com.intel.analytics.bigdl.python.api

PythonBigDLValidator

class PythonBigDLValidator[T] extends PythonBigDL[T]

Linear Supertypes
PythonBigDL[T], Serializable, Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. PythonBigDLValidator
  2. PythonBigDL
  3. Serializable
  4. Serializable
  5. AnyRef
  6. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

Instance Constructors

  1. new PythonBigDLValidator()(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

Value Members

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  8. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

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

    Definition Classes
    PythonBigDL

  10. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  11. def createAbs(): Abs[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  22. 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]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  26. def createBilinearFiller(): BilinearFiller.type

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  31. def createBytesToMat(byteKey: String): BytesToMat

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  35. def createCDivTable(): CDivTable[T]

    Definition Classes
    PythonBigDL

  36. def createCMaxTable(): CMaxTable[T]

    Definition Classes
    PythonBigDL

  37. def createCMinTable(): CMinTable[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  39. def createCMulTable(): CMulTable[T]

    Definition Classes
    PythonBigDL

  40. def createCSubTable(): CSubTable[T]

    Definition Classes
    PythonBigDL

  41. def createCategoricalCrossEntropy(): CategoricalCrossEntropy[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  44. def createChannelOrder(): ChannelOrder

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  48. 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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  50. def createConcatTable(): ConcatTable[T]

    Definition Classes
    PythonBigDL

  51. def createConstInitMethod(value: Double): ConstInitMethod

    Definition Classes
    PythonBigDL

  52. def createContiguous(): Contiguous[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  54. 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]

    Definition Classes
    PythonBigDL

  55. 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]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  57. def createCosineDistance(): CosineDistance[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  60. def createCosineProximityCriterion(): CosineProximityCriterion[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  68. def createDLImageTransformer(transformer: FeatureTransformer): DLImageTransformer

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  71. def createDefault(): Default

    Definition Classes
    PythonBigDL

  72. def createDenseToSparse(): DenseToSparse[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  78. 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]]

    Definition Classes
    PythonBigDL

  79. 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]]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  81. def createDotProduct(): DotProduct[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  85. def createEcho(): Echo[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  87. def createEveryEpoch(): Trigger

    Definition Classes
    PythonBigDL

  88. def createExp(): Exp[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  94. def createFlattenTable(): FlattenTable[T]

    Definition Classes
    PythonBigDL

  95. 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]

    Definition Classes
    PythonBigDL

  96. 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]

    Definition Classes
    PythonBigDL

  97. def createGaussianCriterion(): GaussianCriterion[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  100. def createGaussianSampler(): GaussianSampler[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  102. def createHFlip(): HFlip

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  104. def createHardSigmoid: HardSigmoid[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  110. def createIdentity(): Identity[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  115. def createInput(): ModuleNode[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  118. def createKullbackLeiblerDivergenceCriterion: KullbackLeiblerDivergenceCriterion[T]

    Definition Classes
    PythonBigDL

  119. def createL1Cost(): L1Cost[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  125. 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
    PythonBigDL

  126. 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
    PythonBigDL

  127. 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
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  129. 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
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  131. 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
    PythonBigDL

  132. 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
    PythonBigDL

  133. 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
    PythonBigDL

  134. def createLog(): Log[T]

    Definition Classes
    PythonBigDL

  135. def createLogSigmoid(): LogSigmoid[T]

    Definition Classes
    PythonBigDL

  136. def createLogSoftMax(): LogSoftMax[T]

    Definition Classes
    PythonBigDL

  137. 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
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  140. def createMAE(): ValidationMethod[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  142. def createMSECriterion: MSECriterion[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  147. def createMaskedSelect(): MaskedSelect[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  152. def createMaxEpoch(max: Int): Trigger

    Definition Classes
    PythonBigDL

  153. def createMaxIteration(max: Int): Trigger

    Definition Classes
    PythonBigDL

  154. def createMaxScore(max: Float): Trigger

    Definition Classes
    PythonBigDL

  155. 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
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  157. def createMeanAbsolutePercentageCriterion: MeanAbsolutePercentageCriterion[T]

    Definition Classes
    PythonBigDL

  158. def createMeanSquaredLogarithmicCriterion: MeanSquaredLogarithmicCriterion[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  160. def createMinLoss(min: Float): Trigger

