PythonInferenceModel

Value Members

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

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

   Definition Classes

   AnyRef → Any

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

   

   Definition Classes

   AnyRef → Any

4. def activityToJTensors(outputActivity: Activity): List[bigdl.python.api.JTensor]

   

   Definition Classes

   PythonBigDL

5. def activityToList(outputActivity: Activity): List[AnyRef]

   

   Definition Classes

   PythonZoo

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

   

   Definition Classes

   PythonBigDL

7. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

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

   

   Definition Classes

   PythonBigDL

9. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... )

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

    

    Definition Classes

    PythonBigDLKeras

11. def createAbs(): Abs[T]

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

14. def createAdadelta(decayRate: Double, Epsilon: Double): Adadelta[T]

    

    Definition Classes

    PythonBigDL

15. def createAdagrad(learningRate: Double, learningRateDecay: Double, weightDecay: Double): Adagrad[T]

    

    Definition Classes

    PythonBigDL

16. def createAdam(learningRate: Double, learningRateDecay: Double, beta1: Double, beta2: Double, Epsilon: Double): Adam[T]

    

    Definition Classes

    PythonBigDL

17. def createAdamax(learningRate: Double, beta1: Double, beta2: Double, Epsilon: Double): Adamax[T]

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

21. def createBCECriterion(weights: bigdl.python.api.JTensor, sizeAverage: Boolean): BCECriterion[T]

    

    Definition Classes

    PythonBigDL

22. def createBatchNormalization(nOutput: Int, eps: Double, momentum: Double, affine: Boolean, initWeight: bigdl.python.api.JTensor, initBias: bigdl.python.api.JTensor, initGradWeight: bigdl.python.api.JTensor, initGradBias: bigdl.python.api.JTensor): BatchNormalization[T]

    

    Definition Classes

    PythonBigDL

23. def createBiRecurrent(merge: AbstractModule[Table, Tensor[T], T]): 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, wRegularizer: Regularizer[T], bRegularizer: Regularizer[T]): 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, withGraph: Boolean): BinaryTreeLSTM[T]

    

    Definition Classes

    PythonBigDL

29. def createBottle(module: AbstractModule[Activity, Activity, T], nInputDim: Int, nOutputDim1: Int): 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]): CAdd[T]

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

34. def createCAveTable(inplace: Boolean): 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]): 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, stdG: Double, stdB: Double): FeatureTransformer

    

    Definition Classes

    PythonBigDL

44. def createChannelOrder(): ChannelOrder

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

47. def createClassNLLCriterion(weights: bigdl.python.api.JTensor, sizeAverage: Boolean, logProbAsInput: Boolean): ClassNLLCriterion[T]

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

49. def createColorJitter(brightnessProb: Double, brightnessDelta: Double, contrastProb: Double, contrastLower: Double, contrastUpper: Double, hueProb: Double, hueDelta: Double, saturationProb: Double, saturationLower: Double, saturationUpper: Double, randomOrderProb: Double, shuffle: Boolean): ColorJitter

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

51. def createConcatTable(): ConcatTable[T]

    

    Definition Classes

    PythonBigDL

52. def createConstInitMethod(value: Double): ConstInitMethod

    

    Definition Classes

    PythonBigDL

53. def createContiguous(): Contiguous[T]

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

58. def createCosineDistance(): CosineDistance[T]

    

    Definition Classes

    PythonBigDL

59. def createCosineDistanceCriterion(sizeAverage: Boolean): CosineDistanceCriterion[T]

    

    Definition Classes

    PythonBigDL

60. def createCosineEmbeddingCriterion(margin: Double, sizeAverage: Boolean): CosineEmbeddingCriterion[T]

    

    Definition Classes

    PythonBigDL

61. def createCosineProximityCriterion(): CosineProximityCriterion[T]

    

    Definition Classes

    PythonBigDL

62. def createCropping2D(heightCrop: List[Int], widthCrop: List[Int], dataFormat: String): Cropping2D[T]

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

64. def createCrossEntropyCriterion(weights: bigdl.python.api.JTensor, sizeAverage: Boolean): CrossEntropyCriterion[T]

