com.intel.analytics.bigdl.nn.abstractnn

AbstractModule

abstract class AbstractModule[A <: Activity, B <: Activity, T] extends Serializable

Module is the basic component of a neural network. It forward activities and backward gradients. Modules can connect to others to construct a complex neural network.

A

Input data type

B

Output data type

T

Numeric type of parameter(e.g. weight, bias). Only support float/double now

Linear Supertypes
Serializable, Serializable, AnyRef, Any
Known Subclasses
Abs, ActivityRegularization, Adapter, Add, AddConstant, All, Any, ApproximateEqual, ArgMax, Assert, Assign, AssignGrad, AvgPoolGrad, BatchMatMul, BatchNormalization, BiRecurrent, BiasAdd, BiasAddGrad, BifurcateSplitTable, Bilinear, BinaryThreshold, BinaryTreeLSTM, Bottle, BroadcastGradientArgs, CAdd, CAddTable, CAveTable, CDivTable, CMaxTable, CMinTable, CMul, CMulTable, CSubTable, Cast, Ceil, Cell, Clamp, Compare, Concat, ConcatTable, ConcatV2LoadTF, Container, Contiguous, ControlOps, Conv2D, Conv2DBackFilter, Conv2DTranspose, Conv3D, Conv3DBackpropFilter, Conv3DBackpropFilterV2, Conv3DBackpropInput, Conv3DBackpropInputV2, ConvLSTMPeephole, ConvLSTMPeephole3D, Cosine, CosineDistance, Cropping2D, Cropping3D, CrossEntropy, DecodeGif, DecodeImage, DecodeJpeg, DecodePng, DecodeRaw, DenseToSparse, DepthwiseConv2D, DepthwiseConv2DBackpropFilter, DepthwiseConv2DBackpropInput, DetectionOutputFrcnn, DetectionOutputSSD, Digamma, Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput, DotProduct, Dropout, DynamicGraph, ELU, Echo, EluGrad, Equal, Erf, Erfc, Euclidean, Exp, Exp, ExpandDimsLoadTF, Expm1, FlattenTable, Floor, FloorDiv, FloorMod, FusedBatchNorm, FusedBatchNormGrad, GRU, GaussianDropout, GaussianNoise, GaussianSampler, GradientReversal, Graph, Greater, GreaterEqual, HardShrink, HardSigmoid, HardTanh, Identity, IdentityControl, InTopK, Index, InferReshape, Input, Inv, InvGrad, IsFinite, IsInf, IsNan, JoinTable, L1Penalty, L2Loss, LRNGrad, LSTM, LSTMPeephole, LeakyReLU, Less, LessEqual, Lgamma, Linear, LocallyConnected1D, LocallyConnected2D, Log, Log1p, LogSigmoid, LogSoftMax, LogicalAnd, LogicalNot, LogicalOr, LookupTable, LookupTableSparse, MM, MV, MapTable, MaskedSelect, Masking, Max, MaxPool, MaxPoolGrad, Maximum, Maxout, Mean, MeanLoadTF, Min, Minimum, MixtureTable, Mod, ModuleToOperation, Mul, MulConstant, MultiRNNCell, Narrow, NarrowTable, Negative, NoOp, Normalize, NormalizeScale, NotEqual, OneHot, Operation, PReLU, Pack, Pad, PadLoadTF, Padding, PairwiseDistance, ParallelTable, ParseExample, Pow, Power, PriorBox, Prod, ProdLoadTF, Proposal, QuantizedModule, RReLU, RandomUniform, RangeOps, Rank, ReLU, ReLU6, Recurrent, RecurrentDecoder, Relu6Grad, ReluGrad, Replicate, Reshape, ReshapeLoadTF, ResizeBilinear, ResizeBilinearOps, Reverse, Rint, RnnCell, RoiPooling, Round, RsqrtGrad, SReLU, Scale, SegmentSum, Select, Select, SelectTable, Sequential, Sigmoid, SigmoidGrad, Sign, Slice, SliceLoadTF, SoftMax, SoftMin, SoftPlus, SoftShrink, SoftSign, SoftplusGrad, SoftsignGrad, SparseJoinTable, SparseLinear, SpatialAveragePooling, SpatialBatchNormalization, SpatialContrastiveNormalization, SpatialConvolution, SpatialConvolutionMap, SpatialCrossMapLRN, SpatialDilatedConvolution, SpatialDivisiveNormalization, SpatialDropout1D, SpatialDropout2D, SpatialDropout3D, SpatialFullConvolution, SpatialMaxPooling, SpatialSeperableConvolution, SpatialShareConvolution, SpatialSubtractiveNormalization, SpatialWithinChannelLRN, SpatialZeroPadding, SplitLoadTF, SplitTable, Sqrt, SqrtGrad, Square, SquaredDifference, Squeeze, StaticGraph, StridedSliceLoadTF, Substr, Sum, Sum, Tanh, TanhGrad, TanhShrink, TemporalConvolution, TemporalMaxPooling, TensorModule, Threshold, Tile, Tile, TimeDistributed, TopK, TopKV2LoadTF, Transpose, TransposeLoadTF, TreeLSTM, TruncateDiv, TruncatedNormal, UnaryGrad, Unsqueeze, UpSampling1D, UpSampling2D, UpSampling3D, Variable, View, VolumetricAveragePooling, VolumetricConvolution, VolumetricFullConvolution, VolumetricMaxPooling
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Instance Constructors

