PythonBigDL

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

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

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3. final def ##(): Int

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

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

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

7. final def asInstanceOf[T0]: T0

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8. def clone(): AnyRef

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9. def createAbs(): Abs[T]

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

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

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

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

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

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

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

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

18. 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]

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

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

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

22. def createBilinearFiller(): BilinearFiller.type

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

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

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

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

27. def createCDivTable(): CDivTable[T]

28. def createCMaxTable(): CMaxTable[T]

29. def createCMinTable(): CMinTable[T]

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

31. def createCMulTable(): CMulTable[T]

32. def createCSubTable(): CSubTable[T]

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

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

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

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

37. def createConcatTable(): ConcatTable[T]

38. def createConstInitMethod(value: Double): ConstInitMethod

39. def createContiguous(): Contiguous[T]

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

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

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

43. def createCosineDistance(): CosineDistance[T]

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

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

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

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

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

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

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

51. def createDefault(): Default

52. def createDenseToSparse(): DenseToSparse[T]

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

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

55. def createDotProduct(): DotProduct[T]

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

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

58. def createEcho(): Echo[T]

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

60. def createEveryEpoch(): Trigger

61. def createExp(): Exp[T]

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

63. def createFlattenTable(): FlattenTable[T]

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

65. def createGaussianCriterion(): GaussianCriterion[T]

66. def createGaussianSampler(): GaussianSampler[T]

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

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

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

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

71. def createIdentity(): Identity[T]

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

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

74. def createInput(): ModuleNode[T]

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

76. def createKLDCriterion(): KLDCriterion[T]

77. def createL1Cost(): L1Cost[T]

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

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

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

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

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

83. 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]

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

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

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

87. 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]

88. def createLog(): Log[T]

89. def createLogSigmoid(): LogSigmoid[T]

90. def createLogSoftMax(): LogSoftMax[T]

91. 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]

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

93. def createMAE(): ValidationMethod[T]

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

95. def createMSECriterion: MSECriterion[T]

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

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

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

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

100. def createMaskedSelect(): MaskedSelect[T]

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

102. def createMaxEpoch(max: Int): Trigger

103. def createMaxIteration(max: Int): Trigger

104. def createMaxScore(max: Float): Trigger

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

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

107. def createMinLoss(min: Float): Trigger

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

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

110. def createMul(): Mul[T]

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

112. def createMultiCriterion(): MultiCriterion[T]

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

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

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

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

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

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

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

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

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

122. def createOnes(): Ones.type

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

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

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

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

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

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

129. def createParallelTable(): ParallelTable[T]

130. 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

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

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

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

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

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

136. def createRandomUniform(): InitializationMethod

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

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

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

140. def createRecurrent(): Recurrent[T]

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

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

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

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

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

146. 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]

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

148. 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]

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

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

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

152. def createSequential(): Sequential[T]

153. def createSeveralIteration(interval: Int): Trigger

154. def createSigmoid(): Sigmoid[T]

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

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

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

158. def createSoftMax(): SoftMax[T]

159. def createSoftMin(): SoftMin[T]

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

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

162. def createSoftSign(): SoftSign[T]

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

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

165. 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]

166. 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]

167. 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): SpatialBatchNormalization[T]

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

169. 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]

170. 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]

171. def createSpatialCrossMapLRN(size: Int = 5, alpha: Double = 1.0, beta: Double = 0.75, k: Double = 1.0): SpatialCrossMapLRN[T]

172. 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]

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

174. 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]

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

176. 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]

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

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

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

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

181. def createSqrt(): Sqrt[T]

182. def createSquare(): Square[T]

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

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

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

186. def createTanh(): Tanh[T]

187. def createTanhShrink(): TanhShrink[T]

188. 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]

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

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

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

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

193. def createTop1Accuracy(): ValidationMethod[T]

194. def createTop5Accuracy(): ValidationMethod[T]

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

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

197. def createTreeNNAccuracy(): ValidationMethod[T]

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

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

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

201. 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]

202. 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]

203. 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]

204. def createXavier(): Xavier.type

205. def createZeros(): Zeros.type

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

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

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

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

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

     Definition Classes

     AnyRef

211. def equals(arg0: Any): Boolean

     Definition Classes

     AnyRef → Any

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

213. def finalize(): Unit

     Attributes

     protected[java.lang]

     Definition Classes

     AnyRef

     Annotations

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

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

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

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

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

     Definition Classes

     AnyRef → Any

218. def getHiddenStates(rec: Recurrent[T]): List[JTensor]

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

220. def hashCode(): Int

     Definition Classes

     AnyRef → Any

221. def initEngine(): Unit

222. final def isInstanceOf[T0]: Boolean

     Definition Classes

     Any

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

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

225. def loadBigDLModule(path: String): AbstractModule[Activity, Activity, T]

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

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

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

229. def loadTF(path: String, inputs: List[String], outputs: List[String], byteOrder: String): AbstractModule[Activity, Activity, T]

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

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

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

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

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

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

236. def modelPredictRDD(model: AbstractModule[Activity, Activity, T], dataRdd: JavaRDD[Sample]): JavaRDD[JTensor]

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

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

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239. final def notify(): Unit

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240. final def notifyAll(): Unit

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

242. def redirectSparkLogs(logPath: String): Unit

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

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

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

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

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

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

     Save tensor dictionary to a Java hashmap object file

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

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

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

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

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

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

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

256. def setHiddenStates(rec: Recurrent[T], hiddenStates: List[JTensor], isTable: Boolean): Unit

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

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

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

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

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

262. def setModelSeed(seed: Long): Unit

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

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

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

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

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

268. def showBigDlInfoLogs(): Unit

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

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

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

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272. def testSample(sample: Sample): Sample

273. def testTensor(jTensor: JTensor): JTensor

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

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

276. def toSample(record: Sample): dataset.Sample[T]

277. def toString(): String

     Definition Classes

     AnyRef → Any

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

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

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

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

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

283. final def wait(): Unit

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     @throws( ... )

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

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

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     @throws( ... )