    

    Definition Classes

    PythonBigDL

65. def createCrossProduct(numTensor: Int, embeddingSize: Int): CrossProduct[T]

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

69. def createDLImageTransformer(transformer: FeatureTransformer): DLImageTransformer

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

72. def createDefault(): Default

    

    Definition Classes

    PythonBigDL

73. def createDenseToSparse(): DenseToSparse[T]

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

75. def createDetectionOutputFrcnn(nmsThresh: Float, nClasses: Int, bboxVote: Boolean, maxPerImage: Int, thresh: Double): DetectionOutputFrcnn

    

    Definition Classes

    PythonBigDL

76. 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

77. def createDiceCoefficientCriterion(sizeAverage: Boolean, epsilon: Float): DiceCoefficientCriterion[T]

    

    Definition Classes

    PythonBigDL

78. def createDistKLDivCriterion(sizeAverage: Boolean): DistKLDivCriterion[T]

    

    Definition Classes

    PythonBigDL

79. 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

80. 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

81. def createDistributedImageFrame(imageRdd: JavaRDD[bigdl.python.api.JTensor], labelRdd: JavaRDD[bigdl.python.api.JTensor]): DistributedImageFrame

    

    Definition Classes

    PythonBigDL

82. def createDotProduct(): DotProduct[T]

    

    Definition Classes

    PythonBigDL

83. def createDotProductCriterion(sizeAverage: Boolean): DotProductCriterion[T]

    

    Definition Classes

    PythonBigDL

84. def createDropout(initP: Double, inplace: Boolean, scale: Boolean): Dropout[T]

    

    Definition Classes

    PythonBigDL

85. def createELU(alpha: Double, inplace: Boolean): ELU[T]

    

    Definition Classes

    PythonBigDL

86. def createEcho(): Echo[T]

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

88. def createEveryEpoch(): Trigger

    

    Definition Classes

    PythonBigDL

89. def createExp(): Exp[T]

    

    Definition Classes

    PythonBigDL

90. def createExpand(meansR: Int, meansG: Int, meansB: Int, minExpandRatio: Double, maxExpandRatio: Double): Expand

    

    Definition Classes

    PythonBigDL

91. def createExponential(decayStep: Int, decayRate: Double, stairCase: Boolean): Exponential

    

    Definition Classes

    PythonBigDL

92. def createFiller(startX: Double, startY: Double, endX: Double, endY: Double, value: Int): Filler

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

95. def createFlattenTable(): FlattenTable[T]

    

    Definition Classes

    PythonBigDL

96. def createFtrl(learningRate: Double, learningRatePower: Double, initialAccumulatorValue: Double, l1RegularizationStrength: Double, l2RegularizationStrength: Double, l2ShrinkageRegularizationStrength: Double): Ftrl[T]

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

98. def createGaussianCriterion(): GaussianCriterion[T]

    

    Definition Classes

    PythonBigDL

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

    

    Definition Classes

    PythonBigDL

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

     

     Definition Classes

     PythonBigDL

101. def createGaussianSampler(): GaussianSampler[T]

     

     Definition Classes

     PythonBigDL

102. def createGradientReversal(lambda: Double): GradientReversal[T]

     

     Definition Classes

     PythonBigDL

103. def createHFlip(): HFlip

     

     Definition Classes

     PythonBigDL

104. def createHardShrink(lambda: Double): HardShrink[T]

     

     Definition Classes

     PythonBigDL

105. def createHardSigmoid: HardSigmoid[T]

     

     Definition Classes

     PythonBigDL

106. def createHardTanh(minValue: Double, maxValue: Double, inplace: Boolean): HardTanh[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

108. def createHingeEmbeddingCriterion(margin: Double, sizeAverage: Boolean): HingeEmbeddingCriterion[T]

     

     Definition Classes

     PythonBigDL

109. def createHitRatio(k: Int, negNum: Int): ValidationMethod[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

111. def createIdentity(): Identity[T]

     

     Definition Classes

     PythonBigDL

112. def createImageFeature(data: bigdl.python.api.JTensor, label: bigdl.python.api.JTensor, uri: String): ImageFeature

