MathOps

Instance Constructors

new MathOps(output: Output)

Value Members

def !=(other: Output): Output

$OpDocMathNotEqual
$OpDocMathNotEqual
returns
Result as a new tensor.
final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
def %(other: Output): Output

$OpDocMathMod
$OpDocMathMod
returns
Result as a new tensor.
def &&(other: Output): Output

$OpDocMathLogicalAnd
$OpDocMathLogicalAnd
returns
Result as a new tensor.
def *(other: Output): Output

$OpDocMathMultiply
$OpDocMathMultiply
returns
Result as a new tensor.
def **(other: Output): Output

$OpDocMathPow
$OpDocMathPow
returns
Result as a new tensor.
def +(other: Output): Output

$OpDocMathAdd
$OpDocMathAdd
returns
Result as a new tensor.
def -(other: Output): Output

$OpDocMathSubtract
$OpDocMathSubtract
returns
Result as a new tensor.
def /(other: Output): Output

$OpDocMathDivide
$OpDocMathDivide
returns
Result as a new tensor.
def <(other: Output): Output

$OpDocMathLess
$OpDocMathLess
returns
Result as a new tensor.
def <=(other: Output): Output

$OpDocMathLessEqual
$OpDocMathLessEqual
returns
Result as a new tensor.
def ==(other: Output): Output

$OpDocMathEqual
$OpDocMathEqual
returns
Result as a new tensor.
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def >(other: Output): Output

$OpDocMathGreater
$OpDocMathGreater
returns
Result as a new tensor.
def >=(other: Output): Output

$OpDocMathGreaterEqual
$OpDocMathGreaterEqual
returns
Result as a new tensor.
def ^(other: Output): Output

$OpDocMathPow
$OpDocMathPow
returns
Result as a new tensor.
def abs: Output

$OpDocMathAbs
$OpDocMathAbs
returns
Result as a new tensor.
def acos: Output

$OpDocMathAcos
$OpDocMathAcos
returns
Result as a new tensor.
def acosh: Output

$OpDocMathAcosh
$OpDocMathAcosh
returns
Result as a new tensor.
def add(other: Output): Output

$OpDocMathAdd
$OpDocMathAdd
returns
Result as a new tensor.
def all(axes: Output = null, keepDims: Boolean = false): Output

$OpDocMathAll
$OpDocMathAll
axes
Integer tensor containing the axes to reduce. If null, then all axes are reduced.
keepDims
If true, retain the reduced axes.
returns
Result as a new tensor.
def angle: Output

$OpDocMathAngle
$OpDocMathAngle
returns
Result as a new tensor.
def any(axes: Output = null, keepDims: Boolean = false): Output

$OpDocMathAny
$OpDocMathAny
axes
Integer tensor containing the axes to reduce. If null, then all axes are reduced.
keepDims
If true, retain the reduced axes.
returns
Result as a new tensor.
def approximatelyEqual(other: Output): Output

$OpDocMathApproximatelyEqual
$OpDocMathApproximatelyEqual
returns
Result as a new tensor.
def argmax(axes: Output = 0, outputDataType: types.DataType = INT64): Output

$OpDocMathArgmax
$OpDocMathArgmax
axes
Integer tensor containing the axes to reduce. If null, then all axes are reduced.
outputDataType
Data type for the output tensor. Must be INT32 or INT64.
returns
Result as a new tensor.
def argmin(axes: Output = 0, outputDataType: types.DataType = INT64): Output

$OpDocMathArgmin
$OpDocMathArgmin
axes
Integer tensor containing the axes to reduce. If null, then all axes are reduced.
outputDataType
Data type for the output tensor. Must be INT32 or INT64.
returns
Result as a new tensor.
final def asInstanceOf[T0]: T0

Definition Classes
Any
def asin: Output

$OpDocMathAsin
$OpDocMathAsin
returns
Result as a new tensor.
def asinh: Output

$OpDocMathAsinh
$OpDocMathAsinh
returns
Result as a new tensor.
def atan: Output

$OpDocMathAtan
$OpDocMathAtan
returns
Result as a new tensor.
def atan2(other: Output): Output

