PythonBigDL

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

   

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2. final def ##(): Int

   

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

   

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

   

5. final def asInstanceOf[T0]: T0

   

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6. def clone(): AnyRef

   

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7. def createAbs(): Abs[T]

   

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

   

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

   

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

    

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

    

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

    

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

    

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

    

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

    

16. 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]

    

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

    

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

    

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

    

20. def createBilinearFiller(): BilinearFiller.type

    

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

    

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

    

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

    

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

    

25. def createCDivTable(): CDivTable[T]

    

26. def createCMaxTable(): CMaxTable[T]

    

27. def createCMinTable(): CMinTable[T]

    

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

    

29. def createCMulTable(): CMulTable[T]

    

30. def createCSubTable(): CSubTable[T]

    

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

    

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

    

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

    

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

    

35. def createConcatTable(): ConcatTable[T]

    

36. def createConstInitMethod(value: Double): ConstInitMethod

    

37. def createContiguous(): Contiguous[T]

    

38. 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]

    

39. 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]

    

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

    

41. def createCosineDistance(): CosineDistance[T]

    

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

    

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

    

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

    

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

    

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

    

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

    

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

    

49. def createDefault(): Default

    

50. def createDenseToSparse(): DenseToSparse[T]

    

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

    

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

    

53. def createDotProduct(): DotProduct[T]

    

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

    

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

    

56. def createEcho(): Echo[T]

    

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

    

58. def createEveryEpoch(): Trigger

    

59. def createExp(): Exp[T]

    

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

    

61. def createFlattenTable(): FlattenTable[T]

    

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

    

63. def createGaussianCriterion(): GaussianCriterion[T]

    

64. def createGaussianSampler(): GaussianSampler[T]

    

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

    

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

    

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

    

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

    

69. def createIdentity(): Identity[T]

    

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

    

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

    

72. def createInput(): ModuleNode[T]

    

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

    

74. def createKLDCriterion(): KLDCriterion[T]

    

75. def createL1Cost(): L1Cost[T]

    

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

    

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

    

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

    

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

    

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

    

81. 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]

    

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

    

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

    

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

    

85. 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]

    

86. def createLog(): Log[T]

    

87. def createLogSigmoid(): LogSigmoid[T]

    

88. def createLogSoftMax(): LogSoftMax[T]

    

89. 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]

    

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

    

91. def createMAE(): ValidationMethod[T]

    

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

    

93. def createMSECriterion: MSECriterion[T]

    

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

    

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

    

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

    

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

    

98. def createMaskedSelect(): MaskedSelect[T]

    

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

    

100. def createMaxEpoch(max: Int): Trigger

     

101. def createMaxIteration(max: Int): Trigger

     

102. def createMaxScore(max: Float): Trigger

     

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

     

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

     

105. def createMinLoss(min: Float): Trigger

     

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

     

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

     

108. def createMul(): Mul[T]

     

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

     

110. def createMultiCriterion(): MultiCriterion[T]

     

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

     

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

     

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

     

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

     

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

     

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

     

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

     

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

     

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

     

120. def createOnes(): Ones.type

     

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

     

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

     

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

     

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

     

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

     

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

     

127. def createParallelTable(): ParallelTable[T]

     

128. 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

     

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

     

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

     

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

     

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

     

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

     

134. def createRandomUniform(): InitializationMethod

     

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

     

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

     

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

     

138. def createRecurrent(): Recurrent[T]

     

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

     

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

     

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

     

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

     

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

     

144. 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]

     

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

     

146. 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]

     

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

     

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

     

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

     

150. def createSequential(): Sequential[T]

     

151. def createSeveralIteration(interval: Int): Trigger

     

152. def createSigmoid(): Sigmoid[T]

     

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

     

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

     

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

     

156. def createSoftMax(): SoftMax[T]

     

157. def createSoftMin(): SoftMin[T]

     

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

     

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

     

160. def createSoftSign(): SoftSign[T]

     

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

     

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

     

163. 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]

     

164. 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]

     

165. 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]

     

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

     

167. 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]

     

168. 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]

     

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

     

170. 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]

     

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

     

172. 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]

     

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

     

174. 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]

     

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

     

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

     

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

     

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

     

179. def createSqrt(): Sqrt[T]

     

180. def createSquare(): Square[T]

     

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

     

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

     

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

     

184. def createTanh(): Tanh[T]

     

185. def createTanhShrink(): TanhShrink[T]

     

186. 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]

     

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

     

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

     

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

     

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

     

191. def createTop1Accuracy(): ValidationMethod[T]

     

192. def createTop5Accuracy(): ValidationMethod[T]

     

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

     

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

     

195. def createTreeNNAccuracy(): ValidationMethod[T]

     

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

     

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

     

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

     

199. 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]

     

200. 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]

     

201. 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]

     

202. def createXavier(): Xavier.type

     

203. def createZeros(): Zeros.type

     

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

     

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

     

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

     

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

     

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

     

     Definition Classes

     AnyRef

209. def equals(arg0: Any): Boolean

     

     Definition Classes

     AnyRef → Any

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

     

211. def finalize(): Unit

     

     Attributes

     protected[java.lang]

     Definition Classes

     AnyRef

     Annotations

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

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

     

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

     

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

     

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

     

     Definition Classes

     AnyRef → Any

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

     

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

     

218. def hashCode(): Int

     

     Definition Classes

     AnyRef → Any

219. def initEngine(): Unit

     

220. final def isInstanceOf[T0]: Boolean

     

     Definition Classes

     Any

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

     

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

     

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

     

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

     

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

     

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

     

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

     

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

     

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

     

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

     

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

     

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

     

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

     

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

     

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

     

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

     

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237. final def notify(): Unit

     

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238. final def notifyAll(): Unit

     

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

     

240. def redirectSparkLogs(logPath: String): Unit

     

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

     

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

     

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

     

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

     

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

     

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

     

     Save tensor dictionary to a Java hashmap object file

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

     

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

     

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

     

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

     

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

     

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

     

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

     

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

     

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

     

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

     

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

     

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

     

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

     

260. def setModelSeed(seed: Long): Unit

     

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

     

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

     

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

     

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

     

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

     

266. def showBigDlInfoLogs(): Unit

     

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

     

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

     

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

     

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270. def testSample(sample: Sample): Sample

     

271. def testTensor(jTensor: JTensor): JTensor

     

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

     

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

     

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

     

275. def toString(): String

     

     Definition Classes

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

     

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

     

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

     

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

     

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

     

281. final def wait(): Unit

     

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

     

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     @throws( ... )

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

     

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     @throws( ... )