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

116. def createInferenceModel(supportedConcurrentNum: Int = 1): InferenceModel

     

117. def createInput(): ModuleNode[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

123. def createKerasAveragePooling1D(poolLength: Int, stride: Int, borderMode: String, inputShape: List[Int]): AveragePooling1D[T]

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

126. def createKerasBatchNormalization(epsilon: Double, momentum: Double, betaInit: String, gammaInit: String, dimOrdering: String, inputShape: List[Int]): BatchNormalization[T]

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

128. def createKerasConvLSTM2D(nbFilter: Int, nbKernel: Int, activation: String, innerActivation: String, dimOrdering: String, subsample: Int, wRegularizer: Regularizer[T], uRegularizer: Regularizer[T], bRegularizer: Regularizer[T], returnSequences: Boolean, goBackwards: Boolean, inputShape: List[Int]): ConvLSTM2D[T]

     

     Definition Classes

     PythonBigDLKeras

129. def createKerasConvolution1D(nbFilter: Int, filterLength: Int, init: String, activation: String, borderMode: String, subsampleLength: Int, wRegularizer: Regularizer[T], bRegularizer: Regularizer[T], bias: Boolean, inputShape: List[Int]): Convolution1D[T]

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

158. def createKerasMaxPooling1D(poolLength: Int, stride: Int, borderMode: String, inputShape: List[Int]): MaxPooling1D[T]

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

167. def createKerasSReLU(tLeftInit: String, aLeftInit: String, tRightInit: String, aRightInit: String, sharedAxes: List[Int], inputShape: List[Int]): SReLU[T]

     

     Definition Classes

     PythonBigDLKeras

168. def createKerasSeparableConvolution2D(nbFilter: Int, nbRow: Int, nbCol: Int, init: String, activation: String, borderMode: String, subsample: List[Int], depthMultiplier: Int, dimOrdering: String, depthwiseRegularizer: Regularizer[T], pointwiseRegularizer: Regularizer[T], bRegularizer: Regularizer[T], bias: Boolean, inputShape: List[Int]): SeparableConvolution2D[T]

     

     Definition Classes

     PythonBigDLKeras

169. def createKerasSequential(): Sequential[T]

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

172. def createKerasSpatialDropout2D(p: Double, dimOrdering: String, inputShape: List[Int]): SpatialDropout2D[T]

     

     Definition Classes

     PythonBigDLKeras

173. def createKerasSpatialDropout3D(p: Double, dimOrdering: String, inputShape: List[Int]): SpatialDropout3D[T]

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

182. def createKullbackLeiblerDivergenceCriterion: KullbackLeiblerDivergenceCriterion[T]

     

     Definition Classes

     PythonBigDL

183. def createL1Cost(): L1Cost[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

189. def createLBFGS(maxIter: Int, maxEval: Double, tolFun: Double, tolX: Double, nCorrection: Int, learningRate: Double, verbose: Boolean, lineSearch: LineSearch[T], lineSearchOptions: Map[Any, Any]): LBFGS[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

195. def createLocalOptimizer(features: List[bigdl.python.api.JTensor], y: bigdl.python.api.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

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

     

     Definition Classes

     PythonBigDL

197. def createLocallyConnected2D(nInputPlane: Int, inputWidth: Int, inputHeight: Int, nOutputPlane: Int, kernelW: Int, kernelH: Int, strideW: Int, strideH: Int, padW: Int, padH: Int, propagateBack: Boolean, wRegularizer: Regularizer[T], bRegularizer: Regularizer[T], initWeight: bigdl.python.api.JTensor, initBias: bigdl.python.api.JTensor, initGradWeight: bigdl.python.api.JTensor, initGradBias: bigdl.python.api.JTensor, withBias: Boolean, dataFormat: String): LocallyConnected2D[T]

     

     Definition Classes

     PythonBigDL

198. def createLog(): Log[T]

     

     Definition Classes

     PythonBigDL

199. def createLogSigmoid(): LogSigmoid[T]

     

     Definition Classes

     PythonBigDL

200. def createLogSoftMax(): LogSoftMax[T]

     

