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trait Math extends AnyRef

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Math
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  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##: Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. def abs[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  5. def acos[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  6. def acosh[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  7. def add[T](x: Tensor[T], y: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Tensor[T]
  8. def addN[T](inputs: Seq[Tensor[T]])(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T]): Tensor[T]
  9. def all[I](input: Tensor[Boolean], axes: Tensor[I] = null, keepDims: Boolean = false)(implicit arg0: IntDefault[I], arg1: core.types.TF[I], arg2: core.types.IsIntOrLong[I]): Tensor[Boolean]
  10. def angleDouble[TL[A] <: TensorLike[A]](input: TL[core.types.ComplexDouble], name: String = "Angle")(implicit ev: Aux[TL, core.types.ComplexDouble]): TL[Double]
  11. def angleFloat[TL[A] <: TensorLike[A]](input: TL[core.types.ComplexFloat], name: String = "Angle")(implicit ev: Aux[TL, core.types.ComplexFloat]): TL[Float]
  12. def any[I](input: Tensor[Boolean], axes: Tensor[I] = null, keepDims: Boolean = false)(implicit arg0: IntDefault[I], arg1: core.types.TF[I], arg2: core.types.IsIntOrLong[I]): Tensor[Boolean]
  13. def approximatelyEqual[T](x: Tensor[T], y: Tensor[T], tolerance: Float = 0.00001f)(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T]): Tensor[Boolean]
  14. def argmax[T, I, IR](input: Tensor[T], axes: Tensor[I], outputDataType: core.types.DataType[IR])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], arg2: core.types.TF[I], arg3: core.types.IsIntOrLong[I], arg4: core.types.TF[IR], arg5: core.types.IsIntOrLong[IR]): Tensor[IR]
  15. def argmax[T, I](input: Tensor[T], axes: Tensor[I])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], arg2: core.types.TF[I], arg3: core.types.IsIntOrLong[I]): Tensor[Long]
  16. def argmin[T, I, IR](input: Tensor[T], axes: Tensor[I], outputDataType: core.types.DataType[IR])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], arg2: core.types.TF[I], arg3: core.types.IsIntOrLong[I], arg4: core.types.TF[IR], arg5: core.types.IsIntOrLong[IR]): Tensor[IR]
  17. def argmin[T, I](input: Tensor[T], axes: Tensor[I])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], arg2: core.types.TF[I], arg3: core.types.IsIntOrLong[I]): Tensor[Long]
  18. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  19. def asin[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  20. def asinh[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  21. def atan[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  22. def atan2[T](x: Tensor[T], y: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsFloatOrDouble[T]): Tensor[T]
  23. def atanh[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  24. def binCount[T](input: Tensor[Int], dataType: core.types.DataType[T], weights: Tensor[T] = null, minLength: Tensor[Int] = null, maxLength: Tensor[Int] = null)(implicit arg0: core.types.TF[T], arg1: core.types.IsIntOrLongOrFloatOrDouble[T]): Tensor[T]
  25. def bucketize[T](input: Tensor[T], boundaries: Seq[Float])(implicit arg0: core.types.TF[T], arg1: core.types.IsIntOrLongOrFloatOrDouble[T]): Tensor[T]
  26. def ceil[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsHalfOrFloatOrDouble[T], ev: Aux[TL, T]): TL[T]
  27. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
  28. def complexDouble(real: Tensor[Double], imag: Tensor[Double]): Tensor[core.types.ComplexDouble]
  29. def complexFloat(real: Tensor[Float], imag: Tensor[Float]): Tensor[core.types.ComplexFloat]
  30. def conjugate[T, TL[A] <: TensorLike[A]](input: TL[T])(implicit arg0: core.types.TF[T], ev: Aux[TL, T]): TL[T]
  31. def cos[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  32. def cosh[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  33. def countNonZero[T, I](input: Tensor[T], axes: Tensor[I] = null, keepDims: Boolean = false)(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T], arg2: IntDefault[I], arg3: core.types.TF[I], arg4: core.types.IsIntOrLong[I]): Tensor[Long]
  34. def cross[T](a: Tensor[T], b: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T]): Tensor[T]
  35. def cumprod[T, I](input: Tensor[T], axis: Tensor[I], exclusive: Boolean = false, reverse: Boolean = false)(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], arg2: core.types.TF[I], arg3: core.types.IsIntOrLong[I]): Tensor[T]
