sealed trait Tensor extends AnyRef
- Self Type
- Tensor
- Source
- Tensors.scala
- Note
There are three kinds of Tensor.
- InlineTensor and TransformedTensor are like a
@inline def
, which can be merged into a larger kernel and will be recalculated whenever a slow action is performed. - NonInlineTensor is like a
@noinline def
, which is never merged into a larger kernel and will be recalculated whenever a slow action is performed. - CachedTensor is like a
lazy val
, which has an associated data buffer on the compute device and will be calculated only once even when slow actions are performed more than once.
- InlineTensor and TransformedTensor are like a
- Grouped
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- Tensor
- AnyRef
- Any
- by any2stringadd
- by StringFormat
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- by ArrowAssoc
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Abstract Value Members
Concrete Value Members
-
final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
##(): Int
- Definition Classes
- AnyRef → Any
- def %(rightHandSide: Tensor): InlineTensor
- def *(rightHandSide: Tensor): InlineTensor
- def +(rightHandSide: Tensor): InlineTensor
- def -(rightHandSide: Tensor): InlineTensor
- def ->[B](y: B): (Tensor, B)
- def /(rightHandSide: Tensor): InlineTensor
-
final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
- def broadcast(newShape: Array[Int]): Tensor
-
def
clone(): AnyRef
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @native() @throws( ... )
-
def
doCache: Do[CachedTensor]
Allocates device-side cache that are managed by the RAII.scala library.
- def ensuring(cond: (Tensor) ⇒ Boolean, msg: ⇒ Any): Tensor
- def ensuring(cond: (Tensor) ⇒ Boolean): Tensor
- def ensuring(cond: Boolean, msg: ⇒ Any): Tensor
- def ensuring(cond: Boolean): Tensor
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
finalize(): Unit
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
-
def
flatArray: Future[Array[Trees.FloatTrees.FloatTerm.JvmValue]]
Returns an asynchronous task to read this Tensor into a scala.Array, which is linearized in row-major order.
-
def
flatBuffer: Do[FloatBuffer]
Returns a RAII managed asynchronous task to read this Tensor into an off-heap memory, which is linearized in row-major order.
- def formatted(fmtstr: String): String
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
- def permute(dimensions: Array[Int]): TransformedTensor
-
def
read1DArray: Future[Array[Float]]
Returns an asynchronous task to read this Tensor into a scala.Array
-
def
read1DSeq: Future[Seq[Float]]
Returns an asynchronous task to read this Tensor into a scala.Seq
-
def
read2DArray: Future[Array[Array[Float]]]
Returns an asynchronous task to read this Tensor into a 2D scala.Array
-
def
read2DSeq: Future[Seq[Seq[Float]]]
Returns an asynchronous task to read this Tensor into a 2D scala.Seq
-
def
read3DArray: Future[Array[Array[Array[Float]]]]
Returns an asynchronous task to read this Tensor into a 3D scala.Array
-
def
read3DSeq: Future[Seq[Seq[Seq[Float]]]]
Returns an asynchronous task to read this Tensor into a 3D scala.Seq
-
def
read4DArray: Future[Array[Array[Array[Array[Float]]]]]
Returns an asynchronous task to read this Tensor into a 4D scala.Array
-
def
read4DSeq: Future[Seq[Seq[Seq[Seq[Float]]]]]
Returns an asynchronous task to read this Tensor into a 4D scala.Seq
-
def
read5DArray: Future[Array[Array[Array[Array[Array[Float]]]]]]
Returns an asynchronous task to read this Tensor into a 5D scala.Array
-
def
read5DSeq: Future[Seq[Seq[Seq[Seq[Seq[Float]]]]]]
Returns an asynchronous task to read this Tensor into a 5D scala.Seq
-
def
readScalar: Future[Float]
Returns an asynchronous task to read this Tensor into a scala.Float
-
def
reshape(newShape: Array[Int]): NonInlineTensor
Returns a new Tensor of new shape and the same data of this Tensor.
- Note
The data in this Tensor is considered as row-major order when reshape. You can create another column-major version reshape by reversing the shape:
def columnMajorReshape[Category <: Tensors](tensor: Category#Tensor, newShape: Array[Int]): Category#Tensor = { tensor.permute(tensor.shape.indices.reverse.toArray).reshape(newShape.reverse).permute(newShape.indices.reverse.toArray) }
- def scale(newShape: Array[Int]): TransformedTensor
- def split(dimension: Int): IndexedSeq[TransformedTensor]
- def sum: NonInlineTensor
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- Tensor → AnyRef → Any
- def translate(offset: Array[Double], newShape: Array[Int] = shape): TransformedTensor
- def transpose: TransformedTensor
- def unary_+: Tensor.this.type
- def unary_-: InlineTensor
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @native() @throws( ... )
- def →[B](y: B): (Tensor, B)
Shadowed Implicit Value Members
-
def
+(other: String): String
- Implicit
- This member is added by an implicit conversion from Tensor to any2stringadd[Tensor] performed by method any2stringadd in scala.Predef.
- Shadowing
- This implicitly inherited member is shadowed by one or more members in this class.
To access this member you can use a type ascription:(tensor: any2stringadd[Tensor]).+(other)
- Definition Classes
- any2stringadd
General information
Methods that provides general information of this Tensor.
Slow actions
Actions that can actually perform delayed operations in order to read the data from the device to JVM, or change the internal state of this Tensor.
Delayed operators
Operators that return new Tensors of delay-evaluated computational graphs. The actually computation will be only performed when Slow actions are called.