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  165. def createMul(): Mul[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  167. def createMultiCriterion(): MultiCriterion[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  181. def createOnes(): Ones.type

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  187. 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
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  189. def createParallelTable(): ParallelTable[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  191. def createPixelBytesToMat(byteKey: String): PixelBytesToMat

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  193. 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

  194. def createPoissonCriterion: PoissonCriterion[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  197. 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

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  204. def createRandomSampler(): FeatureTransformer

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  206. def createRandomUniform(): InitializationMethod

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  210. def createRecurrent(): Recurrent[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  217. 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

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

    Definition Classes
    PythonBigDL

  219. def createRoiNormalize(): RoiNormalize

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  221. def createRoiProject(needMeetCenterConstraint: Boolean): RoiProject

    Definition Classes
    PythonBigDL

  222. def createRoiResize(normalized: Boolean): RoiResize

    Definition Classes
    PythonBigDL

  223. 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

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  230. def createSequentialSchedule(iterationPerEpoch: Int): SequentialSchedule

    Definition Classes
    PythonBigDL

  231. def createSeveralIteration(interval: Int): Trigger

    Definition Classes
    PythonBigDL

  232. def createSigmoid(): Sigmoid[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  236. def createSoftMax(): SoftMax[T]

    Definition Classes
    PythonBigDL

  237. def createSoftMin(): SoftMin[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  240. def createSoftSign(): SoftSign[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  243. 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

  244. 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

  245. 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

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

    Definition Classes
    PythonBigDL

  247. 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

  248. 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

  249. 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

  250. 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

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  255. 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

  256. 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

  257. 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

  258. 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

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  263. def createSqrt(): Sqrt[T]

    Definition Classes
    PythonBigDL

  264. def createSquare(): Square[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  268. def createTanh(): Tanh[T]

    Definition Classes
    PythonBigDL

  269. def createTanhShrink(): TanhShrink[T]

    Definition Classes
    PythonBigDL

  270. 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

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  277. def createTop1Accuracy(): ValidationMethod[T]

    Definition Classes
    PythonBigDL

  278. def createTop5Accuracy(): ValidationMethod[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  282. def createTreeNNAccuracy(): ValidationMethod[T]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  289. 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

  290. 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

  291. 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

  292. 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

  293. def createWarmup(delta: Double): Warmup

    Definition Classes
    PythonBigDL

  294. def createXavier(): Xavier.type

    Definition Classes
    PythonBigDL

  295. def createZeros(): Zeros.type

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    AnyRef

  310. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  313. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    AnyRef → Any

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  322. def getNodeAndCoreNumber(): Array[Int]

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  327. def hashCode(): Int

    Definition Classes
    AnyRef → Any

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  330. def imageFeatureToLabelTensor(imageFeature: ImageFeature): JTensor

    Definition Classes
    PythonBigDL

  331. def initEngine(): Unit

    Definition Classes
    PythonBigDL

  332. def isDistributed(imageFrame: ImageFrame): Boolean

    Definition Classes
    PythonBigDL

  333. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  334. def isLocal(imageFrame: ImageFrame): Boolean

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  342. 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

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    AnyRef

  359. final def notify(): Unit

    Definition Classes
    AnyRef

  360. final def notifyAll(): Unit

    Definition Classes
    AnyRef

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  366. def redirectSparkLogs(logPath: String): Unit

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  372. 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

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  391. def setModelSeed(seed: Long): Unit

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  402. def showBigDlInfoLogs(): Unit

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    AnyRef

  406. def testActivityWithTableOfTable(): JActivity

  407. def testActivityWithTableOfTensor(): JActivity

  408. def testActivityWithTensor(): JActivity

  409. def testDict(): Map[String, String]

  410. def testDictJMapJTensor(): Map[String, Map[String, JTensor]]

  411. def testDictJTensor(): Map[String, JTensor]

  412. def testSample(sample: Sample): Sample

    Definition Classes
    PythonBigDL

  413. def testTensor(jTensor: JTensor): JTensor

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  419. def toString(): String

    Definition Classes
    AnyRef → Any

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

    Definition Classes
    PythonBigDL

  421. 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

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

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

    Definition Classes
    PythonBigDL

  427. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    PythonBigDL

Inherited from PythonBigDL[T]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped