com.intel.analytics.bigdl.python.api

PythonBigDLKeras

class PythonBigDLKeras[T] extends PythonBigDL[T]

Linear Supertypes
PythonBigDL[T], Serializable, Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. PythonBigDLKeras
  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 PythonBigDLKeras()(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 compile(module: KerasModel[T], optimizer: OptimMethod[T], loss: Criterion[T], metrics: List[ValidationMethod[T]] = null): Unit

  12. def createAbs(): Abs[T]

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

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

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

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

    Definition Classes
    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]

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

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

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

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

    Definition Classes
    PythonBigDL
  22. def createAttention(hiddenSize: Int, numHeads: Int, attentionDropout: Float): Attention[T]

    Definition Classes
    PythonBigDL
  23. def createBCECriterion(weights: JTensor = null, sizeAverage: Boolean = true): BCECriterion[T]

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

    Definition Classes
    PythonBigDL
  26. def createBifurcateSplitTable(dimension: Int): BifurcateSplitTable[T]

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

    Definition Classes
    PythonBigDL
  28. def createBilinearFiller(): BilinearFiller.type

    Definition Classes
    PythonBigDL
  29. def createBinaryThreshold(th: Double, ip: Boolean): BinaryThreshold[T]

    Definition Classes
    PythonBigDL
  30. def createBinaryTreeLSTM(inputSize: Int, hiddenSize: Int, gateOutput: Boolean = true, withGraph: Boolean = true): BinaryTreeLSTM[T]

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

    Definition Classes
    PythonBigDL
  32. def createBrightness(deltaLow: Double, deltaHigh: Double): Brightness

    Definition Classes
    PythonBigDL
  33. def createBytesToMat(byteKey: String): BytesToMat

    Definition Classes
    PythonBigDL
  34. def createCAdd(size: List[Int], bRegularizer: Regularizer[T] = null): CAdd[T]

    Definition Classes
    PythonBigDL
  35. def createCAddTable(inplace: Boolean = false): CAddTable[T, T]

    Definition Classes
    PythonBigDL
  36. def createCAveTable(inplace: Boolean = false): CAveTable[T]

    Definition Classes
    PythonBigDL
  37. def createCDivTable(): CDivTable[T]

    Definition Classes
    PythonBigDL
  38. def createCMaxTable(): CMaxTable[T]

    Definition Classes
    PythonBigDL
  39. def createCMinTable(): CMinTable[T]

    Definition Classes
    PythonBigDL
  40. def createCMul(size: List[Int], wRegularizer: Regularizer[T] = null): CMul[T]

    Definition Classes
    PythonBigDL
  41. def createCMulTable(): CMulTable[T]

    Definition Classes
    PythonBigDL
  42. def createCSubTable(): CSubTable[T]

    Definition Classes
    PythonBigDL
  43. def createCategoricalCrossEntropy(): CategoricalCrossEntropy[T]

    Definition Classes
    PythonBigDL
  44. def createCenterCrop(cropWidth: Int, cropHeight: Int, isClip: Boolean): CenterCrop

    Definition Classes
    PythonBigDL
  45. def createChannelNormalize(meanR: Double, meanG: Double, meanB: Double, stdR: Double = 1, stdG: Double = 1, stdB: Double = 1): FeatureTransformer

    Definition Classes
    PythonBigDL
  46. def createChannelOrder(): ChannelOrder

    Definition Classes
    PythonBigDL
  47. def createChannelScaledNormalizer(meanR: Int, meanG: Int, meanB: Int, scale: Double): ChannelScaledNormalizer

    Definition Classes
    PythonBigDL
  48. def createClamp(min: Int, max: Int): Clamp[T]

    Definition Classes
    PythonBigDL
  49. def createClassNLLCriterion(weights: JTensor = null, sizeAverage: Boolean = true, logProbAsInput: Boolean = true): ClassNLLCriterion[T]

    Definition Classes
    PythonBigDL
  50. def createClassSimplexCriterion(nClasses: Int): ClassSimplexCriterion[T]

    Definition Classes
    PythonBigDL
  51. 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
  52. def createConcat(dimension: Int): Concat[T]

    Definition Classes
    PythonBigDL
  53. def createConcatTable(): ConcatTable[T]

    Definition Classes
    PythonBigDL
  54. def createConstInitMethod(value: Double): ConstInitMethod

    Definition Classes
    PythonBigDL
  55. def createContiguous(): Contiguous[T]

    Definition Classes
    PythonBigDL
  56. def createContrast(deltaLow: Double, deltaHigh: Double): Contrast

    Definition Classes
    PythonBigDL
  57. 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
  58. 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
  59. def createCosine(inputSize: Int, outputSize: Int): Cosine[T]

    Definition Classes
    PythonBigDL
  60. def createCosineDistance(): CosineDistance[T]

    Definition Classes
    PythonBigDL
  61. def createCosineDistanceCriterion(sizeAverage: Boolean = true): CosineDistanceCriterion[T]

    Definition Classes
    PythonBigDL
  62. def createCosineEmbeddingCriterion(margin: Double = 0.0, sizeAverage: Boolean = true): CosineEmbeddingCriterion[T]

    Definition Classes
    PythonBigDL
  63. def createCosineProximityCriterion(): CosineProximityCriterion[T]

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

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

    Definition Classes
    PythonBigDL
  66. def createCrossEntropyCriterion(weights: JTensor = null, sizeAverage: Boolean = true): CrossEntropyCriterion[T]

    Definition Classes
    PythonBigDL
  67. def createCrossProduct(numTensor: Int = 0, embeddingSize: Int = 0): CrossProduct[T]

    Definition Classes
    PythonBigDL
  68. def createDLClassifier(model: Module[T], criterion: Criterion[T], featureSize: ArrayList[Int], labelSize: ArrayList[Int]): DLClassifier[T]

    Definition Classes
    PythonBigDL
  69. def createDLClassifierModel(model: Module[T], featureSize: ArrayList[Int]): DLClassifierModel[T]

    Definition Classes
    PythonBigDL
  70. def createDLEstimator(model: Module[T], criterion: Criterion[T], featureSize: ArrayList[Int], labelSize: ArrayList[Int]): DLEstimator[T]

    Definition Classes
    PythonBigDL
  71. def createDLImageTransformer(transformer: FeatureTransformer): DLImageTransformer

