Math

Type Members

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

Value Members

final def !=(arg0: Any): Boolean

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

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def abs[T <: OutputLike](x: T, name: String = "Abs")(implicit arg0: OutputOps[T]): T

$OpDocMathAbs
$OpDocMathAbs
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def accumulateN(inputs: Seq[Output], shape: core.Shape = null, name: String = "AccumulateN"): Output

$OpDocMathAccumulateN
$OpDocMathAccumulateN
inputs
Input tensors.
shape
Shape of the elements of inputs (in case it's not known statically and needs to be retained).
name
Created op name.
returns
Created op output.

Definition Classes
Math
Annotations
@throws( ... )
Exceptions thrown
InvalidArgumentException If any of the inputs has a different data type and/or shape than the rest.
def acos[T](x: T, name: String = "Acos")(implicit arg0: OutputOps[T]): T

$OpDocMathAcos
$OpDocMathAcos
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def acosh[T](x: T, name: String = "ACosh")(implicit arg0: OutputOps[T]): T

$OpDocMathAcosh
$OpDocMathAcosh
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def add(x: Output, y: Output, name: String = "Add"): Output

$OpDocMathAdd
$OpDocMathAdd
x
First input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, UINT8, INT8, INT16, INT32, INT64, COMPLEX64, COMPLEX128, or STRING.
y
Second input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, UINT8, INT8, INT16, INT32, INT64, COMPLEX64, COMPLEX128, or STRING.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def addN(inputs: Seq[Output], name: String = "AddN"): Output

$OpDocMathAddN
$OpDocMathAddN
inputs
Input tensors.
name
Created op name.
returns
Created op output.

Definition Classes
Math
def all(input: Output, axes: Output = null, keepDims: Boolean = false, name: String = "All"): Output

$OpDocMathAll
$OpDocMathAll
input
Input tensor to reduce.
axes
Integer tensor containing the axes to reduce. If null, then all axes are reduced.
keepDims
If true, retain the reduced axes.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def angle[T <: OutputLike](input: T, name: String = "Angle")(implicit arg0: OutputOps[T]): T

$OpDocMathAngle
$OpDocMathAngle
input
Input tensor.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
Annotations
@throws( ... )
Exceptions thrown
IllegalArgumentException If the provided tensor is not numeric.
def any(input: Output, axes: Output = null, keepDims: Boolean = false, name: String = "Any"): Output

$OpDocMathAny
$OpDocMathAny
input
Input tensor to reduce.
axes
Integer tensor containing the axes to reduce. If null, then all axes are reduced.
keepDims
If true, retain the reduced axes.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def approximatelyEqual(x: Output, y: Output, tolerance: Float = 0.00001f, name: String = "ApproximatelyEqual"): Output

$OpDocMathApproximatelyEqual
$OpDocMathApproximatelyEqual
x
First input tensor.
y
Second input tensor.
tolerance
Comparison tolerance value.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def argmax(input: Output, axes: Output = 0, outputDataType: types.DataType = INT64, name: String = "ArgMax"): Output

$OpDocMathArgmax
$OpDocMathArgmax
input
Input tensor.
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.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def argmin(input: Output, axes: Output = 0, outputDataType: types.DataType = INT64, name: String = "ArgMin"): Output

$OpDocMathArgmin
$OpDocMathArgmin
input
Input tensor.
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.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
Annotations
@throws( ... )
Exceptions thrown
IllegalArgumentException If axes data type or outputDataType is not INT32 or INT64.
final def asInstanceOf[T0]: T0

Definition Classes
Any
def asin[T](x: T, name: String = "Asin")(implicit arg0: OutputOps[T]): T

$OpDocMathAsin
$OpDocMathAsin
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def asinh[T](x: T, name: String = "ASinh")(implicit arg0: OutputOps[T]): T

$OpDocMathAsinh
$OpDocMathAsinh
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def atan[T](x: T, name: String = "Atan")(implicit arg0: OutputOps[T]): T

$OpDocMathAtan
$OpDocMathAtan
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def atan2(x: Output, y: Output, name: String = "ATan2"): Output

$OpDocMathAtan2
$OpDocMathAtan2
x
First input tensor that must be one of the following types: FLOAT32, or FLOAT64.
y
Second input tensor that must be one of the following types: FLOAT32, or FLOAT64.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def atanh[T](x: T, name: String = "ATanh")(implicit arg0: OutputOps[T]): T

$OpDocMathAtanh
$OpDocMathAtanh
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def binCount(input: Output, weights: Output = null, minLength: Output = null, maxLength: Output = null, dataType: types.DataType = INT32, name: String = "BinCount"): Output

$OpDocMathBinCount
$OpDocMathBinCount
input
INT32 tensor containing non-negative values.
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).
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def bucketize(input: Output, boundaries: Seq[Float], name: String = "Bucketize"): Output

$OpDocMathBucketize
$OpDocMathBucketize
input
Numeric tensor to bucketize.
boundaries
Sorted sequence of Floats specifying the boundaries of the buckets.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def ceil[T](x: T, name: String = "Ceil")(implicit arg0: OutputOps[T]): T

$OpDocMathCeil
$OpDocMathCeil
x
Input tensor that must be one of the following types: HALF, FLOAT32, or FLOAT64.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def complex(real: Output, imag: Output, name: String = "Complex"): Output

$OpDocMathComplex
$OpDocMathComplex
real
Tensor containing the real component. Must have FLOAT32 or FLOAT64 data type.
imag
Tensor containing the imaginary component. Must have FLOAT32 or FLOAT64 data type.
name
Name for the created op.
returns
Created op output with data type being either COMPLEX64 or COMPLEX128.

