createKerasSimpleRNN(outputDim: Int, activation: String = "tanh", returnSequences: Boolean = false, goBackwards: Boolean = false, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, inputShape: List[Int] = null): SimpleRNN[T]

171. def createKerasSpatialDropout1D(p: Double = 0.5, inputShape: List[Int] = null): SpatialDropout1D[T]

172. def createKerasSpatialDropout2D(p: Double = 0.5, dimOrdering: String = "th", inputShape: List[Int] = null): SpatialDropout2D[T]

173. def createKerasSpatialDropout3D(p: Double = 0.5, dimOrdering: String = "th", inputShape: List[Int] = null): SpatialDropout3D[T]

174. def createKerasThresholdedReLU(theta: Double = 1.0, inputShape: List[Int] = null): ThresholdedReLU[T]

175. def createKerasTimeDistributed(layer: KerasLayer[Tensor[T], Tensor[T], T], inputShape: List[Int] = null): TimeDistributed[T]

176. def createKerasUpSampling1D(length: Int = 2, inputShape: List[Int] = null): UpSampling1D[T]

177. def createKerasUpSampling2D(size: List[Int], dimOrdering: String = "th", inputShape: List[Int] = null): UpSampling2D[T]

178. def createKerasUpSampling3D(size: List[Int], dimOrdering: String = "th", inputShape: List[Int] = null): UpSampling3D[T]

179. def createKerasZeroPadding1D(padding: List[Int], inputShape: List[Int] = null): ZeroPadding1D[T]

180. def createKerasZeroPadding2D(padding: List[Int], dimOrdering: String = "th", inputShape: List[Int] = null): ZeroPadding2D[T]

181. def createKerasZeroPadding3D(padding: List[Int], dimOrdering: String = "th", inputShape: List[Int] = null): ZeroPadding3D[T]

182. def createKullbackLeiblerDivergenceCriterion: KullbackLeiblerDivergenceCriterion[T]

     Definition Classes

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183. def createL1Cost(): L1Cost[T]

     Definition Classes

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184. def createL1HingeEmbeddingCriterion(margin: Double = 1): L1HingeEmbeddingCriterion[T]

     Definition Classes

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185. def createL1L2Regularizer(l1: Double, l2: Double): L1L2Regularizer[T]

     Definition Classes

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186. def createL1Penalty(l1weight: Int, sizeAverage: Boolean = false, provideOutput: Boolean = true): L1Penalty[T]

     Definition Classes

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187. def createL1Regularizer(l1: Double): L1Regularizer[T]

     Definition Classes

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188. def createL2Regularizer(l2: Double): L2Regularizer[T]

     Definition Classes

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189. def createLBFGS(maxIter: Int = 20, maxEval: Double = Double.MaxValue, tolFun: Double = 1e-5, tolX: Double = 1e-9, nCorrection: Int = 100, learningRate: Double = 1.0, verbose: Boolean = false, lineSearch: LineSearch[T] = null, lineSearchOptions: Map[Any, Any] = null): LBFGS[T]

     Definition Classes

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190. def createLSTM(inputSize: Int, hiddenSize: Int, p: Double = 0, activation: TensorModule[T] = null, innerActivation: TensorModule[T] = null, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null): LSTM[T]

     Definition Classes

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191. def createLSTMPeephole(inputSize: Int, hiddenSize: Int, p: Double = 0, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null): LSTMPeephole[T]

     Definition Classes

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192. def createLeakyReLU(negval: Double = 0.01, inplace: Boolean = false): LeakyReLU[T]

     Definition Classes

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193. def createLinear(inputSize: Int, outputSize: Int, withBias: Boolean, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, initWeight: JTensor = null, initBias: JTensor = null, initGradWeight: JTensor = null, initGradBias: JTensor = null): Linear[T]

     Definition Classes

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194. def createLocalImageFrame(images: List[JTensor], labels: List[JTensor]): LocalImageFrame

     Definition Classes

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195. def createLocalOptimizer(features: List[JTensor], y: JTensor, model: AbstractModule[Activity, Activity, T], criterion: Criterion[T], optimMethod: Map[String, OptimMethod[T]], endTrigger: Trigger, batchSize: Int, localCores: Int): Optimizer[T, MiniBatch[T]]

     Definition Classes

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196. def createLocallyConnected1D(nInputFrame: Int, inputFrameSize: Int, outputFrameSize: Int, kernelW: Int, strideW: Int = 1, propagateBack: Boolean = true, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, initWeight: JTensor = null, initBias: JTensor = null, initGradWeight: JTensor = null, initGradBias: JTensor = null): LocallyConnected1D[T]

     Definition Classes

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197. def createLocallyConnected2D(nInputPlane: Int, inputWidth: Int, inputHeight: Int, nOutputPlane: Int, kernelW: Int, kernelH: Int, strideW: Int = 1, strideH: Int = 1, padW: Int = 0, padH: Int = 0, propagateBack: Boolean = true, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, initWeight: JTensor = null, initBias: JTensor = null, initGradWeight: JTensor = null, initGradBias: JTensor = null, withBias: Boolean = true, dataFormat: String = "NCHW"): LocallyConnected2D[T]

     Definition Classes

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198. def createLog(): Log[T]

     Definition Classes

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199. def createLogSigmoid(): LogSigmoid[T]

     Definition Classes

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200. def createLogSoftMax(): LogSoftMax[T]

     Definition Classes

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201. def createLookupTable(nIndex: Int, nOutput: Int, paddingValue: Double = 0, maxNorm: Double = Double.MaxValue, normType: Double = 2.0, shouldScaleGradByFreq: Boolean = false, wRegularizer: Regularizer[T] = null): LookupTable[T]

     Definition Classes

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202. def createLookupTableSparse(nIndex: Int, nOutput: Int, combiner: String = "sum", maxNorm: Double = 1, wRegularizer: Regularizer[T] = null): LookupTableSparse[T]

     Definition Classes

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203. def createLoss(criterion: Criterion[T]): ValidationMethod[T]

     Definition Classes

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204. def createMAE(): ValidationMethod[T]

     Definition Classes

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205. def createMM(transA: Boolean = false, transB: Boolean = false): MM[T]

     Definition Classes

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206. def createMSECriterion: MSECriterion[T]

     Definition Classes

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207. def createMV(trans: Boolean = false): MV[T]

     Definition Classes

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208. def createMapTable(module: AbstractModule[Activity, Activity, T] = null): MapTable[T]

     Definition Classes

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209. def createMarginCriterion(margin: Double = 1.0, sizeAverage: Boolean = true, squared: Boolean = false): MarginCriterion[T]

