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 createAdd(inputSize: Int): Add[T]

   

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

    

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

    

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

    

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

    

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

    

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

    

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

    

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

    

18. def createCDivTable(): CDivTable[T]

    

19. def createCMaxTable(): CMaxTable[T]

    

20. def createCMinTable(): CMinTable[T]

    

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

    

22. def createCMulTable(): CMulTable[T]

    

23. def createCSubTable(): CSubTable[T]

    

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

    

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

    

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

    

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

    

28. def createConcatTable(): ConcatTable[T]

    

29. def createContiguous(): Contiguous[T]

    

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

    

31. def createCosineDistance(): CosineDistance[T]

    

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

    

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

    

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

    

35. def createDotProduct(): DotProduct[T]

    

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

    

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

    

38. def createEcho(): Echo[T]

    

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

    

40. def createEveryEpoch(): Trigger

    

41. def createExp(): Exp[T]

    

42. def createFlattenTable(): FlattenTable[T]

    

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

    

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

    

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

    

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

    

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

    

48. def createIdentity(): Identity[T]

    

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

    

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

    

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

    

52. def createL1Cost(): L1Cost[T]

    

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

    

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

    

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

    

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

    

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

    

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

    

59. def createLog(): Log[T]

    

60. def createLogSigmoid(): LogSigmoid[T]

    

61. def createLogSoftMax(): LogSoftMax[T]

    

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

    

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

    

64. def createMSECriterion: MSECriterion[T]

    

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

    

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

    

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

    

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

    

69. def createMaskedSelect(): MaskedSelect[T]

    

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

    

71. def createMaxEpoch(max: Int): Trigger

    

72. def createMaxIteration(max: Int): Trigger

    

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

    

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

    

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

    

76. def createMul(): Mul[T]

    

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

    

78. def createMultiCriterion(): MultiCriterion[T]

    

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

    

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

    

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

    

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

    

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

    

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

    

85. 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]]

    

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

    

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

    

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

    

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

    

90. def createParallelTable(): ParallelTable[T]

    

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

    

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

    

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

    

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

    

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

    

96. def createRecurrent(): Recurrent[T]

    

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

    

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

    

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

    

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

     

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

     

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

     

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

     

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

     

105. def createSequential(): Sequential[T]

     

106. def createSeveralIteration(interval: Int): Trigger

     

107. def createSigmoid(): Sigmoid[T]

     

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

     

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

     

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

     

111. def createSoftMax(): SoftMax[T]

     

112. def createSoftMin(): SoftMin[T]

     

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

     

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

     

115. def createSoftSign(): SoftSign[T]

     

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

     

117. 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]

     

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

     

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

     

120. 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]

     

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

     

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

     

123. 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]

     

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

     

125. 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]

     

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

     

127. 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]

     

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

     

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

     

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

     

131. def createSqrt(): Sqrt[T]

     

132. def createSquare(): Square[T]

     

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

     

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

     

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

     

136. def createTanh(): Tanh[T]

     

137. def createTanhShrink(): TanhShrink[T]

     

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

     

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

     

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

     

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

     

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

     

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

     

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

     

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

     

146. def criterionBackward(criterion: AbstractCriterion[Activity, Activity, T], input: List[JTensor], target: List[JTensor]): List[JTensor]

     

147. def criterionForward(criterion: AbstractCriterion[Activity, Activity, T], input: List[JTensor], target: List[JTensor]): 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 jTensorsToActivity(input: List[JTensor]): Activity

     

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

     

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

     

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

     

159. def modelBackward(model: AbstractModule[Activity, Activity, T], input: List[JTensor], gradOutput: List[JTensor]): List[JTensor]

     

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

     

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

     

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

     

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

     

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

     

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165. final def notify(): Unit

     

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166. final def notifyAll(): Unit

     

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

     

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

     

169. def setModelSeed(seed: Long): Unit

     

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

     

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

     

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

     

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

     

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

     

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

     

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176. def testTensor(jTensor: JTensor): JTensor

     

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

     

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

     

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

     

180. def toString(): String

     

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

     

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

     

183. final def wait(): Unit

     

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

     

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

     

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