$OpDocMathAtan2
$OpDocMathAtan2
returns
Result as a new tensor.
def atanh: Output

$OpDocMathAtanh
$OpDocMathAtanh
returns
Result as a new tensor.
def binCount(weights: Output = null, minLength: Output = null, maxLength: Output = null, dataType: types.DataType = INT32): Output

$OpDocMathBinCount
$OpDocMathBinCount
weights
If not null, this tensor must have the same shape as input. For each value in input, the corresponding bin count will be incremented by the corresponding weight instead of 1.
minLength
If not null, this ensures the output has length at least minLength, padding with zeros at the end, if necessary.
maxLength
If not null, this skips values in input that are equal or greater than maxLength, ensuring that the output has length at most maxLength.
dataType
If weights is null, this determines the data type used for the output tensor (i.e., the tensor containing the bin counts).
returns
Result as a new tensor.
def bitcast(dataType: types.DataType): Output

$OpDocMathBitcast
$OpDocMathBitcast
dataType
Target data type.
returns
Result as a new tensor.
def bucketize(boundaries: Seq[Float]): Output

$OpDocMathBucketize
$OpDocMathBucketize
boundaries
Sorted sequence of Floats specifying the boundaries of the buckets.
returns
Result as a new tensor.
def cast(dataType: types.DataType): Output

$OpDocMathCast
$OpDocMathCast
dataType
Target data type.
returns
Result as a new tensor.
def ceil: Output

$OpDocMathCeil
$OpDocMathCeil
returns
Result as a new tensor.
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def conjugate: Output

$OpDocMathConjugate
$OpDocMathConjugate
returns
Result as a new tensor.
def cos: Output

$OpDocMathCos
$OpDocMathCos
returns
Result as a new tensor.
def cosh: Output

$OpDocMathCosh
$OpDocMathCosh
returns
Result as a new tensor.
def countNonZero(axes: Output = null, keepDims: Boolean = false): Output

$OpDocMathCountNonZero
$OpDocMathCountNonZero
axes
Integer tensor containing the axes to reduce. If null, then all axes are reduced.
keepDims
If true, retain the reduced axes.
returns
Result as a new tensor.
def cross(other: Output): Output

$OpDocMathCross
$OpDocMathCross
returns
Result as a new tensor.
def cumprod(axis: Output = 0, exclusive: Boolean = false, reverse: Boolean = false): Output

$OpDocMathCumprod
$OpDocMathCumprod
axis
INT32 tensor containing the axis along which to perform the cumulative product.
exclusive
Boolean value indicating whether to perform an exclusive cumulative product.
reverse
Boolean value indicating whether to perform a reverse cumulative product.
returns
Result as a new tensor.
def cumsum(axis: Output = 0, exclusive: Boolean = false, reverse: Boolean = false): Output

$OpDocMathCumsum
$OpDocMathCumsum
axis
INT32 tensor containing the axis along which to perform the cumulative sum.
exclusive
Boolean value indicating whether to perform an exclusive cumulative sum.
reverse
Boolean value indicating whether to perform a reverse cumulative sum.
returns
Result as a new tensor.
def diag: Output

$OpDocMathDiag
$OpDocMathDiag
returns
Result as a new tensor.
def diagPart: Output

$OpDocMathDiagPart
$OpDocMathDiagPart
returns
Result as a new tensor.
def digamma: Output

$OpDocMathDigamma
$OpDocMathDigamma
returns
Result as a new tensor.
def divide(other: Output): Output

$OpDocMathDivide
$OpDocMathDivide
returns
Result as a new tensor.
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equal(other: Output): Output

$OpDocMathEqual
$OpDocMathEqual
returns
Result as a new tensor.
def erc: Output

$OpDocMathErfc
$OpDocMathErfc
returns
Result as a new tensor.
def erf: Output

$OpDocMathErf
$OpDocMathErf
returns
Result as a new tensor.
def exp: Output

$OpDocMathExp
$OpDocMathExp
returns
Result as a new tensor.
def expm1: Output

$OpDocMathExpm1
$OpDocMathExpm1
returns
Result as a new tensor.
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def floor: Output