     Definition Classes

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210. def createMarginRankingCriterion(margin: Double = 1.0, sizeAverage: Boolean = true): MarginRankingCriterion[T]

     Definition Classes

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211. def createMaskedSelect(): MaskedSelect[T]

     Definition Classes

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212. def createMasking(maskValue: Double): Masking[T]

     Definition Classes

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213. def createMatToFloats(validHeight: Int = 300, validWidth: Int = 300, validChannels: Int = 3, outKey: String = ImageFeature.floats, shareBuffer: Boolean = true): MatToFloats

     Definition Classes

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214. def createMatToTensor(toRGB: Boolean = false, tensorKey: String = ImageFeature.imageTensor): MatToTensor[T]

     Definition Classes

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215. def createMax(dim: Int = 1, numInputDims: Int = Int.MinValue): Max[T]

     Definition Classes

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216. def createMaxEpoch(max: Int): Trigger

     Definition Classes

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217. def createMaxIteration(max: Int): Trigger

     Definition Classes

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218. def createMaxScore(max: Float): Trigger

     Definition Classes

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219. def createMaxout(inputSize: Int, outputSize: Int, maxoutNumber: Int, withBias: Boolean = true, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, initWeight: Tensor[T] = null, initBias: Tensor[T] = null): Maxout[T]

     Definition Classes

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220. def createMean(dimension: Int = 1, nInputDims: Int = 1, squeeze: Boolean = true): Mean[T]

     Definition Classes

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221. def createMeanAbsolutePercentageCriterion: MeanAbsolutePercentageCriterion[T]

     Definition Classes

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222. def createMeanSquaredLogarithmicCriterion: MeanSquaredLogarithmicCriterion[T]

     Definition Classes

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223. def createMin(dim: Int = 1, numInputDims: Int = Int.MinValue): Min[T]

     Definition Classes

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224. def createMinLoss(min: Float): Trigger

     Definition Classes

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225. def createMixtureTable(dim: Int = Int.MaxValue): MixtureTable[T]

     Definition Classes

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226. def createModel(input: List[ModuleNode[T]], output: List[ModuleNode[T]]): Graph[T]

     Definition Classes

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227. def createModelPreprocessor(preprocessor: AbstractModule[Activity, Activity, T], trainable: AbstractModule[Activity, Activity, T]): Graph[T]

     Definition Classes

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228. def createMsraFiller(varianceNormAverage: Boolean = true): MsraFiller

     Definition Classes

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229. def createMul(): Mul[T]

     Definition Classes

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230. def createMulConstant(scalar: Double, inplace: Boolean = false): MulConstant[T]

     Definition Classes

     PythonBigDL

231. def createMultiCriterion(): MultiCriterion[T]

     Definition Classes

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232. def createMultiLabelMarginCriterion(sizeAverage: Boolean = true): MultiLabelMarginCriterion[T]

     Definition Classes

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233. def createMultiLabelSoftMarginCriterion(weights: JTensor = null, sizeAverage: Boolean = true): MultiLabelSoftMarginCriterion[T]

     Definition Classes

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234. def createMultiMarginCriterion(p: Int = 1, weights: JTensor = null, margin: Double = 1.0, sizeAverage: Boolean = true): MultiMarginCriterion[T]

     Definition Classes

     PythonBigDL

235. def createMultiRNNCell(cells: List[Cell[T]]): MultiRNNCell[T]

     Definition Classes

     PythonBigDL

236. def createMultiStep(stepSizes: List[Int], gamma: Double): MultiStep

     Definition Classes

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237. def createNDCG(k: Int = 10, negNum: Int = 100): ValidationMethod[T]

     Definition Classes

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238. def createNarrow(dimension: Int, offset: Int, length: Int = 1): Narrow[T]

     Definition Classes

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239. def createNarrowTable(offset: Int, length: Int = 1): NarrowTable[T]

     Definition Classes

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240. def createNegative(inplace: Boolean): Negative[T]

     Definition Classes

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241. def createNegativeEntropyPenalty(beta: Double): NegativeEntropyPenalty[T]

     Definition Classes

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242. def createNode(module: AbstractModule[Activity, Activity, T], x: List[ModuleNode[T]]): ModuleNode[T]

     Definition Classes

     PythonBigDL

243. def createNormalize(p: Double, eps: Double = 1e-10): Normalize[T]

     Definition Classes

     PythonBigDL

244. def createNormalizeScale(p: Double, eps: Double = 1e-10, scale: Double, size: List[Int], wRegularizer: Regularizer[T] = null): NormalizeScale[T]

     Definition Classes

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245. def createOnes(): Ones.type

     Definition Classes

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246. def createPGCriterion(sizeAverage: Boolean = false): PGCriterion[T]

     Definition Classes

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247. def createPReLU(nOutputPlane: Int = 0): PReLU[T]

     Definition Classes

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248. def createPack(dimension: Int): Pack[T]

     Definition Classes

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249. def createPadding(dim: Int, pad: Int, nInputDim: Int, value: Double = 0.0, nIndex: Int = 1): Padding[T]

     Definition Classes

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250. def createPairwiseDistance(norm: Int = 2): PairwiseDistance[T]

     Definition Classes

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251. def createParallelAdam(learningRate: Double = 1e-3, learningRateDecay: Double = 0.0, beta1: Double = 0.9, beta2: Double = 0.999, Epsilon: Double = 1e-8, parallelNum: Int = Engine.coreNumber()): ParallelAdam[T]

     Definition Classes

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252. def createParallelCriterion(repeatTarget: Boolean = false): ParallelCriterion[T]

     Definition Classes

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253. def createParallelTable(): ParallelTable[T]

     Definition Classes

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254. def createPipeline(list: List[FeatureTransformer]): FeatureTransformer

     Definition Classes

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255. def createPixelBytesToMat(byteKey: String): PixelBytesToMat

     Definition Classes

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256. def createPixelNormalize(means: List[Double]): PixelNormalizer

     Definition Classes

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257. def createPlateau(monitor: String, factor: Float = 0.1f, patience: Int = 10, mode: String = "min", epsilon: Float = 1e-4f, cooldown: Int = 0, minLr: Float = 0): Plateau

     Definition Classes

     PythonBigDL

258. def createPoissonCriterion: PoissonCriterion[T]

     Definition Classes

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259. def createPoly(power: Double, maxIteration: Int): Poly

     Definition Classes

     PythonBigDL

260. def createPower(power: Double, scale: Double = 1, shift: Double = 0): Power[T]

     Definition Classes

     PythonBigDL

261. def createPriorBox(minSizes: List[Double], maxSizes: List[Double] = null, aspectRatios: List[Double] = null, isFlip: Boolean = true, isClip: Boolean = false, variances: List[Double] = null, offset: Float = 0.5f, imgH: Int = 0, imgW: Int = 0, imgSize: Int = 0, stepH: Float = 0, stepW: Float = 0, step: Float = 0): PriorBox[T]

     Definition Classes

     PythonBigDL

262. def createProposal(preNmsTopN: Int, postNmsTopN: Int, ratios: List[Double], scales: List[Double], rpnPreNmsTopNTrain: Int = 12000, rpnPostNmsTopNTrain: Int = 2000): Proposal

     Definition Classes

     PythonBigDL

263. def createRMSprop(learningRate: Double = 1e-2, learningRateDecay: Double = 0.0, decayRate: Double = 0.99, Epsilon: Double = 1e-8): RMSprop[T]

     Definition Classes

     PythonBigDL

264. def createRReLU(lower: Double = 1.0 / 8, upper: Double = 1.0 / 3, inplace: Boolean = false): RReLU[T]

     Definition Classes

     PythonBigDL

265. def createRandomAlterAspect(min_area_ratio: Float, max_area_ratio: Int, min_aspect_ratio_change: Float, interp_mode: String, cropLength: Int): RandomAlterAspect

     Definition Classes

     PythonBigDL

266. def createRandomAspectScale(scales: List[Int], scaleMultipleOf: Int = 1, maxSize: Int = 1000): RandomAspectScale

     Definition Classes

     PythonBigDL

267. def createRandomCrop(cropWidth: Int, cropHeight: Int, isClip: Boolean): RandomCrop

     Definition Classes

     PythonBigDL

268. def createRandomCropper(cropWidth: Int, cropHeight: Int, mirror: Boolean, cropperMethod: String, channels: Int): RandomCropper

     Definition Classes

     PythonBigDL

269. def createRandomNormal(mean: Double, stdv: Double): RandomNormal

     Definition Classes

     PythonBigDL

270. def createRandomResize(minSize: Int, maxSize: Int): RandomResize

     Definition Classes

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271. def createRandomSampler(): FeatureTransformer

     Definition Classes

     PythonBigDL

272. def createRandomTransformer(transformer: FeatureTransformer, prob: Double): RandomTransformer

     Definition Classes

     PythonBigDL

273. def createRandomUniform(): InitializationMethod

     Definition Classes

     PythonBigDL

274. def createRandomUniform(lower: Double, upper: Double): InitializationMethod

     Definition Classes

     PythonBigDL

275. def createReLU(ip: Boolean = false): ReLU[T]

     Definition Classes

     PythonBigDL

276. def createReLU6(inplace: Boolean = false): ReLU6[T]

     Definition Classes

     PythonBigDL

277. def createRecurrent(): Recurrent[T]

     Definition Classes

     PythonBigDL

278. def createRecurrentDecoder(outputLength: Int): RecurrentDecoder[T]

     Definition Classes

     PythonBigDL

279. def createReplicate(nFeatures: Int, dim: Int = 1, nDim: Int = Int.MaxValue): Replicate[T]

     Definition Classes

     PythonBigDL

280. def createReshape(size: List[Int], batchMode: Boolean = null): Reshape[T]

     Definition Classes

     PythonBigDL

281. def createResize(resizeH: Int, resizeW: Int, resizeMode: Int = Imgproc.INTER_LINEAR, useScaleFactor: Boolean): Resize

     Definition Classes

     PythonBigDL

282. def createResizeBilinear(outputHeight: Int, outputWidth: Int, alignCorner: Boolean, dataFormat: String): ResizeBilinear[T]

     Definition Classes

     PythonBigDL

283. def createReverse(dimension: Int = 1, isInplace: Boolean = false): Reverse[T]

     Definition Classes

     PythonBigDL

284. def createRnnCell(inputSize: Int, hiddenSize: Int, activation: TensorModule[T], isInputWithBias: Boolean = true, isHiddenWithBias: Boolean = true, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null): RnnCell[T]

     Definition Classes

     PythonBigDL

285. def createRoiHFlip(normalized: Boolean = true): RoiHFlip

     Definition Classes

     PythonBigDL

286. def createRoiNormalize(): RoiNormalize

     Definition Classes

     PythonBigDL

287. def createRoiPooling(pooled_w: Int, pooled_h: Int, spatial_scale: Double): RoiPooling[T]

     Definition Classes

     PythonBigDL

288. def createRoiProject(needMeetCenterConstraint: Boolean): RoiProject

     Definition Classes

     PythonBigDL

289. def createRoiResize(normalized: Boolean): RoiResize

     Definition Classes

     PythonBigDL

290. def createSGD(learningRate: Double = 1e-3, learningRateDecay: Double = 0.0, weightDecay: Double = 0.0, momentum: Double = 0.0, dampening: Double = Double.MaxValue, nesterov: Boolean = false, leaningRateSchedule: LearningRateSchedule = SGD.Default(), learningRates: JTensor = null, weightDecays: JTensor = null): SGD[T]

     Definition Classes

     PythonBigDL

291. def createSReLU(shape: ArrayList[Int], shareAxes: ArrayList[Int] = null): SReLU[T]

     Definition Classes

     PythonBigDL

292. def createSaturation(deltaLow: Double, deltaHigh: Double): Saturation

     Definition Classes

     PythonBigDL

293. def createScale(size: List[Int]): Scale[T]

     Definition Classes

     PythonBigDL

294. def createSelect(dimension: Int, index: Int): Select[T]

     Definition Classes

     PythonBigDL

295. def createSelectTable(dimension: Int): SelectTable[T]

     Definition Classes

     PythonBigDL

296. def createSequential(): Container[Activity, Activity, T]

     Definition Classes

     PythonBigDL

297. def createSequentialSchedule(iterationPerEpoch: Int): SequentialSchedule

     Definition Classes

     PythonBigDL

298. def createSeveralIteration(interval: Int): Trigger

     Definition Classes

     PythonBigDL

299. def createSigmoid(): Sigmoid[T]

     Definition Classes

     PythonBigDL

300. def createSmoothL1Criterion(sizeAverage: Boolean = true): SmoothL1Criterion[T]

     Definition Classes

     PythonBigDL

301. def createSmoothL1CriterionWithWeights(sigma: Double, num: Int = 0): SmoothL1CriterionWithWeights[T]

     Definition Classes

     PythonBigDL

302. def createSoftMarginCriterion(sizeAverage: Boolean = true): SoftMarginCriterion[T]

     Definition Classes

     PythonBigDL

303. def createSoftMax(): SoftMax[T]

     Definition Classes

     PythonBigDL

304. def createSoftMin(): SoftMin[T]

     Definition Classes

     PythonBigDL

305. def createSoftPlus(beta: Double = 1.0): SoftPlus[T]