     Definition Classes

     PythonBigDL

201. def createLookupTable(nIndex: Int, nOutput: Int, paddingValue: Double, maxNorm: Double, normType: Double, shouldScaleGradByFreq: Boolean, wRegularizer: Regularizer[T]): LookupTable[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

204. def createMAE(): ValidationMethod[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

206. def createMSECriterion: MSECriterion[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

211. def createMaskedSelect(): MaskedSelect[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

213. def createMatToFloats(validHeight: Int, validWidth: Int, validChannels: Int, outKey: String, shareBuffer: Boolean): MatToFloats

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

216. def createMaxEpoch(max: Int): Trigger

     

     Definition Classes

     PythonBigDL

217. def createMaxIteration(max: Int): Trigger

     

     Definition Classes

     PythonBigDL

218. def createMaxScore(max: Float): Trigger

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

221. def createMeanAbsolutePercentageCriterion: MeanAbsolutePercentageCriterion[T]

     

     Definition Classes

     PythonBigDL

222. def createMeanSquaredLogarithmicCriterion: MeanSquaredLogarithmicCriterion[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

224. def createMinLoss(min: Float): Trigger

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

228. def createMsraFiller(varianceNormAverage: Boolean): MsraFiller

     

     Definition Classes

     PythonBigDL

229. def createMul(): Mul[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

231. def createMultiCriterion(): MultiCriterion[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

233. def createMultiLabelSoftMarginCriterion(weights: bigdl.python.api.JTensor, sizeAverage: Boolean): MultiLabelSoftMarginCriterion[T]

     

     Definition Classes

     PythonBigDL

234. def createMultiMarginCriterion(p: Int, weights: bigdl.python.api.JTensor, margin: Double, sizeAverage: Boolean): MultiMarginCriterion[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

243. def createNormalize(p: Double, eps: Double): Normalize[T]

     

     Definition Classes

     PythonBigDL

244. def createNormalizeScale(p: Double, eps: Double, scale: Double, size: List[Int], wRegularizer: Regularizer[T]): NormalizeScale[T]

     

     Definition Classes

     PythonBigDL

245. def createOnes(): Ones.type

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

251. def createParallelAdam(learningRate: Double, learningRateDecay: Double, beta1: Double, beta2: Double, Epsilon: Double, parallelNum: Int): ParallelAdam[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

253. def createParallelTable(): ParallelTable[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

255. def createPixelBytesToMat(byteKey: String): PixelBytesToMat

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

257. def createPlateau(monitor: String, factor: Float, patience: Int, mode: String, epsilon: Float, cooldown: Int, minLr: Float): Plateau

     

     Definition Classes

     PythonBigDL

258. def createPoissonCriterion: PoissonCriterion[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

263. def createRMSprop(learningRate: Double, learningRateDecay: Double, decayRate: Double, Epsilon: Double): RMSprop[T]

     

     Definition Classes

     PythonBigDL

264. def createRReLU(lower: Double, upper: Double, inplace: Boolean): RReLU[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

271. def createRandomSampler(): FeatureTransformer

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

273. def createRandomUniform(): InitializationMethod

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

277. def createRecurrent(): Recurrent[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

285. def createRoiHFlip(normalized: Boolean): RoiHFlip

     

     Definition Classes

     PythonBigDL

286. def createRoiNormalize(): RoiNormalize

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

288. def createRoiProject(needMeetCenterConstraint: Boolean): RoiProject

     

     Definition Classes

     PythonBigDL

289. def createRoiResize(normalized: Boolean): RoiResize

     

     Definition Classes

     PythonBigDL

290. def createSGD(learningRate: Double, learningRateDecay: Double, weightDecay: Double, momentum: Double, dampening: Double, nesterov: Boolean, leaningRateSchedule: LearningRateSchedule, learningRates: bigdl.python.api.JTensor, weightDecays: bigdl.python.api.JTensor): SGD[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

297. def createSequentialSchedule(iterationPerEpoch: Int): SequentialSchedule

     

     Definition Classes

     PythonBigDL

298. def createSeveralIteration(interval: Int): Trigger

     