    Definition Classes
    PythonBigDL
  72. def createDLModel(model: Module[T], featureSize: ArrayList[Int]): DLModel[T]

    Definition Classes
    PythonBigDL
  73. def createDatasetFromImageFrame(imageFrame: ImageFrame): DataSet[ImageFeature]

    Definition Classes
    PythonBigDL
  74. def createDefault(): Default

    Definition Classes
    PythonBigDL
  75. def createDenseToSparse(): DenseToSparse[T]

    Definition Classes
    PythonBigDL
  76. def createDetectionCrop(roiKey: String, normalized: Boolean): DetectionCrop

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

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

    Definition Classes
    PythonBigDL
  80. def createDistKLDivCriterion(sizeAverage: Boolean = true): DistKLDivCriterion[T]

    Definition Classes
    PythonBigDL
  81. 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
  82. 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
  83. def createDistributedImageFrame(imageRdd: JavaRDD[JTensor], labelRdd: JavaRDD[JTensor]): DistributedImageFrame

    Definition Classes
    PythonBigDL
  84. def createDotProduct(): DotProduct[T]

    Definition Classes
    PythonBigDL
  85. def createDotProductCriterion(sizeAverage: Boolean = false): DotProductCriterion[T]

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

    Definition Classes
    PythonBigDL
  87. def createELU(alpha: Double = 1.0, inplace: Boolean = false): ELU[T]

    Definition Classes
    PythonBigDL
  88. def createEcho(): Echo[T]

    Definition Classes
    PythonBigDL
  89. def createEuclidean(inputSize: Int, outputSize: Int, fastBackward: Boolean = true): Euclidean[T]

    Definition Classes
    PythonBigDL
  90. def createEveryEpoch(): Trigger

    Definition Classes
    PythonBigDL
  91. def createExp(): Exp[T]

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

    Definition Classes
    PythonBigDL
  93. def createExpandSize(targetSizes: List[Int]): ExpandSize[T]

    Definition Classes
    PythonBigDL
  94. def createExponential(decayStep: Int, decayRate: Double, stairCase: Boolean = false): Exponential

    Definition Classes
    PythonBigDL
  95. def createFPN(in_channels_list: List[Int], out_channels: Int, top_blocks: Int = 0, in_channels_of_p6p7: Int = 0, out_channels_of_p6p7: Int = 0): FPN[T]

    Definition Classes
    PythonBigDL
  96. def createFeedForwardNetwork(hiddenSize: Int, filterSize: Int, reluDropout: Float): FeedForwardNetwork[T]

    Definition Classes
    PythonBigDL
  97. def createFiller(startX: Double, startY: Double, endX: Double, endY: Double, value: Int = 255): Filler

    Definition Classes
    PythonBigDL
  98. def createFixExpand(eh: Int, ew: Int): FixExpand

    Definition Classes
    PythonBigDL
  99. def createFixedCrop(wStart: Double, hStart: Double, wEnd: Double, hEnd: Double, normalized: Boolean, isClip: Boolean): FixedCrop

    Definition Classes
    PythonBigDL
  100. def createFlattenTable(): FlattenTable[T]

    Definition Classes
    PythonBigDL
  101. 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
  102. 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
  103. def createGaussianCriterion(): GaussianCriterion[T]

    Definition Classes
    PythonBigDL
  104. def createGaussianDropout(rate: Double): GaussianDropout[T]

    Definition Classes
    PythonBigDL
  105. def createGaussianNoise(stddev: Double): GaussianNoise[T]

    Definition Classes
    PythonBigDL
  106. def createGaussianSampler(): GaussianSampler[T]

    Definition Classes
    PythonBigDL
  107. def createGradientReversal(lambda: Double = 1): GradientReversal[T]

    Definition Classes
    PythonBigDL
  108. def createHFlip(): HFlip

    Definition Classes
    PythonBigDL
  109. def createHardShrink(lambda: Double = 0.5): HardShrink[T]

    Definition Classes
    PythonBigDL
  110. def createHardSigmoid: HardSigmoid[T]

    Definition Classes
    PythonBigDL
  111. def createHardTanh(minValue: Double = 1, maxValue: Double = 1, inplace: Boolean = false): HardTanh[T]

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

    Definition Classes
    PythonBigDL
  113. def createHingeEmbeddingCriterion(margin: Double = 1, sizeAverage: Boolean = true): HingeEmbeddingCriterion[T]

    Definition Classes
    PythonBigDL
  114. def createHitRatio(k: Int = 10, negNum: Int = 100): ValidationMethod[T]

    Definition Classes
    PythonBigDL
  115. def createHue(deltaLow: Double, deltaHigh: Double): Hue

    Definition Classes
    PythonBigDL
  116. def createIdentity(): Identity[T]

    Definition Classes
    PythonBigDL
  117. def createImageFeature(data: JTensor = null, label: JTensor = null, uri: String = null): ImageFeature

    Definition Classes
    PythonBigDL
  118. def createImageFrameToSample(inputKeys: List[String], targetKeys: List[String], sampleKey: String): ImageFrameToSample[T]

    Definition Classes
    PythonBigDL
  119. def createIndex(dimension: Int): Index[T]

    Definition Classes
    PythonBigDL
  120. def createInferReshape(size: List[Int], batchMode: Boolean = false): InferReshape[T]

    Definition Classes
    PythonBigDL
  121. def createInput(): ModuleNode[T]

    Definition Classes
    PythonBigDL
  122. def createJoinTable(dimension: Int, nInputDims: Int): JoinTable[T]

    Definition Classes
    PythonBigDL
  123. def createKLDCriterion(sizeAverage: Boolean): KLDCriterion[T]

    Definition Classes
    PythonBigDL
  124. def createKerasActivation(activation: String, inputShape: List[Int] = null): Activation[T]

  125. 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]

  126. 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]

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

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

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

  130. 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]

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

  132. 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]

  133. 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]

  134. 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]

  135. 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]

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

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

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

  139. 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]

  140. 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]

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

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

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

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

  145. 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]

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

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

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

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

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

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

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

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

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

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

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

  157. 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]

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

  159. 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]

  160. 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]

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

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

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

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

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

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

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

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

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

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

  171. 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]

  172. 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]