     Definition Classes

     PythonBigDL

306. def createSoftShrink(lambda: Double = 0.5): SoftShrink[T]

     Definition Classes

     PythonBigDL

307. def createSoftSign(): SoftSign[T]

     Definition Classes

     PythonBigDL

308. def createSoftmaxWithCriterion(ignoreLabel: Integer = null, normalizeMode: String = "VALID"): SoftmaxWithCriterion[T]

     Definition Classes

     PythonBigDL

309. def createSparseJoinTable(dimension: Int): SparseJoinTable[T]

     Definition Classes

     PythonBigDL

310. def createSparseLinear(inputSize: Int, outputSize: Int, withBias: Boolean, backwardStart: Int = 1, backwardLength: Int = 1, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, initWeight: JTensor = null, initBias: JTensor = null, initGradWeight: JTensor = null, initGradBias: JTensor = null): SparseLinear[T]

     Definition Classes

     PythonBigDL

311. def createSpatialAveragePooling(kW: Int, kH: Int, dW: Int = 1, dH: Int = 1, padW: Int = 0, padH: Int = 0, globalPooling: Boolean = false, ceilMode: Boolean = false, countIncludePad: Boolean = true, divide: Boolean = true, format: String = "NCHW"): SpatialAveragePooling[T]

     Definition Classes

     PythonBigDL

312. def createSpatialBatchNormalization(nOutput: Int, eps: Double = 1e-5, momentum: Double = 0.1, affine: Boolean = true, initWeight: JTensor = null, initBias: JTensor = null, initGradWeight: JTensor = null, initGradBias: JTensor = null, dataFormat: String = "NCHW"): SpatialBatchNormalization[T]

     Definition Classes

     PythonBigDL

313. def createSpatialContrastiveNormalization(nInputPlane: Int = 1, kernel: JTensor = null, threshold: Double = 1e-4, thresval: Double = 1e-4): SpatialContrastiveNormalization[T]

     Definition Classes

     PythonBigDL

314. def createSpatialConvolution(nInputPlane: Int, nOutputPlane: Int, kernelW: Int, kernelH: Int, strideW: Int = 1, strideH: Int = 1, padW: Int = 0, padH: Int = 0, nGroup: Int = 1, propagateBack: Boolean = true, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, initWeight: JTensor = null, initBias: JTensor = null, initGradWeight: JTensor = null, initGradBias: JTensor = null, withBias: Boolean = true, dataFormat: String = "NCHW"): SpatialConvolution[T]

     Definition Classes

     PythonBigDL

315. def createSpatialConvolutionMap(connTable: JTensor, kW: Int, kH: Int, dW: Int = 1, dH: Int = 1, padW: Int = 0, padH: Int = 0, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null): SpatialConvolutionMap[T]

     Definition Classes

     PythonBigDL

316. def createSpatialCrossMapLRN(size: Int = 5, alpha: Double = 1.0, beta: Double = 0.75, k: Double = 1.0, dataFormat: String = "NCHW"): SpatialCrossMapLRN[T]

     Definition Classes

     PythonBigDL

317. def createSpatialDilatedConvolution(nInputPlane: Int, nOutputPlane: Int, kW: Int, kH: Int, dW: Int = 1, dH: Int = 1, padW: Int = 0, padH: Int = 0, dilationW: Int = 1, dilationH: Int = 1, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null): SpatialDilatedConvolution[T]

     Definition Classes

     PythonBigDL

318. def createSpatialDivisiveNormalization(nInputPlane: Int = 1, kernel: JTensor = null, threshold: Double = 1e-4, thresval: Double = 1e-4): SpatialDivisiveNormalization[T]

     Definition Classes

     PythonBigDL

319. def createSpatialDropout1D(initP: Double = 0.5): SpatialDropout1D[T]

     Definition Classes

     PythonBigDL

320. def createSpatialDropout2D(initP: Double = 0.5, dataFormat: String = "NCHW"): SpatialDropout2D[T]

     Definition Classes

     PythonBigDL

321. def createSpatialDropout3D(initP: Double = 0.5, dataFormat: String = "NCHW"): SpatialDropout3D[T]

     Definition Classes

     PythonBigDL

322. def createSpatialFullConvolution(nInputPlane: Int, nOutputPlane: Int, kW: Int, kH: Int, dW: Int = 1, dH: Int = 1, padW: Int = 0, padH: Int = 0, adjW: Int = 0, adjH: Int = 0, nGroup: Int = 1, noBias: Boolean = false, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null): SpatialFullConvolution[T]

     Definition Classes

     PythonBigDL

323. def createSpatialMaxPooling(kW: Int, kH: Int, dW: Int, dH: Int, padW: Int = 0, padH: Int = 0, ceilMode: Boolean = false, format: String = "NCHW"): SpatialMaxPooling[T]

     Definition Classes

     PythonBigDL

324. def createSpatialSeparableConvolution(nInputChannel: Int, nOutputChannel: Int, depthMultiplier: Int, kW: Int, kH: Int, sW: Int = 1, sH: Int = 1, pW: Int = 0, pH: Int = 0, withBias: Boolean = true, dataFormat: String = "NCHW", wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, pRegularizer: Regularizer[T] = null): SpatialSeparableConvolution[T]

     Definition Classes

     PythonBigDL

325. def createSpatialShareConvolution(nInputPlane: Int, nOutputPlane: Int, kernelW: Int, kernelH: Int, strideW: Int = 1, strideH: Int = 1, padW: Int = 0, padH: Int = 0, nGroup: Int = 1, propagateBack: Boolean = true, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, initWeight: JTensor = null, initBias: JTensor = null, initGradWeight: JTensor = null, initGradBias: JTensor = null, withBias: Boolean = true): SpatialShareConvolution[T]

     Definition Classes

     PythonBigDL

326. def createSpatialSubtractiveNormalization(nInputPlane: Int = 1, kernel: JTensor = null): SpatialSubtractiveNormalization[T]

     Definition Classes

     PythonBigDL

327. def createSpatialWithinChannelLRN(size: Int = 5, alpha: Double = 1.0, beta: Double = 0.75): SpatialWithinChannelLRN[T]

     Definition Classes

     PythonBigDL

328. def createSpatialZeroPadding(padLeft: Int, padRight: Int, padTop: Int, padBottom: Int): SpatialZeroPadding[T]

     Definition Classes

     PythonBigDL

329. def createSplitTable(dimension: Int, nInputDims: Int = 1): SplitTable[T]

     Definition Classes

     PythonBigDL

330. def createSqrt(): Sqrt[T]

     Definition Classes

     PythonBigDL

331. def createSquare(): Square[T]