Definition Classes
Math
def conjugate[T <: OutputLike](input: T, name: String = "Conjugate")(implicit arg0: OutputOps[T]): T

$OpDocMathConjugate
$OpDocMathConjugate
input
Input tensor.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
Annotations
@throws( ... )
Exceptions thrown
IllegalArgumentException If the provided tensor is not numeric.
def cos[T](x: T, name: String = "Cos")(implicit arg0: OutputOps[T]): T

$OpDocMathCos
$OpDocMathCos
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def cosh[T](x: T, name: String = "Cosh")(implicit arg0: OutputOps[T]): T

$OpDocMathCosh
$OpDocMathCosh
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def countNonZero(input: Output, axes: Output = null, keepDims: Boolean = false, name: String = "CountNonZero"): Output

$OpDocMathCountNonZero
$OpDocMathCountNonZero
input
Input tensor to reduce.
axes
Integer array containing the axes to reduce. If null, then all axes are reduced.
keepDims
If true, retain the reduced axes.
name
Name for the created op.
returns
Created op output with INT64 data type.

Definition Classes
Math
def countNonZeroSparse[T <: OutputLike](input: T, name: String = "CountNonZero"): Output

$OpDocMathCountNonZero
$OpDocMathCountNonZero
input
Input tensor for which to count the number of non-zero entries.
name
Name for the created op.
returns
Created op output with INT64 data type.

Definition Classes
Math
def cross(a: Output, b: Output, name: String = "Cross"): Output

$OpDocMathCross
$OpDocMathCross
a
First input tensor.
b
Second input tensor.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def cumprod(input: Output, axis: Output = 0, exclusive: Boolean = false, reverse: Boolean = false, name: String = "CumProd"): Output

$OpDocMathCumprod
$OpDocMathCumprod
input
Input tensor.
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.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def cumsum(input: Output, axis: Output = 0, exclusive: Boolean = false, reverse: Boolean = false, name: String = "CumSum"): Output

$OpDocMathCumsum
$OpDocMathCumsum
input
Input tensor.
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.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def diag(diagonal: Output, name: String = "Diag"): Output

$OpDocMathDiag
$OpDocMathDiag
diagonal
Diagonal values, represented as a rank-K tensor, where K can be at most 3.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def diagPart(input: Output, name: String = "DiagPart"): Output

$OpDocMathDiagPart
$OpDocMathDiagPart
input
Rank-K input tensor, where K is either 2, 4, or 6.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def digamma[T](x: T, name: String = "Digamma")(implicit arg0: OutputOps[T]): T

$OpDocMathDigamma
$OpDocMathDigamma
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def divide(x: Output, y: Output, name: String = "Div"): Output

$OpDocMathDivide
$OpDocMathDivide
x
First input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, UINT8, INT8, INT16, INT32, INT64, COMPLEX64, or COMPLEX128.
y
Second input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, UINT8, INT8, INT16, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equal(x: Output, y: Output, name: String = "Equal"): Output

$OpDocMathEqual
$OpDocMathEqual
x
First input tensor.
y
Second input tensor.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def erf[T](x: T, name: String = "Erf")(implicit arg0: OutputOps[T]): T

$OpDocMathErf
$OpDocMathErf
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def erfc[T](x: T, name: String = "Erfc")(implicit arg0: OutputOps[T]): T

$OpDocMathErfc
$OpDocMathErfc
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def exp[T](x: T, name: String = "Exp")(implicit arg0: OutputOps[T]): T

$OpDocMathExp
$OpDocMathExp
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def expm1[T](x: T, name: String = "Expm1")(implicit arg0: OutputOps[T]): T

$OpDocMathExpm1
$OpDocMathExpm1
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def floor[T](x: T, name: String = "Floor")(implicit arg0: OutputOps[T]): T

$OpDocMathFloor
$OpDocMathFloor
x
Input tensor that must be one of the following types: HALF, FLOAT32, or FLOAT64.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def floorMod(x: Output, y: Output, name: String = "FloorMod"): Output

$OpDocMathFloorMod
$OpDocMathFloorMod
x
First input tensor that must be one of the following types: FLOAT32, FLOAT64, INT32, or INT64.
y
Second input tensor that must be one of the following types: FLOAT32, FLOAT64, INT32, or INT64.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def greater(x: Output, y: Output, name: String = "Greater"): Output

OpDocMathGreater
OpDocMathGreater
x
First input tensor.
y
Second input tensor.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def greaterEqual(x: Output, y: Output, name: String = "GreaterEqual"): Output

OpDocMathGreaterEqual
OpDocMathGreaterEqual
x
First input tensor.
y
Second input tensor.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def hashCode(): Int

Definition Classes
AnyRef → Any
def igamma(a: Output, x: Output, name: String = "Igamma"): Output

$OpDocMathIgamma
$OpDocMathIgamma
a
First input tensor that must be one of the following types: FLOAT32, or FLOAT64.
x
Second input tensor that must be one of the following types: FLOAT32, or FLOAT64.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def igammac(a: Output, x: Output, name: String = "Igammac"): Output

$OpDocMathIgammac
$OpDocMathIgammac
a
First input tensor that must be one of the following types: FLOAT32, or FLOAT64.
x
Second input tensor that must be one of the following types: FLOAT32, or FLOAT64.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def imag[T <: OutputLike](input: T, name: String = "Imag")(implicit arg0: OutputOps[T]): T

$OpDocMathImag
$OpDocMathImag
input
Input tensor.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def incompleteBeta(a: Output, b: Output, x: Output, name: String = "IncompleteBeta"): Output

$OpDocMathIncompleteBeta
$OpDocMathIncompleteBeta
a
First input tensor that must be one of the following types: FLOAT32, or FLOAT64.
b
Second input tensor that must be one of the following types: FLOAT32, or FLOAT64.
x
Third input tensor that must be one of the following types: FLOAT32, or FLOAT64.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def isFinite[T](x: T, name: String = "IsFinite")(implicit arg0: OutputOps[T]): T

$OpDocMathIsFinite
$OpDocMathIsFinite
x
Input tensor that must be one of the following types: HALF, FLOAT32, or FLOAT64.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def isInf[T](x: T, name: String = "IsInf")(implicit arg0: OutputOps[T]): T

$OpDocMathIsInf
$OpDocMathIsInf
x
Input tensor that must be one of the following types: HALF, FLOAT32, or FLOAT64.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def isNaN[T](x: T, name: String = "IsNaN")(implicit arg0: OutputOps[T]): T

$OpDocMathIsNaN
$OpDocMathIsNaN
x
Input tensor that must be one of the following types: HALF, FLOAT32, or FLOAT64.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def less(x: Output, y: Output, name: String = "Less"): Output

OpDocMathLess
OpDocMathLess
x
First input tensor.
y
Second input tensor.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def lessEqual(x: Output, y: Output, name: String = "LessEqual"): Output

OpDocMathLessEqual
OpDocMathLessEqual
x
First input tensor.
y
Second input tensor.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def linspace(start: Output, stop: Output, numberOfValues: Output, name: String = "LinSpace"): Output

$OpDocMathLinspace
$OpDocMathLinspace
start
Rank 0 (i.e., scalar) tensor that contains the starting value of the number sequence.
stop
Rank 0 (i.e., scalar) tensor that contains the ending value (inclusive) of the number sequence.
numberOfValues
Rank 0 (i.e., scalar) tensor that contains the number of values in the number sequence.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def log[T](x: T, name: String = "Log")(implicit arg0: OutputOps[T]): T

$OpDocMathLog
$OpDocMathLog
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def log1p[T](x: T, name: String = "Log1p")(implicit arg0: OutputOps[T]): T

$OpDocMathLog1p
$OpDocMathLog1p
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def logGamma[T](x: T, name: String = "Lgamma")(implicit arg0: OutputOps[T]): T

$OpDocMathLogGamma
$OpDocMathLogGamma
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def logSigmoid[T](x: T, name: String = "LogSigmoid")(implicit arg0: OutputOps[T]): T

$OpDocMathLogSigmoid
$OpDocMathLogSigmoid
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def logSumExp(input: Output, axes: Output = null, keepDims: Boolean = false, name: String = "LogSumExp"): Output

$OpDocMathLogSumExp
$OpDocMathLogSumExp
input
Input tensor to reduce.
axes
Integer tensor containing the axes to reduce. If null, then all axes are reduced.
keepDims
If true, retain the reduced axes.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def logicalAnd(x: Output, y: Output, name: String = "LogicalAnd"): Output

$OpDocMathLogicalAnd
$OpDocMathLogicalAnd
x
First input tensor.
y
Second input tensor.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def logicalNot(x: Output, name: String = "LogicalNot"): Output

$OpDocMathLogicalNot
$OpDocMathLogicalNot
x
Input tensor.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def logicalOr(x: Output, y: Output, name: String = "LogicalOr"): Output

$OpDocMathLogicalOr
$OpDocMathLogicalOr
x
First input tensor.
y
Second input tensor.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def logicalXOr(x: Output, y: Output, name: String = "LogicalXOr"): Output

$OpDocMathLogicalXOr
$OpDocMathLogicalXOr
x
First input tensor.
y
Second input tensor.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def matmul(a: Output, b: Output, transposeA: Boolean = false, transposeB: Boolean = false, conjugateA: Boolean = false, conjugateB: Boolean = false, aIsSparse: Boolean = false, bIsSparse: Boolean = false, name: String = "MatMul"): Output

$OpDocMathMatmul
$OpDocMathMatmul
a
First input tensor with data type one of: BFLOAT16, FLOAT16, FLOAT32, FLOAT64, INT32, COMPLEX64, COMPLEX128.
b
Second input tensor with data type one of: BFLOAT16, FLOAT16, FLOAT32, FLOAT64, INT32, COMPLEX64, COMPLEX128.
transposeA
If true, a is transposed before the multiplication.
transposeB
If true, b is transposed before the multiplication.
conjugateA
If true, a is conjugated before the multiplication.
conjugateB
If true, b is conjugated before the multiplication.
aIsSparse
If true, a is treated as a sparse matrix (i.e., it is assumed it contains many zeros).
bIsSparse
If true, b is treated as a sparse matrix (i.e., it is assumed it contains many zeros).
name
Name for the created op.
returns
Created op output that has the same data type as a and b and where each inner-most matrix is the product of the corresponding matrices in a and b.