  1. new AbstractModule()(implicit arg0: ClassTag[A], arg1: ClassTag[B], arg2: ClassTag[T], ev: TensorNumeric[T])

Abstract Value Members

  1. abstract def updateGradInput(input: A, gradOutput: B): A

    Computing the gradient of the module with respect to its own input.

    Computing the gradient of the module with respect to its own input. This is returned in gradInput. Also, the gradInput state variable is updated accordingly.

    input
    gradOutput
    returns

  2. abstract def updateOutput(input: A): B

    Computes the output using the current parameter set of the class and input.

    Computes the output using the current parameter set of the class and input. This function returns the result which is stored in the output field.

    input
    returns

Concrete Value Members

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  6. def accGradParameters(input: A, gradOutput: B): Unit

    Computing the gradient of the module with respect to its own parameters.

    Computing the gradient of the module with respect to its own parameters. Many modules do not perform this step as they do not have any parameters. The state variable name for the parameters is module dependent. The module is expected to accumulate the gradients with respect to the parameters in some variable.

    input
    gradOutput

  7. def apply(name: String): Option[AbstractModule[Activity, Activity, T]]

    Find a module with given name.

    Find a module with given name. If there is no module with given name, it will return None. If there are multiple modules with the given name, an exception will be thrown.

    name
    returns

  8. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  9. def backward(input: A, gradOutput: B): A

    Performs a back-propagation step through the module, with respect to the given input.

    Performs a back-propagation step through the module, with respect to the given input. In general this method makes the assumption forward(input) has been called before, with the same input. This is necessary for optimization reasons. If you do not respect this rule, backward() will compute incorrect gradients.

    input

    input data

    gradOutput

    gradient of next layer

    returns

    gradient corresponding to input data

  10. var backwardTime: Long

    Attributes
    protected

  11. def canEqual(other: Any): Boolean

  12. def checkEngineType(): AbstractModule.this.type

    get execution engine type

  13. def clearState(): AbstractModule.this.type

    Clear cached activities to save storage space or network bandwidth.

    Clear cached activities to save storage space or network bandwidth. Note that we use Tensor.set to keep some information like tensor share

    The subclass should override this method if it allocate some extra resource, and call the super.clearState in the override method

    returns

  14. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  15. def cloneModule(): AbstractModule[A, B, T]

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

    Definition Classes
    AnyRef

  17. def equals(other: Any): Boolean

    Definition Classes
    AbstractModule → AnyRef → Any

  18. def evaluate(dataSet: LocalDataSet[MiniBatch[T]], vMethods: Array[ValidationMethod[T]]): Array[(ValidationResult, ValidationMethod[T])]

  19. def evaluate(dataset: RDD[Sample[T]], vMethods: Array[ValidationMethod[T]], batchSize: Option[Int] = None): Array[(ValidationResult, ValidationMethod[T])]

    use ValidationMethod to evaluate module

    use ValidationMethod to evaluate module

    dataset

    dataset for test

    vMethods

    validation methods

    batchSize

    total batchsize of all partitions, optional param and default 4 * partitionNum of dataset

    returns

  20. def evaluate(): AbstractModule.this.type

  21. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  22. final def forward(input: A): B

    Takes an input object, and computes the corresponding output of the module.

    Takes an input object, and computes the corresponding output of the module. After a forward, the output state variable should have been updated to the new value.

    input

    input data

    returns

    output data

  23. var forwardTime: Long

    Attributes
    protected

  24. def freeze(names: String*): AbstractModule.this.type

    freeze the module, i.

    freeze the module, i.e. their parameters(weight/bias, if exists) are not changed in training process if names is not empty, set an array of layers that match the given names to be "freezed",

    names

    an array of layer names

    returns

    current graph model

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

    Definition Classes
    AnyRef → Any

  26. def getClassTagNumerics(): (Array[ClassTag[_]], Array[TensorNumeric[_]])

  27. def getExtraParameter(): Array[Tensor[T]]

    Get extra parameter in this module.

    Get extra parameter in this module. Extra parameter means the trainable parameters beside weight and bias. Such as runningMean and runningVar in BatchNormalization.

    The subclass should override this method if it has some parameters besides weight and bias.

    returns

    an array of tensor

  28. def getName(): String

    Get the module name, default name is className@namePostfix

    Get the module name, default name is className@namePostfix

    returns

  29. def getNamePostfix: String

  30. def getNumericType(): TensorDataType

    returns

    Float or Double

  31. def getParameters(): (Tensor[T], Tensor[T])

    This method compact all parameters and gradients of the model into two tensors.

    This method compact all parameters and gradients of the model into two tensors. So it's easier to use optim method

    returns

  32. def getParametersTable(): Table

    This function returns a table contains ModuleName, the parameter names and parameter value in this module.

    This function returns a table contains ModuleName, the parameter names and parameter value in this module. The result table is a structure of Table(ModuleName -> Table(ParameterName -> ParameterValue)), and the type is Table[String, Table[String, Tensor[T]]].

    For example, get the weight of a module named conv1: table[Table]("conv1")[Tensor[T]]("weight").

    Custom modules should override this function if they have parameters.

    returns

    Table

  33. def getPrintName(): String

    Attributes
    protected

  34. def getScaleB(): Double

    Get the scale of gradientBias

  35. def getScaleW(): Double

    Get the scale of gradientWeight

  36. def getTimes(): Array[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)]

  37. def getWeightsBias(): Array[Tensor[T]]

    Get weight and bias for the module

    Get weight and bias for the module

    returns

    array of weights and bias

  38. var gradInput: A

    The cached gradient of activities.

    The cached gradient of activities. So we don't compute it again when need it

  39. def hasName: Boolean

  40. def hashCode(): Int

    Definition Classes
    AbstractModule → AnyRef → Any

  41. def inputs(first: (ModuleNode[T], Int), nodesWithIndex: (ModuleNode[T], Int)*): ModuleNode[T]

    Build graph: some other modules point to current module

    Build graph: some other modules point to current module

    first

    distinguish from another inputs when input parameter list is empty

    nodesWithIndex

    upstream module nodes and the output tensor index. The start index is 1.

    returns

    node containing current module

  42. def inputs(nodes: Array[ModuleNode[T]]): ModuleNode[T]

    Build graph: some other modules point to current module

    Build graph: some other modules point to current module

    nodes

    upstream module nodes in an array

    returns

    node containing current module

  43. def inputs(nodes: ModuleNode[T]*): ModuleNode[T]

    Build graph: some other modules point to current module

    Build graph: some other modules point to current module

    nodes

    upstream module nodes

    returns

    node containing current module

  44. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  45. final def isTraining(): Boolean

  46. var line: String

    Attributes
    protected

  47. def loadModelWeights(srcModel: Module[Float], matchAll: Boolean = true): AbstractModule.this.type

    copy weights from another model, mapping by layer name

    copy weights from another model, mapping by layer name

    srcModel

    model to copy from

    matchAll

    whether to match all layers' weights and bias,

    returns

    current module

  48. def loadWeights(weightPath: String, matchAll: Boolean = true): AbstractModule.this.type

    load pretrained weights and bias to current module

    load pretrained weights and bias to current module

    weightPath

    file to store weights and bias

    matchAll

    whether to match all layers' weights and bias, if not, only load existing pretrained weights and bias

    returns

    current module

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

    Definition Classes
    AnyRef

  50. final def notify(): Unit

    Definition Classes
    AnyRef

  51. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  52. var output: B

    The cached output.

    The cached output. So we don't compute it again when need it

  53. def parameters(): (Array[Tensor[T]], Array[Tensor[T]])

    This function returns two arrays.

    This function returns two arrays. One for the weights and the other the gradients Custom modules should override this function if they have parameters

    returns

    (Array of weights, Array of grad)

  54. def predict(dataset: RDD[Sample[T]], batchSize: Int = 1, shareBuffer: Boolean = false): RDD[Activity]

    module predict, return the probability distribution

    module predict, return the probability distribution

    dataset

    dataset for prediction

    batchSize

    total batchSize for all partitions. if -1, default is 4 * partitionNumber of datatset

    shareBuffer

    whether to share same memory for each batch predict results

  55. def predictClass(dataset: RDD[Sample[T]], batchSize: Int = 1): RDD[Int]

    module predict, return the predict label

    module predict, return the predict label

    dataset

    dataset for prediction

    batchSize

    total batchSize for all partitions. if -1, default is 4 * partitionNumber of dataset

  56. def predictImage(imageFrame: ImageFrame, outputLayer: String = null, shareBuffer: Boolean = false, batchPerPartition: Int = 4, predictKey: String = ImageFeature.predict): ImageFrame

    model predict images, return imageFrame with predicted tensor

    model predict images, return imageFrame with predicted tensor

    imageFrame

    imageFrame that contains images

    outputLayer

    if outputLayer is not null, the output of layer that matches outputLayer will be used as predicted output

    shareBuffer

    whether to share same memory for each batch predict results

    batchPerPartition

    batch size per partition, default is 4

    predictKey

    key to store predicted result

    returns

  57. def quantize(): Module[T]

  58. def reset(): Unit

  59. def resetTimes(): Unit

  60. def saveCaffe(prototxtPath: String, modelPath: String, useV2: Boolean = true, overwrite: Boolean = false): AbstractModule.this.type

  61. def saveDefinition(path: String, overWrite: Boolean = false): AbstractModule.this.type

    Save this module definition to path.

    Save this module definition to path.

    path

    path to save module, local file system, HDFS and Amazon S3 is supported. HDFS path should be like "hdfs://[host]:[port]/xxx" Amazon S3 path should be like "s3a://bucket/xxx"

    overWrite

    if overwrite

    returns

    self

  62. def saveModule(path: String, weightPath: String = null, overWrite: Boolean = false): AbstractModule.this.type

    Save this module to path with protobuf format

    Save this module to path with protobuf format

    path

    path to save module, local file system, HDFS and Amazon S3 is supported. HDFS path should be like "hdfs://[host]:[port]/xxx" Amazon S3 path should be like "s3a://bucket/xxx"

    weightPath

    where to store weight

    overWrite

    if overwrite

    returns

    self

  63. def saveTF(inputs: Seq[(String, Seq[Int])], path: String, byteOrder: ByteOrder = ByteOrder.LITTLE_ENDIAN, dataFormat: TensorflowDataFormat = TensorflowDataFormat.NHWC): AbstractModule.this.type

  64. def saveTorch(path: String, overWrite: Boolean = false): AbstractModule.this.type

  65. def saveWeights(path: String, overWrite: Boolean): Unit

    save weights and bias to file

    save weights and bias to file

    path

    file to save

    overWrite

    whether to overwrite or not

  66. var scaleB: Double

    Attributes
    protected

  67. var scaleW: Double

    The scale of gradient weight and gradient bias before gradParameters being accumulated.

    The scale of gradient weight and gradient bias before gradParameters being accumulated.

    Attributes
    protected

  68. def setExtraParameter(extraParam: Array[Tensor[T]]): AbstractModule.this.type

    Set extra parameter to this module.

    Set extra parameter to this module. Extra parameter means the trainable parameters beside weight and bias. Such as runningMean and runningVar in BatchNormalization.

    returns

    this

  69. def setLine(line: String): AbstractModule.this.type

  70. def setName(name: String): AbstractModule.this.type

    Set the module name

    Set the module name

    name
    returns

  71. def setNamePostfix(namePostfix: String): Unit

  72. def setScaleB(b: Double): AbstractModule.this.type

    Set the scale of gradientBias

    Set the scale of gradientBias

    b

    the value of the scale of gradientBias

    returns

    this

  73. def setScaleW(w: Double): AbstractModule.this.type

    Set the scale of gradientWeight

    Set the scale of gradientWeight

    w

    the value of the scale of gradientWeight

    returns

    this

  74. def setWeightsBias(newWeights: Array[Tensor[T]]): AbstractModule.this.type

    Set weight and bias for the module

    Set weight and bias for the module

    newWeights

    array of weights and bias

    returns

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

    Definition Classes
    AnyRef

  76. def toGraph(startNodes: ModuleNode[T]*): Graph[T]

    Generate graph module with start nodes

    Generate graph module with start nodes

    startNodes
    returns

  77. def toString(): String

    Definition Classes
    AbstractModule → AnyRef → Any

  78. var train: Boolean

    Module status.

    Module status. It is useful for modules like dropout/batch normalization

    Attributes
    protected

  79. def training(): AbstractModule.this.type

  80. def unFreeze(names: String*): AbstractModule.this.type

    "unfreeze" module, i.

    "unfreeze" module, i.e. make the module parameters(weight/bias, if exists) to be trained(updated) in training process if names is not empty, unfreeze layers that match given names

    names

    array of module names to unFreeze

  81. def updateParameters(learningRate: T): Unit

  82. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  85. def zeroGradParameters(): Unit

    If the module has parameters, this will zero the accumulation of the gradients with respect to these parameters.

    If the module has parameters, this will zero the accumulation of the gradients with respect to these parameters. Otherwise, it does nothing.

Deprecated Value Members

  1. def save(path: String, overWrite: Boolean = false): AbstractModule.this.type

    Save this module to path.

    Save this module to path.

    path

    path to save module, local file system, HDFS and Amazon S3 is supported. HDFS path should be like "hdfs://[host]:[port]/xxx" Amazon S3 path should be like "s3a://bucket/xxx"

    overWrite

    if overwrite

    returns

    self

    Annotations
    @deprecated
    Deprecated

    please use recommended saveModule(path, overWrite)

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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