     Definition Classes

     PythonBigDL

332. def createSqueeze(dim: Int = Int.MinValue, numInputDims: Int = Int.MinValue): Squeeze[T]

     Definition Classes

     PythonBigDL

333. def createStep(stepSize: Int, gamma: Double): Step

     Definition Classes

     PythonBigDL

334. def createSum(dimension: Int = 1, nInputDims: Int = 1, sizeAverage: Boolean = false, squeeze: Boolean = true): Sum[T]

     Definition Classes

     PythonBigDL

335. def createTanh(): Tanh[T]

     Definition Classes

     PythonBigDL

336. def createTanhShrink(): TanhShrink[T]

     Definition Classes

     PythonBigDL

337. def createTemporalConvolution(inputFrameSize: Int, outputFrameSize: Int, kernelW: Int, strideW: Int = 1, propagateBack: Boolean = true, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, initWeight: JTensor = null, initBias: JTensor = null, initGradWeight: JTensor = null, initGradBias: JTensor = null): TemporalConvolution[T]

     Definition Classes

     PythonBigDL

338. def createTemporalMaxPooling(kW: Int, dW: Int): TemporalMaxPooling[T]

     Definition Classes

     PythonBigDL

339. def createThreshold(th: Double = 1e-6, v: Double = 0.0, ip: Boolean = false): Threshold[T]

     Definition Classes

     PythonBigDL

340. def createTile(dim: Int, copies: Int): Tile[T]

     Definition Classes

     PythonBigDL

341. def createTimeDistributed(layer: TensorModule[T]): TimeDistributed[T]

     Definition Classes

     PythonBigDL

342. def createTimeDistributedCriterion(critrn: TensorCriterion[T], sizeAverage: Boolean = false): TimeDistributedCriterion[T]

     Definition Classes

     PythonBigDL

343. def createTimeDistributedMaskCriterion(critrn: TensorCriterion[T], paddingValue: Int = 0): TimeDistributedMaskCriterion[T]

     Definition Classes

     PythonBigDL

344. def createTop1Accuracy(): ValidationMethod[T]

     Definition Classes

     PythonBigDL

345. def createTop5Accuracy(): ValidationMethod[T]

     Definition Classes

     PythonBigDL

346. def createTrainSummary(logDir: String, appName: String): TrainSummary

     Definition Classes

     PythonBigDL

347. def createTransformerCriterion(criterion: AbstractCriterion[Activity, Activity, T], inputTransformer: AbstractModule[Activity, Activity, T] = null, targetTransformer: AbstractModule[Activity, Activity, T] = null): TransformerCriterion[T]

     Definition Classes

     PythonBigDL

348. def createTranspose(permutations: List[List[Int]]): Transpose[T]

     Definition Classes

     PythonBigDL

349. def createTreeNNAccuracy(): ValidationMethod[T]

     Definition Classes

     PythonBigDL

350. def createTriggerAnd(first: Trigger, others: List[Trigger]): Trigger

     Definition Classes

     PythonBigDL

351. def createTriggerOr(first: Trigger, others: List[Trigger]): Trigger

     Definition Classes

     PythonBigDL

352. def createUnsqueeze(pos: Int, numInputDims: Int = Int.MinValue): Unsqueeze[T]

     Definition Classes

     PythonBigDL

353. def createUpSampling1D(length: Int): UpSampling1D[T]

     Definition Classes

     PythonBigDL

354. def createUpSampling2D(size: List[Int], dataFormat: String): UpSampling2D[T]

     Definition Classes

     PythonBigDL

355. def createUpSampling3D(size: List[Int]): UpSampling3D[T]

     Definition Classes

     PythonBigDL

356. def createValidationSummary(logDir: String, appName: String): ValidationSummary

     Definition Classes

     PythonBigDL

357. def createView(sizes: List[Int], num_input_dims: Int = 0): View[T]

     Definition Classes

     PythonBigDL

358. def createVolumetricAveragePooling(kT: Int, kW: Int, kH: Int, dT: Int, dW: Int, dH: Int, padT: Int = 0, padW: Int = 0, padH: Int = 0, countIncludePad: Boolean = true, ceilMode: Boolean = false): VolumetricAveragePooling[T]

     Definition Classes

     PythonBigDL

359. def createVolumetricConvolution(nInputPlane: Int, nOutputPlane: Int, kT: Int, kW: Int, kH: Int, dT: Int = 1, dW: Int = 1, dH: Int = 1, padT: Int = 0, padW: Int = 0, padH: Int = 0, withBias: Boolean = true, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null): VolumetricConvolution[T]

     Definition Classes

     PythonBigDL

360. def createVolumetricFullConvolution(nInputPlane: Int, nOutputPlane: Int, kT: Int, kW: Int, kH: Int, dT: Int = 1, dW: Int = 1, dH: Int = 1, padT: Int = 0, padW: Int = 0, padH: Int = 0, adjT: Int = 0, adjW: Int = 0, adjH: Int = 0, nGroup: Int = 1, noBias: Boolean = false, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null): VolumetricFullConvolution[T]

     Definition Classes

     PythonBigDL

361. def createVolumetricMaxPooling(kT: Int, kW: Int, kH: Int, dT: Int, dW: Int, dH: Int, padT: Int = 0, padW: Int = 0, padH: Int = 0): VolumetricMaxPooling[T]

     Definition Classes

     PythonBigDL

362. def createWarmup(delta: Double): Warmup

     Definition Classes

     PythonBigDL

363. def createXavier(): Xavier.type

     Definition Classes

     PythonBigDL

364. def createZeros(): Zeros.type

     Definition Classes

     PythonBigDL

365. def criterionBackward(criterion: AbstractCriterion[Activity, Activity, T], input: List[JTensor], inputIsTable: Boolean, target: List[JTensor], targetIsTable: Boolean): List[JTensor]

     Definition Classes

     PythonBigDL

366. def criterionForward(criterion: AbstractCriterion[Activity, Activity, T], input: List[JTensor], inputIsTable: Boolean, target: List[JTensor], targetIsTable: Boolean): T

     Definition Classes

     PythonBigDL

367. def disableClip(optimizer: Optimizer[T, MiniBatch[T]]): Unit

     Definition Classes

     PythonBigDL

368. def distributedImageFrameRandomSplit(imageFrame: DistributedImageFrame, weights: List[Double]): Array[ImageFrame]

     Definition Classes

     PythonBigDL

369. def distributedImageFrameToImageTensorRdd(imageFrame: DistributedImageFrame, floatKey: String = ImageFeature.floats, toChw: Boolean = true): JavaRDD[JTensor]