Definition Classes
Math
def matrixBandPart(input: Output, numSubDiagonals: Output, numSuperDiagonals: Output, name: String = "MatrixBandPart"): Output

$OpDocMathMatrixBandPart
$OpDocMathMatrixBandPart
input
Input tensor.
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.
name
Name for the created op.

Definition Classes
Math
def matrixDiag(diagonal: Output, name: String = "MatrixDiag"): Output

$OpDocMathMatrixDiag
$OpDocMathMatrixDiag
diagonal
Rank-K input tensor, where K >= 1.
name
Name for the created op.
returns
Created op output with rank equal to K + 1 and shape equal to the shape of diagonal, with its last dimension duplicated.

Definition Classes
Math
def matrixDiagPart(input: Output, name: String = "MatrixDiagPart"): Output

$OpDocMathMatrixDiagPart
$OpDocMathMatrixDiagPart
input
Rank-K tensor, where K >= 2.
name
Name for the created op.
returns
Created op output containing the diagonal(s) and having shape equal to input.shape[:-2] + [min(input.shape[-2:])].

Definition Classes
Math
def matrixSetDiag(input: Output, diagonal: Output, name: String = "MatrixSetDiag"): Output

$OpDocMathMatrixSetDiag
$OpDocMathMatrixSetDiag
input
Rank-K+1 tensor, where K >= 2.
diagonal
Rank-K tensor, where K >= 1.
name
Name for the created op.
returns
Created op output with rank equal to K + 1 and shape equal to the shape of input.

Definition Classes
Math
def max(input: Output, axes: Output = null, keepDims: Boolean = false, name: String = "Max"): Output

$OpDocMathMax
$OpDocMathMax
input
Input tensor to reduce.
axes
Integer tensor containing the axes to reduce. If null, then all axes are reduced.
keepDims
If true, retain the reduced axes.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def maximum(x: Output, y: Output, name: String = "Maximum"): Output

$OpDocMathMaximum
$OpDocMathMaximum
x
First input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, or INT64.
y
Second input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, or INT64.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def mean(input: Output, axes: Output = null, keepDims: Boolean = false, name: String = "Mean"): Output

$OpDocMathMean
$OpDocMathMean
input
Input tensor to reduce.
axes
Integer tensor containing the axes to reduce. If null, then all axes are reduced.
keepDims
If true, retain the reduced axes.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def min(input: Output, axes: Output = null, keepDims: Boolean = false, name: String = "Min"): Output

$OpDocMathMin
$OpDocMathMin
input
Input tensor to reduce.
axes
Integer tensor containing the axes to reduce. If null, then all axes are reduced.
keepDims
If true, retain the reduced axes.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def minimum(x: Output, y: Output, name: String = "Minimum"): Output

$OpDocMathMinimum
$OpDocMathMinimum
x
First input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, or INT64.
y
Second input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, or INT64.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def mod(x: Output, y: Output, name: String = "Mod"): Output

$OpDocMathMod
$OpDocMathMod
x
First input tensor that must be one of the following types: FLOAT32, FLOAT64, INT32, or INT64.
y
Second input tensor that must be one of the following types: FLOAT32, FLOAT64, INT32, or INT64.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def multiply(x: Output, y: Output, name: String = "Mul"): Output

$OpDocMathMultiply
$OpDocMathMultiply
x
First input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, UINT8, INT8, INT16, INT32, INT64, COMPLEX64, or COMPLEX128.
y
Second input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, UINT8, INT8, INT16, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def negate[T](x: T, name: String = "Negate")(implicit arg0: OutputOps[T]): T

$OpDocMathNegate
$OpDocMathNegate
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def notEqual(x: Output, y: Output, name: String = "NotEqual"): Output

$OpDocMathNotEqual
$OpDocMathNotEqual
x
First input tensor.
y
Second input tensor.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def polygamma(n: Output, x: Output, name: String = "Polygamma"): Output

$OpDocMathPolygamma
$OpDocMathPolygamma
n
First input tensor that must be one of the following types: FLOAT32, or FLOAT64.
x
Second input tensor that must be one of the following types: FLOAT32, or FLOAT64.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def pow(x: Output, y: Output, name: String = "Pow"): Output

$OpDocMathPow
$OpDocMathPow
x
First input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
y
Second input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def prod(input: Output, axes: Output = null, keepDims: Boolean = false, name: String = "Prod"): Output

$OpDocMathProd
$OpDocMathProd
input
Input tensor to reduce.
axes
Integer tensor containing the axes to reduce. If null, then all axes are reduced.
keepDims
If true, retain the reduced axes.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def range(start: Output, limit: Output, delta: Output = Basic.constant(1), dataType: types.DataType = null, name: String = "Range"): Output

$OpDocMathRange
$OpDocMathRange
start
Rank 0 (i.e., scalar) tensor that contains the starting value of the number sequence.
limit
Rank 0 (i.e., scalar) tensor that contains the ending value (exclusive) of the number sequence.
delta
Rank 0 (i.e., scalar) tensor that contains the difference between consecutive numbers in the sequence.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def real[T <: OutputLike](input: T, name: String = "Real")(implicit arg0: OutputOps[T]): T

