com.intel.analytics.bigdl.nn

LookupTable

class LookupTable[T] extends TensorModule[T] with Initializable

This layer is a particular case of a convolution, where the width of the convolution would be 1. Input should be a 1D or 2D tensor filled with indices. Indices are corresponding to the position in weight. For each index element of input, it outputs the selected index part of weight. This layer is often used in word embedding.

T

The numeric type in the criterion, usually which are Float or Double

Annotations
@SerialVersionUID( 4832171200145114633L )
Linear Supertypes
Initializable, TensorModule[T], AbstractModule[Tensor[T], Tensor[T], T], Serializable, Serializable, AnyRef, Any
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  1. LookupTable
  2. Initializable
  3. TensorModule
  4. AbstractModule
  5. Serializable
  6. Serializable
  7. AnyRef
  8. Any
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Instance Constructors

  1. new LookupTable(nIndex: Int, nOutput: Int, paddingValue: Double = 0, maxNorm: Double = scala.Double.MaxValue, normType: Double = 2.0, shouldScaleGradByFreq: Boolean = false, wRegularizer: Regularizer[T] = null)(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

    nIndex

    Indices of input row

    nOutput

    the last dimension size of output

    paddingValue

    padding value, default 0

    maxNorm

    max norm, defalt Double.MaxValue

    normType

    norm regularization number, default 2

    shouldScaleGradByFreq

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: Tensor[T], gradOutput: Tensor[T]): 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

    Definition Classes
    LookupTableAbstractModule
  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

    Definition Classes
    AbstractModule
  8. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  9. def backward(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T]

    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

    Definition Classes
    AbstractModule
  10. var backwardTime: Long

    Attributes
    protected
    Definition Classes
    AbstractModule
  11. var biasInitMethod: InitializationMethod

    Attributes
    protected
    Definition Classes
    Initializable
  12. def canEqual(other: Any): Boolean

    Definition Classes
    LookupTableAbstractModule
  13. def checkEngineType(): LookupTable.this.type

    get execution engine type

    get execution engine type

    Definition Classes
    AbstractModule
  14. def clearState(): LookupTable.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

    Definition Classes
    LookupTableAbstractModule
  15. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  16. def cloneModule(): AbstractModule[Tensor[T], Tensor[T], T]

    Definition Classes
    AbstractModule
  17. def copyStatus(src: Module[T]): LookupTable.this.type

    Copy the useful running status from src to this.

    Copy the useful running status from src to this.

    The subclass should override this method if it has some parameters besides weight and bias. Such as runningMean and runningVar of BatchNormalization.

    src

    source Module

    returns

    this

    Definition Classes
    AbstractModule
  18. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  19. def equals(other: Any): Boolean

    Definition Classes
    LookupTableAbstractModule → AnyRef → Any
  20. def evaluate(dataSet: LocalDataSet[MiniBatch[T]], vMethods: Array[ValidationMethod[T]]): Array[(ValidationResult, ValidationMethod[T])]

    Definition Classes
    AbstractModule
  21. 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

    Definition Classes
    AbstractModule
  22. def evaluate(): LookupTable.this.type

    Definition Classes
    AbstractModule
  23. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  24. final def forward(input: Tensor[T]): Tensor[T]

    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

    Definition Classes
    AbstractModule
  25. var forwardTime: Long

    Attributes
    protected
    Definition Classes
    AbstractModule
  26. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  27. def getName(): String

    Get the module name, default name is className@namePostfix

    Get the module name, default name is className@namePostfix

    returns

    Definition Classes
    AbstractModule
  28. def getNumericType(): TensorDataType

    returns

    Float or Double

    Definition Classes
    AbstractModule
  29. 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

    Definition Classes
    AbstractModule
  30. 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

    Definition Classes
    LookupTableAbstractModule
  31. def getPrintName(): String

    Attributes
    protected
    Definition Classes
    AbstractModule
  32. def getScaleB(): Double

    Get the scale of gradientBias

    Get the scale of gradientBias

    Definition Classes
    AbstractModule
  33. def getScaleW(): Double

    Get the scale of gradientWeight

    Get the scale of gradientWeight

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

    Definition Classes
    AbstractModule
  35. 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

    Definition Classes
    AbstractModule
  36. var gradInput: Tensor[T]

    The cached gradient of activities.

