PythonBigDLKeras

Instance Constructors

new PythonBigDLKeras()(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def activityToJTensors(outputActivity: Activity): List[JTensor]

Definition Classes
PythonBigDL
def addScheduler(seq: SequentialSchedule, scheduler: LearningRateSchedule, maxIteration: Int): SequentialSchedule

Definition Classes
PythonBigDL
final def asInstanceOf[T0]: T0

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

Definition Classes
PythonBigDL
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def compile(module: KerasModel[T], optimizer: OptimMethod[T], loss: Criterion[T], metrics: List[ValidationMethod[T]] = null): Unit
def createAbs(): Abs[T]

Definition Classes
PythonBigDL
def createAbsCriterion(sizeAverage: Boolean = true): AbsCriterion[T]

Definition Classes
PythonBigDL
def createActivityRegularization(l1: Double, l2: Double): ActivityRegularization[T]

Definition Classes
PythonBigDL
def createAdadelta(decayRate: Double = 0.9, Epsilon: Double = 1e-10): Adadelta[T]

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

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

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

Definition Classes
PythonBigDL
def createAdd(inputSize: Int): Add[T]

Definition Classes
PythonBigDL
def createAddConstant(constant_scalar: Double, inplace: Boolean = false): AddConstant[T]

Definition Classes
PythonBigDL
def createAspectScale(scale: Int, scaleMultipleOf: Int, maxSize: Int, resizeMode: Int = 1, useScaleFactor: Boolean = true, minScale: Double = 1): FeatureTransformer

Definition Classes
PythonBigDL
def createAttention(hiddenSize: Int, numHeads: Int, attentionDropout: Float): Attention[T]

Definition Classes
PythonBigDL
def createBCECriterion(weights: JTensor = null, sizeAverage: Boolean = true): BCECriterion[T]

Definition Classes
PythonBigDL
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]

Definition Classes
PythonBigDL
def createBiRecurrent(merge: AbstractModule[Table, Tensor[T], T] = null): BiRecurrent[T]

Definition Classes
PythonBigDL
def createBifurcateSplitTable(dimension: Int): BifurcateSplitTable[T]

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

Definition Classes
PythonBigDL
def createBilinearFiller(): BilinearFiller.type

Definition Classes
PythonBigDL
def createBinaryThreshold(th: Double, ip: Boolean): BinaryThreshold[T]

Definition Classes
PythonBigDL
def createBinaryTreeLSTM(inputSize: Int, hiddenSize: Int, gateOutput: Boolean = true, withGraph: Boolean = true): BinaryTreeLSTM[T]

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

Definition Classes
PythonBigDL
def createBrightness(deltaLow: Double, deltaHigh: Double): Brightness

Definition Classes
PythonBigDL
def createBytesToMat(byteKey: String): BytesToMat

Definition Classes
PythonBigDL
def createCAdd(size: List[Int], bRegularizer: Regularizer[T] = null): CAdd[T]

Definition Classes
PythonBigDL
def createCAddTable(inplace: Boolean = false): CAddTable[T, T]

Definition Classes
PythonBigDL
def createCAveTable(inplace: Boolean = false): CAveTable[T]

Definition Classes
PythonBigDL
def createCDivTable(): CDivTable[T]

Definition Classes
PythonBigDL
def createCMaxTable(): CMaxTable[T]

Definition Classes
PythonBigDL
def createCMinTable(): CMinTable[T]

Definition Classes
PythonBigDL
def createCMul(size: List[Int], wRegularizer: Regularizer[T] = null): CMul[T]

Definition Classes
PythonBigDL
def createCMulTable(): CMulTable[T]

Definition Classes
PythonBigDL
def createCSubTable(): CSubTable[T]

Definition Classes
PythonBigDL
def createCategoricalCrossEntropy(): CategoricalCrossEntropy[T]

Definition Classes
PythonBigDL
def createCenterCrop(cropWidth: Int, cropHeight: Int, isClip: Boolean): CenterCrop

Definition Classes
PythonBigDL
def createChannelNormalize(meanR: Double, meanG: Double, meanB: Double, stdR: Double = 1, stdG: Double = 1, stdB: Double = 1): FeatureTransformer

Definition Classes
PythonBigDL
def createChannelOrder(): ChannelOrder

Definition Classes
PythonBigDL
def createChannelScaledNormalizer(meanR: Int, meanG: Int, meanB: Int, scale: Double): ChannelScaledNormalizer

Definition Classes
PythonBigDL
def createClamp(min: Int, max: Int): Clamp[T]

Definition Classes
PythonBigDL
def createClassNLLCriterion(weights: JTensor = null, sizeAverage: Boolean = true, logProbAsInput: Boolean = true): ClassNLLCriterion[T]

Definition Classes
PythonBigDL
def createClassSimplexCriterion(nClasses: Int): ClassSimplexCriterion[T]

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

Definition Classes
PythonBigDL
def createConcat(dimension: Int): Concat[T]

Definition Classes
PythonBigDL
def createConcatTable(): ConcatTable[T]

Definition Classes
PythonBigDL
def createConstInitMethod(value: Double): ConstInitMethod

Definition Classes
PythonBigDL
def createContiguous(): Contiguous[T]

Definition Classes
PythonBigDL
def createContrast(deltaLow: Double, deltaHigh: Double): Contrast

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

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

Definition Classes
PythonBigDL
def createCosine(inputSize: Int, outputSize: Int): Cosine[T]

Definition Classes
PythonBigDL
def createCosineDistance(): CosineDistance[T]

Definition Classes
PythonBigDL
def createCosineDistanceCriterion(sizeAverage: Boolean = true): CosineDistanceCriterion[T]

Definition Classes
PythonBigDL
def createCosineEmbeddingCriterion(margin: Double = 0.0, sizeAverage: Boolean = true): CosineEmbeddingCriterion[T]

Definition Classes
PythonBigDL
def createCosineProximityCriterion(): CosineProximityCriterion[T]

Definition Classes
PythonBigDL
def createCropping2D(heightCrop: List[Int], widthCrop: List[Int], dataFormat: String = "NCHW"): Cropping2D[T]

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

Definition Classes
PythonBigDL
def createCrossEntropyCriterion(weights: JTensor = null, sizeAverage: Boolean = true): CrossEntropyCriterion[T]

Definition Classes
PythonBigDL
def createCrossProduct(numTensor: Int = 0, embeddingSize: Int = 0): CrossProduct[T]

Definition Classes
PythonBigDL
def createDLClassifier(model: Module[T], criterion: Criterion[T], featureSize: ArrayList[Int], labelSize: ArrayList[Int]): DLClassifier[T]

Definition Classes
PythonBigDL
def createDLClassifierModel(model: Module[T], featureSize: ArrayList[Int]): DLClassifierModel[T]

Definition Classes
PythonBigDL
def createDLEstimator(model: Module[T], criterion: Criterion[T], featureSize: ArrayList[Int], labelSize: ArrayList[Int]): DLEstimator[T]

Definition Classes
PythonBigDL
def createDLImageTransformer(transformer: FeatureTransformer): DLImageTransformer

Definition Classes
PythonBigDL
def createDLModel(model: Module[T], featureSize: ArrayList[Int]): DLModel[T]

Definition Classes
PythonBigDL
def createDatasetFromImageFrame(imageFrame: ImageFrame): DataSet[ImageFeature]

Definition Classes
PythonBigDL
def createDefault(): Default

Definition Classes
PythonBigDL
def createDenseToSparse(): DenseToSparse[T]

Definition Classes
PythonBigDL
def createDetectionCrop(roiKey: String, normalized: Boolean): DetectionCrop

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

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

Definition Classes
PythonBigDL
def createDiceCoefficientCriterion(sizeAverage: Boolean = true, epsilon: Float = 1.0f): DiceCoefficientCriterion[T]

Definition Classes
PythonBigDL
def createDistKLDivCriterion(sizeAverage: Boolean = true): DistKLDivCriterion[T]

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

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

Definition Classes
PythonBigDL
def createDistributedImageFrame(imageRdd: JavaRDD[JTensor], labelRdd: JavaRDD[JTensor]): DistributedImageFrame

Definition Classes
PythonBigDL
def createDotProduct(): DotProduct[T]

Definition Classes
PythonBigDL
def createDotProductCriterion(sizeAverage: Boolean = false): DotProductCriterion[T]

Definition Classes
PythonBigDL
def createDropout(initP: Double = 0.5, inplace: Boolean = false, scale: Boolean = true): Dropout[T]

Definition Classes
PythonBigDL
def createELU(alpha: Double = 1.0, inplace: Boolean = false): ELU[T]

Definition Classes
PythonBigDL
def createEcho(): Echo[T]

Definition Classes
PythonBigDL
def createEuclidean(inputSize: Int, outputSize: Int, fastBackward: Boolean = true): Euclidean[T]

Definition Classes
PythonBigDL
def createEveryEpoch(): Trigger

Definition Classes
PythonBigDL
def createExp(): Exp[T]

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

Definition Classes
PythonBigDL
def createExpandSize(targetSizes: List[Int]): ExpandSize[T]

Definition Classes
PythonBigDL
def createExponential(decayStep: Int, decayRate: Double, stairCase: Boolean = false): Exponential

Definition Classes
PythonBigDL
def createFPN(in_channels_list: List[Int], out_channels: Int, top_blocks: Int = 0, in_channels_of_p6p7: Int = 0, out_channels_of_p6p7: Int = 0): FPN[T]

Definition Classes
PythonBigDL
def createFeedForwardNetwork(hiddenSize: Int, filterSize: Int, reluDropout: Float): FeedForwardNetwork[T]

Definition Classes
PythonBigDL
def createFiller(startX: Double, startY: Double, endX: Double, endY: Double, value: Int = 255): Filler

Definition Classes
PythonBigDL
def createFixExpand(eh: Int, ew: Int): FixExpand

Definition Classes
PythonBigDL
def createFixedCrop(wStart: Double, hStart: Double, wEnd: Double, hEnd: Double, normalized: Boolean, isClip: Boolean): FixedCrop

Definition Classes
PythonBigDL
def createFlattenTable(): FlattenTable[T]

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

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

Definition Classes
PythonBigDL
def createGaussianCriterion(): GaussianCriterion[T]

Definition Classes
PythonBigDL
def createGaussianDropout(rate: Double): GaussianDropout[T]

Definition Classes
PythonBigDL
def createGaussianNoise(stddev: Double): GaussianNoise[T]

Definition Classes
PythonBigDL
def createGaussianSampler(): GaussianSampler[T]

Definition Classes
PythonBigDL
def createGradientReversal(lambda: Double = 1): GradientReversal[T]

Definition Classes
PythonBigDL
def createHFlip(): HFlip

Definition Classes
PythonBigDL
def createHardShrink(lambda: Double = 0.5): HardShrink[T]

Definition Classes
PythonBigDL
def createHardSigmoid: HardSigmoid[T]

Definition Classes
PythonBigDL
def createHardTanh(minValue: Double = 1, maxValue: Double = 1, inplace: Boolean = false): HardTanh[T]

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

Definition Classes
PythonBigDL
def createHingeEmbeddingCriterion(margin: Double = 1, sizeAverage: Boolean = true): HingeEmbeddingCriterion[T]

Definition Classes
PythonBigDL
def createHitRatio(k: Int = 10, negNum: Int = 100): ValidationMethod[T]

Definition Classes
PythonBigDL
def createHue(deltaLow: Double, deltaHigh: Double): Hue

Definition Classes
PythonBigDL
def createIdentity(): Identity[T]

Definition Classes
PythonBigDL
def createImageFeature(data: JTensor = null, label: JTensor = null, uri: String = null): ImageFeature

Definition Classes
PythonBigDL
def createImageFrameToSample(inputKeys: List[String], targetKeys: List[String], sampleKey: String): ImageFrameToSample[T]

Definition Classes
PythonBigDL
def createIndex(dimension: Int): Index[T]

Definition Classes
PythonBigDL
def createInferReshape(size: List[Int], batchMode: Boolean = false): InferReshape[T]

Definition Classes
PythonBigDL
def createInput(): ModuleNode[T]

Definition Classes
PythonBigDL
def createJoinTable(dimension: Int, nInputDims: Int): JoinTable[T]

Definition Classes
PythonBigDL
def createKLDCriterion(sizeAverage: Boolean): KLDCriterion[T]

Definition Classes
PythonBigDL
def createKerasActivation(activation: String, inputShape: List[Int] = null): Activation[T]
def createKerasAtrousConvolution1D(nbFilter: Int, filterLength: Int, init: String = "glorot_uniform", activation: String = null, subsampleLength: Int = 1, atrousRate: Int = 1, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, inputShape: List[Int] = null): AtrousConvolution1D[T]
def createKerasAtrousConvolution2D(nbFilter: Int, nbRow: Int, nbCol: Int, init: String = "glorot_uniform", activation: String = null, subsample: List[Int], atrousRate: List[Int], dimOrdering: String = "th", wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, inputShape: List[Int] = null): AtrousConvolution2D[T]
def createKerasAveragePooling1D(poolLength: Int = 2, stride: Int = 1, borderMode: String = "valid", inputShape: List[Int] = null): AveragePooling1D[T]
def createKerasAveragePooling2D(poolSize: List[Int], strides: List[Int], borderMode: String = "valid", dimOrdering: String = "th", inputShape: List[Int] = null): AveragePooling2D[T]
def createKerasAveragePooling3D(poolSize: List[Int], strides: List[Int], dimOrdering: String = "th", inputShape: List[Int] = null): AveragePooling3D[T]
def createKerasBatchNormalization(epsilon: Double = 0.001, momentum: Double = 0.99, betaInit: String = "zero", gammaInit: String = "one", dimOrdering: String = "th", inputShape: List[Int] = null): BatchNormalization[T]
def createKerasBidirectional(layer: Recurrent[T], mergeMode: String = "concat", inputShape: List[Int] = null): Bidirectional[T]
def createKerasConvLSTM2D(nbFilter: Int, nbKernel: Int, activation: String = "tanh", innerActivation: String = "hard_sigmoid", dimOrdering: String = "th", subsample: Int = 1, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, returnSequences: Boolean = false, goBackwards: Boolean = false, inputShape: List[Int] = null): ConvLSTM2D[T]
def createKerasConvolution1D(nbFilter: Int, filterLength: Int, init: String = "glorot_uniform", activation: String = null, borderMode: String = "valid", subsampleLength: Int = 1, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: List[Int] = null): Convolution1D[T]
def createKerasConvolution2D(nbFilter: Int, nbRow: Int, nbCol: Int, init: String = "glorot_uniform", activation: String = null, borderMode: String = "valid", subsample: List[Int], dimOrdering: String = "th", wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: List[Int] = null): Convolution2D[T]
def createKerasConvolution3D(nbFilter: Int, kernelDim1: Int, kernelDim2: Int, kernelDim3: Int, init: String = "glorot_uniform", activation: String = null, borderMode: String = "valid", subsample: List[Int], dimOrdering: String = "th", wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: List[Int] = null): Convolution3D[T]
def createKerasCropping1D(cropping: List[Int], inputShape: List[Int] = null): Cropping1D[T]
def createKerasCropping2D(heightCrop: List[Int], widthCrop: List[Int], dimOrdering: String = "th", inputShape: List[Int] = null): Cropping2D[T]
def createKerasCropping3D(dim1Crop: List[Int], dim2Crop: List[Int], dim3Crop: List[Int], dimOrdering: String = "th", inputShape: List[Int] = null): Cropping3D[T]
def createKerasDeconvolution2D(nbFilter: Int, nbRow: Int, nbCol: Int, init: String = "glorot_uniform", activation: String = null, subsample: List[Int], dimOrdering: String = "th", wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: List[Int] = null): Deconvolution2D[T]
def createKerasDense(outputDim: Int, init: String = "glorot_uniform", activation: String = null, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: List[Int] = null): Dense[T]
def createKerasDropout(p: Double, inputShape: List[Int] = null): Dropout[T]
def createKerasELU(alpha: Double = 1.0, inputShape: List[Int] = null): ELU[T]
def createKerasEmbedding(inputDim: Int, outputDim: Int, init: String = "uniform", wRegularizer: Regularizer[T] = null, inputShape: List[Int] = null): Embedding[T]
def createKerasFlatten(inputShape: List[Int] = null): Flatten[T]
def createKerasGRU(outputDim: Int, activation: String = "tanh", innerActivation: String = "hard_sigmoid", returnSequences: Boolean = false, goBackwards: Boolean = false, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, inputShape: List[Int] = null): GRU[T]
def createKerasGaussianDropout(p: Double, inputShape: List[Int] = null): GaussianDropout[T]
def createKerasGaussianNoise(sigma: Double, inputShape: List[Int] = null): GaussianNoise[T]
def createKerasGlobalAveragePooling1D(inputShape: List[Int] = null): GlobalAveragePooling1D[T]
def createKerasGlobalAveragePooling2D(dimOrdering: String = "th", inputShape: List[Int] = null): GlobalAveragePooling2D[T]
def createKerasGlobalAveragePooling3D(dimOrdering: String = "th", inputShape: List[Int] = null): GlobalAveragePooling3D[T]
def createKerasGlobalMaxPooling1D(inputShape: List[Int] = null): GlobalMaxPooling1D[T]
def createKerasGlobalMaxPooling2D(dimOrdering: String = "th", inputShape: List[Int] = null): GlobalMaxPooling2D[T]
def createKerasGlobalMaxPooling3D(dimOrdering: String = "th", inputShape: List[Int] = null): GlobalMaxPooling3D[T]
def createKerasHighway(activation: String = null, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: List[Int] = null): Highway[T]
def createKerasInput(name: String = null, inputShape: List[Int] = null): ModuleNode[T]
def createKerasInputLayer(inputShape: List[Int] = null): KerasLayer[Activity, Activity, T]
def createKerasLSTM(outputDim: Int, activation: String = "tanh", innerActivation: String = "hard_sigmoid", returnSequences: Boolean = false, goBackwards: Boolean = false, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, inputShape: List[Int] = null): LSTM[T]
def createKerasLeakyReLU(alpha: Double = 0.01, inputShape: List[Int] = null): LeakyReLU[T]
def createKerasLocallyConnected1D(nbFilter: Int, filterLength: Int, activation: String = null, subsampleLength: Int = 1, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: List[Int] = null): LocallyConnected1D[T]
def createKerasLocallyConnected2D(nbFilter: Int, nbRow: Int, nbCol: Int, activation: String = null, borderMode: String = "valid", subsample: List[Int], dimOrdering: String = "th", wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: List[Int] = null): LocallyConnected2D[T]
def createKerasMasking(maskValue: Double = 0.0, inputShape: List[Int] = null): Masking[T]
def createKerasMaxPooling1D(poolLength: Int = 2, stride: Int = 1, borderMode: String = "valid", inputShape: List[Int] = null): MaxPooling1D[T]
def createKerasMaxPooling2D(poolSize: List[Int], strides: List[Int], borderMode: String = "valid", dimOrdering: String = "th", inputShape: List[Int] = null): MaxPooling2D[T]
def createKerasMaxPooling3D(poolSize: List[Int], strides: List[Int], dimOrdering: String = "th", inputShape: List[Int] = null): MaxPooling3D[T]
def createKerasMaxoutDense(outputDim: Int, nbFeature: Int = 4, wRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: List[Int] = null): MaxoutDense[T]
def createKerasMerge(layers: List[AbstractModule[Activity, Activity, T]] = null, mode: String = "sum", concatAxis: Int = 1, inputShape: List[List[Int]]): Merge[T]
def createKerasModel(input: List[ModuleNode[T]], output: List[ModuleNode[T]]): Model[T]
def createKerasPermute(dims: List[Int], inputShape: List[Int] = null): Permute[T]
def createKerasRepeatVector(n: Int, inputShape: List[Int] = null): RepeatVector[T]
def createKerasReshape(targetShape: List[Int], inputShape: List[Int] = null): Reshape[T]
def createKerasSReLU(tLeftInit: String = "zero", aLeftInit: String = "glorot_uniform", tRightInit: String = "glorot_uniform", aRightInit: String = "one", sharedAxes: List[Int] = null, inputShape: List[Int] = null): SReLU[T]
def createKerasSeparableConvolution2D(nbFilter: Int, nbRow: Int, nbCol: Int, init: String = "glorot_uniform", activation: String = null, borderMode: String = "valid", subsample: List[Int], depthMultiplier: Int = 1, dimOrdering: String = "th", depthwiseRegularizer: Regularizer[T] = null, pointwiseRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, bias: Boolean = true, inputShape: List[Int] = null): SeparableConvolution2D[T]
def createKerasSequential(): Sequential[T]
def createKerasSimpleRNN(outputDim: Int, activation: String = "tanh", returnSequences: Boolean = false, goBackwards: Boolean = false, wRegularizer: Regularizer[T] = null, uRegularizer: Regularizer[T] = null, bRegularizer: Regularizer[T] = null, inputShape: List[Int] = null): SimpleRNN[T]
def createKerasSpatialDropout1D(p: Double = 0.5, inputShape: List[Int] = null): SpatialDropout1D[T]
def createKerasSpatialDropout2D(p: Double = 0.5, dimOrdering: String = "th", inputShape: List[Int] = null): SpatialDropout2D[T]
def createKerasSpatialDropout3D(p: Double = 0.5, dimOrdering: String = "th", inputShape: List[Int] = null): SpatialDropout3D[T]
def createKerasThresholdedReLU(theta: Double = 1.0, inputShape: List[Int] = null): ThresholdedReLU[T]
def createKerasTimeDistributed(layer: KerasLayer[Tensor[T], Tensor[T], T], inputShape: List[Int] = null): TimeDistributed[T]
def createKerasUpSampling1D(length: Int = 2, inputShape: List[Int] = null): UpSampling1D[T]
def createKerasUpSampling2D(size: List[Int], dimOrdering: String = "th", inputShape: List[Int] = null): UpSampling2D[T]
def createKerasUpSampling3D(size: List[Int], dimOrdering: String = "th", inputShape: List[Int] = null): UpSampling3D[T]
def createKerasZeroPadding1D(padding: List[Int], inputShape: List[Int] = null): ZeroPadding1D[T]
def createKerasZeroPadding2D(padding: List[Int], dimOrdering: String = "th", inputShape: List[Int] = null): ZeroPadding2D[T]
def createKerasZeroPadding3D(padding: List[Int], dimOrdering: String = "th", inputShape: List[Int] = null): ZeroPadding3D[T]
def createKullbackLeiblerDivergenceCriterion: KullbackLeiblerDivergenceCriterion[T]

Definition Classes
PythonBigDL
def createL1Cost(): L1Cost[T]

Definition Classes
PythonBigDL
def createL1HingeEmbeddingCriterion(margin: Double = 1): L1HingeEmbeddingCriterion[T]

Definition Classes
PythonBigDL
def createL1L2Regularizer(l1: Double, l2: Double): L1L2Regularizer[T]

Definition Classes
PythonBigDL
def createL1Penalty(l1weight: Int, sizeAverage: Boolean = false, provideOutput: Boolean = true): L1Penalty[T]

Definition Classes
PythonBigDL
def createL1Regularizer(l1: Double): L1Regularizer[T]

Definition Classes
PythonBigDL
def createL2Regularizer(l2: Double): L2Regularizer[T]

Definition Classes
PythonBigDL
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]

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

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

Definition Classes
PythonBigDL
def createLayerNormalization(hiddenSize: Int): LayerNormalization[T]

Definition Classes
PythonBigDL
def createLeakyReLU(negval: Double = 0.01, inplace: Boolean = false): LeakyReLU[T]

Definition Classes
PythonBigDL
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]

Definition Classes
PythonBigDL
def createLocalImageFrame(images: List[JTensor], labels: List[JTensor]): LocalImageFrame

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

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

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

Definition Classes
PythonBigDL
def createLog(): Log[T]

Definition Classes
PythonBigDL
def createLogSigmoid(): LogSigmoid[T]

Definition Classes
PythonBigDL
def createLogSoftMax(): LogSoftMax[T]

Definition Classes
PythonBigDL
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]

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

Definition Classes
PythonBigDL
def createLoss(criterion: Criterion[T]): ValidationMethod[T]

Definition Classes
PythonBigDL
def createMAE(): ValidationMethod[T]

Definition Classes
PythonBigDL
def createMM(transA: Boolean = false, transB: Boolean = false): MM[T]

Definition Classes
PythonBigDL
def createMSECriterion: MSECriterion[T]

Definition Classes
PythonBigDL
def createMV(trans: Boolean = false): MV[T]

Definition Classes
PythonBigDL
def createMapTable(module: AbstractModule[Activity, Activity, T] = null): MapTable[T]

Definition Classes
PythonBigDL
def createMarginCriterion(margin: Double = 1.0, sizeAverage: Boolean = true, squared: Boolean = false): MarginCriterion[T]

Definition Classes
PythonBigDL
def createMarginRankingCriterion(margin: Double = 1.0, sizeAverage: Boolean = true): MarginRankingCriterion[T]

Definition Classes
PythonBigDL
def createMaskedSelect(): MaskedSelect[T]

Definition Classes
PythonBigDL
def createMasking(maskValue: Double): Masking[T]

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

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

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

Definition Classes
PythonBigDL
def createMaxEpoch(max: Int): Trigger

Definition Classes
PythonBigDL
def createMaxIteration(max: Int): Trigger

Definition Classes
PythonBigDL
def createMaxScore(max: Float): Trigger

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

Definition Classes
PythonBigDL
def createMean(dimension: Int = 1, nInputDims: Int = 1, squeeze: Boolean = true): Mean[T]

Definition Classes
PythonBigDL
def createMeanAbsolutePercentageCriterion: MeanAbsolutePercentageCriterion[T]

Definition Classes
PythonBigDL
def createMeanAveragePrecision(k: Int, classes: Int): ValidationMethod[T]

Definition Classes
PythonBigDL
def createMeanAveragePrecisionObjectDetection(classes: Int, iou: Float, useVoc2007: Boolean, skipClass: Int): ValidationMethod[T]

Definition Classes
PythonBigDL
def createMeanSquaredLogarithmicCriterion: MeanSquaredLogarithmicCriterion[T]

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

Definition Classes
PythonBigDL
def createMinLoss(min: Float): Trigger

Definition Classes
PythonBigDL
def createMixtureTable(dim: Int = Int.MaxValue): MixtureTable[T]

Definition Classes
PythonBigDL
def createModel(input: List[ModuleNode[T]], output: List[ModuleNode[T]]): Graph[T]

Definition Classes
PythonBigDL
def createModelPreprocessor(preprocessor: AbstractModule[Activity, Activity, T], trainable: AbstractModule[Activity, Activity, T]): Graph[T]

Definition Classes
PythonBigDL
def createMsraFiller(varianceNormAverage: Boolean = true): MsraFiller

Definition Classes
PythonBigDL
def createMul(): Mul[T]

Definition Classes
PythonBigDL
def createMulConstant(scalar: Double, inplace: Boolean = false): MulConstant[T]

Definition Classes
PythonBigDL
def createMultiCriterion(): MultiCriterion[T]

Definition Classes
PythonBigDL
def createMultiLabelMarginCriterion(sizeAverage: Boolean = true): MultiLabelMarginCriterion[T]

Definition Classes
PythonBigDL
def createMultiLabelSoftMarginCriterion(weights: JTensor = null, sizeAverage: Boolean = true): MultiLabelSoftMarginCriterion[T]

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

Definition Classes
PythonBigDL
def createMultiRNNCell(cells: List[Cell[T]]): MultiRNNCell[T]

Definition Classes
PythonBigDL
def createMultiStep(stepSizes: List[Int], gamma: Double): MultiStep

Definition Classes
PythonBigDL
def createNDCG(k: Int = 10, negNum: Int = 100): ValidationMethod[T]

Definition Classes
PythonBigDL
def createNarrow(dimension: Int, offset: Int, length: Int = 1): Narrow[T]

Definition Classes
PythonBigDL
def createNarrowTable(offset: Int, length: Int = 1): NarrowTable[T]

Definition Classes
PythonBigDL
def createNegative(inplace: Boolean): Negative[T]

Definition Classes
PythonBigDL
def createNegativeEntropyPenalty(beta: Double): NegativeEntropyPenalty[T]

Definition Classes
PythonBigDL
def createNode(module: AbstractModule[Activity, Activity, T], x: List[ModuleNode[T]]): ModuleNode[T]

Definition Classes
PythonBigDL
def createNormalize(p: Double, eps: Double = 1e-10): Normalize[T]

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

Definition Classes
PythonBigDL
def createOnes(): Ones.type

Definition Classes
PythonBigDL
def createPGCriterion(sizeAverage: Boolean = false): PGCriterion[T]

Definition Classes
PythonBigDL
def createPReLU(nOutputPlane: Int = 0): PReLU[T]

Definition Classes
PythonBigDL
def createPack(dimension: Int): Pack[T]

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

Definition Classes
PythonBigDL
def createPairwiseDistance(norm: Int = 2): PairwiseDistance[T]