$OpDocMathReal
$OpDocMathReal
input
Input tensor.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def realDivide(x: Output, y: Output, name: String = "RealDiv"): Output

$OpDocMathRealDivide
$OpDocMathRealDivide
x
First input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, UINT8, INT8, INT16, INT32, INT64, COMPLEX64, or COMPLEX128.
y
Second input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, UINT8, INT8, INT16, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def reciprocal[T](x: T, name: String = "Reciprocal")(implicit arg0: OutputOps[T]): T

$OpDocMathReciprocal
$OpDocMathReciprocal
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def round[T](x: T, name: String = "Round")(implicit arg0: OutputOps[T]): T

$OpDocMathRound
$OpDocMathRound
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def roundInt[T](x: T, name: String = "RoundInt")(implicit arg0: OutputOps[T]): T

$OpDocMathRoundInt
$OpDocMathRoundInt
x
Input tensor that must be one of the following types: HALF, FLOAT32, or FLOAT64.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def rsqrt[T](x: T, name: String = "Rsqrt")(implicit arg0: OutputOps[T]): T

$OpDocMathRsqrt
$OpDocMathRsqrt
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def scalarMul[T](scalar: Output, tensor: T, name: String = "ScalarMul")(implicit arg0: OutputOps[T]): T

$OpDocMathScalarMul
$OpDocMathScalarMul
scalar
Scalar tensor.
tensor
Tensor to multiply the scalar tensor with.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def segmentMax(data: Output, segmentIndices: Output, name: String = "SegmentMax"): Output

$OpDocMathSegmentMax
$OpDocMathSegmentMax
data
Data (must have a numeric data type -- i.e., representing a number).
segmentIndices
Segment indices (must have data type of INT32 or INT64). Values should be sorted and can be repeated.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def segmentMean(data: Output, segmentIndices: Output, name: String = "SegmentMean"): Output

$OpDocMathSegmentMean
$OpDocMathSegmentMean
data
Data (must have a numeric data type -- i.e., representing a number).
segmentIndices
Segment indices (must have data type of INT32 or INT64). Values should be sorted and can be repeated.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def segmentMin(data: Output, segmentIndices: Output, name: String = "SegmentMin"): Output

$OpDocMathSegmentMin
$OpDocMathSegmentMin
data
Data (must have a numeric data type -- i.e., representing a number).
segmentIndices
Segment indices (must have data type of INT32 or INT64). Values should be sorted and can be repeated.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def segmentProd(data: Output, segmentIndices: Output, name: String = "SegmentProd"): Output

$OpDocMathSegmentProd
$OpDocMathSegmentProd
data
Data (must have a numeric data type -- i.e., representing a number).
segmentIndices
Segment indices (must have data type of INT32 or INT64). Values should be sorted and can be repeated.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def segmentSum(data: Output, segmentIndices: Output, name: String = "SegmentSum"): Output

$OpDocMathSegmentSum
$OpDocMathSegmentSum
data
Data (must have a numeric data type -- i.e., representing a number).
segmentIndices
Segment indices (must have data type of INT32 or INT64). Values should be sorted and can be repeated.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def select(condition: Output, x: Output, y: Output, name: String = "Select"): Output

$OpDocMathSelect
$OpDocMathSelect
condition
Boolean condition tensor.
x
Tensor which may have the same shape as condition. If condition has rank 1, then t may have a higher rank, but its first dimension must match the size of condition.
y
Tensor with the same data type and shape as t.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def sigmoid[T](x: T, name: String = "Sigmoid")(implicit arg0: OutputOps[T]): T

$OpDocMathSigmoid
$OpDocMathSigmoid
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def sign[T](x: T, name: String = "Sign")(implicit arg0: OutputOps[T]): T

$OpDocMathSign
$OpDocMathSign
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def sin[T](x: T, name: String = "Sin")(implicit arg0: OutputOps[T]): T

$OpDocMathSin
$OpDocMathSin
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def sinh[T](x: T, name: String = "Sinh")(implicit arg0: OutputOps[T]): T

$OpDocMathSinh
$OpDocMathSinh
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def sparseSegmentMean(data: Output, indices: Output, segmentIndices: Output, numSegments: Output = null, name: String = "SparseSegmentMean"): Output

$OpDocMathSparseSegmentMean
$OpDocMathSparseSegmentMean
data
Data (must have a numeric data type -- i.e., representing a number).
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.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def sparseSegmentSum(data: Output, indices: Output, segmentIndices: Output, numSegments: Output = null, name: String = "SparseSegmentSum"): Output

$OpDocMathSparseSegmentSum
$OpDocMathSparseSegmentSum
data
Data (must have a numeric data type -- i.e., representing a number).
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.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def sparseSegmentSumSqrtN(data: Output, indices: Output, segmentIndices: Output, numSegments: Output = null, name: String = "SparseSegmentSumSqrtN"): Output

$OpDocMathSparseSegmentSumSqrtN
$OpDocMathSparseSegmentSumSqrtN
data
Data (must have a numeric data type -- i.e., representing a number).
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.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def sqrt[T](x: T, name: String = "Sqrt")(implicit arg0: OutputOps[T]): T

$OpDocMathSqrt
$OpDocMathSqrt
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def square[T](x: T, name: String = "Square")(implicit arg0: OutputOps[T]): T

$OpDocMathSquare
$OpDocMathSquare
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def squaredDifference(x: Output, y: Output, name: String = "SquaredDifference"): Output

$OpDocMathSquaredDifference
$OpDocMathSquaredDifference
x
First input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
y
Second input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def subtract(x: Output, y: Output, name: String = "Sub"): Output

$OpDocMathSubtract
$OpDocMathSubtract
x
First input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
y
Second input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def sum(input: Output, axes: Output = null, keepDims: Boolean = false, name: String = "Sum"): Output

$OpDocMathSum
$OpDocMathSum
input
Input tensor to reduce.
axes
Integer tensor containing the axes to reduce. If null, then all axes are reduced.
keepDims
If true, retain the reduced axes.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def tan[T](x: T, name: String = "Tan")(implicit arg0: OutputOps[T]): T

$OpDocMathTan
$OpDocMathTan
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def tanh[T](x: T, name: String = "Tanh")(implicit arg0: OutputOps[T]): T

$OpDocMathTanh
$OpDocMathTanh
x
Input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def tensorDot(a: Output, b: Output, axesA: Seq[Int], axesB: Seq[Int], name: String): Output

$OpDocMathTensorDot
$OpDocMathTensorDot
a
First tensor.
b
Second tensor.
axesA
Axes to contract in a.
axesB
Axes to contract in b.
name
Name for the created ops.
returns
Created op output.

Definition Classes
Math
def tensorDot(a: Output, b: Output, axesA: Seq[Int], axesB: Seq[Int]): Output

$OpDocMathTensorDot
$OpDocMathTensorDot
a
First tensor.
b
Second tensor.
axesA
Axes to contract in a.
axesB
Axes to contract in b.
returns
Created op output.

Definition Classes
Math
def tensorDot(a: Output, b: Output, numAxes: Int, name: String): Output

$OpDocMathTensorDot
$OpDocMathTensorDot
a
First tensor.
b
Second tensor.
numAxes
Number of axes to contract.
name
Name for the created ops.
returns
Created op output.

Definition Classes
Math
def tensorDot(a: Output, b: Output, numAxes: Int): Output

$OpDocMathTensorDot
$OpDocMathTensorDot
a
First tensor.
b
Second tensor.
numAxes
Number of axes to contract.
returns
Created op output.

Definition Classes
Math
def tensorDotDynamic(a: Output, b: Output, axesA: Output, axesB: Output, name: String = "TensorDot"): Output

Dynamic version (i.e., where axesA and axesB may be symbolic tensors) of the tensorDot op.
Dynamic version (i.e., where axesA and axesB may be symbolic tensors) of the tensorDot op.
$OpDocMathTensorDot
a
First tensor.
b
Second tensor.
axesA
Axes to contract in a.
axesB
Axes to contract in b.
name
Name for the created ops.
returns
Created op output.

Definition Classes
Math
def tensorDotDynamic(a: Output, b: Output, axesA: Output, axesB: Output): Output

Dynamic version (i.e., where axesA and axesB may be symbolic tensors) of the tensorDot op.
Dynamic version (i.e., where axesA and axesB may be symbolic tensors) of the tensorDot op.
$OpDocMathTensorDot
a
First tensor.
b
Second tensor.
axesA
Axes to contract in a.
axesB
Axes to contract in b.
returns
Created op output.

Definition Classes
Math
def tensorDotDynamic(a: Output, b: 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
a
First tensor.
b
Second tensor.
numAxes
Number of axes to contract.
name
Name for the created ops.
returns
Created op output.

Definition Classes
Math
def tensorDotDynamic(a: Output, b: 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
a
First tensor.
b
Second tensor.
numAxes
Number of axes to contract.
returns
Created op output.

Definition Classes
Math
def toString(): String

Definition Classes
AnyRef → Any
def trace(input: Output, name: String = "Trace"): Output

$OpDocMathTrace
$OpDocMathTrace
input
Input tensor.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def truncateDivide(x: Output, y: Output, name: String = "TruncateDiv"): Output

$OpDocMathTruncateDivide
$OpDocMathTruncateDivide
x
First input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, UINT8, INT8, INT16, INT32, INT64, COMPLEX64, or COMPLEX128.
y
Second input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, UINT8, INT8, INT16, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def truncateMod(x: Output, y: Output, name: String = "TruncateMod"): Output

$OpDocMathTruncateMod
$OpDocMathTruncateMod
x
First input tensor that must be one of the following types: FLOAT32, FLOAT64, INT32, or INT64.
y
Second input tensor that must be one of the following types: FLOAT32, FLOAT64, INT32, or INT64.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def unsortedSegmentMax(data: Output, segmentIndices: Output, segmentsNumber: Output, name: String = "UnsortedSegmentMax"): Output

$OpDocMathUnsortedSegmentMax
$OpDocMathUnsortedSegmentMax
data
Data (must have a numeric data type -- i.e., representing a number).
segmentIndices
Segment indices (must have data type of INT32 or INT64).
segmentsNumber
Number of segments (must have data type of INT32).
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def unsortedSegmentMean(data: Output, segmentIndices: Output, segmentsNumber: Output, name: String = "UnsortedSegmentMean"): Output

