com.intel.analytics.bigdl.nn

LocallyConnected2D

class LocallyConnected2D[T] extends TensorModule[T] with Initializable

The LocallyConnected2D layer works similarly to the SpatialConvolution layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input.

T

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

Linear Supertypes
Initializable, TensorModule[T], AbstractModule[Tensor[T], Tensor[T], T], Serializable, Serializable, AnyRef, Any
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  1. LocallyConnected2D
  2. Initializable
  3. TensorModule
  4. AbstractModule
  5. Serializable
  6. Serializable
  7. AnyRef
  8. Any
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Instance Constructors

  1. new LocallyConnected2D(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: Tensor[T] = null, initBias: Tensor[T] = null, initGradWeight: Tensor[T] = null, initGradBias: Tensor[T] = null, withBias: Boolean = true, format: DataFormat = ...)(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

    nInputPlane

    The number of expected input planes in the image given into forward()

    inputWidth

    The input width

    inputHeight

    The input height

    nOutputPlane

    The number of output planes the convolution layer will produce.

    kernelW

    The kernel width of the convolution

    kernelH

    The kernel height of the convolution

    strideW

    The step of the convolution in the width dimension

    strideH

    The step of the convolution in the height dimension

    padW

    The additional zeros added per width to the input planes

    padH

    The additional zeros added per height to the input planes

    propagateBack

    propagate gradient back

    wRegularizer

    weight regularizer

    bRegularizer

    bias regularizer

    initWeight

    initial weight

    initBias

    initial bias

    initGradWeight

    initial gradient weight

    initGradBias

    initial gradient bias

    withBias

    if has bias

    format

    data format NCHW, NHWC

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. val _1x1: Boolean

    Attributes
    protected
  7. 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
    LocallyConnected2DAbstractModule
  8. def accGradParametersFrame(gradOutput: Tensor[T], gradWeight: Tensor[T], gradBias: Tensor[T], fInput: Tensor[T], scaleW: T, scaleB: T)(implicit ev: TensorNumeric[T]): Unit

    Attributes
    protected
  9. 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
  10. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  11. var bRegularizer: Regularizer[T]

    bias regularizer

  12. 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
  13. var backwardTime: Long

    Attributes
    protected
    Definition Classes
    AbstractModule
  14. val bias: Tensor[T]

  15. var biasInitMethod: InitializationMethod

    Attributes
    protected
    Definition Classes
    Initializable
  16. def calcGradParametersFrame(gradOutput: Tensor[T], gradWeight: Tensor[T], gradBias: Tensor[T], fInput: Tensor[T], scaleW: T, scaleB: T)(implicit ev: TensorNumeric[T]): Unit

    Attributes
    protected
  17. def canEqual(other: Any): Boolean

    Definition Classes
    AbstractModule
  18. def checkEngineType(): LocallyConnected2D.this.type

    get execution engine type

    get execution engine type

    Definition Classes
    AbstractModule
  19. def clearState(): LocallyConnected2D.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
    LocallyConnected2DAbstractModule
  20. def clone(): AnyRef

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

    Definition Classes
    AbstractModule
  22. var col2imTime: Long

    Attributes
    protected
  23. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  24. def equals(obj: Any): Boolean

    Definition Classes
    LocallyConnected2DAbstractModule → AnyRef → Any
  25. def evaluate(dataSet: LocalDataSet[MiniBatch[T]], vMethods: Array[ValidationMethod[T]]): Array[(ValidationResult, ValidationMethod[T])]

    Definition Classes
    AbstractModule
  26. 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
  27. def evaluate(): LocallyConnected2D.this.type

    Definition Classes
    AbstractModule
  28. var fGradInput: Tensor[T]

  29. var fInput: Tensor[T]

  30. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  31. val format: DataFormat

    data format NCHW, NHWC

  32. 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
  33. var forwardTime: Long

    Attributes
    protected
    Definition Classes
    AbstractModule
  34. def freeze(names: String*): LocallyConnected2D.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

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

    Definition Classes
    AnyRef → Any
  36. def getClassTagNumerics(): (Array[ClassTag[_]], Array[TensorNumeric[_]])

    Definition Classes
    AbstractModule
  37. def getCol2ImgTime(): Double

  38. 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

    Definition Classes
    AbstractModule
  39. def getIm2ColTime(): Double

  40. 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
  41. def getNamePostfix: String

    Definition Classes
    AbstractModule
  42. def getNumericType(): TensorDataType

    returns

    Float or Double

    Definition Classes
    AbstractModule
  43. def getPadding(inputHeight: Int, inputWidth: Int): (Int, Int, Int, Int)

    Attributes
    protected
  44. 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
  45. 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
    LocallyConnected2DAbstractModule
  46. def getPrintName(): String

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

    Get the scale of gradientBias

    Get the scale of gradientBias

    Definition Classes
    AbstractModule
  48. def getScaleW(): Double

    Get the scale of gradientWeight

    Get the scale of gradientWeight

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

    Definition Classes
    AbstractModule
  50. 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
  51. val gradBias: Tensor[T]

  52. var gradBiasWindow: Tensor[T]

    Attributes
    protected
  53. 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
  54. val gradWeight: Tensor[T]

  55. var gradWeightMM: Tensor[T]

    Attributes
    protected
  56. var gradWeightMMInBatch: Tensor[T]

    Attributes
    protected
  57. val gradientBiasMT: Tensor[T]

    Attributes
    protected
  58. def hasName: Boolean

    Definition Classes
    AbstractModule
  59. def hashCode(): Int

    Definition Classes
    LocallyConnected2DAbstractModule → AnyRef → Any
  60. var im2colTime: Long

    Attributes
    protected
  61. val initBias: Tensor[T]

    initial bias

  62. val initGradBias: Tensor[T]

    initial gradient bias

  63. val initGradWeight: Tensor[T]

    initial gradient weight

  64. val initWeight: Tensor[T]

    initial weight

  65. val inputHeight: Int

    The input height

  66. val inputWidth: Int

    The input width

  67. 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

    Definition Classes
    AbstractModule
  68. 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

    Definition Classes
    AbstractModule
  69. 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

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

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

    Definition Classes
    AbstractModule
  72. val kernelH: Int

    The kernel height of the convolution

  73. val kernelW: Int

    The kernel width of the convolution

  74. var line: String

    Attributes
    protected
    Definition Classes
    AbstractModule
  75. def loadModelWeights(srcModel: Module[Float], matchAll: Boolean = true): LocallyConnected2D.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
  76. def loadWeights(weightPath: String, matchAll: Boolean = true): LocallyConnected2D.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
  77. val nInputPlane: Int

    The number of expected input planes in the image given into forward()

  78. val nOutputPlane: Int

    The number of output planes the convolution layer will produce.

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

    Definition Classes
    AnyRef
  80. final def notify(): Unit

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

    Definition Classes
    AnyRef
  82. val ones: Tensor[T]

    Attributes
    protected
  83. val onesBatch: Tensor[T]

    Attributes
    protected
  84. val onesBias: Tensor[T]

    Attributes
    protected
  85. var output: Tensor[T]

    The cached output.

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

    Definition Classes
    AbstractModule
  86. val outputHeight: Int

  87. val outputWidth: Int

  88. val padBottom: Int

  89. val padH: Int

    The additional zeros added per height to the input planes

  90. val padLeft: Int

  91. val padRight: Int

  92. val padTop: Int

  93. val padW: Int

    The additional zeros added per width to the input planes

  94. 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
    LocallyConnected2DAbstractModule
  95. 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

    Definition Classes
    AbstractModule
  96. 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

    Definition Classes
    AbstractModule
  97. 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

    Definition Classes
    AbstractModule
  98. val propagateBack: Boolean

    propagate gradient back

  99. def quantize(): Module[T]

    Definition Classes
    AbstractModule
  100. def reset(): Unit

  101. def resetTimes(): Unit

    Definition Classes
    AbstractModule
  102. var results: Array[Future[Unit]]

    Attributes
    protected
  103. def saveCaffe(prototxtPath: String, modelPath: String, useV2: Boolean = true, overwrite: Boolean = false): LocallyConnected2D.this.type

    Definition Classes
    AbstractModule
  104. def saveDefinition(path: String, overWrite: Boolean = false): LocallyConnected2D.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

    Definition Classes
    AbstractModule
  105. def saveModule(path: String, weightPath: String = null, overWrite: Boolean = false): LocallyConnected2D.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

    Definition Classes
    AbstractModule
  106. def saveTF(inputs: Seq[(String, Seq[Int])], path: String, byteOrder: ByteOrder = ByteOrder.LITTLE_ENDIAN, dataFormat: TensorflowDataFormat = TensorflowDataFormat.NHWC): LocallyConnected2D.this.type

    Definition Classes
    AbstractModule
  107. def saveTorch(path: String, overWrite: Boolean = false): LocallyConnected2D.this.type

    Definition Classes
    AbstractModule
  108. 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
  109. var scaleB: Double

    Attributes
    protected
    Definition Classes
    AbstractModule
  110. 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
  111. def setExtraParameter(extraParam: Array[Tensor[T]]): LocallyConnected2D.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

    Definition Classes
    AbstractModule
  112. def setInitMethod(weightInitMethod: InitializationMethod = null, biasInitMethod: InitializationMethod = null): LocallyConnected2D.this.type

    Definition Classes
    Initializable
  113. def setLine(line: String): LocallyConnected2D.this.type

    Definition Classes
    AbstractModule
  114. def setName(name: String): LocallyConnected2D.this.type

    Set the module name

    Set the module name

    name
    returns

    Definition Classes
    AbstractModule
  115. def setNamePostfix(namePostfix: String): Unit

    Definition Classes
    AbstractModule
  116. def setScaleB(b: Double): LocallyConnected2D.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
  117. def setScaleW(w: Double): LocallyConnected2D.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
  118. def setWeightsBias(newWeights: Array[Tensor[T]]): LocallyConnected2D.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
  119. val sizes: Array[Int]

  120. val strideH: Int

    The step of the convolution in the height dimension

  121. val strideW: Int

    The step of the convolution in the width dimension

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

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

    Generate graph module with start nodes

    Generate graph module with start nodes

    startNodes
    returns

    Definition Classes
    AbstractModule
  124. def toString(): String

    Definition Classes
    LocallyConnected2DAbstractModule → AnyRef → Any
  125. var train: Boolean

    Module status.

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

    Attributes
    protected
    Definition Classes
    AbstractModule
  126. def training(): LocallyConnected2D.this.type

    Definition Classes
    AbstractModule
  127. def unFreeze(names: String*): LocallyConnected2D.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

    Definition Classes
    AbstractModule
  128. 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
    LocallyConnected2DAbstractModule
  129. def updateGradInputFrame(gradInput: Tensor[T], gradOutput: Tensor[T], weight: Tensor[T], fgradInput: Tensor[T], kW: Int, kH: Int, dW: Int, dH: Int, padLeft: Int, padTop: Int, padRight: Int, padBottom: Int)(implicit ev: TensorNumeric[T]): Unit

    Attributes
    protected
  130. 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
    LocallyConnected2DAbstractModule
  131. def updateOutputFrame(input: Tensor[T], output: Tensor[T], weight: Tensor[T], bias: Tensor[T], fInput: Tensor[T], kW: Int, kH: Int, dW: Int, dH: Int, padLeft: Int, padTop: Int, padRight: Int, padBottom: Int, nInputPlane: Int, inputWidth: Int, inputHeight: Int, nOutputPlane: Int, outputWidth: Int, outputHeight: Int)(implicit ev: TensorNumeric[T]): Unit

    Attributes
    protected
  132. def updateParameters(learningRate: T): Unit

    Definition Classes
    LocallyConnected2DAbstractModule
  133. var wRegularizer: Regularizer[T]

    weight regularizer

  134. final def wait(): Unit

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

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

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

  138. var weightInitMethod: InitializationMethod

    Attributes
    protected
    Definition Classes
    Initializable
  139. val withBias: Boolean

    if has bias

  140. 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
    LocallyConnected2DAbstractModule

Deprecated Value Members

  1. def save(path: String, overWrite: Boolean = false): LocallyConnected2D.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
    Annotations
    @deprecated
    Deprecated

    please use recommended saveModule(path, overWrite)

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