$OpDocMathFloor
$OpDocMathFloor
returns
Result as a new tensor.
def floorMod(other: Output): Output

$OpDocMathFloorMod
$OpDocMathFloorMod
returns
Result as a new tensor.
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def greater(other: Output): Output

$OpDocMathGreater
$OpDocMathGreater
returns
Result as a new tensor.
def greaterEqual(other: Output): Output

$OpDocMathGreaterEqual
$OpDocMathGreaterEqual
returns
Result as a new tensor.
def igamma(other: Output): Output

$OpDocMathIgamma
$OpDocMathIgamma
returns
Result as a new tensor.
def igammac(other: Output): Output

$OpDocMathIgammac
$OpDocMathIgammac
returns
Result as a new tensor.
def imag: Output

$OpDocMathImag
$OpDocMathImag
returns
Result as a new tensor.
def isFinite: Output

$OpDocMathIsFinite
$OpDocMathIsFinite
returns
Result as a new tensor.
def isInf: Output

$OpDocMathIsInf
$OpDocMathIsInf
returns
Result as a new tensor.
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def isNaN: Output

$OpDocMathIsNaN
$OpDocMathIsNaN
returns
Result as a new tensor.
def less(other: Output): Output

$OpDocMathLess
$OpDocMathLess
returns
Result as a new tensor.
def lessEqual(other: Output): Output

$OpDocMathLessEqual
$OpDocMathLessEqual
returns
Result as a new tensor.
def log: Output

$OpDocMathLog
$OpDocMathLog
returns
Result as a new tensor.
def log1p: Output

$OpDocMathLog1p
$OpDocMathLog1p
returns
Result as a new tensor.
def logGamma: Output

$OpDocMathLogGamma
$OpDocMathLogGamma
returns
Result as a new tensor.
def logSumExp(axes: Output = null, keepDims: Boolean = false): Output

$OpDocMathLogSumExp
$OpDocMathLogSumExp
axes
Integer tensor containing the axes to reduce. If null, then all axes are reduced.
keepDims
If true, retain the reduced axes.
returns
Result as a new tensor.
def logicalAnd(other: Output): Output

$OpDocMathLogicalAnd
$OpDocMathLogicalAnd
returns
Result as a new tensor.
def logicalNot: Output

$OpDocMathLogicalNot
$OpDocMathLogicalNot
returns
Result as a new tensor.
def logicalOr(other: Output): Output

$OpDocMathLogicalOr
$OpDocMathLogicalOr
returns
Result as a new tensor.
def logicalXOr(other: Output): Output

$OpDocMathLogicalXOr
$OpDocMathLogicalXOr
returns
Result as a new tensor.
def matmul(other: Output, transposeA: Boolean = false, transposeB: Boolean = false, conjugateA: Boolean = false, conjugateB: Boolean = false, aIsSparse: Boolean = false, bIsSparse: Boolean = false): Output

$OpDocMathMatmul
$OpDocMathMatmul
other
Output to multiply with, with data type one of: BFLOAT16, FLOAT16, FLOAT32, FLOAT64, INT32, COMPLEX64, COMPLEX128.
transposeA
If true, this tensor is transposed before the multiplication.
transposeB
If true, other is transposed before the multiplication.
conjugateA
If true, this tensor is conjugated before the multiplication.
conjugateB
If true, other is conjugated before the multiplication.
aIsSparse
If true, this tensor is treated as a sparse matrix (i.e., it is assumed it contains many zeros).
bIsSparse
If true, other is treated as a sparse matrix (i.e., it is assumed it contains many zeros).
returns
Result as a new tensor.
def matrixBandPart(numSubDiagonals: Output, numSuperDiagonals: Output): Output

$OpDocMathMatrixBandPart
$OpDocMathMatrixBandPart
numSubDiagonals
Scalar INT64 tensor that contains the number of sub-diagonals to keep. If negative, the entire lower triangle is kept.
numSuperDiagonals
Scalar INT64 tensor that contains the number of super-diagonals to keep. If negative, the entire upper triangle is kept.
returns
Result as a new tensor containing the expected banded tensor and has rank K and same shape as input.
def matrixDiag: Output

$OpDocMathMatrixDiag
$OpDocMathMatrixDiag
returns
Result as a new tensor with rank equal to K + 1 and shape equal to the shape of diagonal, with its last dimension duplicated.
def matrixDiagPart: Output

$OpDocMathMatrixDiagPart
$OpDocMathMatrixDiagPart
returns
Result as a new tensor containing the diagonal(s) and having shape equal to input.shape[:-2] + [min(input.shape[-2:])].
def matrixSetDiag(diagonal: Output): Output

$OpDocMathMatrixSetDiag
$OpDocMathMatrixSetDiag
diagonal
Rank-K tensor, where K >= 1.
returns
Result as a new tensor with rank equal to K + 1 and shape equal to the shape of input.
def max(axes: Output = null, keepDims: Boolean = false): Output

$OpDocMathMax
$OpDocMathMax
axes
Integer tensor containing the axes to reduce. If null, then all axes are reduced.
keepDims
If true, retain the reduced axes.
returns
Result as a new tensor.
def maximum(other: Output): Output

$OpDocMathMaximum
$OpDocMathMaximum
returns
Result as a new tensor.
def mean(axes: Output = null, keepDims: Boolean = false): Output

$OpDocMathMean
$OpDocMathMean
axes
Integer tensor containing the axes to reduce. If null, then all axes are reduced.
keepDims
If true, retain the reduced axes.
returns
Result as a new tensor.
def min(axes: Output = null, keepDims: Boolean = false): Output

$OpDocMathMin
$OpDocMathMin
axes
Integer tensor containing the axes to reduce. If null, then all axes are reduced.
keepDims
If true, retain the reduced axes.
returns
Result as a new tensor.
def minimum(other: Output): Output

$OpDocMathMinimum
$OpDocMathMinimum
returns
Result as a new tensor.
def mod(other: Output): Output

$OpDocMathMod
$OpDocMathMod
returns
Result as a new tensor.
def multiply(other: Output): Output

$OpDocMathMultiply
$OpDocMathMultiply
returns
Result as a new tensor.
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def negate: Output

$OpDocMathNegate
$OpDocMathNegate
returns
Result as a new tensor.
def notEqual(other: Output): Output

$OpDocMathNotEqual
$OpDocMathNotEqual
returns
Result as a new tensor.
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val output: Output
def polygamma(other: Output): Output

$OpDocMathPolygamma
$OpDocMathPolygamma
returns
Result as a new tensor.
def pow(other: Output): Output

$OpDocMathPow
$OpDocMathPow
returns
Result as a new tensor.
def prod(axes: Output = null, keepDims: Boolean = false): Output

$OpDocMathProd
$OpDocMathProd
axes
Integer tensor containing the axes to reduce. If null, then all axes are reduced.
keepDims
If true, retain the reduced axes.
returns
Result as a new tensor.
def real: Output

$OpDocMathReal
$OpDocMathReal
returns
Result as a new tensor.
def realDivide(other: Output): Output

$OpDocMathRealDivide
$OpDocMathRealDivide
returns
Result as a new tensor.
def reciprocal: Output

$OpDocMathReciprocal
$OpDocMathReciprocal
returns
Result as a new tensor.
def round: Output

$OpDocMathRound
$OpDocMathRound
returns
Result as a new tensor.
def roundInt: Output

$OpDocMathRoundInt
$OpDocMathRoundInt
returns
Result as a new tensor.
def rsqrt: Output

$OpDocMathRsqrt
$OpDocMathRsqrt
returns
Result as a new tensor.
def segmentMax(segmentIndices: Output): Output

$OpDocMathSegmentMax
$OpDocMathSegmentMax
segmentIndices
Segment indices (must have data type of INT32 or INT64). Values should be sorted and can be repeated.
returns
Result as a new tensor.
def segmentMean(segmentIndices: Output): Output

$OpDocMathSegmentMean
$OpDocMathSegmentMean
segmentIndices
Segment indices (must have data type of INT32 or INT64). Values should be sorted and can be repeated.
returns
Result as a new tensor.
def segmentMin(segmentIndices: Output): Output