  173. def createKerasSequential(): Sequential[T]

  174. 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]

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

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

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

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

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

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

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

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

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

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

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

  186. def createKullbackLeiblerDivergenceCriterion: KullbackLeiblerDivergenceCriterion[T]

    Definition Classes
    PythonBigDL
  187. def createL1Cost(): L1Cost[T]

    Definition Classes
    PythonBigDL
  188. def createL1HingeEmbeddingCriterion(margin: Double = 1): L1HingeEmbeddingCriterion[T]

    Definition Classes
    PythonBigDL
  189. def createL1L2Regularizer(l1: Double, l2: Double): L1L2Regularizer[T]

    Definition Classes
    PythonBigDL
  190. def createL1Penalty(l1weight: Int, sizeAverage: Boolean = false, provideOutput: Boolean = true): L1Penalty[T]

    Definition Classes
    PythonBigDL
  191. def createL1Regularizer(l1: Double): L1Regularizer[T]

    Definition Classes
    PythonBigDL
  192. def createL2Regularizer(l2: Double): L2Regularizer[T]

    Definition Classes
    PythonBigDL
  193. 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
  194. 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
  195. 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
  196. def createLayerNormalization(hiddenSize: Int): LayerNormalization[T]

    Definition Classes
    PythonBigDL
  197. def createLeakyReLU(negval: Double = 0.01, inplace: Boolean = false): LeakyReLU[T]

    Definition Classes
    PythonBigDL
  198. 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
  199. def createLocalImageFrame(images: List[JTensor], labels: List[JTensor]): LocalImageFrame

    Definition Classes
    PythonBigDL
  200. 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
  201. 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
  202. 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
  203. def createLog(): Log[T]

    Definition Classes
    PythonBigDL
  204. def createLogSigmoid(): LogSigmoid[T]

    Definition Classes
    PythonBigDL
  205. def createLogSoftMax(): LogSoftMax[T]

    Definition Classes
    PythonBigDL
  206. 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
  207. def createLookupTableSparse(nIndex: Int, nOutput: Int, combiner: String = "sum", maxNorm: Double = 1, wRegularizer: Regularizer[T] = null): LookupTableSparse[T]

    Definition Classes
    PythonBigDL
  208. def createLoss(criterion: Criterion[T]): ValidationMethod[T]

    Definition Classes
    PythonBigDL
  209. def createMAE(): ValidationMethod[T]

    Definition Classes
    PythonBigDL
  210. def createMM(transA: Boolean = false, transB: Boolean = false): MM[T]

    Definition Classes
    PythonBigDL
  211. def createMSECriterion: MSECriterion[T]

    Definition Classes
    PythonBigDL
  212. def createMV(trans: Boolean = false): MV[T]

    Definition Classes
    PythonBigDL
  213. def createMapTable(module: AbstractModule[Activity, Activity, T] = null): MapTable[T]

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

    Definition Classes
    PythonBigDL
  215. def createMarginRankingCriterion(margin: Double = 1.0, sizeAverage: Boolean = true): MarginRankingCriterion[T]

    Definition Classes
    PythonBigDL
  216. def createMaskedSelect(): MaskedSelect[T]

    Definition Classes
    PythonBigDL
  217. def createMasking(maskValue: Double): Masking[T]

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

    Definition Classes
    PythonBigDL
  219. def createMatToTensor(toRGB: Boolean = false, tensorKey: String = ImageFeature.imageTensor): MatToTensor[T]

    Definition Classes
    PythonBigDL
  220. def createMax(dim: Int = 1, numInputDims: Int = Int.MinValue): Max[T]

    Definition Classes
    PythonBigDL
  221. def createMaxEpoch(max: Int): Trigger

    Definition Classes
    PythonBigDL
  222. def createMaxIteration(max: Int): Trigger

    Definition Classes
    PythonBigDL
  223. def createMaxScore(max: Float): Trigger

    Definition Classes
    PythonBigDL
  224. 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
  225. def createMean(dimension: Int = 1, nInputDims: Int = 1, squeeze: Boolean = true): Mean[T]

    Definition Classes
    PythonBigDL
  226. def createMeanAbsolutePercentageCriterion: MeanAbsolutePercentageCriterion[T]

    Definition Classes
    PythonBigDL
  227. def createMeanAveragePrecision(k: Int, classes: Int): ValidationMethod[T]

    Definition Classes
    PythonBigDL
  228. def createMeanAveragePrecisionObjectDetection(classes: Int, iou: Float, useVoc2007: Boolean, skipClass: Int): ValidationMethod[T]

    Definition Classes
    PythonBigDL
  229. def createMeanSquaredLogarithmicCriterion: MeanSquaredLogarithmicCriterion[T]

    Definition Classes
    PythonBigDL
  230. def createMin(dim: Int = 1, numInputDims: Int = Int.MinValue): Min[T]

    Definition Classes
    PythonBigDL
  231. def createMinLoss(min: Float): Trigger

    Definition Classes
    PythonBigDL
  232. def createMixtureTable(dim: Int = Int.MaxValue): MixtureTable[T]

    Definition Classes
    PythonBigDL
  233. def createModel(input: List[ModuleNode[T]], output: List[ModuleNode[T]]): Graph[T]

    Definition Classes
    PythonBigDL
  234. def createModelPreprocessor(preprocessor: AbstractModule[Activity, Activity, T], trainable: AbstractModule[Activity, Activity, T]): Graph[T]

    Definition Classes
    PythonBigDL
  235. def createMsraFiller(varianceNormAverage: Boolean = true): MsraFiller

    Definition Classes
    PythonBigDL
  236. def createMul(): Mul[T]

    Definition Classes
    PythonBigDL
  237. def createMulConstant(scalar: Double, inplace: Boolean = false): MulConstant[T]

    Definition Classes
    PythonBigDL
  238. def createMultiCriterion(): MultiCriterion[T]

    Definition Classes
    PythonBigDL
  239. def createMultiLabelMarginCriterion(sizeAverage: Boolean = true): MultiLabelMarginCriterion[T]

    Definition Classes
    PythonBigDL
  240. def createMultiLabelSoftMarginCriterion(weights: JTensor = null, sizeAverage: Boolean = true): MultiLabelSoftMarginCriterion[T]