     Definition Classes

     PythonBigDL

370. def distributedImageFrameToLabelTensorRdd(imageFrame: DistributedImageFrame): JavaRDD[JTensor]

     Definition Classes

     PythonBigDL

371. def distributedImageFrameToPredict(imageFrame: DistributedImageFrame, key: String): JavaRDD[List[Any]]

     Definition Classes

     PythonBigDL

372. def distributedImageFrameToSample(imageFrame: DistributedImageFrame, key: String): JavaRDD[Sample]

     Definition Classes

     PythonBigDL

373. def distributedImageFrameToUri(imageFrame: DistributedImageFrame, key: String): JavaRDD[String]

     Definition Classes

     PythonBigDL

374. def dlClassifierModelTransform(dlClassifierModel: DLClassifierModel[T], dataSet: DataFrame): DataFrame

     Definition Classes

     PythonBigDL

375. def dlImageTransform(dlImageTransformer: DLImageTransformer, dataSet: DataFrame): DataFrame

     Definition Classes

     PythonBigDL

376. def dlModelTransform(dlModel: DLModel[T], dataSet: DataFrame): DataFrame

     Definition Classes

     PythonBigDL

377. def dlReadImage(path: String, sc: JavaSparkContext, minParitions: Int): DataFrame

     Definition Classes

     PythonBigDL

378. final def eq(arg0: AnyRef): Boolean

     Definition Classes

     AnyRef

379. def equals(arg0: Any): Boolean

     Definition Classes

     AnyRef → Any

380. def evaluate(module: KerasModel[T], x: JavaRDD[Sample], batchSize: Int = 32): List[EvaluatedResult]

381. def evaluate(module: AbstractModule[Activity, Activity, T]): AbstractModule[Activity, Activity, T]

     Definition Classes

     PythonBigDL

382. def featureTransformDataset(dataset: DataSet[ImageFeature], transformer: FeatureTransformer): DataSet[ImageFeature]

     Definition Classes

     PythonBigDL

383. def finalize(): Unit

     Attributes

     protected[java.lang]

     Definition Classes

     AnyRef

     Annotations

     @throws( classOf[java.lang.Throwable] )

384. def findGraphNode(model: Graph[T], name: String): ModuleNode[T]

     Definition Classes

     PythonBigDL

385. def fit(module: KerasModel[T], xTrain: List[JTensor], yTrain: JTensor, batchSize: Int, epochs: Int, xVal: List[JTensor], yVal: JTensor, localCores: Int): Unit

386. def fit(module: KerasModel[T], x: DataSet[ImageFeature], batchSize: Int, epochs: Int, validationData: DataSet[ImageFeature]): Unit

387. def fit(module: KerasModel[T], x: JavaRDD[Sample], batchSize: Int = 32, epochs: Int = 10, validationData: JavaRDD[Sample] = null): Unit

388. def fitClassifier(classifier: DLClassifier[T], dataSet: DataFrame): DLModel[T]

     Definition Classes

     PythonBigDL

389. def fitEstimator(estimator: DLEstimator[T], dataSet: DataFrame): DLModel[T]

     Definition Classes

     PythonBigDL

390. def freeze(model: AbstractModule[Activity, Activity, T], freezeLayers: List[String]): AbstractModule[Activity, Activity, T]

     Definition Classes

     PythonBigDL

391. final def getClass(): Class[_]

     Definition Classes

     AnyRef → Any

392. def getContainerModules(module: Container[Activity, Activity, T]): List[AbstractModule[Activity, Activity, T]]

     Definition Classes

     PythonBigDL

393. def getFlattenModules(module: Container[Activity, Activity, T], includeContainer: Boolean): List[AbstractModule[Activity, Activity, T]]

     Definition Classes

     PythonBigDL

394. def getHiddenState(rec: Recurrent[T]): JActivity

     Definition Classes

     PythonBigDL

395. def getInputShape(module: Container[Activity, Activity, T]): List[List[Int]]

396. def getNodeAndCoreNumber(): Array[Int]

     Definition Classes

     PythonBigDL

397. def getOutputShape(module: Container[Activity, Activity, T]): List[List[Int]]

398. def getRealClassNameOfJValue(module: AbstractModule[Activity, Activity, T]): String

     Definition Classes

     PythonBigDL

399. def getRunningMean(module: BatchNormalization[T]): JTensor

400. def getRunningMean(module: BatchNormalization[T]): JTensor

     Definition Classes

     PythonBigDL

401. def getRunningStd(module: BatchNormalization[T]): JTensor

402. def getRunningStd(module: BatchNormalization[T]): JTensor

     Definition Classes

     PythonBigDL

403. def getWeights(model: AbstractModule[Activity, Activity, T]): List[JTensor]

     Definition Classes

     PythonBigDL

404. def hashCode(): Int

     Definition Classes

     AnyRef → Any

405. def imageFeatureGetKeys(imageFeature: ImageFeature): List[String]

     Definition Classes

     PythonBigDL

406. def imageFeatureToImageTensor(imageFeature: ImageFeature, floatKey: String = ImageFeature.floats, toChw: Boolean = true): JTensor

     Definition Classes

     PythonBigDL

407. def imageFeatureToLabelTensor(imageFeature: ImageFeature): JTensor

     Definition Classes

     PythonBigDL

408. def initEngine(): Unit

     Definition Classes

     PythonBigDL

409. def isDistributed(imageFrame: ImageFrame): Boolean

     Definition Classes

     PythonBigDL

410. final def isInstanceOf[T0]: Boolean

     Definition Classes

     Any

411. def isLocal(imageFrame: ImageFrame): Boolean

     Definition Classes

     PythonBigDL

412. def isWithWeights(module: Module[T]): Boolean

     Definition Classes

     PythonBigDL

413. def jTensorsToActivity(input: List[JTensor], isTable: Boolean): Activity

     Definition Classes

     PythonBigDL

414. def loadBigDL(path: String): AbstractModule[Activity, Activity, T]

     Definition Classes

     PythonBigDL

415. def loadBigDLModule(modulePath: String, weightPath: String): AbstractModule[Activity, Activity, T]

     Definition Classes

     PythonBigDL

416. def loadCaffe(model: AbstractModule[Activity, Activity, T], defPath: String, modelPath: String, matchAll: Boolean = true): AbstractModule[Activity, Activity, T]

     Definition Classes

     PythonBigDL

417. def loadCaffeModel(defPath: String, modelPath: String): AbstractModule[Activity, Activity, T]

     Definition Classes

     PythonBigDL

418. def loadOptimMethod(path: String): OptimMethod[T]

     Definition Classes

     PythonBigDL

419. def loadTF(path: String, inputs: List[String], outputs: List[String], byteOrder: String, binFile: String = null, generatedBackward: Boolean = true): AbstractModule[Activity, Activity, T]

     Definition Classes

     PythonBigDL

420. def loadTorch(path: String): AbstractModule[Activity, Activity, T]

     Definition Classes

     PythonBigDL

421. def localImageFrameToImageTensor(imageFrame: LocalImageFrame, floatKey: String = ImageFeature.floats, toChw: Boolean = true): List[JTensor]

     Definition Classes

     PythonBigDL

422. def localImageFrameToLabelTensor(imageFrame: LocalImageFrame): List[JTensor]

     Definition Classes

     PythonBigDL

423. def localImageFrameToPredict(imageFrame: LocalImageFrame, key: String): List[List[Any]]

     Definition Classes

     PythonBigDL

424. def localImageFrameToSample(imageFrame: LocalImageFrame, key: String): List[Sample]

     Definition Classes

     PythonBigDL

425. def localImageFrameToUri(imageFrame: LocalImageFrame, key: String): List[String]

     Definition Classes

     PythonBigDL

426. def modelBackward(model: AbstractModule[Activity, Activity, T], input: List[JTensor], inputIsTable: Boolean, gradOutput: List[JTensor], gradOutputIsTable: Boolean): List[JTensor]

     Definition Classes

     PythonBigDL

427. def modelEvaluate(model: AbstractModule[Activity, Activity, T], valRDD: JavaRDD[Sample], batchSize: Int, valMethods: List[ValidationMethod[T]]): List[EvaluatedResult]

     Definition Classes

     PythonBigDL

428. def modelEvaluateImageFrame(model: AbstractModule[Activity, Activity, T], imageFrame: ImageFrame, batchSize: Int, valMethods: List[ValidationMethod[T]]): List[EvaluatedResult]

     Definition Classes

     PythonBigDL

429. def modelForward(model: AbstractModule[Activity, Activity, T], input: List[JTensor], inputIsTable: Boolean): List[JTensor]

     Definition Classes

     PythonBigDL

430. def modelGetParameters(model: AbstractModule[Activity, Activity, T]): Map[Any, Map[Any, List[List[Any]]]]

     Definition Classes

     PythonBigDL

431. def modelPredictClass(model: AbstractModule[Activity, Activity, T], dataRdd: JavaRDD[Sample]): JavaRDD[Int]

     Definition Classes

     PythonBigDL

432. def modelPredictImage(model: AbstractModule[Activity, Activity, T], imageFrame: ImageFrame, featLayerName: String, shareBuffer: Boolean, batchPerPartition: Int, predictKey: String): ImageFrame

     Definition Classes

     PythonBigDL

433. def modelPredictRDD(model: AbstractModule[Activity, Activity, T], dataRdd: JavaRDD[Sample], batchSize: Int = 1): JavaRDD[JTensor]

     Definition Classes

     PythonBigDL

434. def modelSave(module: AbstractModule[Activity, Activity, T], path: String, overWrite: Boolean): Unit

     Definition Classes

     PythonBigDL

435. final def ne(arg0: AnyRef): Boolean

     Definition Classes

     AnyRef

436. final def notify(): Unit

     Definition Classes

     AnyRef

437. final def notifyAll(): Unit

     Definition Classes

     AnyRef

438. def predictLocal(model: AbstractModule[Activity, Activity, T], features: List[JTensor], batchSize: Int = 1): List[JTensor]

     Definition Classes

     PythonBigDL

439. def predictLocalClass(model: AbstractModule[Activity, Activity, T], features: List[JTensor]): List[Int]

     Definition Classes

     PythonBigDL

440. def quantize(module: AbstractModule[Activity, Activity, T]): Module[T]

     Definition Classes

     PythonBigDL

441. def read(path: String, sc: JavaSparkContext, minPartitions: Int): ImageFrame

     Definition Classes

     PythonBigDL

442. def readParquet(path: String, sc: JavaSparkContext): DistributedImageFrame

     Definition Classes

     PythonBigDL

443. def redirectSparkLogs(logPath: String): Unit

     Definition Classes

     PythonBigDL

444. def saveBigDLModule(module: AbstractModule[Activity, Activity, T], modulePath: String, weightPath: String, overWrite: Boolean): Unit

     Definition Classes

     PythonBigDL

445. def saveCaffe(module: AbstractModule[Activity, Activity, T], prototxtPath: String, modelPath: String, useV2: Boolean = true, overwrite: Boolean = false): Unit

     Definition Classes

     PythonBigDL

446. def saveGraphTopology(model: Graph[T], logPath: String): Graph[T]

     Definition Classes

     PythonBigDL

447. def saveOptimMethod(method: OptimMethod[T], path: String, overWrite: Boolean = false): Unit

     Definition Classes

     PythonBigDL

448. def saveTF(model: AbstractModule[Activity, Activity, T], inputs: List[Any], path: String, byteOrder: String, dataFormat: String): Unit

     Definition Classes

     PythonBigDL

449. def saveTensorDictionary(tensors: HashMap[String, JTensor], path: String): Unit

     Save tensor dictionary to a Java hashmap object file

     Save tensor dictionary to a Java hashmap object file

     Definition Classes

     PythonBigDL

450. def seqFilesToImageFrame(url: String, sc: JavaSparkContext, classNum: Int, partitionNum: Int): ImageFrame

     Definition Classes

     PythonBigDL

451. def setBatchSizeDLClassifier(classifier: DLClassifier[T], batchSize: Int): DLClassifier[T]

     Definition Classes

     PythonBigDL

452. def setBatchSizeDLClassifierModel(dlClassifierModel: DLClassifierModel[T], batchSize: Int): DLClassifierModel[T]

     Definition Classes

     PythonBigDL

453. def setBatchSizeDLEstimator(estimator: DLEstimator[T], batchSize: Int): DLEstimator[T]

     Definition Classes

     PythonBigDL

454. def setBatchSizeDLModel(dlModel: DLModel[T], batchSize: Int): DLModel[T]

     Definition Classes

     PythonBigDL

455. def setCheckPoint(optimizer: Optimizer[T, MiniBatch[T]], trigger: Trigger, checkPointPath: String, isOverwrite: Boolean): Unit

     Definition Classes

     PythonBigDL

456. def setConstantClip(optimizer: Optimizer[T, MiniBatch[T]], min: Float, max: Float): Unit

     Definition Classes

     PythonBigDL

457. def setCriterion(optimizer: Optimizer[T, MiniBatch[T]], criterion: Criterion[T]): Unit

     Definition Classes

     PythonBigDL

458. def setFeatureSizeDLClassifierModel(dlClassifierModel: DLClassifierModel[T], featureSize: ArrayList[Int]): DLClassifierModel[T]