  36. def cumsum[T, I](input: Tensor[T], axis: Tensor[I], exclusive: Boolean = false, reverse: Boolean = false)(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], arg2: core.types.TF[I], arg3: core.types.IsIntOrLong[I]): Tensor[T]
  37. def diag[T](diagonal: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Tensor[T]
  38. def diagPart[T](input: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Tensor[T]
  39. def digamma[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsFloatOrDouble[T], ev: Aux[TL, T]): TL[T]
  40. def divide[T](x: Tensor[T], y: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Tensor[T]
  41. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  42. def equal[T](x: Tensor[T], y: Tensor[T])(implicit arg0: core.types.TF[T]): Tensor[Boolean]
  43. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  44. def erf[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  45. def erfc[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  46. def exp[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  47. def expm1[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  48. def floor[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsHalfOrFloatOrDouble[T], ev: Aux[TL, T]): TL[T]
  49. def floorMod[T](x: Tensor[T], y: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Tensor[T]
  50. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  51. def greater[T](x: Tensor[T], y: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T]): Tensor[Boolean]
  52. def greaterEqual[T](x: Tensor[T], y: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T]): Tensor[Boolean]
  53. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  54. def igamma[T](a: Tensor[T], x: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsFloatOrDouble[T]): Tensor[T]
  55. def igammac[T](a: Tensor[T], x: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsFloatOrDouble[T]): Tensor[T]
  56. def imagDouble[TL[A] <: TensorLike[A]](input: TL[core.types.ComplexDouble], name: String = "Imag")(implicit ev: Aux[TL, core.types.ComplexDouble]): TL[Double]
  57. def imagFloat[TL[A] <: TensorLike[A]](input: TL[core.types.ComplexFloat], name: String = "Imag")(implicit ev: Aux[TL, core.types.ComplexFloat]): TL[Float]
  58. def incompleteBeta[T](a: Tensor[T], b: Tensor[T], x: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsFloatOrDouble[T]): Tensor[T]
  59. def isFinite[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsHalfOrFloatOrDouble[T], ev: Aux[TL, T]): TL[Boolean]
  60. def isInf[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsHalfOrFloatOrDouble[T], ev: Aux[TL, T]): TL[Boolean]
  61. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  62. def isNaN[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsHalfOrFloatOrDouble[T], ev: Aux[TL, T]): TL[Boolean]
  63. def less[T](x: Tensor[T], y: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T]): Tensor[Boolean]
  64. def lessEqual[T](x: Tensor[T], y: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T]): Tensor[Boolean]
  65. def linspace[T, I](start: Tensor[T], stop: Tensor[T], numberOfValues: Tensor[I])(implicit arg0: core.types.TF[T], arg1: core.types.IsTruncatedHalfOrFloatOrDouble[T], arg2: core.types.TF[I], arg3: core.types.IsIntOrLong[I]): Tensor[T]
  66. def log[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  67. def log1p[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  68. def logGamma[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsFloatOrDouble[T], ev: Aux[TL, T]): TL[T]
  69. def logSigmoid[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T], ev: Aux[TL, T]): TL[T]
  70. def logSumExp[T](input: Tensor[T], axes: Seq[Int] = null, keepDims: Boolean = false)(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Tensor[T]
  71. def logicalAnd(x: Tensor[Boolean], y: Tensor[Boolean]): Tensor[Boolean]
  72. def logicalNot(x: Tensor[Boolean]): Tensor[Boolean]
  73. def logicalOr(x: Tensor[Boolean], y: Tensor[Boolean]): Tensor[Boolean]
  74. def logicalXOr(x: Tensor[Boolean], y: Tensor[Boolean]): Tensor[Boolean]
  75. def magnitudeDouble[TL[A] <: TensorLike[A]](input: TL[core.types.ComplexDouble], name: String = "Magnitude")(implicit ev: Aux[TL, core.types.ComplexDouble]): TL[Double]
  76. def magnitudeFloat[TL[A] <: TensorLike[A]](input: TL[core.types.ComplexFloat], name: String = "Magnitude")(implicit ev: Aux[TL, core.types.ComplexFloat]): TL[Float]
  77. def matmul[T](a: Tensor[T], b: Tensor[T], transposeA: Boolean = false, transposeB: Boolean = false, conjugateA: Boolean = false, conjugateB: Boolean = false, aIsSparse: Boolean = false, bIsSparse: Boolean = false)(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Tensor[T]
