object Tensor
- Source
- Tensors.scala
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
- Definition Classes
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- def abs(leftHandSide: Tensor): InlineTensor
- def apply[A](elements: A, padding: Float = 0.0f)(implicit tensorBuilder: Aux[A, Float]): NonInlineTensor
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final
def
asInstanceOf[T0]: T0
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def
clone(): AnyRef
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
- Definition Classes
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- def exp(leftHandSide: Tensor): InlineTensor
- def fill(value: Float, shape0: Array[Int], padding: Float = 0.0f): InlineTensor
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def
finalize(): Unit
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
- Definition Classes
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- def join(tensors0: Seq[Tensor]): NonInlineTensor
- def join(tensors0: Seq[Tensor], dimension: Int): Tensor
- def log(leftHandSide: Tensor): InlineTensor
- def max(leftHandSide: Tensor, rightHandSide: Tensor): InlineTensor
- def min(leftHandSide: Tensor, rightHandSide: Tensor): InlineTensor
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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
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- def random(shape: Array[Int], seed: Int = Random.nextInt(), padding: Float = 0.0f): NonInlineTensor
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def
randomNormal(shape: Array[Int], seed: Int = Random.nextInt(), padding: Float = 0.0f): NonInlineTensor
Generate random numbers in normal distribution.
- def scalar(value: Float, padding: Float = 0.0f): InlineTensor
- def sqrt(leftHandSide: Tensor): InlineTensor
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final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
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- def tanh(leftHandSide: Tensor): InlineTensor
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def
toString(): String
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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