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

    Definition Classes
    PythonBigDL
  242. def createMultiRNNCell(cells: List[Cell[T]]): MultiRNNCell[T]

    Definition Classes
    PythonBigDL
  243. def createMultiStep(stepSizes: List[Int], gamma: Double): MultiStep

    Definition Classes
    PythonBigDL
  244. def createNDCG(k: Int = 10, negNum: Int = 100): ValidationMethod[T]

    Definition Classes
    PythonBigDL
  245. def createNarrow(dimension: Int, offset: Int, length: Int = 1): Narrow[T]

    Definition Classes
    PythonBigDL
  246. def createNarrowTable(offset: Int, length: Int = 1): NarrowTable[T]

    Definition Classes
    PythonBigDL
  247. def createNegative(inplace: Boolean): Negative[T]

    Definition Classes
    PythonBigDL
  248. def createNegativeEntropyPenalty(beta: Double): NegativeEntropyPenalty[T]

    Definition Classes
    PythonBigDL
  249. def createNode(module: AbstractModule[Activity, Activity, T], x: List[ModuleNode[T]]): ModuleNode[T]

    Definition Classes
    PythonBigDL
  250. def createNormalize(p: Double, eps: Double = 1e-10): Normalize[T]

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

    Definition Classes
    PythonBigDL
  252. def createOnes(): Ones.type

    Definition Classes
    PythonBigDL
  253. def createPGCriterion(sizeAverage: Boolean = false): PGCriterion[T]

    Definition Classes
    PythonBigDL
  254. def createPReLU(nOutputPlane: Int = 0): PReLU[T]

    Definition Classes
    PythonBigDL
  255. def createPack(dimension: Int): Pack[T]

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

    Definition Classes
    PythonBigDL
  257. def createPairwiseDistance(norm: Int = 2): PairwiseDistance[T]

    Definition Classes
    PythonBigDL
  258. 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
  259. def createParallelCriterion(repeatTarget: Boolean = false): ParallelCriterion[T]

    Definition Classes
    PythonBigDL
  260. def createParallelTable(): ParallelTable[T]

    Definition Classes
    PythonBigDL
  261. def createPipeline(list: List[FeatureTransformer]): FeatureTransformer

    Definition Classes
    PythonBigDL
  262. def createPixelBytesToMat(byteKey: String): PixelBytesToMat

    Definition Classes
    PythonBigDL
  263. def createPixelNormalize(means: List[Double]): PixelNormalizer

    Definition Classes
    PythonBigDL
  264. 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
  265. def createPoissonCriterion: PoissonCriterion[T]

    Definition Classes
    PythonBigDL
  266. def createPoly(power: Double, maxIteration: Int): Poly

    Definition Classes
    PythonBigDL
  267. def createPooler(resolution: Int, scales: List[Double], sampling_ratio: Int): Pooler[T]

    Definition Classes
    PythonBigDL
  268. def createPower(power: Double, scale: Double = 1, shift: Double = 0): Power[T]

    Definition Classes
    PythonBigDL
  269. 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
  270. def createProposal(preNmsTopN: Int, postNmsTopN: Int, ratios: List[Double], scales: List[Double], rpnPreNmsTopNTrain: Int = 12000, rpnPostNmsTopNTrain: Int = 2000): Proposal

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

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

    Definition Classes
    PythonBigDL
  273. def createRandomAlterAspect(min_area_ratio: Float, max_area_ratio: Int, min_aspect_ratio_change: Float, interp_mode: String, cropLength: Int): RandomAlterAspect

    Definition Classes
    PythonBigDL
  274. def createRandomAspectScale(scales: List[Int], scaleMultipleOf: Int = 1, maxSize: Int = 1000): RandomAspectScale

    Definition Classes
    PythonBigDL
  275. def createRandomCrop(cropWidth: Int, cropHeight: Int, isClip: Boolean): RandomCrop

    Definition Classes
    PythonBigDL
  276. def createRandomCropper(cropWidth: Int, cropHeight: Int, mirror: Boolean, cropperMethod: String, channels: Int): RandomCropper

    Definition Classes
    PythonBigDL
  277. def createRandomNormal(mean: Double, stdv: Double): RandomNormal

    Definition Classes
    PythonBigDL
  278. def createRandomResize(minSize: Int, maxSize: Int): RandomResize

    Definition Classes
    PythonBigDL
  279. def createRandomSampler(): FeatureTransformer

    Definition Classes
    PythonBigDL
  280. def createRandomTransformer(transformer: FeatureTransformer, prob: Double): RandomTransformer

    Definition Classes
    PythonBigDL
  281. def createRandomUniform(): InitializationMethod

    Definition Classes
    PythonBigDL
  282. def createRandomUniform(lower: Double, upper: Double): InitializationMethod

    Definition Classes
    PythonBigDL
  283. def createReLU(ip: Boolean = false): ReLU[T]

    Definition Classes
    PythonBigDL
  284. def createReLU6(inplace: Boolean = false): ReLU6[T]

    Definition Classes
    PythonBigDL
  285. def createRecurrent(): Recurrent[T]

    Definition Classes
    PythonBigDL
  286. def createRecurrentDecoder(outputLength: Int): RecurrentDecoder[T]

    Definition Classes
    PythonBigDL
  287. def createReplicate(nFeatures: Int, dim: Int = 1, nDim: Int = Int.MaxValue): Replicate[T]

    Definition Classes
    PythonBigDL
  288. def createReshape(size: List[Int], batchMode: Boolean = null): Reshape[T]

    Definition Classes
    PythonBigDL
  289. def createResize(resizeH: Int, resizeW: Int, resizeMode: Int = Imgproc.INTER_LINEAR, useScaleFactor: Boolean): Resize

    Definition Classes
    PythonBigDL
  290. def createResizeBilinear(outputHeight: Int, outputWidth: Int, alignCorner: Boolean, dataFormat: String): ResizeBilinear[T]

    Definition Classes
    PythonBigDL
  291. def createReverse(dimension: Int = 1, isInplace: Boolean = false): Reverse[T]

    Definition Classes
    PythonBigDL
  292. 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
  293. def createRoiAlign(spatial_scale: Double, sampling_ratio: Int, pooled_h: Int, pooled_w: Int): RoiAlign[T]

    Definition Classes
    PythonBigDL
  294. def createRoiHFlip(normalized: Boolean = true): RoiHFlip

    Definition Classes
    PythonBigDL
  295. def createRoiNormalize(): RoiNormalize

    Definition Classes
    PythonBigDL
  296. def createRoiPooling(pooled_w: Int, pooled_h: Int, spatial_scale: Double): RoiPooling[T]

    Definition Classes
    PythonBigDL
  297. def createRoiProject(needMeetCenterConstraint: Boolean): RoiProject

    Definition Classes
    PythonBigDL
  298. def createRoiResize(normalized: Boolean): RoiResize

    Definition Classes
    PythonBigDL
  299. 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
  300. def createSReLU(shape: ArrayList[Int], shareAxes: ArrayList[Int] = null): SReLU[T]

    Definition Classes
    PythonBigDL
  301. def createSaturation(deltaLow: Double, deltaHigh: Double): Saturation

    Definition Classes
    PythonBigDL
  302. def createScale(size: List[Int]): Scale[T]

    Definition Classes
    PythonBigDL
  303. def createSelect(dimension: Int, index: Int): Select[T]

    Definition Classes
    PythonBigDL
  304. def createSelectTable(dimension: Int): SelectTable[T]

    Definition Classes
    PythonBigDL
  305. def createSequenceBeamSearch(vocabSize: Int, beamSize: Int, alpha: Float, decodeLength: Int, eosId: Float, paddingValue: Float, numHiddenLayers: Int, hiddenSize: Int): SequenceBeamSearch[T]

    Definition Classes
    PythonBigDL
  306. def createSequential(): Container[Activity, Activity, T]

    Definition Classes
    PythonBigDL
  307. def createSequentialSchedule(iterationPerEpoch: Int): SequentialSchedule

    Definition Classes
    PythonBigDL
  308. def createSeveralIteration(interval: Int): Trigger

    Definition Classes
    PythonBigDL
  309. def createSigmoid(): Sigmoid[T]

    Definition Classes
    PythonBigDL
  310. def createSmoothL1Criterion(sizeAverage: Boolean = true): SmoothL1Criterion[T]

    Definition Classes
    PythonBigDL
  311. def createSmoothL1CriterionWithWeights(sigma: Double, num: Int = 0): SmoothL1CriterionWithWeights[T]

    Definition Classes
    PythonBigDL
  312. def createSoftMarginCriterion(sizeAverage: Boolean = true): SoftMarginCriterion[T]

    Definition Classes
    PythonBigDL
  313. def createSoftMax(pos: Int = 1): SoftMax[T]

    Definition Classes
    PythonBigDL
  314. def createSoftMin(): SoftMin[T]

    Definition Classes
    PythonBigDL
  315. def createSoftPlus(beta: Double = 1.0): SoftPlus[T]

    Definition Classes
    PythonBigDL
  316. def createSoftShrink(lambda: Double = 0.5): SoftShrink[T]

    Definition Classes
    PythonBigDL
  317. def createSoftSign(): SoftSign[T]

    Definition Classes
    PythonBigDL
  318. def createSoftmaxWithCriterion(ignoreLabel: Integer = null, normalizeMode: String = "VALID"): SoftmaxWithCriterion[T]

    Definition Classes
    PythonBigDL
  319. def createSparseJoinTable(dimension: Int): SparseJoinTable[T]

    Definition Classes
    PythonBigDL
  320. 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
  321. 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
  322. 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
  323. def createSpatialContrastiveNormalization(nInputPlane: Int = 1, kernel: JTensor = null, threshold: Double = 1e-4, thresval: Double = 1e-4): SpatialContrastiveNormalization[T]

    Definition Classes
    PythonBigDL
  324. 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
  325. 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
  326. 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
  327. 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
  328. def createSpatialDivisiveNormalization(nInputPlane: Int = 1, kernel: JTensor = null, threshold: Double = 1e-4, thresval: Double = 1e-4): SpatialDivisiveNormalization[T]

    Definition Classes
    PythonBigDL
  329. def createSpatialDropout1D(initP: Double = 0.5): SpatialDropout1D[T]

    Definition Classes
    PythonBigDL
  330. def createSpatialDropout2D(initP: Double = 0.5, dataFormat: String = "NCHW"): SpatialDropout2D[T]

    Definition Classes
    PythonBigDL
  331. def createSpatialDropout3D(initP: Double = 0.5, dataFormat: String = "NCHW"): SpatialDropout3D[T]

    Definition Classes
    PythonBigDL
  332. 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
  333. 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
  334. 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
  335. 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
  336. def createSpatialSubtractiveNormalization(nInputPlane: Int = 1, kernel: JTensor = null): SpatialSubtractiveNormalization[T]

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

    Definition Classes
    PythonBigDL
  338. def createSpatialZeroPadding(padLeft: Int, padRight: Int, padTop: Int, padBottom: Int): SpatialZeroPadding[T]

    Definition Classes
    PythonBigDL
  339. def createSplitTable(dimension: Int, nInputDims: Int = 1): SplitTable[T]

    Definition Classes
    PythonBigDL
  340. def createSqrt(): Sqrt[T]

    Definition Classes
    PythonBigDL
  341. def createSquare(): Square[T]

    Definition Classes
    PythonBigDL
  342. def createSqueeze(dim: Int = Int.MinValue, numInputDims: Int = Int.MinValue): Squeeze[T]

    Definition Classes
    PythonBigDL
  343. def createStep(stepSize: Int, gamma: Double): Step

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

    Definition Classes
    PythonBigDL
  345. def createTableOperation(operationLayer: AbstractModule[Table, Tensor[T], T]): TableOperation[T]

    Definition Classes
    PythonBigDL
  346. def createTanh(): Tanh[T]

    Definition Classes
    PythonBigDL
  347. def createTanhShrink(): TanhShrink[T]

    Definition Classes
    PythonBigDL
  348. 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
  349. def createTemporalMaxPooling(kW: Int, dW: Int): TemporalMaxPooling[T]

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

    Definition Classes
    PythonBigDL
  351. def createTile(dim: Int, copies: Int): Tile[T]

    Definition Classes
    PythonBigDL
  352. def createTimeDistributed(layer: TensorModule[T]): TimeDistributed[T]

    Definition Classes
    PythonBigDL
  353. def createTimeDistributedCriterion(critrn: TensorCriterion[T], sizeAverage: Boolean = false, dimension: Int = 2): TimeDistributedCriterion[T]

    Definition Classes
    PythonBigDL
  354. def createTimeDistributedMaskCriterion(critrn: TensorCriterion[T], paddingValue: Int = 0): TimeDistributedMaskCriterion[T]

    Definition Classes
    PythonBigDL
  355. def createTop1Accuracy(): ValidationMethod[T]

    Definition Classes
    PythonBigDL
  356. def createTop5Accuracy(): ValidationMethod[T]

    Definition Classes
    PythonBigDL
  357. def createTrainSummary(logDir: String, appName: String): TrainSummary

    Definition Classes
    PythonBigDL
  358. def createTransformer(vocabSize: Int, hiddenSize: Int, numHeads: Int, filterSize: Int, numHiddenlayers: Int, postprocessDropout: Double, attentionDropout: Double, reluDropout: Double): Transformer[T]

    Definition Classes
    PythonBigDL
  359. def createTransformerCriterion(criterion: AbstractCriterion[Activity, Activity, T], inputTransformer: AbstractModule[Activity, Activity, T] = null, targetTransformer: AbstractModule[Activity, Activity, T] = null): TransformerCriterion[T]

    Definition Classes
    PythonBigDL
  360. def createTranspose(permutations: List[List[Int]]): Transpose[T]

    Definition Classes
    PythonBigDL
  361. def createTreeNNAccuracy(): ValidationMethod[T]

    Definition Classes
    PythonBigDL
  362. def createTriggerAnd(first: Trigger, others: List[Trigger]): Trigger

    Definition Classes
    PythonBigDL
  363. def createTriggerOr(first: Trigger, others: List[Trigger]): Trigger

    Definition Classes
    PythonBigDL
  364. def createUnsqueeze(pos: List[Int], numInputDims: Int = Int.MinValue): Unsqueeze[T]

    Definition Classes
    PythonBigDL
  365. def createUpSampling1D(length: Int): UpSampling1D[T]

    Definition Classes
    PythonBigDL
  366. def createUpSampling2D(size: List[Int], dataFormat: String): UpSampling2D[T]

    Definition Classes
    PythonBigDL
  367. def createUpSampling3D(size: List[Int]): UpSampling3D[T]

    Definition Classes
    PythonBigDL
  368. def createValidationSummary(logDir: String, appName: String): ValidationSummary

    Definition Classes
    PythonBigDL
  369. def createView(sizes: List[Int], num_input_dims: Int = 0): View[T]

    Definition Classes
    PythonBigDL
  370. 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
  371. 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
  372. 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
  373. 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
  374. def createWarmup(delta: Double): Warmup

    Definition Classes
    PythonBigDL
  375. def createXavier(): Xavier.type

    Definition Classes
    PythonBigDL
  376. def createZeros(): Zeros.type

    Definition Classes
    PythonBigDL
  377. def criterionBackward(criterion: AbstractCriterion[Activity, Activity, T], input: List[_ <: AnyRef], inputIsTable: Boolean, target: List[_ <: AnyRef], targetIsTable: Boolean): List[JTensor]

    Definition Classes
    PythonBigDL
  378. def criterionForward(criterion: AbstractCriterion[Activity, Activity, T], input: List[_ <: AnyRef], inputIsTable: Boolean, target: List[_ <: AnyRef], targetIsTable: Boolean): T

    Definition Classes
    PythonBigDL
  379. def disableClip(optimizer: Optimizer[T, MiniBatch[T]]): Unit

    Definition Classes
    PythonBigDL
  380. def distributedImageFrameRandomSplit(imageFrame: DistributedImageFrame, weights: List[Double]): Array[ImageFrame]

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

    Definition Classes
    PythonBigDL
  382. def distributedImageFrameToLabelTensorRdd(imageFrame: DistributedImageFrame): JavaRDD[JTensor]

    Definition Classes
    PythonBigDL
  383. def distributedImageFrameToPredict(imageFrame: DistributedImageFrame, key: String): JavaRDD[List[Any]]

    Definition Classes
    PythonBigDL
  384. def distributedImageFrameToSample(imageFrame: DistributedImageFrame, key: String): JavaRDD[Sample]

    Definition Classes
    PythonBigDL
  385. def distributedImageFrameToUri(imageFrame: DistributedImageFrame, key: String): JavaRDD[String]

    Definition Classes
    PythonBigDL
  386. def dlClassifierModelTransform(dlClassifierModel: DLClassifierModel[T], dataSet: DataFrame): DataFrame

    Definition Classes
    PythonBigDL
  387. def dlImageTransform(dlImageTransformer: DLImageTransformer, dataSet: DataFrame): DataFrame

    Definition Classes
    PythonBigDL
  388. def dlModelTransform(dlModel: DLModel[T], dataSet: DataFrame): DataFrame

    Definition Classes
    PythonBigDL
  389. def dlReadImage(path: String, sc: JavaSparkContext, minParitions: Int): DataFrame

    Definition Classes
    PythonBigDL
  390. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  391. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  392. def evaluate(module: KerasModel[T], x: JavaRDD[Sample], batchSize: Int = 32): List[EvaluatedResult]

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

    Definition Classes
    PythonBigDL
  394. def featureTransformDataset(dataset: DataSet[ImageFeature], transformer: FeatureTransformer): DataSet[ImageFeature]

    Definition Classes
    PythonBigDL
  395. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  396. def findGraphNode(model: Graph[T], name: String): ModuleNode[T]

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

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

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

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

    Definition Classes
    PythonBigDL
  401. def fitEstimator(estimator: DLEstimator[T], dataSet: DataFrame): DLModel[T]

    Definition Classes
    PythonBigDL
  402. def freeze(model: AbstractModule[Activity, Activity, T], freezeLayers: List[String]): AbstractModule[Activity, Activity, T]

    Definition Classes
    PythonBigDL
  403. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  404. def getContainerModules(module: Container[Activity, Activity, T]): List[AbstractModule[Activity, Activity, T]]

    Definition Classes
    PythonBigDL
  405. def getEngineType(): String

    Definition Classes
    PythonBigDL
  406. def getFlattenModules(module: Container[Activity, Activity, T], includeContainer: Boolean): List[AbstractModule[Activity, Activity, T]]

    Definition Classes
    PythonBigDL
  407. def getHiddenState(rec: Recurrent[T]): JActivity

    Definition Classes
    PythonBigDL
  408. def getInputShape(module: Container[Activity, Activity, T]): List[List[Int]]

  409. def getNodeAndCoreNumber(): Array[Int]

    Definition Classes
    PythonBigDL
  410. def getOptimizerVersion(): String

    Definition Classes
    PythonBigDL
  411. def getOutputShape(module: Container[Activity, Activity, T]): List[List[Int]]

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

    Definition Classes
    PythonBigDL
  413. def getRunningMean(module: BatchNormalization[T]): JTensor

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

    Definition Classes
    PythonBigDL
  415. def getRunningStd(module: BatchNormalization[T]): JTensor

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

    Definition Classes
    PythonBigDL
  417. def getWeights(model: AbstractModule[Activity, Activity, T]): List[JTensor]

    Definition Classes
    PythonBigDL
  418. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  419. def imageFeatureGetKeys(imageFeature: ImageFeature): List[String]

    Definition Classes
    PythonBigDL
  420. def imageFeatureToImageTensor(imageFeature: ImageFeature, floatKey: String = ImageFeature.floats, toChw: Boolean = true): JTensor

    Definition Classes
    PythonBigDL
  421. def imageFeatureToLabelTensor(imageFeature: ImageFeature): JTensor

    Definition Classes
    PythonBigDL
  422. def initEngine(): Unit

    Definition Classes
    PythonBigDL
  423. def isDistributed(imageFrame: ImageFrame): Boolean

    Definition Classes
    PythonBigDL
  424. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  425. def isLocal(imageFrame: ImageFrame): Boolean

    Definition Classes
    PythonBigDL
  426. def isWithWeights(module: Module[T]): Boolean

    Definition Classes
    PythonBigDL
  427. def jTensorsToActivity(input: List[_ <: AnyRef], isTable: Boolean): Activity

    Definition Classes
    PythonBigDL
  428. def loadBigDL(path: String): AbstractModule[Activity, Activity, T]

    Definition Classes
    PythonBigDL
  429. def loadBigDLModule(modulePath: String, weightPath: String): AbstractModule[Activity, Activity, T]

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

    Definition Classes
    PythonBigDL
  431. def loadCaffeModel(defPath: String, modelPath: String): AbstractModule[Activity, Activity, T]

    Definition Classes
    PythonBigDL
  432. def loadOptimMethod(path: String): OptimMethod[T]

    Definition Classes
    PythonBigDL
  433. 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
  434. def loadTorch(path: String): AbstractModule[Activity, Activity, T]

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

    Definition Classes
    PythonBigDL
  436. def localImageFrameToLabelTensor(imageFrame: LocalImageFrame): List[JTensor]

    Definition Classes
    PythonBigDL
  437. def localImageFrameToPredict(imageFrame: LocalImageFrame, key: String): List[List[Any]]

    Definition Classes
    PythonBigDL
  438. def localImageFrameToSample(imageFrame: LocalImageFrame, key: String): List[Sample]

    Definition Classes
    PythonBigDL
  439. def localImageFrameToUri(imageFrame: LocalImageFrame, key: String): List[String]

    Definition Classes
    PythonBigDL
  440. def modelBackward(model: AbstractModule[Activity, Activity, T], input: List[_ <: AnyRef], inputIsTable: Boolean, gradOutput: List[_ <: AnyRef], gradOutputIsTable: Boolean): List[JTensor]

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

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

    Definition Classes
    PythonBigDL
  443. def modelForward(model: AbstractModule[Activity, Activity, T], input: List[_ <: AnyRef], inputIsTable: Boolean): List[JTensor]

    Definition Classes
    PythonBigDL
  444. def modelGetParameters(model: AbstractModule[Activity, Activity, T]): Map[Any, Map[Any, List[List[Any]]]]

    Definition Classes
    PythonBigDL
  445. def modelPredictClass(model: AbstractModule[Activity, Activity, T], dataRdd: JavaRDD[Sample]): JavaRDD[Int]

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

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

    Definition Classes
    PythonBigDL
  448. def modelSave(module: AbstractModule[Activity, Activity, T], path: String, overWrite: Boolean): Unit

    Definition Classes
    PythonBigDL
  449. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  450. final def notify(): Unit

    Definition Classes
    AnyRef
  451. final def notifyAll(): Unit

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

    Definition Classes
    PythonBigDL
  453. def predictLocalClass(model: AbstractModule[Activity, Activity, T], features: List[JTensor]): List[Int]

    Definition Classes
    PythonBigDL
  454. def quantize(module: AbstractModule[Activity, Activity, T]): Module[T]

    Definition Classes
    PythonBigDL
  455. def read(path: String, sc: JavaSparkContext, minPartitions: Int): ImageFrame

    Definition Classes
    PythonBigDL
  456. def readParquet(path: String, sc: JavaSparkContext): DistributedImageFrame

    Definition Classes
    PythonBigDL
  457. def redirectSparkLogs(logPath: String): Unit

    Definition Classes
    PythonBigDL
  458. def saveBigDLModule(module: AbstractModule[Activity, Activity, T], modulePath: String, weightPath: String, overWrite: Boolean): Unit

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

    Definition Classes
    PythonBigDL
  460. def saveGraphTopology(model: Graph[T], logPath: String): Graph[T]

    Definition Classes
    PythonBigDL
  461. def saveOptimMethod(method: OptimMethod[T], path: String, overWrite: Boolean = false): Unit

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

    Definition Classes
    PythonBigDL
  463. 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
  464. def seqFilesToImageFrame(url: String, sc: JavaSparkContext, classNum: Int, partitionNum: Int): ImageFrame

    Definition Classes
    PythonBigDL
  465. def setBatchSizeDLClassifier(classifier: DLClassifier[T], batchSize: Int): DLClassifier[T]

    Definition Classes
    PythonBigDL
  466. def setBatchSizeDLClassifierModel(dlClassifierModel: DLClassifierModel[T], batchSize: Int): DLClassifierModel[T]

    Definition Classes
    PythonBigDL
  467. def setBatchSizeDLEstimator(estimator: DLEstimator[T], batchSize: Int): DLEstimator[T]

    Definition Classes
    PythonBigDL
  468. def setBatchSizeDLModel(dlModel: DLModel[T], batchSize: Int): DLModel[T]

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

    Definition Classes
    PythonBigDL
  470. def setConstantClip(optimizer: Optimizer[T, MiniBatch[T]], min: Float, max: Float): Unit

    Definition Classes
    PythonBigDL
  471. def setCriterion(optimizer: Optimizer[T, MiniBatch[T]], criterion: Criterion[T]): Unit

    Definition Classes
    PythonBigDL
  472. def setFeatureSizeDLClassifierModel(dlClassifierModel: DLClassifierModel[T], featureSize: ArrayList[Int]): DLClassifierModel[T]

    Definition Classes
    PythonBigDL
  473. def setFeatureSizeDLModel(dlModel: DLModel[T], featureSize: ArrayList[Int]): DLModel[T]

    Definition Classes
    PythonBigDL
  474. def setInitMethod(layer: Initializable, initMethods: ArrayList[InitializationMethod]): layer.type

    Definition Classes
    PythonBigDL
  475. def setInitMethod(layer: Initializable, weightInitMethod: InitializationMethod, biasInitMethod: InitializationMethod): layer.type

    Definition Classes
    PythonBigDL
  476. def setInputFormats(graph: StaticGraph[T], inputFormat: List[Int]): StaticGraph[T]

    Definition Classes
    PythonBigDL
  477. def setL2NormClip(optimizer: Optimizer[T, MiniBatch[T]], normValue: Float): Unit

    Definition Classes
    PythonBigDL
  478. def setLabel(labelMap: Map[String, Float], imageFrame: ImageFrame): Unit

    Definition Classes
    PythonBigDL
  479. def setLearningRateDLClassifier(classifier: DLClassifier[T], lr: Double): DLClassifier[T]

    Definition Classes
    PythonBigDL
  480. def setLearningRateDLEstimator(estimator: DLEstimator[T], lr: Double): DLEstimator[T]

    Definition Classes
    PythonBigDL
  481. def setMaxEpochDLClassifier(classifier: DLClassifier[T], maxEpoch: Int): DLClassifier[T]

    Definition Classes
    PythonBigDL
  482. def setMaxEpochDLEstimator(estimator: DLEstimator[T], maxEpoch: Int): DLEstimator[T]

    Definition Classes
    PythonBigDL
  483. def setModelSeed(seed: Long): Unit

    Definition Classes
    PythonBigDL
  484. def setOptimizerVersion(version: String): Unit

    Definition Classes
    PythonBigDL
  485. def setOutputFormats(graph: StaticGraph[T], outputFormat: List[Int]): StaticGraph[T]

    Definition Classes
    PythonBigDL
  486. def setRunningMean(module: BatchNormalization[T], runningMean: JTensor): Unit

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

    Definition Classes
    PythonBigDL
  488. def setRunningStd(module: BatchNormalization[T], runningStd: JTensor): Unit

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

    Definition Classes
    PythonBigDL
  490. def setStopGradient(model: Graph[T], layers: List[String]): Graph[T]

    Definition Classes
    PythonBigDL
  491. def setTrainData(optimizer: Optimizer[T, MiniBatch[T]], trainingRdd: JavaRDD[Sample], batchSize: Int): Unit

    Definition Classes
    PythonBigDL
  492. def setTrainSummary(optimizer: Optimizer[T, MiniBatch[T]], summary: TrainSummary): Unit

    Definition Classes
    PythonBigDL
  493. def setValSummary(optimizer: Optimizer[T, MiniBatch[T]], summary: ValidationSummary): Unit

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

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

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

    Definition Classes
    PythonBigDL
  497. def setWeights(model: AbstractModule[Activity, Activity, T], weights: List[JTensor]): Unit

    Definition Classes
    PythonBigDL
  498. def shapeToJList(shape: Shape): List[List[Int]]

  499. def showBigDlInfoLogs(): Unit

    Definition Classes
    PythonBigDL
  500. def summaryReadScalar(summary: Summary, tag: String): List[List[Any]]

    Definition Classes
    PythonBigDL
  501. def summarySetTrigger(summary: TrainSummary, summaryName: String, trigger: Trigger): TrainSummary

    Definition Classes
    PythonBigDL
  502. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  503. def testSample(sample: Sample): Sample

    Definition Classes
    PythonBigDL
  504. def testTensor(jTensor: JTensor): JTensor

    Definition Classes
    PythonBigDL
  505. def toGraph(sequential: Sequential[T]): StaticGraph[T]

    Definition Classes
    PythonBigDL
  506. def toJSample(psamples: RDD[Sample]): RDD[dataset.Sample[T]]

    Definition Classes
    PythonBigDL
  507. def toJSample(record: Sample): dataset.Sample[T]

    Definition Classes
    PythonBigDL
  508. def toJTensor(tensor: Tensor[T]): JTensor

    Definition Classes
    PythonBigDL
  509. def toPySample(sample: dataset.Sample[T]): Sample

    Definition Classes
    PythonBigDL
  510. def toSampleArray(Xs: List[Tensor[T]], y: Tensor[T] = null): Array[dataset.Sample[T]]

    Definition Classes
    PythonBigDL
  511. def toScalaArray(list: List[Int]): Array[Int]

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

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

  514. def toString(): String

    Definition Classes
    AnyRef → Any
  515. def toTensor(jTensor: JTensor): Tensor[T]

    Definition Classes
    PythonBigDL
  516. 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
  517. def transformImageFeature(transformer: FeatureTransformer, feature: ImageFeature): ImageFeature

    Definition Classes
    PythonBigDL
  518. def transformImageFrame(transformer: FeatureTransformer, imageFrame: ImageFrame): ImageFrame

    Definition Classes
    PythonBigDL
  519. def unFreeze(model: AbstractModule[Activity, Activity, T], names: List[String]): AbstractModule[Activity, Activity, T]

    Definition Classes
    PythonBigDL
  520. def uniform(a: Double, b: Double, size: List[Int]): JTensor

    Definition Classes
    PythonBigDL
  521. def updateParameters(model: AbstractModule[Activity, Activity, T], lr: Double): Unit

    Definition Classes
    PythonBigDL
  522. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  523. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  524. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  525. 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