  78. def matrixBandPart[T, I](input: Tensor[T], numSubDiagonals: Tensor[I], numSuperDiagonals: Tensor[I])(implicit arg0: core.types.TF[T], arg1: core.types.TF[I], arg2: core.types.IsIntOrLong[I]): Tensor[T]
  79. def matrixDiag[T](diagonal: Tensor[T])(implicit arg0: core.types.TF[T]): Tensor[T]
  80. def matrixDiagPart[T](input: Tensor[T])(implicit arg0: core.types.TF[T]): Tensor[T]
  81. def matrixSetDiag[T](input: Tensor[T], diagonal: Tensor[T])(implicit arg0: core.types.TF[T]): Tensor[T]
  82. def max[T, I](input: Tensor[T], axes: Tensor[I] = null, keepDims: Boolean = false)(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], arg2: IntDefault[I], arg3: core.types.TF[I], arg4: core.types.IsIntOrLong[I]): Tensor[T]
  83. def maximum[T](x: Tensor[T], y: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Tensor[T]
  84. def mean[T, I](input: Tensor[T], axes: Tensor[I] = null, keepDims: Boolean = false)(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T], arg2: IntDefault[I], arg3: core.types.TF[I], arg4: core.types.IsIntOrLong[I]): Tensor[T]
  85. def min[T, I](input: Tensor[T], axes: Tensor[I] = null, keepDims: Boolean = false)(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], arg2: IntDefault[I], arg3: core.types.TF[I], arg4: core.types.IsIntOrLong[I]): Tensor[T]
  86. def minimum[T](x: Tensor[T], y: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Tensor[T]
  87. def mod[T](x: Tensor[T], y: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Tensor[T]
  88. def multiply[T](x: Tensor[T], y: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Tensor[T]
  89. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  90. def negate[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  91. def notEqual[T](x: Tensor[T], y: Tensor[T])(implicit arg0: core.types.TF[T]): Tensor[Boolean]
  92. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  93. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  94. def polygamma[T](n: Tensor[T], x: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsFloatOrDouble[T]): Tensor[T]
  95. def pow[T](x: Tensor[T], y: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Tensor[T]
  96. def prod[T, I](input: Tensor[T], axes: Tensor[I] = null, keepDims: Boolean = false)(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], arg2: IntDefault[I], arg3: core.types.TF[I], arg4: core.types.IsIntOrLong[I]): Tensor[T]
  97. def range[T](start: Tensor[T], limit: Tensor[T], delta: Tensor[T] = null)(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T]): Tensor[T]
  98. def realDivide[T](x: Tensor[T], y: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Tensor[T]
  99. def realDouble[TL[A] <: TensorLike[A]](input: TL[core.types.ComplexDouble], name: String = "Real")(implicit ev: Aux[TL, core.types.ComplexDouble]): TL[Double]
  100. def realFloat[TL[A] <: TensorLike[A]](input: TL[core.types.ComplexFloat], name: String = "Real")(implicit ev: Aux[TL, core.types.ComplexFloat]): TL[Float]
  101. def reciprocal[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  102. def round[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  103. def roundInt[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsHalfOrFloatOrDouble[T], ev: Aux[TL, T]): TL[T]
  104. def rsqrt[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  105. def scalarMul[T, TL[A] <: TensorLike[A]](scalar: Tensor[T], tensor: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  106. def segmentMax[T, I](data: Tensor[T], segmentIndices: Tensor[I])(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T], arg2: core.types.TF[I], arg3: core.types.IsIntOrLong[I]): Tensor[T]
  107. def segmentMean[T, I](data: Tensor[T], segmentIndices: Tensor[I])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], arg2: core.types.TF[I], arg3: core.types.IsIntOrLong[I]): Tensor[T]
  108. def segmentMin[T, I](data: Tensor[T], segmentIndices: Tensor[I])(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T], arg2: core.types.TF[I], arg3: core.types.IsIntOrLong[I]): Tensor[T]
  109. def segmentProd[T, I](data: Tensor[T], segmentIndices: Tensor[I])(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T], arg2: core.types.TF[I], arg3: core.types.IsIntOrLong[I]): Tensor[T]
  110. def segmentSum[T, I](data: Tensor[T], segmentIndices: Tensor[I])(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T], arg2: core.types.TF[I], arg3: core.types.IsIntOrLong[I]): Tensor[T]
  111. def select[T](condition: Tensor[Boolean], x: Tensor[T], y: Tensor[T])(implicit arg0: core.types.TF[T]): Tensor[T]