$OpDocMathUnsortedSegmentMean
$OpDocMathUnsortedSegmentMean
data
Data (must have a numeric data type -- i.e., representing a number).
segmentIndices
Segment indices (must have data type of INT32 or INT64).
segmentsNumber
Number of segments (must have data type of INT32).
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def unsortedSegmentMin(data: Output, segmentIndices: Output, segmentsNumber: Output, name: String = "UnsortedSegmentMin"): Output

$OpDocMathUnsortedSegmentMin
$OpDocMathUnsortedSegmentMin
data
Data (must have a numeric data type -- i.e., representing a number).
segmentIndices
Segment indices (must have data type of INT32 or INT64).
segmentsNumber
Number of segments (must have data type of INT32).
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def unsortedSegmentN(data: Output, segmentIndices: Output, segmentsNumber: Output, name: String = "UnsortedSegmentN"): Output

Helper function for unsortedSegmentMean and unsortedSegmentSqrtN that computes the number of segment entries with zero entries set to 1, in order to allow for division by N.
Helper function for unsortedSegmentMean and unsortedSegmentSqrtN that computes the number of segment entries with zero entries set to 1, in order to allow for division by N.
data
Data (must have a numeric data type -- i.e., representing a number).
segmentIndices
Segment indices (must have data type of INT32 or INT64).
segmentsNumber
Number of segments (must have data type of INT32).
returns
Created op output.

Attributes
protected
Definition Classes
Math
def unsortedSegmentProd(data: Output, segmentIndices: Output, segmentsNumber: Output, name: String = "UnsortedSegmentProd"): Output

$OpDocMathUnsortedSegmentProd
$OpDocMathUnsortedSegmentProd
data
Data (must have a numeric data type -- i.e., representing a number).
segmentIndices
Segment indices (must have data type of INT32 or INT64).
segmentsNumber
Number of segments (must have data type of INT32).
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def unsortedSegmentSqrtN(data: Output, segmentIndices: Output, segmentsNumber: Output, name: String = "UnsortedSegmentSqrtN"): Output

$OpDocMathUnsortedSegmentSqrtN
$OpDocMathUnsortedSegmentSqrtN
data
Data (must have a numeric data type -- i.e., representing a number).
segmentIndices
Segment indices (must have data type of INT32 or INT64).
segmentsNumber
Number of segments (must have data type of INT32).
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
def unsortedSegmentSum(data: Output, segmentIndices: Output, segmentsNumber: Output, name: String = "UnsortedSegmentSum"): Output

$OpDocMathUnsortedSegmentSum
$OpDocMathUnsortedSegmentSum
data
Data (must have a numeric data type -- i.e., representing a number).
segmentIndices
Segment indices (must have data type of INT32 or INT64).
segmentsNumber
Number of segments (must have data type of INT32).
name
Name for the created op.
returns
Created op output.

Definition Classes
Math
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(input: Output, name: String = "ZerosFraction"): Output

$OpDocMathZerosFraction
$OpDocMathZerosFraction
input
Input tensor.
name
Name for the created op.
returns
Created op output, with FLOAT32 data type.

Definition Classes
Math
def zeta(x: Output, q: Output, name: String = "Zeta"): Output

$OpDocMathZeta
$OpDocMathZeta
x
First input tensor that must be one of the following types: FLOAT32, or FLOAT64.
q
Second input tensor that must be one of the following types: FLOAT32, or FLOAT64.
name
Name for the created op.
returns
Created op output.

Definition Classes
Math

Deprecated Value Members

def floorDivide(x: Output, y: Output, name: String = "FloorDiv"): Output

$OpDocMathFloorDivide
$OpDocMathFloorDivide
x
First input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, UINT8, INT8, INT16, INT32, INT64, COMPLEX64, or COMPLEX128.
y
Second input tensor that must be one of the following types: HALF, FLOAT32, FLOAT64, UINT8, INT8, INT16, INT32, INT64, COMPLEX64, or COMPLEX128.
name
Name for the created op.
returns
Created op output.

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

Related Doc: package ops

object Math extends Math

Type Members

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

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def abs[T <: OutputLike](x: T, name: String = "Abs")(implicit arg0: OutputOps[T]): T

def accumulateN(inputs: Seq[Output], shape: core.Shape = null, name: String = "AccumulateN"): Output

def acos[T](x: T, name: String = "Acos")(implicit arg0: OutputOps[T]): T

def acosh[T](x: T, name: String = "ACosh")(implicit arg0: OutputOps[T]): T

def add(x: Output, y: Output, name: String = "Add"): Output

def addN(inputs: Seq[Output], name: String = "AddN"): Output

def all(input: Output, axes: Output = null, keepDims: Boolean = false, name: String = "All"): Output

def angle[T <: OutputLike](input: T, name: String = "Angle")(implicit arg0: OutputOps[T]): T

def any(input: Output, axes: Output = null, keepDims: Boolean = false, name: String = "Any"): Output

def approximatelyEqual(x: Output, y: Output, tolerance: Float = 0.00001f, name: String = "ApproximatelyEqual"): Output

def argmax(input: Output, axes: Output = 0, outputDataType: types.DataType = INT64, name: String = "ArgMax"): Output

def argmin(input: Output, axes: Output = 0, outputDataType: types.DataType = INT64, name: String = "ArgMin"): Output

final def asInstanceOf[T0]: T0

def asin[T](x: T, name: String = "Asin")(implicit arg0: OutputOps[T]): T

def asinh[T](x: T, name: String = "ASinh")(implicit arg0: OutputOps[T]): T

def atan[T](x: T, name: String = "Atan")(implicit arg0: OutputOps[T]): T

def atan2(x: Output, y: Output, name: String = "ATan2"): Output

def atanh[T](x: T, name: String = "ATanh")(implicit arg0: OutputOps[T]): T

def binCount(input: Output, weights: Output = null, minLength: Output = null, maxLength: Output = null, dataType: types.DataType = INT32, name: String = "BinCount"): Output

def bucketize(input: Output, boundaries: Seq[Float], name: String = "Bucketize"): Output

def ceil[T](x: T, name: String = "Ceil")(implicit arg0: OutputOps[T]): T

def clone(): AnyRef

def complex(real: Output, imag: Output, name: String = "Complex"): Output

def conjugate[T <: OutputLike](input: T, name: String = "Conjugate")(implicit arg0: OutputOps[T]): T

def cos[T](x: T, name: String = "Cos")(implicit arg0: OutputOps[T]): T

def cosh[T](x: T, name: String = "Cosh")(implicit arg0: OutputOps[T]): T

def countNonZero(input: Output, axes: Output = null, keepDims: Boolean = false, name: String = "CountNonZero"): Output

def countNonZeroSparse[T <: OutputLike](input: T, name: String = "CountNonZero"): Output

def cross(a: Output, b: Output, name: String = "Cross"): Output

def cumprod(input: Output, axis: Output = 0, exclusive: Boolean = false, reverse: Boolean = false, name: String = "CumProd"): Output

def cumsum(input: Output, axis: Output = 0, exclusive: Boolean = false, reverse: Boolean = false, name: String = "CumSum"): Output

def diag(diagonal: Output, name: String = "Diag"): Output

def diagPart(input: Output, name: String = "DiagPart"): Output

def digamma[T](x: T, name: String = "Digamma")(implicit arg0: OutputOps[T]): T

def divide(x: Output, y: Output, name: String = "Div"): Output

final def eq(arg0: AnyRef): Boolean

def equal(x: Output, y: Output, name: String = "Equal"): Output

def equals(arg0: Any): Boolean

def erf[T](x: T, name: String = "Erf")(implicit arg0: OutputOps[T]): T

def erfc[T](x: T, name: String = "Erfc")(implicit arg0: OutputOps[T]): T

def exp[T](x: T, name: String = "Exp")(implicit arg0: OutputOps[T]): T

def expm1[T](x: T, name: String = "Expm1")(implicit arg0: OutputOps[T]): T

def finalize(): Unit

def floor[T](x: T, name: String = "Floor")(implicit arg0: OutputOps[T]): T

def floorMod(x: Output, y: Output, name: String = "FloorMod"): Output

final def getClass(): Class[_]

def greater(x: Output, y: Output, name: String = "Greater"): Output

def greaterEqual(x: Output, y: Output, name: String = "GreaterEqual"): Output

def hashCode(): Int

def igamma(a: Output, x: Output, name: String = "Igamma"): Output

def igammac(a: Output, x: Output, name: String = "Igammac"): Output

def imag[T <: OutputLike](input: T, name: String = "Imag")(implicit arg0: OutputOps[T]): T

def incompleteBeta(a: Output, b: Output, x: Output, name: String = "IncompleteBeta"): Output

def isFinite[T](x: T, name: String = "IsFinite")(implicit arg0: OutputOps[T]): T

def isInf[T](x: T, name: String = "IsInf")(implicit arg0: OutputOps[T]): T

final def isInstanceOf[T0]: Boolean

def isNaN[T](x: T, name: String = "IsNaN")(implicit arg0: OutputOps[T]): T

def less(x: Output, y: Output, name: String = "Less"): Output

def lessEqual(x: Output, y: Output, name: String = "LessEqual"): Output

def linspace(start: Output, stop: Output, numberOfValues: Output, name: String = "LinSpace"): Output

def log[T](x: T, name: String = "Log")(implicit arg0: OutputOps[T]): T

def log1p[T](x: T, name: String = "Log1p")(implicit arg0: OutputOps[T]): T

def logGamma[T](x: T, name: String = "Lgamma")(implicit arg0: OutputOps[T]): T

def logSigmoid[T](x: T, name: String = "LogSigmoid")(implicit arg0: OutputOps[T]): T

def logSumExp(input: Output, axes: Output = null, keepDims: Boolean = false, name: String = "LogSumExp"): Output

def logicalAnd(x: Output, y: Output, name: String = "LogicalAnd"): Output

def logicalNot(x: Output, name: String = "LogicalNot"): Output

def logicalOr(x: Output, y: Output, name: String = "LogicalOr"): Output

def logicalXOr(x: Output, y: Output, name: String = "LogicalXOr"): Output

def matmul(a: Output, b: Output, transposeA: Boolean = false, transposeB: Boolean = false, conjugateA: Boolean = false, conjugateB: Boolean = false, aIsSparse: Boolean = false, bIsSparse: Boolean = false, name: String = "MatMul"): Output

def matrixBandPart(input: Output, numSubDiagonals: Output, numSuperDiagonals: Output, name: String = "MatrixBandPart"): Output