     Definition Classes

     PythonBigDL

459. def setFeatureSizeDLModel(dlModel: DLModel[T], featureSize: ArrayList[Int]): DLModel[T]

     Definition Classes

     PythonBigDL

460. def setInitMethod(layer: Initializable, initMethods: ArrayList[InitializationMethod]): layer.type

     Definition Classes

     PythonBigDL

461. def setInitMethod(layer: Initializable, weightInitMethod: InitializationMethod, biasInitMethod: InitializationMethod): layer.type

     Definition Classes

     PythonBigDL

462. def setL2NormClip(optimizer: Optimizer[T, MiniBatch[T]], normValue: Float): Unit

     Definition Classes

     PythonBigDL

463. def setLabel(labelMap: Map[String, Float], imageFrame: ImageFrame): Unit

     Definition Classes

     PythonBigDL

464. def setLearningRateDLClassifier(classifier: DLClassifier[T], lr: Double): DLClassifier[T]

     Definition Classes

     PythonBigDL

465. def setLearningRateDLEstimator(estimator: DLEstimator[T], lr: Double): DLEstimator[T]

     Definition Classes

     PythonBigDL

466. def setMaxEpochDLClassifier(classifier: DLClassifier[T], maxEpoch: Int): DLClassifier[T]

     Definition Classes

     PythonBigDL

467. def setMaxEpochDLEstimator(estimator: DLEstimator[T], maxEpoch: Int): DLEstimator[T]

     Definition Classes

     PythonBigDL

468. def setModelSeed(seed: Long): Unit

     Definition Classes

     PythonBigDL

469. def setRunningMean(module: BatchNormalization[T], runningMean: JTensor): Unit

470. def setRunningMean(module: BatchNormalization[T], runningMean: JTensor): Unit

     Definition Classes

     PythonBigDL

471. def setRunningStd(module: BatchNormalization[T], runningStd: JTensor): Unit

472. def setRunningStd(module: BatchNormalization[T], runningStd: JTensor): Unit

     Definition Classes

     PythonBigDL

473. def setStopGradient(model: Graph[T], layers: List[String]): Graph[T]

     Definition Classes

     PythonBigDL

474. def setTrainData(optimizer: Optimizer[T, MiniBatch[T]], trainingRdd: JavaRDD[Sample], batchSize: Int): Unit

     Definition Classes

     PythonBigDL

475. def setTrainSummary(optimizer: Optimizer[T, MiniBatch[T]], summary: TrainSummary): Unit

     Definition Classes

     PythonBigDL

476. def setValSummary(optimizer: Optimizer[T, MiniBatch[T]], summary: ValidationSummary): Unit

     Definition Classes

     PythonBigDL

477. def setValidation(optimizer: Optimizer[T, MiniBatch[T]], batchSize: Int, trigger: Trigger, xVal: List[JTensor], yVal: JTensor, vMethods: List[ValidationMethod[T]]): Unit

     Definition Classes

     PythonBigDL

478. def setValidation(optimizer: Optimizer[T, MiniBatch[T]], batchSize: Int, trigger: Trigger, valRdd: JavaRDD[Sample], vMethods: List[ValidationMethod[T]]): Unit

     Definition Classes

     PythonBigDL

479. def setValidationFromDataSet(optimizer: Optimizer[T, MiniBatch[T]], batchSize: Int, trigger: Trigger, valDataSet: DataSet[ImageFeature], vMethods: List[ValidationMethod[T]]): Unit

     Definition Classes

     PythonBigDL

480. def setWeights(model: AbstractModule[Activity, Activity, T], weights: List[JTensor]): Unit

     Definition Classes

     PythonBigDL

481. def shapeToJList(shape: Shape): List[List[Int]]

482. def showBigDlInfoLogs(): Unit

     Definition Classes

     PythonBigDL

483. def summaryReadScalar(summary: Summary, tag: String): List[List[Any]]

     Definition Classes

     PythonBigDL

484. def summarySetTrigger(summary: TrainSummary, summaryName: String, trigger: Trigger): TrainSummary

     Definition Classes

     PythonBigDL

485. final def synchronized[T0](arg0: ⇒ T0): T0

     Definition Classes

     AnyRef

486. def testSample(sample: Sample): Sample

     Definition Classes

     PythonBigDL

487. def testTensor(jTensor: JTensor): JTensor

     Definition Classes

     PythonBigDL

488. def toJSample(psamples: RDD[Sample]): RDD[dataset.Sample[T]]

     Definition Classes

     PythonBigDL

489. def toJSample(record: Sample): dataset.Sample[T]

     Definition Classes

     PythonBigDL

490. def toJTensor(tensor: Tensor[T]): JTensor

     Definition Classes

     PythonBigDL

491. def toPySample(sample: dataset.Sample[T]): Sample

     Definition Classes

     PythonBigDL

492. def toSampleArray(Xs: List[Tensor[T]], y: Tensor[T] = null): Array[dataset.Sample[T]]

     Definition Classes

     PythonBigDL

493. def toScalaArray(list: List[Int]): Array[Int]

494. def toScalaMultiShape(inputShape: List[List[Int]]): Shape

495. def toScalaShape(inputShape: List[Int]): Shape

496. def toString(): String

     Definition Classes

     AnyRef → Any

497. def toTensor(jTensor: JTensor): Tensor[T]

     Definition Classes

     PythonBigDL

498. def trainTF(modelPath: String, output: String, samples: JavaRDD[Sample], optMethod: OptimMethod[T], criterion: Criterion[T], batchSize: Int, endWhen: Trigger): AbstractModule[Activity, Activity, T]

     Definition Classes

     PythonBigDL

499. def transformImageFeature(transformer: FeatureTransformer, feature: ImageFeature): ImageFeature

     Definition Classes

     PythonBigDL

500. def transformImageFrame(transformer: FeatureTransformer, imageFrame: ImageFrame): ImageFrame

     Definition Classes

     PythonBigDL

501. def unFreeze(model: AbstractModule[Activity, Activity, T], names: List[String]): AbstractModule[Activity, Activity, T]

     Definition Classes

     PythonBigDL

502. def uniform(a: Double, b: Double, size: List[Int]): JTensor

     Definition Classes

     PythonBigDL

503. def updateParameters(model: AbstractModule[Activity, Activity, T], lr: Double): Unit

     Definition Classes

     PythonBigDL

504. final def wait(): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

505. final def wait(arg0: Long, arg1: Int): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

506. final def wait(arg0: Long): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

507. def writeParquet(path: String, output: String, sc: JavaSparkContext, partitionNum: Int = 1): Unit

     Definition Classes

     PythonBigDL