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

Definition Classes
PythonBigDL
def createParallelCriterion(repeatTarget: Boolean = false): ParallelCriterion[T]

Definition Classes
PythonBigDL
def createParallelTable(): ParallelTable[T]

Definition Classes
PythonBigDL
def createPipeline(list: List[FeatureTransformer]): FeatureTransformer

Definition Classes
PythonBigDL
def createPixelBytesToMat(byteKey: String): PixelBytesToMat

Definition Classes
PythonBigDL
def createPixelNormalize(means: List[Double]): PixelNormalizer

Definition Classes
PythonBigDL
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

Definition Classes
PythonBigDL
def createPoissonCriterion: PoissonCriterion[T]

Definition Classes
PythonBigDL
def createPoly(power: Double, maxIteration: Int): Poly

Definition Classes
PythonBigDL
def createPooler(resolution: Int, scales: List[Double], sampling_ratio: Int): Pooler[T]

Definition Classes
PythonBigDL
def createPower(power: Double, scale: Double = 1, shift: Double = 0): Power[T]

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

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

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

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

Definition Classes
PythonBigDL
def createRandomAlterAspect(min_area_ratio: Float, max_area_ratio: Int, min_aspect_ratio_change: Float, interp_mode: String, cropLength: Int): RandomAlterAspect

Definition Classes
PythonBigDL
def createRandomAspectScale(scales: List[Int], scaleMultipleOf: Int = 1, maxSize: Int = 1000): RandomAspectScale

Definition Classes
PythonBigDL
def createRandomCrop(cropWidth: Int, cropHeight: Int, isClip: Boolean): RandomCrop

Definition Classes
PythonBigDL
def createRandomCropper(cropWidth: Int, cropHeight: Int, mirror: Boolean, cropperMethod: String, channels: Int): RandomCropper

Definition Classes
PythonBigDL
def createRandomNormal(mean: Double, stdv: Double): RandomNormal

Definition Classes
PythonBigDL
def createRandomResize(minSize: Int, maxSize: Int): RandomResize

Definition Classes
PythonBigDL
def createRandomSampler(): FeatureTransformer

Definition Classes
PythonBigDL
def createRandomTransformer(transformer: FeatureTransformer, prob: Double): RandomTransformer

Definition Classes
PythonBigDL
def createRandomUniform(): InitializationMethod

Definition Classes
PythonBigDL
def createRandomUniform(lower: Double, upper: Double): InitializationMethod

Definition Classes
PythonBigDL
def createReLU(ip: Boolean = false): ReLU[T]

Definition Classes
PythonBigDL
def createReLU6(inplace: Boolean = false): ReLU6[T]

Definition Classes
PythonBigDL
def createRecurrent(): Recurrent[T]

Definition Classes
PythonBigDL
def createRecurrentDecoder(outputLength: Int): RecurrentDecoder[T]

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

Definition Classes
PythonBigDL
def createReshape(size: List[Int], batchMode: Boolean = null): Reshape[T]

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

Definition Classes
PythonBigDL
def createResizeBilinear(outputHeight: Int, outputWidth: Int, alignCorner: Boolean, dataFormat: String): ResizeBilinear[T]

Definition Classes
PythonBigDL
def createReverse(dimension: Int = 1, isInplace: Boolean = false): Reverse[T]

Definition Classes
PythonBigDL
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]

Definition Classes
PythonBigDL
def createRoiAlign(spatial_scale: Double, sampling_ratio: Int, pooled_h: Int, pooled_w: Int): RoiAlign[T]

Definition Classes
PythonBigDL
def createRoiHFlip(normalized: Boolean = true): RoiHFlip

Definition Classes
PythonBigDL
def createRoiNormalize(): RoiNormalize

Definition Classes
PythonBigDL
def createRoiPooling(pooled_w: Int, pooled_h: Int, spatial_scale: Double): RoiPooling[T]

Definition Classes
PythonBigDL
def createRoiProject(needMeetCenterConstraint: Boolean): RoiProject

Definition Classes
PythonBigDL
def createRoiResize(normalized: Boolean): RoiResize

Definition Classes
PythonBigDL
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]

Definition Classes
PythonBigDL
def createSReLU(shape: ArrayList[Int], shareAxes: ArrayList[Int] = null): SReLU[T]

Definition Classes
PythonBigDL
def createSaturation(deltaLow: Double, deltaHigh: Double): Saturation

Definition Classes
PythonBigDL
def createScale(size: List[Int]): Scale[T]

Definition Classes
PythonBigDL
def createSelect(dimension: Int, index: Int): Select[T]

Definition Classes
PythonBigDL
def createSelectTable(dimension: Int): SelectTable[T]

Definition Classes
PythonBigDL
def createSequenceBeamSearch(vocabSize: Int, beamSize: Int, alpha: Float, decodeLength: Int, eosId: Float, paddingValue: Float, numHiddenLayers: Int, hiddenSize: Int): SequenceBeamSearch[T]

Definition Classes
PythonBigDL
def createSequential(): Container[Activity, Activity, T]

Definition Classes
PythonBigDL
def createSequentialSchedule(iterationPerEpoch: Int): SequentialSchedule

Definition Classes
PythonBigDL
def createSeveralIteration(interval: Int): Trigger

Definition Classes
PythonBigDL
def createSigmoid(): Sigmoid[T]

Definition Classes
PythonBigDL
def createSmoothL1Criterion(sizeAverage: Boolean = true): SmoothL1Criterion[T]

Definition Classes
PythonBigDL
def createSmoothL1CriterionWithWeights(sigma: Double, num: Int = 0): SmoothL1CriterionWithWeights[T]

Definition Classes
PythonBigDL
def createSoftMarginCriterion(sizeAverage: Boolean = true): SoftMarginCriterion[T]

Definition Classes
PythonBigDL
def createSoftMax(pos: Int = 1): SoftMax[T]

Definition Classes
PythonBigDL
def createSoftMin(): SoftMin[T]

Definition Classes
PythonBigDL
def createSoftPlus(beta: Double = 1.0): SoftPlus[T]

Definition Classes
PythonBigDL
def createSoftShrink(lambda: Double = 0.5): SoftShrink[T]

Definition Classes
PythonBigDL
def createSoftSign(): SoftSign[T]

Definition Classes
PythonBigDL
def createSoftmaxWithCriterion(ignoreLabel: Integer = null, normalizeMode: String = "VALID"): SoftmaxWithCriterion[T]

Definition Classes
PythonBigDL
def createSparseJoinTable(dimension: Int): SparseJoinTable[T]

Definition Classes
PythonBigDL
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]

Definition Classes
PythonBigDL
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]

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

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

Definition Classes
PythonBigDL
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]

Definition Classes
PythonBigDL
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]

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

Definition Classes
PythonBigDL
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]

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

Definition Classes
PythonBigDL
def createSpatialDropout1D(initP: Double = 0.5): SpatialDropout1D[T]

Definition Classes
PythonBigDL
def createSpatialDropout2D(initP: Double = 0.5, dataFormat: String = "NCHW"): SpatialDropout2D[T]

Definition Classes
PythonBigDL
def createSpatialDropout3D(initP: Double = 0.5, dataFormat: String = "NCHW"): SpatialDropout3D[T]

Definition Classes
PythonBigDL
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]

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

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

Definition Classes
PythonBigDL
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]

Definition Classes
PythonBigDL
def createSpatialSubtractiveNormalization(nInputPlane: Int = 1, kernel: JTensor = null): SpatialSubtractiveNormalization[T]

Definition Classes
PythonBigDL
def createSpatialWithinChannelLRN(size: Int = 5, alpha: Double = 1.0, beta: Double = 0.75): SpatialWithinChannelLRN[T]

Definition Classes
PythonBigDL
def createSpatialZeroPadding(padLeft: Int, padRight: Int, padTop: Int, padBottom: Int): SpatialZeroPadding[T]

Definition Classes
PythonBigDL
def createSplitTable(dimension: Int, nInputDims: Int = 1): SplitTable[T]

Definition Classes
PythonBigDL
def createSqrt(): Sqrt[T]

Definition Classes
PythonBigDL
def createSquare(): Square[T]

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

Definition Classes
PythonBigDL
def createStep(stepSize: Int, gamma: Double): Step

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

Definition Classes
PythonBigDL
def createTableOperation(operationLayer: AbstractModule[Table, Tensor[T], T]): TableOperation[T]

Definition Classes
PythonBigDL
def createTanh(): Tanh[T]

Definition Classes
PythonBigDL
def createTanhShrink(): TanhShrink[T]

Definition Classes
PythonBigDL
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]

Definition Classes
PythonBigDL
def createTemporalMaxPooling(kW: Int, dW: Int): TemporalMaxPooling[T]

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

Definition Classes
PythonBigDL
def createTile(dim: Int, copies: Int): Tile[T]

Definition Classes
PythonBigDL
def createTimeDistributed(layer: TensorModule[T]): TimeDistributed[T]

Definition Classes
PythonBigDL
def createTimeDistributedCriterion(critrn: TensorCriterion[T], sizeAverage: Boolean = false, dimension: Int = 2): TimeDistributedCriterion[T]

Definition Classes
PythonBigDL
def createTimeDistributedMaskCriterion(critrn: TensorCriterion[T], paddingValue: Int = 0): TimeDistributedMaskCriterion[T]

Definition Classes
PythonBigDL
def createTop1Accuracy(): ValidationMethod[T]

Definition Classes
PythonBigDL
def createTop5Accuracy(): ValidationMethod[T]

Definition Classes
PythonBigDL
def createTrainSummary(logDir: String, appName: String): TrainSummary

Definition Classes
PythonBigDL
def createTransformer(vocabSize: Int, hiddenSize: Int, numHeads: Int, filterSize: Int, numHiddenlayers: Int, postprocessDropout: Double, attentionDropout: Double, reluDropout: Double): Transformer[T]

Definition Classes
PythonBigDL
def createTransformerCriterion(criterion: AbstractCriterion[Activity, Activity, T], inputTransformer: AbstractModule[Activity, Activity, T] = null, targetTransformer: AbstractModule[Activity, Activity, T] = null): TransformerCriterion[T]

Definition Classes
PythonBigDL
def createTranspose(permutations: List[List[Int]]): Transpose[T]

Definition Classes
PythonBigDL
def createTreeNNAccuracy(): ValidationMethod[T]

Definition Classes
PythonBigDL
def createTriggerAnd(first: Trigger, others: List[Trigger]): Trigger

Definition Classes
PythonBigDL
def createTriggerOr(first: Trigger, others: List[Trigger]): Trigger

Definition Classes
PythonBigDL
def createUnsqueeze(pos: List[Int], numInputDims: Int = Int.MinValue): Unsqueeze[T]

Definition Classes
PythonBigDL
def createUpSampling1D(length: Int): UpSampling1D[T]

Definition Classes
PythonBigDL
def createUpSampling2D(size: List[Int], dataFormat: String): UpSampling2D[T]

Definition Classes
PythonBigDL
def createUpSampling3D(size: List[Int]): UpSampling3D[T]

Definition Classes
PythonBigDL
def createValidationSummary(logDir: String, appName: String): ValidationSummary

Definition Classes
PythonBigDL
def createView(sizes: List[Int], num_input_dims: Int = 0): View[T]

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

Definition Classes
PythonBigDL
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]

Definition Classes
PythonBigDL
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]

Definition Classes
PythonBigDL
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]

Definition Classes
PythonBigDL
def createWarmup(delta: Double): Warmup

Definition Classes
PythonBigDL
def createXavier(): Xavier.type

Definition Classes
PythonBigDL
def createZeros(): Zeros.type

Definition Classes
PythonBigDL
def criterionBackward(criterion: AbstractCriterion[Activity, Activity, T], input: List[_ <: AnyRef], inputIsTable: Boolean, target: List[_ <: AnyRef], targetIsTable: Boolean): List[JTensor]

Definition Classes
PythonBigDL
def criterionForward(criterion: AbstractCriterion[Activity, Activity, T], input: List[_ <: AnyRef], inputIsTable: Boolean, target: List[_ <: AnyRef], targetIsTable: Boolean): T

Definition Classes
PythonBigDL
def disableClip(optimizer: Optimizer[T, MiniBatch[T]]): Unit

Definition Classes
PythonBigDL
def distributedImageFrameRandomSplit(imageFrame: DistributedImageFrame, weights: List[Double]): Array[ImageFrame]

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

Definition Classes
PythonBigDL
def distributedImageFrameToLabelTensorRdd(imageFrame: DistributedImageFrame): JavaRDD[JTensor]

Definition Classes
PythonBigDL
def distributedImageFrameToPredict(imageFrame: DistributedImageFrame, key: String): JavaRDD[List[Any]]

Definition Classes
PythonBigDL
def distributedImageFrameToSample(imageFrame: DistributedImageFrame, key: String): JavaRDD[Sample]

Definition Classes
PythonBigDL
def distributedImageFrameToUri(imageFrame: DistributedImageFrame, key: String): JavaRDD[String]

Definition Classes
PythonBigDL
def dlClassifierModelTransform(dlClassifierModel: DLClassifierModel[T], dataSet: DataFrame): DataFrame

Definition Classes
PythonBigDL
def dlImageTransform(dlImageTransformer: DLImageTransformer, dataSet: DataFrame): DataFrame

Definition Classes
PythonBigDL
def dlModelTransform(dlModel: DLModel[T], dataSet: DataFrame): DataFrame

Definition Classes
PythonBigDL
def dlReadImage(path: String, sc: JavaSparkContext, minParitions: Int): DataFrame

Definition Classes
PythonBigDL
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def evaluate(module: KerasModel[T], x: JavaRDD[Sample], batchSize: Int = 32): List[EvaluatedResult]
def evaluate(module: AbstractModule[Activity, Activity, T]): AbstractModule[Activity, Activity, T]

Definition Classes
PythonBigDL
def featureTransformDataset(dataset: DataSet[ImageFeature], transformer: FeatureTransformer): DataSet[ImageFeature]

Definition Classes
PythonBigDL
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def findGraphNode(model: Graph[T], name: String): ModuleNode[T]

Definition Classes
PythonBigDL
def fit(module: KerasModel[T], xTrain: List[JTensor], yTrain: JTensor, batchSize: Int, epochs: Int, xVal: List[JTensor], yVal: JTensor, localCores: Int): Unit
def fit(module: KerasModel[T], x: DataSet[ImageFeature], batchSize: Int, epochs: Int, validationData: DataSet[ImageFeature]): Unit
def fit(module: KerasModel[T], x: JavaRDD[Sample], batchSize: Int = 32, epochs: Int = 10, validationData: JavaRDD[Sample] = null): Unit
def fitClassifier(classifier: DLClassifier[T], dataSet: DataFrame): DLModel[T]

Definition Classes
PythonBigDL
def fitEstimator(estimator: DLEstimator[T], dataSet: DataFrame): DLModel[T]

Definition Classes
PythonBigDL
def freeze(model: AbstractModule[Activity, Activity, T], freezeLayers: List[String]): AbstractModule[Activity, Activity, T]

Definition Classes
PythonBigDL
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def getContainerModules(module: Container[Activity, Activity, T]): List[AbstractModule[Activity, Activity, T]]

Definition Classes
PythonBigDL
def getEngineType(): String

Definition Classes
PythonBigDL
def getFlattenModules(module: Container[Activity, Activity, T], includeContainer: Boolean): List[AbstractModule[Activity, Activity, T]]

Definition Classes
PythonBigDL
def getHiddenState(rec: Recurrent[T]): JActivity

Definition Classes
PythonBigDL
def getInputShape(module: Container[Activity, Activity, T]): List[List[Int]]
def getNodeAndCoreNumber(): Array[Int]

Definition Classes
PythonBigDL
def getOptimizerVersion(): String

Definition Classes
PythonBigDL
def getOutputShape(module: Container[Activity, Activity, T]): List[List[Int]]
def getRealClassNameOfJValue(module: AbstractModule[Activity, Activity, T]): String

Definition Classes
PythonBigDL
def getRunningMean(module: BatchNormalization[T]): JTensor
def getRunningMean(module: BatchNormalization[T]): JTensor

Definition Classes
PythonBigDL
def getRunningStd(module: BatchNormalization[T]): JTensor
def getRunningStd(module: BatchNormalization[T]): JTensor

Definition Classes
PythonBigDL
def getWeights(model: AbstractModule[Activity, Activity, T]): List[JTensor]

Definition Classes
PythonBigDL
def hashCode(): Int

Definition Classes
AnyRef → Any
def imageFeatureGetKeys(imageFeature: ImageFeature): List[String]

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

Definition Classes
PythonBigDL
def imageFeatureToLabelTensor(imageFeature: ImageFeature): JTensor

Definition Classes
PythonBigDL
def initEngine(): Unit

Definition Classes
PythonBigDL
def isDistributed(imageFrame: ImageFrame): Boolean

Definition Classes
PythonBigDL
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def isLocal(imageFrame: ImageFrame): Boolean

Definition Classes
PythonBigDL
def isWithWeights(module: Module[T]): Boolean

Definition Classes
PythonBigDL
def jTensorsToActivity(input: List[_ <: AnyRef], isTable: Boolean): Activity

Definition Classes
PythonBigDL
def loadBigDL(path: String): AbstractModule[Activity, Activity, T]

Definition Classes
PythonBigDL
def loadBigDLModule(modulePath: String, weightPath: String): AbstractModule[Activity, Activity, T]

Definition Classes
PythonBigDL
def loadCaffe(model: AbstractModule[Activity, Activity, T], defPath: String, modelPath: String, matchAll: Boolean = true): AbstractModule[Activity, Activity, T]

Definition Classes
PythonBigDL
def loadCaffeModel(defPath: String, modelPath: String): AbstractModule[Activity, Activity, T]

Definition Classes
PythonBigDL
def loadOptimMethod(path: String): OptimMethod[T]

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

Definition Classes
PythonBigDL
def loadTorch(path: String): AbstractModule[Activity, Activity, T]

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

Definition Classes
PythonBigDL
def localImageFrameToLabelTensor(imageFrame: LocalImageFrame): List[JTensor]

Definition Classes
PythonBigDL
def localImageFrameToPredict(imageFrame: LocalImageFrame, key: String): List[List[Any]]

Definition Classes
PythonBigDL
def localImageFrameToSample(imageFrame: LocalImageFrame, key: String): List[Sample]

Definition Classes
PythonBigDL
def localImageFrameToUri(imageFrame: LocalImageFrame, key: String): List[String]

Definition Classes
PythonBigDL
def modelBackward(model: AbstractModule[Activity, Activity, T], input: List[_ <: AnyRef], inputIsTable: Boolean, gradOutput: List[_ <: AnyRef], gradOutputIsTable: Boolean): List[JTensor]

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

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

Definition Classes
PythonBigDL
def modelForward(model: AbstractModule[Activity, Activity, T], input: List[_ <: AnyRef], inputIsTable: Boolean): List[JTensor]

Definition Classes
PythonBigDL
def modelGetParameters(model: AbstractModule[Activity, Activity, T]): Map[Any, Map[Any, List[List[Any]]]]

Definition Classes
PythonBigDL
def modelPredictClass(model: AbstractModule[Activity, Activity, T], dataRdd: JavaRDD[Sample]): JavaRDD[Int]

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

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

Definition Classes
PythonBigDL
def modelSave(module: AbstractModule[Activity, Activity, T], path: String, overWrite: Boolean): Unit

Definition Classes
PythonBigDL
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def predictLocal(model: AbstractModule[Activity, Activity, T], features: List[JTensor], batchSize: Int = 1): List[JTensor]

Definition Classes
PythonBigDL
def predictLocalClass(model: AbstractModule[Activity, Activity, T], features: List[JTensor]): List[Int]

Definition Classes
PythonBigDL
def quantize(module: AbstractModule[Activity, Activity, T]): Module[T]

Definition Classes
PythonBigDL
def read(path: String, sc: JavaSparkContext, minPartitions: Int): ImageFrame

Definition Classes
PythonBigDL
def readParquet(path: String, sc: JavaSparkContext): DistributedImageFrame

Definition Classes
PythonBigDL
def redirectSparkLogs(logPath: String): Unit

Definition Classes
PythonBigDL
def saveBigDLModule(module: AbstractModule[Activity, Activity, T], modulePath: String, weightPath: String, overWrite: Boolean): Unit

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

Definition Classes
PythonBigDL
def saveGraphTopology(model: Graph[T], logPath: String): Graph[T]

Definition Classes
PythonBigDL
def saveOptimMethod(method: OptimMethod[T], path: String, overWrite: Boolean = false): Unit

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

Definition Classes
PythonBigDL
def saveTensorDictionary(tensors: HashMap[String, JTensor], path: String): Unit

Save tensor dictionary to a Java hashmap object file
Save tensor dictionary to a Java hashmap object file

Definition Classes
PythonBigDL
def seqFilesToImageFrame(url: String, sc: JavaSparkContext, classNum: Int, partitionNum: Int): ImageFrame

Definition Classes
PythonBigDL
def setBatchSizeDLClassifier(classifier: DLClassifier[T], batchSize: Int): DLClassifier[T]

Definition Classes
PythonBigDL
def setBatchSizeDLClassifierModel(dlClassifierModel: DLClassifierModel[T], batchSize: Int): DLClassifierModel[T]

Definition Classes
PythonBigDL
def setBatchSizeDLEstimator(estimator: DLEstimator[T], batchSize: Int): DLEstimator[T]

Definition Classes
PythonBigDL
def setBatchSizeDLModel(dlModel: DLModel[T], batchSize: Int): DLModel[T]

Definition Classes
PythonBigDL
def setCheckPoint(optimizer: Optimizer[T, MiniBatch[T]], trigger: Trigger, checkPointPath: String, isOverwrite: Boolean): Unit

Definition Classes
PythonBigDL
def setConstantClip(optimizer: Optimizer[T, MiniBatch[T]], min: Float, max: Float): Unit

Definition Classes
PythonBigDL
def setCriterion(optimizer: Optimizer[T, MiniBatch[T]], criterion: Criterion[T]): Unit

Definition Classes
PythonBigDL
def setFeatureSizeDLClassifierModel(dlClassifierModel: DLClassifierModel[T], featureSize: ArrayList[Int]): DLClassifierModel[T]

Definition Classes
PythonBigDL
def setFeatureSizeDLModel(dlModel: DLModel[T], featureSize: ArrayList[Int]): DLModel[T]

Definition Classes
PythonBigDL
def setInitMethod(layer: Initializable, initMethods: ArrayList[InitializationMethod]): layer.type

Definition Classes
PythonBigDL
def setInitMethod(layer: Initializable, weightInitMethod: InitializationMethod, biasInitMethod: InitializationMethod): layer.type

Definition Classes
PythonBigDL
def setInputFormats(graph: StaticGraph[T], inputFormat: List[Int]): StaticGraph[T]

Definition Classes
PythonBigDL
def setL2NormClip(optimizer: Optimizer[T, MiniBatch[T]], normValue: Float): Unit

Definition Classes
PythonBigDL
def setLabel(labelMap: Map[String, Float], imageFrame: ImageFrame): Unit

Definition Classes
PythonBigDL
def setLearningRateDLClassifier(classifier: DLClassifier[T], lr: Double): DLClassifier[T]

Definition Classes
PythonBigDL
def setLearningRateDLEstimator(estimator: DLEstimator[T], lr: Double): DLEstimator[T]

Definition Classes
PythonBigDL
def setMaxEpochDLClassifier(classifier: DLClassifier[T], maxEpoch: Int): DLClassifier[T]

Definition Classes
PythonBigDL
def setMaxEpochDLEstimator(estimator: DLEstimator[T], maxEpoch: Int): DLEstimator[T]

Definition Classes
PythonBigDL
def setModelSeed(seed: Long): Unit

Definition Classes
PythonBigDL
def setOptimizerVersion(version: String): Unit

Definition Classes
PythonBigDL
def setOutputFormats(graph: StaticGraph[T], outputFormat: List[Int]): StaticGraph[T]

Definition Classes
PythonBigDL
def setRunningMean(module: BatchNormalization[T], runningMean: JTensor): Unit
def setRunningMean(module: BatchNormalization[T], runningMean: JTensor): Unit

Definition Classes
PythonBigDL
def setRunningStd(module: BatchNormalization[T], runningStd: JTensor): Unit
def setRunningStd(module: BatchNormalization[T], runningStd: JTensor): Unit

Definition Classes
PythonBigDL
def setStopGradient(model: Graph[T], layers: List[String]): Graph[T]

Definition Classes
PythonBigDL
def setTrainData(optimizer: Optimizer[T, MiniBatch[T]], trainingRdd: JavaRDD[Sample], batchSize: Int): Unit

Definition Classes
PythonBigDL
def setTrainSummary(optimizer: Optimizer[T, MiniBatch[T]], summary: TrainSummary): Unit

Definition Classes
PythonBigDL
def setValSummary(optimizer: Optimizer[T, MiniBatch[T]], summary: ValidationSummary): Unit

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

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

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

Definition Classes
PythonBigDL
def setWeights(model: AbstractModule[Activity, Activity, T], weights: List[JTensor]): Unit

Definition Classes
PythonBigDL
def shapeToJList(shape: Shape): List[List[Int]]
def showBigDlInfoLogs(): Unit

Definition Classes
PythonBigDL
def summaryReadScalar(summary: Summary, tag: String): List[List[Any]]

Definition Classes
PythonBigDL
def summarySetTrigger(summary: TrainSummary, summaryName: String, trigger: Trigger): TrainSummary

Definition Classes
PythonBigDL
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def testSample(sample: Sample): Sample

Definition Classes
PythonBigDL
def testTensor(jTensor: JTensor): JTensor

Definition Classes
PythonBigDL
def toGraph(sequential: Sequential[T]): StaticGraph[T]

Definition Classes
PythonBigDL
def toJSample(psamples: RDD[Sample]): RDD[dataset.Sample[T]]

Definition Classes
PythonBigDL
def toJSample(record: Sample): dataset.Sample[T]

Definition Classes
PythonBigDL
def toJTensor(tensor: Tensor[T]): JTensor

Definition Classes
PythonBigDL
def toPySample(sample: dataset.Sample[T]): Sample

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

Definition Classes
PythonBigDL
def toScalaArray(list: List[Int]): Array[Int]
def toScalaMultiShape(inputShape: List[List[Int]]): Shape
def toScalaShape(inputShape: List[Int]): Shape
def toString(): String

Definition Classes
AnyRef → Any
def toTensor(jTensor: JTensor): Tensor[T]

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

Definition Classes
PythonBigDL
def transformImageFeature(transformer: FeatureTransformer, feature: ImageFeature): ImageFeature

Definition Classes
PythonBigDL
def transformImageFrame(transformer: FeatureTransformer, imageFrame: ImageFrame): ImageFrame

Definition Classes
PythonBigDL
def unFreeze(model: AbstractModule[Activity, Activity, T], names: List[String]): AbstractModule[Activity, Activity, T]

Definition Classes
PythonBigDL
def uniform(a: Double, b: Double, size: List[Int]): JTensor

Definition Classes
PythonBigDL
def updateParameters(model: AbstractModule[Activity, Activity, T], lr: Double): Unit

Definition Classes
PythonBigDL
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
def writeParquet(path: String, output: String, sc: JavaSparkContext, partitionNum: Int = 1): Unit

Definition Classes
PythonBigDL

Related Docs: object PythonBigDLKeras | package api

class PythonBigDLKeras[T] extends PythonBigDL[T]

Instance Constructors

new PythonBigDLKeras()(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

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

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

final def asInstanceOf[T0]: T0

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

def clone(): AnyRef

def compile(module: KerasModel[T], optimizer: OptimMethod[T], loss: Criterion[T], metrics: List[ValidationMethod[T]] = null): Unit

def createAbs(): Abs[T]

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

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

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

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

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

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

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

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

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

def createAttention(hiddenSize: Int, numHeads: Int, attentionDropout: Float): Attention[T]

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

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]

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

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

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

def createBilinearFiller(): BilinearFiller.type

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

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

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

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

def createBytesToMat(byteKey: String): BytesToMat

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

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

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

def createCDivTable(): CDivTable[T]

def createCMaxTable(): CMaxTable[T]

def createCMinTable(): CMinTable[T]

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

def createCMulTable(): CMulTable[T]

def createCSubTable(): CSubTable[T]

def createCategoricalCrossEntropy(): CategoricalCrossEntropy[T]

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

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

def createChannelOrder(): ChannelOrder

def createChannelScaledNormalizer(meanR: Int, meanG: Int, meanB: Int, scale: Double): ChannelScaledNormalizer

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

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

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

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

def createConcatTable(): ConcatTable[T]

def createConstInitMethod(value: Double): ConstInitMethod

def createContiguous(): Contiguous[T]

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

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

def createCosineDistance(): CosineDistance[T]

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

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

def createCosineProximityCriterion(): CosineProximityCriterion[T]

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

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

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

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

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

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

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

def createDLImageTransformer(transformer: FeatureTransformer): DLImageTransformer

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

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

def createDefault(): Default

def createDenseToSparse(): DenseToSparse[T]

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

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

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

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