  112. def sigmoid[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  113. def sign[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  114. def sin[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  115. def sinh[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  116. def sparseSegmentMean[T, I1, I2](data: Tensor[T], indices: Tensor[I1], segmentIndices: Tensor[Int], numSegments: Tensor[I2] = null)(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T], arg2: core.types.TF[I1], arg3: core.types.IsIntOrLong[I1], arg4: IntDefault[I2], arg5: core.types.TF[I2], arg6: core.types.IsIntOrLong[I2]): Tensor[T]
  117. def sparseSegmentSum[T, I1, I2](data: Tensor[T], indices: Tensor[I1], segmentIndices: Tensor[Int], numSegments: Tensor[I2] = null)(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T], arg2: core.types.TF[I1], arg3: core.types.IsIntOrLong[I1], arg4: IntDefault[I2], arg5: core.types.TF[I2], arg6: core.types.IsIntOrLong[I2]): Tensor[T]
  118. def sparseSegmentSumSqrtN[T, I1, I2](data: Tensor[T], indices: Tensor[I1], segmentIndices: Tensor[Int], numSegments: Tensor[I2] = null)(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T], arg2: core.types.TF[I1], arg3: core.types.IsIntOrLong[I1], arg4: IntDefault[I2], arg5: core.types.TF[I2], arg6: core.types.IsIntOrLong[I2]): Tensor[T]
  119. def sqrt[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  120. def square[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  121. def squaredDifference[T](x: Tensor[T], y: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Tensor[T]
  122. def subtract[T](x: Tensor[T], y: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Tensor[T]
  123. def sum[T, I](input: Tensor[T], axes: Tensor[I] = null, keepDims: Boolean = false)(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T], arg2: IntDefault[I], arg3: core.types.TF[I], arg4: core.types.IsIntOrLong[I]): Tensor[T]
  124. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  125. def tan[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  126. def tanh[T, TL[A] <: TensorLike[A]](x: TL[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T], ev: Aux[TL, T]): TL[T]
  127. def tensorDot[T](a: Tensor[T], b: Tensor[T], axesA: Tensor[Int], axesB: Tensor[Int])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Tensor[T]
    Annotations
    @throws(scala.this.throws.<init>$default$1[org.platanios.tensorflow.api.core.exception.InvalidShapeException])
  128. def tensorDot[T](a: Tensor[T], b: Tensor[T], numAxes: Tensor[Int])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Tensor[T]
    Annotations
    @throws(scala.this.throws.<init>$default$1[org.platanios.tensorflow.api.core.exception.InvalidShapeException])
  129. def toString(): String
    Definition Classes
    AnyRef → Any
  130. def trace[T](input: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T]): Tensor[T]
  131. def truncateDivide[T](x: Tensor[T], y: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Tensor[T]
  132. def truncateMod[T](x: Tensor[T], y: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Tensor[T]
  133. def unsortedSegmentMax[T, I1, I2](data: Tensor[T], segmentIndices: Tensor[I1], segmentsNumber: Tensor[I2])(implicit arg0: core.types.TF[T], arg1: core.types.IsReal[T], arg2: core.types.TF[I1], arg3: core.types.IsIntOrLong[I1], arg4: core.types.TF[I2], arg5: core.types.IsIntOrLong[I2]): Tensor[T]
  134. def unsortedSegmentSum[T, I1, I2](data: Tensor[T], segmentIndices: Tensor[I1], segmentsNumber: Tensor[I2])(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T], arg2: core.types.TF[I1], arg3: core.types.IsIntOrLong[I1], arg4: core.types.TF[I2], arg5: core.types.IsIntOrLong[I2]): Tensor[T]
  135. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  136. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  137. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  138. def zerosFraction[T](input: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNumeric[T]): Tensor[Float]
  139. def zeta[T](x: Tensor[T], q: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsFloatOrDouble[T]): Tensor[T]

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable]) @Deprecated @deprecated
    Deprecated

    (Since version ) see corresponding Javadoc for more information.

  2. def floorDivide[T](x: Tensor[T], y: Tensor[T])(implicit arg0: core.types.TF[T], arg1: core.types.IsNotQuantized[T]): Tensor[T]
    Annotations
    @deprecated
    Deprecated

    (Since version 0.1) Use truncateDivide instead.

Inherited from AnyRef

Inherited from Any

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