     Definition Classes

     PythonBigDL

299. def createSigmoid(): Sigmoid[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

303. def createSoftMax(): SoftMax[T]

     

     Definition Classes

     PythonBigDL

304. def createSoftMin(): SoftMin[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

307. def createSoftSign(): SoftSign[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

311. def createSpatialAveragePooling(kW: Int, kH: Int, dW: Int, dH: Int, padW: Int, padH: Int, globalPooling: Boolean, ceilMode: Boolean, countIncludePad: Boolean, divide: Boolean, format: String): SpatialAveragePooling[T]

     

     Definition Classes

     PythonBigDL

312. def createSpatialBatchNormalization(nOutput: Int, eps: Double, momentum: Double, affine: Boolean, initWeight: bigdl.python.api.JTensor, initBias: bigdl.python.api.JTensor, initGradWeight: bigdl.python.api.JTensor, initGradBias: bigdl.python.api.JTensor, dataFormat: String): SpatialBatchNormalization[T]

     

     Definition Classes

     PythonBigDL

313. def createSpatialContrastiveNormalization(nInputPlane: Int, kernel: bigdl.python.api.JTensor, threshold: Double, thresval: Double): SpatialContrastiveNormalization[T]

     

     Definition Classes

     PythonBigDL

314. def createSpatialConvolution(nInputPlane: Int, nOutputPlane: Int, kernelW: Int, kernelH: Int, strideW: Int, strideH: Int, padW: Int, padH: Int, nGroup: Int, propagateBack: Boolean, wRegularizer: Regularizer[T], bRegularizer: Regularizer[T], initWeight: bigdl.python.api.JTensor, initBias: bigdl.python.api.JTensor, initGradWeight: bigdl.python.api.JTensor, initGradBias: bigdl.python.api.JTensor, withBias: Boolean, dataFormat: String): SpatialConvolution[T]

     

     Definition Classes

     PythonBigDL

315. def createSpatialConvolutionMap(connTable: bigdl.python.api.JTensor, kW: Int, kH: Int, dW: Int, dH: Int, padW: Int, padH: Int, wRegularizer: Regularizer[T], bRegularizer: Regularizer[T]): SpatialConvolutionMap[T]

     

     Definition Classes

     PythonBigDL

316. def createSpatialCrossMapLRN(size: Int, alpha: Double, beta: Double, k: Double, dataFormat: String): SpatialCrossMapLRN[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

318. def createSpatialDivisiveNormalization(nInputPlane: Int, kernel: bigdl.python.api.JTensor, threshold: Double, thresval: Double): SpatialDivisiveNormalization[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

326. def createSpatialSubtractiveNormalization(nInputPlane: Int, kernel: bigdl.python.api.JTensor): SpatialSubtractiveNormalization[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

330. def createSqrt(): Sqrt[T]

     

     Definition Classes

     PythonBigDL

331. def createSquare(): Square[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

335. def createTanh(): Tanh[T]

     

     Definition Classes

     PythonBigDL

336. def createTanhShrink(): TanhShrink[T]

     

     Definition Classes

     PythonBigDL

337. def createTemporalConvolution(inputFrameSize: Int, outputFrameSize: Int, kernelW: Int, strideW: Int, propagateBack: Boolean, wRegularizer: Regularizer[T], bRegularizer: Regularizer[T], initWeight: bigdl.python.api.JTensor, initBias: bigdl.python.api.JTensor, initGradWeight: bigdl.python.api.JTensor, initGradBias: bigdl.python.api.JTensor): TemporalConvolution[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

339. def createThreshold(th: Double, v: Double, ip: Boolean): Threshold[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

344. def createTop1Accuracy(): ValidationMethod[T]

     

     Definition Classes

     PythonBigDL

345. def createTop5Accuracy(): ValidationMethod[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

349. def createTreeNNAccuracy(): ValidationMethod[T]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

362. def createWarmup(delta: Double): Warmup

     

     Definition Classes

     PythonBigDL

363. def createXavier(): Xavier.type

     

     Definition Classes

     PythonBigDL

364. def createZeros(): Zeros.type

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

369. def distributedImageFrameToImageTensorRdd(imageFrame: DistributedImageFrame, floatKey: String, toChw: Boolean): JavaRDD[bigdl.python.api.JTensor]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     AnyRef

379. def equals(arg0: Any): Boolean

     

     Definition Classes

     AnyRef → Any

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

383. def finalize(): Unit

     

     Attributes

     protected[java.lang]

     Definition Classes

     AnyRef

     Annotations

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

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     AnyRef → Any

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDLKeras

396. def getNodeAndCoreNumber(): Array[Int]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

404. def hashCode(): Int

     

     Definition Classes

     AnyRef → Any

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

     

     Definition Classes

     PythonBigDL

406. def imageFeatureToImageTensor(imageFeature: ImageFeature, floatKey: String, toChw: Boolean): bigdl.python.api.JTensor

     

     Definition Classes

     PythonBigDL

407. def imageFeatureToLabelTensor(imageFeature: ImageFeature): bigdl.python.api.JTensor

     

     Definition Classes

     PythonBigDL

408. def inferenceModelLoad(model: InferenceModel, modelPath: String, weightPath: String): Unit

     

409. def inferenceModelLoadCaffe(model: InferenceModel, modelPath: String, weightPath: String): Unit

     

410. def inferenceModelLoadOpenVINO(model: InferenceModel, modelPath: String, weightPath: String): Unit

     

411. def inferenceModelOpenVINOLoadTF(model: InferenceModel, modelPath: String, imageClassificationModelType: String, checkpointPath: String, inputShape: List[Int], ifReverseInputChannels: Boolean, meanValues: List[Double], scale: Double): Unit

     

412. def inferenceModelOpenVINOLoadTF(model: InferenceModel, modelPath: String, objectDetectionModelType: String, pipelineConfigFilePath: String, extensionsConfigFilePath: String): Unit

     

413. def inferenceModelOpenVINOLoadTF(model: InferenceModel, modelPath: String, pipelineConfigFilePath: String, extensionsConfigFilePath: String): Unit

     

414. def inferenceModelOpenVINOLoadTF(model: InferenceModel, modelPath: String, modelType: String): Unit

     

415. def inferenceModelOpenVINOLoadTFAsCalibratedOpenVINO(model: InferenceModel, modelPath: String, modelType: String, checkpointPath: String, inputShape: List[Int], ifReverseInputChannels: Boolean, meanValues: List[Double], scale: Double, networkType: String, validationFilePath: String, subset: Int, opencvLibPath: String): Unit

     

416. def inferenceModelPredict(model: InferenceModel, inputs: List[bigdl.python.api.JTensor], inputIsTable: Boolean): List[AnyRef]

     

417. def inferenceModelTensorFlowLoadTF(model: InferenceModel, modelPath: String, intraOpParallelismThreads: Int, interOpParallelismThreads: Int, usePerSessionThreads: Boolean): Unit

     

418. def initEngine(): Unit

     

     Definition Classes

     PythonBigDL

419. def isDistributed(imageFrame: ImageFrame): Boolean

     

     Definition Classes

     PythonBigDL

420. final def isInstanceOf[T0]: Boolean

     

     Definition Classes

     Any

421. def isLocal(imageFrame: ImageFrame): Boolean

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

423. def jTensorsToActivity(input: List[bigdl.python.api.JTensor], isTable: Boolean): Activity

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

431. def localImageFrameToImageTensor(imageFrame: LocalImageFrame, floatKey: String, toChw: Boolean): List[bigdl.python.api.JTensor]

     

     Definition Classes

     PythonBigDL

432. def localImageFrameToLabelTensor(imageFrame: LocalImageFrame): List[bigdl.python.api.JTensor]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

439. def modelForward(model: AbstractModule[Activity, Activity, T], input: List[bigdl.python.api.JTensor], inputIsTable: Boolean): List[bigdl.python.api.JTensor]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

443. def modelPredictRDD(model: AbstractModule[Activity, Activity, T], dataRdd: JavaRDD[Sample], batchSize: Int): JavaRDD[bigdl.python.api.JTensor]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     AnyRef

446. final def notify(): Unit

     

     Definition Classes

     AnyRef

447. final def notifyAll(): Unit

     

     Definition Classes

     AnyRef

448. def predictLocal(model: AbstractModule[Activity, Activity, T], features: List[bigdl.python.api.JTensor], batchSize: Int): List[bigdl.python.api.JTensor]

     

     Definition Classes

     PythonBigDL

449. def predictLocalClass(model: AbstractModule[Activity, Activity, T], features: List[bigdl.python.api.JTensor]): List[Int]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

453. def redirectSparkLogs(logPath: String): Unit

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

459. def saveTensorDictionary(tensors: HashMap[String, bigdl.python.api.JTensor], path: String): Unit

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

478. def setModelSeed(seed: Long): Unit

     

     Definition Classes

     PythonBigDL

479. def setRunningMean(module: BatchNormalization[T], runningMean: bigdl.python.api.JTensor): Unit

     

     Definition Classes

     PythonBigDLKeras

480. def setRunningMean(module: BatchNormalization[T], runningMean: bigdl.python.api.JTensor): Unit

     

     Definition Classes

     PythonBigDL

481. def setRunningStd(module: BatchNormalization[T], runningStd: bigdl.python.api.JTensor): Unit

     

     Definition Classes

     PythonBigDLKeras

482. def setRunningStd(module: BatchNormalization[T], runningStd: bigdl.python.api.JTensor): Unit

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

490. def setWeights(model: AbstractModule[Activity, Activity, T], weights: List[bigdl.python.api.JTensor]): Unit

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDLKeras

492. def showBigDlInfoLogs(): Unit

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     AnyRef

496. def testSample(sample: Sample): Sample

     

     Definition Classes

     PythonBigDL

497. def testTensor(jTensor: bigdl.python.api.JTensor): bigdl.python.api.JTensor

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

499. def toJSample(record: Sample): Sample[T]

     

     Definition Classes

     PythonBigDL

500. def toJTensor(tensor: Tensor[T]): bigdl.python.api.JTensor

     

     Definition Classes

     PythonZoo → PythonBigDL

501. def toPySample(sample: Sample[T]): Sample

     

     Definition Classes

     PythonBigDL

502. def toSampleArray(Xs: List[Tensor[T]], y: Tensor[T]): Array[Sample[T]]

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

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

     

     Definition Classes

     PythonBigDLKeras

506. def toString(): String

     

     Definition Classes

     AnyRef → Any

507. def toTensor(jTensor: bigdl.python.api.JTensor): Tensor[T]

     

     Definition Classes

     PythonZoo → PythonBigDL

508. 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

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

512. def uniform(a: Double, b: Double, size: List[Int]): bigdl.python.api.JTensor

     

     Definition Classes

     PythonBigDL

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

     

     Definition Classes

     PythonBigDL

514. final def wait(): Unit

     

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

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

     

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

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

     

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

517. def writeParquet(path: String, output: String, sc: JavaSparkContext, partitionNum: Int): Unit

     

     Definition Classes

     PythonBigDL

518. def zooForward(model: AbstractModule[Activity, Activity, T], input: List[bigdl.python.api.JTensor], inputIsTable: Boolean): List[AnyRef]

     

     Definition Classes

     PythonZoo

519. def zooPredict(module: Predictable[T], x: TextSet, batchPerThread: Int): TextSet

     

     Definition Classes

     PythonZoo

520. def zooPredict(module: Predictable[T], x: ImageSet, batchPerThread: Int): ImageSet

     

     Definition Classes

     PythonZoo

521. def zooPredict(module: Module[T], x: List[bigdl.python.api.JTensor], batchPerThread: Int): List[List[AnyRef]]

     

     Definition Classes

     PythonZoo

522. def zooPredict(module: Predictable[T], x: JavaRDD[Sample], batchPerThread: Int): JavaRDD[List[AnyRef]]

     

     Definition Classes

     PythonZoo

523. def zooPredictClasses(module: Predictable[T], x: JavaRDD[Sample], batchPerThread: Int, zeroBasedLabel: Boolean = true): JavaRDD[Int]

     

     Definition Classes

     PythonZoo