$OpDocMathSegmentMin
$OpDocMathSegmentMin
segmentIndices
Segment indices (must have data type of INT32 or INT64). Values should be sorted and can be repeated.
returns
Result as a new tensor.
def segmentProd(segmentIndices: Output): Output

$OpDocMathSegmentProd
$OpDocMathSegmentProd
segmentIndices
Segment indices (must have data type of INT32 or INT64). Values should be sorted and can be repeated.
returns
Result as a new tensor.
def segmentSum(segmentIndices: Output): Output

$OpDocMathSegmentSum
$OpDocMathSegmentSum
segmentIndices
Segment indices (must have data type of INT32 or INT64). Values should be sorted and can be repeated.
returns
Result as a new tensor.
def sigmoid: Output

$OpDocMathSigmoid
$OpDocMathSigmoid
returns
Result as a new tensor.
def sign: Output

$OpDocMathSign
$OpDocMathSign
returns
Result as a new tensor.
def sin: Output

$OpDocMathSin
$OpDocMathSin
returns
Result as a new tensor.
def sinh: Output

$OpDocMathSinh
$OpDocMathSinh
returns
Result as a new tensor.
def sparseSegmentMean(indices: Output, segmentIndices: Output, numSegments: Output = null): Output

$OpDocMathSparseSegmentMean
$OpDocMathSparseSegmentMean
indices
One-dimensional tensor with rank equal to that of segmentIndices.
segmentIndices
Segment indices (must have data type of INT32 or INT64). Values should be sorted and can be repeated.
numSegments
Optional INT32 scalar indicating the size of the output tensor.
returns
Result as a new tensor.
def sparseSegmentSum(indices: Output, segmentIndices: Output, numSegments: Output = null): Output

$OpDocMathSparseSegmentSum
$OpDocMathSparseSegmentSum
indices
One-dimensional tensor with rank equal to that of segmentIndices.
segmentIndices
Segment indices (must have data type of INT32 or INT64). Values should be sorted and can be repeated.
numSegments
Optional INT32 scalar indicating the size of the output tensor.
returns
Result as a new tensor.
def sparseSegmentSumSqrtN(indices: Output, segmentIndices: Output, numSegments: Output = null): Output

$OpDocMathSparseSegmentSumSqrtN
$OpDocMathSparseSegmentSumSqrtN
indices
One-dimensional tensor with rank equal to that of segmentIndices.
segmentIndices
Segment indices (must have data type of INT32 or INT64). Values should be sorted and can be repeated.
numSegments
Optional INT32 scalar indicating the size of the output tensor.
returns
Result as a new tensor.
def sqrt: Output

$OpDocMathSqrt
$OpDocMathSqrt
returns
Result as a new tensor.
def square: Output

$OpDocMathSquare
$OpDocMathSquare
returns
Result as a new tensor.
def squaredDifference(other: Output): Output

$OpDocMathSquaredDifference
$OpDocMathSquaredDifference
returns
Result as a new tensor.
def subtract(other: Output): Output

$OpDocMathSubtract
$OpDocMathSubtract
returns
Result as a new tensor.
def sum(axes: Output = null, keepDims: Boolean = false): Output

$OpDocMathSum
$OpDocMathSum
axes
Integer tensor containing the axes to reduce. If null, then all axes are reduced.
keepDims
If true, retain the reduced axes.
returns
Result as a new tensor.
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def tan: Output

$OpDocMathTan
$OpDocMathTan
returns
Result as a new tensor.
def tanh: Output

$OpDocMathTanh
$OpDocMathTanh
returns
Result as a new tensor.
def tensorDot(other: Output, axes: Seq[Int], axesOther: Seq[Int], name: String): Output

$OpDocMathTensorDot
$OpDocMathTensorDot
other
Tensor to contract with.
axes
Axes to contract in this tensor.
axesOther
Axes to contract in other.
name
Name for the created ops.
returns
Created op output.
def tensorDot(other: Output, axes: Seq[Int], axesOther: Seq[Int]): Output

$OpDocMathTensorDot
$OpDocMathTensorDot
other
Tensor to contract with.
axes
Axes to contract in this tensor.
axesOther
Axes to contract in other.
returns
Created op output.
def tensorDot(other: Output, numAxes: Int, name: String): Output

$OpDocMathTensorDot
$OpDocMathTensorDot
other
Tensor to contract with.
numAxes
Number of axes to contract.
name
Name for the created ops.
returns
Created op output.
def tensorDot(other: Output, numAxes: Int): Output

$OpDocMathTensorDot
$OpDocMathTensorDot
other
Tensor to contract with.
numAxes
Number of axes to contract.
returns
Created op output.
def tensorDotDynamic(other: Output, axes: Output, axesOther: Output, name: String): Output

Dynamic version (i.e., where axes and axesOther may be symbolic tensors) of the tensorDot op.
Dynamic version (i.e., where axes and axesOther may be symbolic tensors) of the tensorDot op.
$OpDocMathTensorDot
other
Tensor to contract with.
axes
Axes to contract in this tensor.
axesOther
Axes to contract in other.
name
Name for the created ops.
returns
Created op output.
def tensorDotDynamic(other: Output, axes: Output, axesOther: Output): Output

Dynamic version (i.e., where axes and axesOther may be symbolic tensors) of the tensorDot op.
Dynamic version (i.e., where axes and axesOther may be symbolic tensors) of the tensorDot op.
$OpDocMathTensorDot
other
Tensor to contract with.
axes
Axes to contract in this tensor.
axesOther
Axes to contract in other.
returns
Created op output.
def tensorDotDynamic(other: Output, numAxes: Output, name: String): Output

Dynamic version (i.e., where numAxes may be a symbolic tensor) of the tensorDot op.
Dynamic version (i.e., where numAxes may be a symbolic tensor) of the tensorDot op.
$OpDocMathTensorDot
other
Tensor to contract with.
numAxes
Number of axes to contract.
name
Name for the created ops.
returns
Created op output.
def tensorDotDynamic(other: Output, numAxes: Output): Output

Dynamic version (i.e., where numAxes may be a symbolic tensor) of the tensorDot op.
Dynamic version (i.e., where numAxes may be a symbolic tensor) of the tensorDot op.
$OpDocMathTensorDot
other
Tensor to contract with.
numAxes
Number of axes to contract.
returns
Created op output.
def trace: Output

$OpDocMathTrace
$OpDocMathTrace
returns
Result as a new tensor.
def truncateDivide(other: Output): Output

$OpDocMathTruncateDivide
$OpDocMathTruncateDivide
returns
Result as a new tensor.
def truncateMod(other: Output): Output

$OpDocMathTruncateMod
$OpDocMathTruncateMod
returns
Result as a new tensor.
def unary_!: Output

$OpDocMathLogicalNot
$OpDocMathLogicalNot
returns
Result as a new tensor.
def unary_-: Output

$OpDocMathNegate
$OpDocMathNegate
returns
Result as a new tensor.
def unsortedSegmentMax(segmentIndices: Output, segmentsNumber: Output): Output

$OpDocMathUnsortedSegmentMax
$OpDocMathUnsortedSegmentMax
segmentIndices
Segment indices (must have data type of INT32 or INT64).
segmentsNumber
Number of segments (must have data type of INT32).
returns
Result as a new tensor.
def unsortedSegmentMean(segmentIndices: Output, segmentsNumber: Output): Output

$OpDocMathUnsortedSegmentMean
$OpDocMathUnsortedSegmentMean
segmentIndices
Segment indices (must have data type of INT32 or INT64).
segmentsNumber
Number of segments (must have data type of INT32).
returns
Result as a new tensor.
def unsortedSegmentMin(segmentIndices: Output, segmentsNumber: Output): Output

$OpDocMathUnsortedSegmentMin
$OpDocMathUnsortedSegmentMin
segmentIndices
Segment indices (must have data type of INT32 or INT64).
segmentsNumber
Number of segments (must have data type of INT32).
returns
Result as a new tensor.
def unsortedSegmentProd(segmentIndices: Output, segmentsNumber: Output): Output

$OpDocMathUnsortedSegmentProd
$OpDocMathUnsortedSegmentProd
segmentIndices
Segment indices (must have data type of INT32 or INT64).
segmentsNumber
Number of segments (must have data type of INT32).
returns
Result as a new tensor.
def unsortedSegmentSqrtN(segmentIndices: Output, segmentsNumber: Output): Output

