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. final def asInstanceOf[T0]: T0

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7. def clone(): AnyRef

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8. def createAbs(): Abs[T]

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

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

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

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

13. def createBatchNormalization(nOutput: Int, eps: Double = 1e-5, momentum: Double = 0.1, affine: Boolean = true): BatchNormalization[T]

14. def createBiRecurrent(): BiRecurrent[T]

15. def createBilinear(inputSize1: Int, inputSize2: Int, outputSize: Int, biasRes: Boolean = true): Bilinear[T]

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

17. def createCAdd(size: List[Int]): CAdd[T]

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

19. def createCDivTable(): CDivTable[T]

20. def createCMaxTable(): CMaxTable[T]

21. def createCMinTable(): CMinTable[T]

22. def createCMul(size: List[Int]): CMul[T]

23. def createCMulTable(): CMulTable[T]

24. def createCSubTable(): CSubTable[T]

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

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

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

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

29. def createConcatTable(): ConcatTable[T]

30. def createContiguous(): Contiguous[T]

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

32. def createCosineDistance(): CosineDistance[T]

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

34. def createCriterionTable(criterion: TensorCriterion[T]): CriterionTable[T]

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

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

37. def createDotProduct(): DotProduct[T]

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

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

40. def createEcho(): Echo[T]

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

42. def createEveryEpoch(): Trigger

43. def createExp(): Exp[T]

44. def createFlattenTable(): FlattenTable[T]

45. def createGRU(inputSize: Int, outputSize: Int): GRU[T]

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

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

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

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

50. def createIdentity(): Identity[T]

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

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

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

54. def createL1Cost(): L1Cost[T]

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

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

57. def createLSTM(inputSize: Int, hiddenSize: Int): LSTM[T]

58. def createLSTMPeephole(inputSize: Int, hiddenSize: Int): LSTMPeephole[T]

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

60. def createLinear(inputSize: Int, outputSize: Int, initMethod: String, withBias: Boolean): Linear[T]

61. def createLog(): Log[T]

62. def createLogSigmoid(): LogSigmoid[T]

63. def createLogSoftMax(): LogSoftMax[T]

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

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

66. def createMSECriterion: MSECriterion[T]

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

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

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

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

71. def createMaskedSelect(): MaskedSelect[T]

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

73. def createMaxEpoch(max: Int): Trigger

74. def createMaxIteration(max: Int): Trigger

75. def createMean(dimension: Int = 1, nInputDims: Int = 1): Mean[T]

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

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

78. def createMul(): Mul[T]

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

80. def createMultiCriterion(): MultiCriterion[T]

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

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

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

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

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

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

87. def createOptimizer(model: AbstractModule[Activity, Activity, T], trainingRdd: JavaRDD[Sample], criterion: Criterion[T], optimMethod: String, state: Map[Any, Any], endTrigger: Trigger, batchSize: Int): Optimizer[T, MiniBatch[T]]

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

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

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

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

92. def createParallelTable(): ParallelTable[T]

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

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

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

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

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

98. def createRecurrent(): Recurrent[T]

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

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

101. def createReverse(dimension: Int = 1): Reverse[T]

102. def createRnnCell(inputSize: Int, hiddenSize: Int, activation: TensorModule[T]): RnnCell[T]

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

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

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

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

107. def createSequential(): Sequential[T]

108. def createSeveralIteration(interval: Int): Trigger

109. def createSigmoid(): Sigmoid[T]

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

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

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

113. def createSoftMax(): SoftMax[T]

114. def createSoftMin(): SoftMin[T]

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

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

117. def createSoftSign(): SoftSign[T]

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

119. def createSpatialAveragePooling(kW: Int, kH: Int, dW: Int = 1, dH: Int = 1, padW: Int = 0, padH: Int = 0, ceilMode: Boolean = false, countIncludePad: Boolean = true, divide: Boolean = true): SpatialAveragePooling[T]

120. def createSpatialBatchNormalization(nOutput: Int, eps: Double = 1e-5, momentum: Double = 0.1, affine: Boolean = true): SpatialBatchNormalization[T]

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

122. 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, initMethod: String = "default"): SpatialConvolution[T]

123. def createSpatialConvolutionMap(connTable: JTensor, kW: Int, kH: Int, dW: Int = 1, dH: Int = 1, padW: Int = 0, padH: Int = 0): SpatialConvolutionMap[T]

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

125. 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, initMethod: String = "default"): SpatialDilatedConvolution[T]

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

127. 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, initMethod: String = "default"): SpatialFullConvolution[Activity, T]

128. def createSpatialMaxPooling(kW: Int, kH: Int, dW: Int, dH: Int, padW: Int = 0, padH: Int = 0, ceilMode: Boolean = false): SpatialMaxPooling[T]

129. 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, initMethod: String = "default"): SpatialShareConvolution[T]

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

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

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

133. def createSqrt(): Sqrt[T]

134. def createSquare(): Square[T]

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

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

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

138. def createTanh(): Tanh[T]

139. def createTanhShrink(): TanhShrink[T]

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

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

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

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

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

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

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

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

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

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149. def equals(arg0: Any): Boolean

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150. def finalize(): Unit

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

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152. def hashCode(): Int

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153. def initEngine(): Unit

154. final def isInstanceOf[T0]: Boolean

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

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

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

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

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

160. def modelTest(model: AbstractModule[Activity, Activity, T], valRDD: JavaRDD[Sample], batchSize: Int, valMethods: List[String]): List[TestResult]

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

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162. final def notify(): Unit

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163. final def notifyAll(): Unit

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164. def predict(model: AbstractModule[Activity, Activity, T], dataRdd: RDD[dataset.Sample[T]]): RDD[dataset.Sample[T]]

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

166. def setModelSeed(seed: Long): Unit

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

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

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

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

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

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

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173. def testTensor(jTensor: JTensor): JTensor

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

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

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

177. def toString(): String

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

179. final def wait(): Unit

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

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

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