PythonBigDL

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3. final def ##(): Int

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

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

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

8. final def asInstanceOf[T0]: T0

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

10. def clone(): AnyRef

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11. def createAbs(): Abs[T]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

26. def createBilinearFiller(): BilinearFiller.type

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

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

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

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

31. def createBytesToMat(byteKey: String): BytesToMat

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

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

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

35. def createCDivTable(): CDivTable[T]

36. def createCMaxTable(): CMaxTable[T]

37. def createCMinTable(): CMinTable[T]

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

39. def createCMulTable(): CMulTable[T]

40. def createCSubTable(): CSubTable[T]

41. def createCategoricalCrossEntropy(): CategoricalCrossEntropy[T]

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

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

44. def createChannelOrder(): ChannelOrder

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

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

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

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

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

50. def createConcatTable(): ConcatTable[T]

51. def createConstInitMethod(value: Double): ConstInitMethod

52. def createContiguous(): Contiguous[T]

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

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

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

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

57. def createCosineDistance(): CosineDistance[T]

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

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

60. def createCosineProximityCriterion(): CosineProximityCriterion[T]

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

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

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

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

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

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

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

68. def createDLImageTransformer(transformer: FeatureTransformer): DLImageTransformer

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

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

71. def createDefault(): Default

72. def createDenseToSparse(): DenseToSparse[T]

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

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

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

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

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

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

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

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

81. def createDotProduct(): DotProduct[T]

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

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

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

85. def createEcho(): Echo[T]

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

87. def createEveryEpoch(): Trigger

88. def createExp(): Exp[T]

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

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

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

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

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

94. def createFlattenTable(): FlattenTable[T]

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

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

97. def createGaussianCriterion(): GaussianCriterion[T]

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

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

100. def createGaussianSampler(): GaussianSampler[T]

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

102. def createHFlip(): HFlip

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

104. def createHardSigmoid: HardSigmoid[T]

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

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

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

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

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

110. def createIdentity(): Identity[T]

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

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

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

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

115. def createInput(): ModuleNode[T]

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

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

118. def createKullbackLeiblerDivergenceCriterion: KullbackLeiblerDivergenceCriterion[T]

119. def createL1Cost(): L1Cost[T]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

134. def createLog(): Log[T]

135. def createLogSigmoid(): LogSigmoid[T]

136. def createLogSoftMax(): LogSoftMax[T]

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

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

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

140. def createMAE(): ValidationMethod[T]

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

142. def createMSECriterion: MSECriterion[T]

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

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

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

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

147. def createMaskedSelect(): MaskedSelect[T]

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

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

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

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

152. def createMaxEpoch(max: Int): Trigger

153. def createMaxIteration(max: Int): Trigger

154. def createMaxScore(max: Float): Trigger

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

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

157. def createMeanAbsolutePercentageCriterion: MeanAbsolutePercentageCriterion[T]

158. def createMeanSquaredLogarithmicCriterion: MeanSquaredLogarithmicCriterion[T]

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

160. def createMinLoss(min: Float): Trigger

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

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

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

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

165. def createMul(): Mul[T]

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

167. def createMultiCriterion(): MultiCriterion[T]

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

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

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

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

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

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

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

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

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

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

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

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

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

181. def createOnes(): Ones.type

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

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

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

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

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

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

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

189. def createParallelTable(): ParallelTable[T]

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

191. def createPixelBytesToMat(byteKey: String): PixelBytesToMat

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

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

194. def createPoissonCriterion: PoissonCriterion[T]

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

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

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

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

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

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

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

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

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

204. def createRandomSampler(): FeatureTransformer

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

206. def createRandomUniform(): InitializationMethod

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

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

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

210. def createRecurrent(): Recurrent[T]

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

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

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

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

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

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

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

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

219. def createRoiNormalize(): RoiNormalize

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

221. def createRoiProject(needMeetCenterConstraint: Boolean): RoiProject

222. def createRoiResize(normalized: Boolean): RoiResize

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

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

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

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

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

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

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

230. def createSequentialSchedule(iterationPerEpoch: Int): SequentialSchedule

231. def createSeveralIteration(interval: Int): Trigger

232. def createSigmoid(): Sigmoid[T]

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

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

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

236. def createSoftMax(): SoftMax[T]

237. def createSoftMin(): SoftMin[T]

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

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

240. def createSoftSign(): SoftSign[T]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

263. def createSqrt(): Sqrt[T]

264. def createSquare(): Square[T]

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

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

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

268. def createTanh(): Tanh[T]

269. def createTanhShrink(): TanhShrink[T]

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

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

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

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

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

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

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

277. def createTop1Accuracy(): ValidationMethod[T]

278. def createTop5Accuracy(): ValidationMethod[T]

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

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

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

282. def createTreeNNAccuracy(): ValidationMethod[T]

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

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

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

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

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

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

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

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

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

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

293. def createWarmup(delta: Double): Warmup

294. def createXavier(): Xavier.type

295. def createZeros(): Zeros.type

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

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

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

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

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

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

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

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

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

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

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

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

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

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

     Definition Classes

     AnyRef

310. def equals(arg0: Any): Boolean

     Definition Classes

     AnyRef → Any

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

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

313. def finalize(): Unit

     Attributes

     protected[java.lang]

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     Annotations

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

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

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

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

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

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

     Definition Classes

     AnyRef → Any

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

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

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

322. def getNodeAndCoreNumber(): Array[Int]

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

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

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

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

327. def hashCode(): Int

     Definition Classes

     AnyRef → Any

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

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

330. def imageFeatureToLabelTensor(imageFeature: ImageFeature): JTensor

331. def initEngine(): Unit

332. def isDistributed(imageFrame: ImageFrame): Boolean

333. final def isInstanceOf[T0]: Boolean

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     Any

334. def isLocal(imageFrame: ImageFrame): Boolean

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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359. final def notify(): Unit

     Definition Classes

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360. final def notifyAll(): Unit

     Definition Classes

     AnyRef

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

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

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

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

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

366. def redirectSparkLogs(logPath: String): Unit

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

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

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

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

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

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

     Save tensor dictionary to a Java hashmap object file

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

391. def setModelSeed(seed: Long): Unit

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

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

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

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

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

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

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

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

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

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

402. def showBigDlInfoLogs(): Unit

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

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

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

     Definition Classes

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406. def testSample(sample: Sample): Sample

407. def testTensor(jTensor: JTensor): JTensor

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

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

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

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

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

413. def toString(): String

     Definition Classes

     AnyRef → Any

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

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

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

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

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

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

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

421. final def wait(): Unit

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     @throws( ... )

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

     Definition Classes

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     @throws( ... )

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

     Definition Classes

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     @throws( ... )

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