$OpDocMathUnsortedSegmentSqrtN
$OpDocMathUnsortedSegmentSqrtN
segmentIndices
Segment indices (must have data type of INT32 or INT64).
segmentsNumber
Number of segments (must have data type of INT32).
returns
Result as a new tensor.
def unsortedSegmentSum(segmentIndices: Output, segmentsNumber: Output): Output

$OpDocMathUnsortedSegmentSum
$OpDocMathUnsortedSegmentSum
segmentIndices
Segment indices (must have data type of INT32 or INT64).
segmentsNumber
Number of segments (must have data type of INT32).
returns
Result as a new tensor.
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
@throws( ... )
def zerosFraction: Output

$OpDocMathZerosFraction
$OpDocMathZerosFraction
returns
Result as a new tensor, with FLOAT32 data type.
def zeta(other: Output): Output

$OpDocMathZeta
$OpDocMathZeta
returns
Result as a new tensor.
def ||(other: Output): Output

$OpDocMathLogicalOr
$OpDocMathLogicalOr
returns
Result as a new tensor.

Deprecated Value Members

def floorDivide(other: Output): Output

$OpDocMathFloorDivide
$OpDocMathFloorDivide
returns
Result as a new tensor.

Annotations
@deprecated
Deprecated
(Since version 0.1) Use truncateDivide instead.

Related Doc: package Math

case class MathOps(output: Output) extends Product with Serializable

Instance Constructors

new MathOps(output: Output)

Value Members

def !=(other: Output): Output

final def !=(arg0: Any): Boolean

final def ##(): Int

def %(other: Output): Output

def &&(other: Output): Output

def *(other: Output): Output

def **(other: Output): Output

def +(other: Output): Output

def -(other: Output): Output

def /(other: Output): Output

def <(other: Output): Output

def <=(other: Output): Output

def ==(other: Output): Output

final def ==(arg0: Any): Boolean

def >(other: Output): Output

def >=(other: Output): Output

def ^(other: Output): Output

def abs: Output

def acos: Output

def acosh: Output

def add(other: Output): Output

def all(axes: Output = null, keepDims: Boolean = false): Output

def angle: Output

def any(axes: Output = null, keepDims: Boolean = false): Output

def approximatelyEqual(other: Output): Output

def argmax(axes: Output = 0, outputDataType: types.DataType = INT64): Output

def argmin(axes: Output = 0, outputDataType: types.DataType = INT64): Output

final def asInstanceOf[T0]: T0

def asin: Output

def asinh: Output

def atan: Output

def atan2(other: Output): Output

def atanh: Output

def binCount(weights: Output = null, minLength: Output = null, maxLength: Output = null, dataType: types.DataType = INT32): Output

def bitcast(dataType: types.DataType): Output

def bucketize(boundaries: Seq[Float]): Output

def cast(dataType: types.DataType): Output

def ceil: Output

def clone(): AnyRef

def conjugate: Output

def cos: Output

def cosh: Output

def countNonZero(axes: Output = null, keepDims: Boolean = false): Output

def cross(other: Output): Output

def cumprod(axis: Output = 0, exclusive: Boolean = false, reverse: Boolean = false): Output

def cumsum(axis: Output = 0, exclusive: Boolean = false, reverse: Boolean = false): Output

def diag: Output

def diagPart: Output

def digamma: Output

def divide(other: Output): Output

final def eq(arg0: AnyRef): Boolean

def equal(other: Output): Output

def erc: Output

def erf: Output

def exp: Output

def expm1: Output

def finalize(): Unit

def floor: Output

def floorMod(other: Output): Output

final def getClass(): Class[_]

def greater(other: Output): Output

def greaterEqual(other: Output): Output

def igamma(other: Output): Output

def igammac(other: Output): Output

def imag: Output

def isFinite: Output

def isInf: Output

final def isInstanceOf[T0]: Boolean

def isNaN: Output

def less(other: Output): Output

def lessEqual(other: Output): Output

def log: Output

def log1p: Output

def logGamma: Output