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

    Definition Classes
    AbstractModule
  37. val gradWeight: Tensor[T]

  38. def hashCode(): Int

    Definition Classes
    LookupTableAbstractModule → AnyRef → Any
  39. def inputs(nodes: ModuleNode[T]*): ModuleNode[T]

    Some other modules point to current module

    Some other modules point to current module

    nodes

    upstream module nodes

    returns

    node containing current module

    Definition Classes
    AbstractModule
  40. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  41. final def isTraining(): Boolean

    Definition Classes
    AbstractModule
  42. var line: String

    Attributes
    protected
    Definition Classes
    AbstractModule
  43. def loadModelWeights(srcModel: Module[Float], matchAll: Boolean = true): LookupTable.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

    Definition Classes
    AbstractModule
  44. def loadWeights(weightPath: String, matchAll: Boolean = true): LookupTable.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

    Definition Classes
    AbstractModule
  45. val maxNorm: Double

    max norm, defalt Double.

    max norm, defalt Double.MaxValue

  46. val nIndex: Int

    Indices of input row

  47. val nOutput: Int

    the last dimension size of output

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

    Definition Classes
    AnyRef
  49. val normType: Double

    norm regularization number, default 2

  50. final def notify(): Unit

    Definition Classes
    AnyRef
  51. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  52. var output: Tensor[T]

    The cached output.

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

    Definition Classes
    AbstractModule
  53. val paddingValue: Double

    padding value, default 0

  54. 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)

    Definition Classes
    LookupTableAbstractModule
  55. def predict(dataset: RDD[Sample[T]]): RDD[Activity]

    module predict, return the probability distribution

    module predict, return the probability distribution

    dataset

    dataset for prediction

    Definition Classes
    AbstractModule
  56. def predictClass(dataset: RDD[Sample[T]]): RDD[Int]

    module predict, return the predict label

    module predict, return the predict label

    dataset

    dataset for prediction

    Definition Classes
    AbstractModule
  57. def reset(): Unit

    Definition Classes
    LookupTableInitializableAbstractModule
  58. def resetTimes(): Unit

    Definition Classes
    AbstractModule
  59. def save(path: String, overWrite: Boolean = false): LookupTable.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

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

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

    Definition Classes
    AbstractModule
  62. def saveTorch(path: String, overWrite: Boolean = false): LookupTable.this.type

    Definition Classes
    AbstractModule
  63. 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

    Definition Classes
    AbstractModule
  64. var scaleB: Double

    Attributes
    protected
    Definition Classes
    AbstractModule
  65. 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
    Definition Classes
    AbstractModule
  66. def setInitMethod(weightInitMethod: InitializationMethod = null, biasInitMethod: InitializationMethod = null): LookupTable.this.type

    Definition Classes
    Initializable
  67. def setLine(line: String): LookupTable.this.type

    Definition Classes
    AbstractModule
  68. def setName(name: String): LookupTable.this.type

    Set the module name

    Set the module name

    name
    returns

    Definition Classes
    AbstractModule
  69. def setScaleB(b: Double): LookupTable.this.type

    Set the scale of gradientBias

    Set the scale of gradientBias

    b

    the value of the scale of gradientBias

    returns

    this

    Definition Classes
    AbstractModule
  70. def setScaleW(w: Double): LookupTable.this.type

    Set the scale of gradientWeight

    Set the scale of gradientWeight

    w

    the value of the scale of gradientWeight

    returns

    this

    Definition Classes
    AbstractModule
  71. def setWeightsBias(newWeights: Array[Tensor[T]]): LookupTable.this.type

    Set weight and bias for the module

    Set weight and bias for the module

    newWeights

    array of weights and bias

    returns

    Definition Classes
    AbstractModule
  72. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  73. def toString(): String

    Definition Classes
    LookupTableAbstractModule → AnyRef → Any
  74. var train: Boolean

    Module status.

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

    Attributes
    protected
    Definition Classes
    AbstractModule
  75. def training(): LookupTable.this.type

    Definition Classes
    AbstractModule
  76. def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T]

    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

    Definition Classes
    LookupTableAbstractModule
  77. def updateOutput(input: Tensor[T]): Tensor[T]

    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

    Definition Classes
    LookupTableAbstractModule
  78. def updateParameters(learningRate: T): Unit

    Definition Classes
    AbstractModule
  79. var wRegularizer: Regularizer[T]

  80. final def wait(): Unit

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  83. val weight: Tensor[T]

  84. var weightInitMethod: InitializationMethod

    Attributes
    protected
    Definition Classes
    Initializable
  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.

    Definition Classes
    LookupTableAbstractModule

Inherited from Initializable

Inherited from TensorModule[T]

Inherited from AbstractModule[Tensor[T], Tensor[T], T]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped