Class/Object

com.intel.analytics.bigdl.nn

NormalizeScale

Related Docs: object NormalizeScale | package nn

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class NormalizeScale[T] extends TensorModule[T]

NormalizeScale is conposed of normalize and scale, this is equal to caffe Normalize layer

T

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

Annotations
@SerialVersionUID()
Linear Supertypes
TensorModule[T], AbstractModule[Tensor[T], Tensor[T], T], InferShape, Serializable, Serializable, AnyRef, Any
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Inherited
  1. NormalizeScale
  2. TensorModule
  3. AbstractModule
  4. InferShape
  5. Serializable
  6. Serializable
  7. AnyRef
  8. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new NormalizeScale(p: Double, eps: Double = 1e-10, scale: Double, size: Array[Int], wRegularizer: Regularizer[T] = null)(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

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    p

    L_p norm

    eps

    smoothing parameter

    scale

    scale parameter

    size

    size of scale input

    wRegularizer

    weight regularizer

Value Members

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

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

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

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    Definition Classes
    AnyRef → Any
  4. def accGradParameters(input: Tensor[T], gradOutput: Tensor[T]): Unit

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    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.

    Definition Classes
    NormalizeScaleAbstractModule
  5. def apply(name: String): Option[AbstractModule[Activity, Activity, T]]

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    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.

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

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

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    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
  8. var backwardTime: Long

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    Attributes
    protected
    Definition Classes
    AbstractModule
  9. def clearState(): NormalizeScale.this.type

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    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

    Definition Classes
    AbstractModule
  10. final def clone(deepCopy: Boolean): AbstractModule[Tensor[T], Tensor[T], T]

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    Clone the module, deep or shallow copy

    Clone the module, deep or shallow copy

    Definition Classes
    AbstractModule
  11. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  12. final def cloneModule(): NormalizeScale.this.type

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    Clone the model

    Clone the model

    Definition Classes
    AbstractModule
  13. val cmul: CMul[T]

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  14. def computeOutputShape(inputShape: Shape): Shape

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    We suppose the first dim is batch

    We suppose the first dim is batch

    Definition Classes
    NormalizeScaleInferShape
  15. val eps: Double

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    smoothing parameter

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

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

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    Definition Classes
    AbstractModule → AnyRef → Any
  18. final def evaluate(dataSet: LocalDataSet[MiniBatch[T]], vMethods: Array[_ <: ValidationMethod[T]]): Array[(ValidationResult, ValidationMethod[T])]

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    use ValidationMethod to evaluate module on the given local dataset

    use ValidationMethod to evaluate module on the given local dataset

    Definition Classes
    AbstractModule
  19. final def evaluate(dataset: RDD[MiniBatch[T]], vMethods: Array[_ <: ValidationMethod[T]]): Array[(ValidationResult, ValidationMethod[T])]

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    use ValidationMethod to evaluate module on the given rdd dataset

    use ValidationMethod to evaluate module on the given rdd dataset

    Definition Classes
    AbstractModule
  20. final def evaluate(dataset: RDD[Sample[T]], vMethods: Array[_ <: ValidationMethod[T]], batchSize: Option[Int] = None): Array[(ValidationResult, ValidationMethod[T])]

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    use ValidationMethod to evaluate module on the given rdd dataset

    use ValidationMethod to evaluate module on the given rdd dataset

    dataset

    dataset for test

    vMethods

    validation methods

    batchSize

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

    Definition Classes
    AbstractModule
  21. def evaluate(): NormalizeScale.this.type

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    Set the module to evaluate mode

    Set the module to evaluate mode

    Definition Classes
    AbstractModule
  22. final def evaluateImage(imageFrame: ImageFrame, vMethods: Array[_ <: ValidationMethod[T]], batchSize: Option[Int] = None): Array[(ValidationResult, ValidationMethod[T])]

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    use ValidationMethod to evaluate module on the given ImageFrame

    use ValidationMethod to evaluate module on the given ImageFrame

    imageFrame

    ImageFrame for valudation

    vMethods

    validation methods

    batchSize

    total batch size of all partitions

    Definition Classes
    AbstractModule
  23. def finalize(): Unit

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

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    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

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    Attributes
    protected
    Definition Classes
    AbstractModule
  26. def freeze(names: String*): NormalizeScale.this.type

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    freeze the module, i.e.

    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

    Definition Classes
    AbstractModule
  27. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  28. def getExtraParameter(): Array[Tensor[T]]

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    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

    Definition Classes
    AbstractModule
  29. final def getInputShape(): Shape

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    Return the inputShape for the current Layer and the first dim is batch.

    Return the inputShape for the current Layer and the first dim is batch.

    Definition Classes
    InferShape
  30. final def getName(): String

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    Get the module name, default name is className@namePostfix

    Get the module name, default name is className@namePostfix

    Definition Classes
    AbstractModule
  31. final def getNumericType(): TensorDataType

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    Get numeric type of module parameters

    Get numeric type of module parameters

    Definition Classes
    AbstractModule
  32. final def getOutputShape(): Shape

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    Return the outputShape for the current Layer and the first dim is batch.

    Return the outputShape for the current Layer and the first dim is batch.

    Definition Classes
    InferShape
  33. def getParametersTable(): Table

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    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").

    The names of the parameters follow such convention:

    1. If there's one parameter, the parameter is named as "weight", the gradient is named as "gradWeight"

    2. If there're two parameters, the first parameter is named as "weight", the first gradient is named as "gradWeight"; the second parameter is named as "bias", the seconcd gradient is named as "gradBias"

    3. If there're more parameters, the weight is named as "weight" with a seq number as suffix, the gradient is named as "gradient" with a seq number as suffix

    Custom modules should override this function the default impl if the convention doesn't meet the requirement.

    returns

    Table

    Definition Classes
    AbstractModule
  34. final def getPrintName(): String

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

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    Get the scale of gradientBias

    Get the scale of gradientBias

    Definition Classes
    AbstractModule
  36. final def getScaleW(): Double

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    Get the scale of gradientWeight

    Get the scale of gradientWeight

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

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    Get the forward/backward cost time for the module or its submodules

    Get the forward/backward cost time for the module or its submodules

    Definition Classes
    AbstractModule
  38. final def getTimesGroupByModuleType(): Array[(String, Long, Long)]

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    Get the forward/backward cost time for the module or its submodules and group by module type.

    Get the forward/backward cost time for the module or its submodules and group by module type.

    returns

    (module type name, forward time, backward time)

    Definition Classes
    AbstractModule
  39. final def getWeightsBias(): Array[Tensor[T]]

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    Get weight and bias for the module

    Get weight and bias for the module

    returns

    array of weights and bias

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

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    The cached gradient of activities.

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

    Definition Classes
    AbstractModule
  41. final def hasName: Boolean

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    Whether user set a name to the module before

    Whether user set a name to the module before

    Definition Classes
    AbstractModule
  42. def hashCode(): Int

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    Definition Classes
    AbstractModule → AnyRef → Any
  43. def inputs(first: (ModuleNode[T], Int), nodesWithIndex: (ModuleNode[T], Int)*): ModuleNode[T]

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    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

    Definition Classes
    AbstractModule
  44. def inputs(nodes: Array[ModuleNode[T]]): ModuleNode[T]

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    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

    Definition Classes
    AbstractModule
  45. def inputs(nodes: ModuleNode[T]*): ModuleNode[T]

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    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

    Definition Classes
    AbstractModule
  46. var inputsFormats: Seq[Int]

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    Attributes
    protected
    Definition Classes
    AbstractModule
  47. final def isInstanceOf[T0]: Boolean

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

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    Check if the model is in training mode

    Check if the model is in training mode

    Definition Classes
    AbstractModule
  49. var line: String

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    Attributes
    protected
    Definition Classes
    AbstractModule
  50. final def loadModelWeights(srcModel: Module[Float], matchAll: Boolean = true): NormalizeScale.this.type

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    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
  51. final def loadWeights(weightPath: String, matchAll: Boolean = true): NormalizeScale.this.type

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

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    Definition Classes
    AnyRef
  53. val normalize: Normalize[T]

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  54. final def notify(): Unit

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

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

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    The cached output.

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

    Definition Classes
    AbstractModule
  57. var outputsFormats: Seq[Int]

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    Attributes
    protected
    Definition Classes
    AbstractModule
  58. val p: Double

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    L_p norm

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

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

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    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

    Definition Classes
    AbstractModule
  61. final def predictClass(dataset: RDD[Sample[T]], batchSize: Int = 1): RDD[Int]

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    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

    Definition Classes
    AbstractModule
  62. final def predictImage(imageFrame: ImageFrame, outputLayer: String = null, shareBuffer: Boolean = false, batchPerPartition: Int = 4, predictKey: String = ImageFeature.predict, featurePaddingParam: Option[PaddingParam[T]] = None): ImageFrame

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    model predict images, return imageFrame with predicted tensor, if you want to call predictImage multiple times, it is recommended to use Predictor for DistributedImageFrame or LocalPredictor for LocalImageFrame

    model predict images, return imageFrame with predicted tensor, if you want to call predictImage multiple times, it is recommended to use Predictor for DistributedImageFrame or LocalPredictor for LocalImageFrame

    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

    featurePaddingParam

    featurePaddingParam if the inputs have variant size

    Definition Classes
    AbstractModule
  63. def processInputs(first: (ModuleNode[T], Int), nodesWithIndex: (ModuleNode[T], Int)*): ModuleNode[T]

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    Attributes
    protected
    Definition Classes
    AbstractModule
  64. def processInputs(nodes: Seq[ModuleNode[T]]): ModuleNode[T]

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    Attributes
    protected
    Definition Classes
    AbstractModule
  65. final def quantize(): Module[T]

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    Quantize this module, which reduces the precision of the parameter.

    Quantize this module, which reduces the precision of the parameter. Get a higher speed with a little accuracy cost.

    Definition Classes
    AbstractModule
  66. def release(): Unit

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    if the model contains native resources such as aligned memory, we should release it by manual.

    if the model contains native resources such as aligned memory, we should release it by manual. JVM GC can't release them reliably.

    Definition Classes
    AbstractModule
  67. def reset(): Unit

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    Reset module parameters, which is re-initialize the parameter with given initMethod

    Reset module parameters, which is re-initialize the parameter with given initMethod

    Definition Classes
    AbstractModule
  68. def resetTimes(): Unit

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    Reset the forward/backward record time for the module or its submodules

    Reset the forward/backward record time for the module or its submodules

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

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    Save this module to path in caffe readable format

    Save this module to path in caffe readable format

    Definition Classes
    AbstractModule
  70. final def saveDefinition(path: String, overWrite: Boolean = false): NormalizeScale.this.type

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    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

    Definition Classes
    AbstractModule
  71. final def saveModule(path: String, weightPath: String = null, overWrite: Boolean = false): NormalizeScale.this.type

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    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

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

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    Save this module to path in tensorflow readable format

    Save this module to path in tensorflow readable format

    Definition Classes
    AbstractModule
  73. final def saveTorch(path: String, overWrite: Boolean = false): NormalizeScale.this.type

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    Save this module to path in torch7 readable format

    Save this module to path in torch7 readable format

    Definition Classes
    AbstractModule
  74. final def saveWeights(path: String, overWrite: Boolean): Unit

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    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
  75. val scale: Double

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    scale parameter

  76. var scaleB: Double

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    Attributes
    protected
    Definition Classes
    AbstractModule
  77. var scaleW: Double

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    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
  78. def setExtraParameter(extraParam: Array[Tensor[T]]): NormalizeScale.this.type

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    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

    Definition Classes
    AbstractModule
  79. def setInputFormats(formats: Seq[Int]): NormalizeScale.this.type

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    set input formats for graph

    set input formats for graph

    Definition Classes
    AbstractModule
  80. final def setLine(line: String): NormalizeScale.this.type

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    Set the line separator when print the module

    Set the line separator when print the module

    Definition Classes
    AbstractModule
  81. final def setName(name: String): NormalizeScale.this.type

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    Set the module name

    Set the module name

    Definition Classes
    AbstractModule
  82. def setOutputFormats(formats: Seq[Int]): NormalizeScale.this.type

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    set output formats for graph

    set output formats for graph

    Definition Classes
    AbstractModule
  83. def setScaleB(b: Double): NormalizeScale.this.type

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    Set the scale of gradientBias

    Set the scale of gradientBias

    b

    the value of the scale of gradientBias

    returns

    this

    Definition Classes
    AbstractModule
  84. def setScaleW(w: Double): NormalizeScale.this.type

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    Set the scale of gradientWeight

    Set the scale of gradientWeight

    w

    the value of the scale of gradientWeight

    returns

    this

    Definition Classes
    NormalizeScaleAbstractModule
  85. final def setWeightsBias(newWeights: Array[Tensor[T]]): NormalizeScale.this.type

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    Set weight and bias for the module

    Set weight and bias for the module

    newWeights

    array of weights and bias

    Definition Classes
    AbstractModule
  86. val size: Array[Int]

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    size of scale input

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

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    Definition Classes
    AnyRef
  88. def toGraph(startNodes: ModuleNode[T]*): Graph[T]

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    Generate graph module with start nodes

    Generate graph module with start nodes

    Definition Classes
    AbstractModule
  89. def toString(): String

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    Definition Classes
    AbstractModule → AnyRef → Any
  90. var train: Boolean

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    Module status.

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

    Attributes
    protected
    Definition Classes
    AbstractModule
  91. def training(): NormalizeScale.this.type

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    Set the module to training mode

    Set the module to training mode

    Definition Classes
    AbstractModule
  92. def unFreeze(names: String*): NormalizeScale.this.type

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    "unfreeze" module, i.e.

    "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

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

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    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.

    Definition Classes
    NormalizeScaleAbstractModule
  94. def updateOutput(input: Tensor[T]): Tensor[T]

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    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.

    Definition Classes
    NormalizeScaleAbstractModule
  95. var wRegularizer: Regularizer[T]

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    weight regularizer

  96. final def wait(): Unit

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

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  99. def zeroGradParameters(): Unit

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    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
    AbstractModule

Deprecated Value Members

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

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    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
    Annotations
    @deprecated
    Deprecated

    (Since version 0.3.0) please use recommended saveModule(path, overWrite)

Inherited from TensorModule[T]

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

Inherited from InferShape

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped