PaddingParam

Feature Padding param for MiniBatch.

For constructing a mini batch, we need to make sure all samples' feature and label in this mini batch have the same size. If the size is different, we will pad them to the same size.

By default, we will pad the first dimension to the longest size with zero in the MiniBatch. If you want to specify the padding values, you can set paddingTensor; If you want to specify the padding length, you can use PaddingLongest or FixedLength.

For example, your feature size is n*m*k, you should provide a 2D tensor in a size of m*k. If your feature is 1D, you can provide a one-element 1D tensor.

For example, we have 3 Sample, and convert them into a MiniBatch. Sample1's feature is a 2*3 tensor {1, 2, 3, 4, 5, 6}

Sample2's feature is a 1*3 tensor {7, 8, 9}

Sample3's feature is a 3*3 tensor {10, 11, 12, 13, 14, 15, 16, 17, 18}

And the paddingTensor is {-1, -2, -3}, use FixedLength(Array(4)), the MiniBatch will be a tensor of 3*4*3: {1, 2, 3, 4, 5, 6, -1, -2, -3, -1, -2, -3

7, 8, 9, -1, -2, -3, -1, -2, -3, -1, -2, -3

10, 11, 12, 13, 14, 15, 16, 17, 18 -1, -2, -3}

T: numeric type
paddingTensor: paddings tensor for the first dimension(by default None, meaning zero padding).
paddingStrategy: See PaddingLongest, FixedLength

Linear Supertypes

Product, Equals, Serializable, Serializable, AnyRef, Any

Instance Constructors

new PaddingParam(paddingTensor: Option[Array[Tensor[T]]] = None, paddingStrategy: PaddingStrategy = new DefaultPadding)(implicit arg0: ClassTag[T])

paddingTensor
paddings tensor for the first dimension(by default None, meaning zero padding).
paddingStrategy
See PaddingLongest, FixedLength

Value Members

final def !=(arg0: Any): Boolean

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AnyRef → Any
final def ##(): Int

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

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def clone(): AnyRef

Attributes
protected[java.lang]
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@throws( ... )
final def eq(arg0: AnyRef): Boolean

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def finalize(): Unit

Attributes
protected[java.lang]
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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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final def isInstanceOf[T0]: Boolean

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final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

Definition Classes
AnyRef
val paddingStrategy: PaddingStrategy

See PaddingLongest, FixedLength
val paddingTensor: Option[Array[Tensor[T]]]

paddings tensor for the first dimension(by default None, meaning zero padding).
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
final def wait(): Unit

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

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@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
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@throws( ... )

Related Doc: package dataset

case class PaddingParam[T](paddingTensor: Option[Array[Tensor[T]]] = None, paddingStrategy: PaddingStrategy = new DefaultPadding)(implicit evidence$14: ClassTag[T]) extends Serializable with Product

Instance Constructors

new PaddingParam(paddingTensor: Option[Array[Tensor[T]]] = None, paddingStrategy: PaddingStrategy = new DefaultPadding)(implicit arg0: ClassTag[T])

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def finalize(): Unit

final def getClass(): Class[_]

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val paddingStrategy: PaddingStrategy

val paddingTensor: Option[Array[Tensor[T]]]

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

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Product

Inherited from Equals

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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