Class CPUOps

Represents a CPU backend ops.

Inheritance

Object

CPUOps

GPUCommandBufferOps

GPUComputeOps

GPUPixelOps

Inherited Members

Object.ToString()

Object.Equals(Object)

Object.Equals(Object, Object)

Object.ReferenceEquals(Object, Object)

Object.GetHashCode()

Object.GetType()

Object.MemberwiseClone()

Namespace: Unity.Sentis

Syntax

public class CPUOps : IOps, IDisposable

Constructors

CPUOps(ITensorAllocator)

Initializes and returns an instance of CPUOps.

Declaration

public CPUOps(ITensorAllocator allocator = null)

Parameters

Type	Name	Description
ITensorAllocator	allocator	The allocator to use when allocating tensors.

Properties

deviceType

Returns the DeviceType for the ops.

Declaration

public virtual DeviceType deviceType { get; }

Property Value

Type	Description
DeviceType

Implements

IOps.deviceType

Methods

Abs(TensorFloat)

Computes an output tensor by applying the element-wise Abs math function: f(x) = f(x) = |x|.

Declaration

public virtual TensorFloat Abs(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Abs(TensorFloat)

Abs(TensorInt)

Computes an output tensor by applying the element-wise Abs math function: f(x) = f(x) = |x|.

Declaration

public virtual TensorInt Abs(TensorInt X)

Parameters

Type	Name	Description
TensorInt	X

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Abs(TensorInt)

Acos(TensorFloat)

Computes an output tensor by applying the element-wise Acos trigonometric function: f(x) = acos(x).

Declaration

public virtual TensorFloat Acos(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Acos(TensorFloat)

Acosh(TensorFloat)

Computes an output tensor by applying the element-wise Acosh trigonometric function: f(x) = acosh(x).

Declaration

public virtual TensorFloat Acosh(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Acosh(TensorFloat)

Add(TensorFloat, TensorFloat)

Performs an element-wise Add math operation: f(a, b) = a + b.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorFloat Add(TensorFloat A, TensorFloat B)

Parameters

Type	Name	Description
TensorFloat	A	The first input tensor.
TensorFloat	B	The second input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Add(TensorFloat, TensorFloat)

Add(TensorInt, TensorInt)

Performs an element-wise Add math operation: f(a, b) = a + b.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorInt Add(TensorInt A, TensorInt B)

Parameters

Type	Name	Description
TensorInt	A	The first input tensor.
TensorInt	B	The second input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Add(TensorInt, TensorInt)

And(TensorInt, TensorInt)

Declaration

public virtual TensorInt And(TensorInt A, TensorInt B)

Parameters

Type	Name	Description
TensorInt	A
TensorInt	B

Returns

Type	Description
TensorInt

Implements

IOps.And(TensorInt, TensorInt)

ArgMax(TensorFloat, Int32, Boolean, Boolean)

Computes the indices of the maximum elements of the input tensor along a given axis.

Declaration

public virtual TensorInt ArgMax(TensorFloat X, int axis, bool keepdim, bool selectLastIndex = false)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32	axis	The axis along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.
Boolean	selectLastIndex	Whether to perform the operation from the back of the axis.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.ArgMax(TensorFloat, Int32, Boolean, Boolean)

ArgMax(TensorInt, Int32, Boolean, Boolean)

Computes the indices of the maximum elements of the input tensor along a given axis.

Declaration

public virtual TensorInt ArgMax(TensorInt X, int axis, bool keepdim, bool selectLastIndex = false)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.
Int32	axis	The axis along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.
Boolean	selectLastIndex	Whether to perform the operation from the back of the axis.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.ArgMax(TensorInt, Int32, Boolean, Boolean)

ArgMin(TensorFloat, Int32, Boolean, Boolean)

Computes the indices of the minimum elements of the input tensor along a given axis.

Declaration

public virtual TensorInt ArgMin(TensorFloat X, int axis, bool keepdim, bool selectLastIndex = false)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32	axis	The axis along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.
Boolean	selectLastIndex	Whether to perform the operation from the back of the axis.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.ArgMin(TensorFloat, Int32, Boolean, Boolean)

ArgMin(TensorInt, Int32, Boolean, Boolean)

Computes the indices of the minimum elements of the input tensor along a given axis.

Declaration

public virtual TensorInt ArgMin(TensorInt X, int axis, bool keepdim, bool selectLastIndex = false)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.
Int32	axis	The axis along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.
Boolean	selectLastIndex	Whether to perform the operation from the back of the axis.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.ArgMin(TensorInt, Int32, Boolean, Boolean)

Asin(TensorFloat)

Computes an output tensor by applying the element-wise Asin trigonometric function: f(x) = asin(x).

Declaration

public virtual TensorFloat Asin(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Asin(TensorFloat)

Asinh(TensorFloat)

Computes an output tensor by applying the element-wise Asinh trigonometric function: f(x) = asinh(x).

Declaration

public virtual TensorFloat Asinh(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Asinh(TensorFloat)

Atan(TensorFloat)

Computes an output tensor by applying the element-wise Atan trigonometric function: f(x) = atan(x).

Declaration

public virtual TensorFloat Atan(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Atan(TensorFloat)

Atanh(TensorFloat)

Computes an output tensor by applying the element-wise Atanh trigonometric function: f(x) = atanh(x).

Declaration

public virtual TensorFloat Atanh(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Atanh(TensorFloat)

AveragePool(TensorFloat, Int32[], Int32[], Int32[])

Calculates an output tensor by pooling the mean values of the input tensor across its spatial dimensions according to the given pool and stride values.

Declaration

public virtual TensorFloat AveragePool(TensorFloat X, int[] pool, int[] stride, int[] pad)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	pool	The size of the kernel along each spatial axis.
Int32[]	stride	The stride along each spatial axis.
Int32[]	pad	The lower and upper padding values for each spatial dimension. For example, [pad_left, pad_right] for 1D, or [pad_top, pad_bottom, pad_left, pad_right] for 2D.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.AveragePool(TensorFloat, Int32[], Int32[], Int32[])

AxisNormalization(TensorFloat, TensorFloat, TensorFloat, Single)

Computes the mean variance on the spatial dimensions of the input tensor and normalizes them according to scale and bias tensors.

Declaration

public virtual TensorFloat AxisNormalization(TensorFloat X, TensorFloat S, TensorFloat B, float epsilon)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
TensorFloat	S	The scale tensor.
TensorFloat	B	The bias tensor.
Single	epsilon	The epsilon value the layer uses to avoid division by zero.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.AxisNormalization(TensorFloat, TensorFloat, TensorFloat, Single)

Bernoulli(TensorFloat, DataType, Nullable<Single>)

Generates an output tensor with values 0 or 1 from a Bernoulli distribution. The input tensor contains the probabilities to use for generating the output values.

Declaration

public virtual Tensor Bernoulli(TensorFloat X, DataType dataType, float? seed)

Parameters

Type	Name	Description
TensorFloat	X
DataType	dataType	The data type of the output tensor.
Nullable<Single>	seed	The optional seed to use for the random number generation. If this is null the operation generates a seed using System.Random().

Returns

Type	Description
Tensor	The computed output tensor.

Implements

IOps.Bernoulli(TensorFloat, DataType, Nullable<Single>)

Cast(Tensor, DataType)

Computes the output tensor using an element-wise Cast function: f(x) = (float)x or f(x) = (int)x depending on the value of toType.

Declaration

public virtual Tensor Cast(Tensor X, DataType toType)

Parameters

Type	Name	Description
Tensor	X
DataType	toType	The data type to cast to as a DataType.

Returns

Type	Description
Tensor	The computed output tensor.

Implements

IOps.Cast(Tensor, DataType)

Cast(TensorFloat)

Declaration

public virtual TensorInt Cast(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorInt

Cast(TensorInt)

Declaration

public virtual TensorFloat Cast(TensorInt X)

Parameters

Type	Name	Description
TensorInt	X

Returns

Type	Description
TensorFloat

Ceil(TensorFloat)

Computes an output tensor by applying the element-wise Ceil math function: f(x) = ceil(x).

Declaration

public virtual TensorFloat Ceil(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Ceil(TensorFloat)

Celu(TensorFloat, Single)

Computes an output tensor by applying the element-wise Celu activation function: f(x) = max(0, x) + min(0, alpha * (exp(x / alpha) - 1)).

Declaration

public virtual TensorFloat Celu(TensorFloat X, float alpha)

Parameters

Type	Name	Description
TensorFloat	X
Single	alpha	The alpha value to use for the Celu activation function.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Celu(TensorFloat, Single)

Clip(TensorFloat, Single, Single)

Computes an output tensor by applying the element-wise Clip math function: f(x) = clamp(x, min, max).

Declaration

public virtual TensorFloat Clip(TensorFloat X, float min, float max)

Parameters

Type	Name	Description
TensorFloat	X
Single	min	The lower clip value.
Single	max	The upper clip value.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Clip(TensorFloat, Single, Single)

Compress(Tensor, TensorInt, Int32)

Selects slices of an input tensor along a given axis according to a condition tensor.

Declaration

public virtual Tensor Compress(Tensor X, TensorInt condition, int axis)

Parameters

Type	Name	Description
Tensor	X	The input tensor.
TensorInt	condition
Int32	axis	The axis along which to compress.

Returns

Type	Description
Tensor	The computed output tensor.

Implements

IOps.Compress(Tensor, TensorInt, Int32)

CompressWithIndices(Tensor, TensorInt, Int32, Int32)

Declaration

protected virtual Tensor CompressWithIndices(Tensor X, TensorInt indices, int numIndices, int axis)

Parameters

Type	Name	Description
Tensor	X
TensorInt	indices
Int32	numIndices
Int32	axis

Returns

Type	Description
Tensor

Concat(Tensor[], Int32)

Calculates an output tensor by concatenating the input tensors along a given axis.

Declaration

public virtual Tensor Concat(Tensor[] tensors, int axis)

Parameters

Type	Name	Description
Tensor[]	tensors	The input tensors.
Int32	axis	The axis along which to concatenate the input tensors.

Returns

Type	Description
Tensor	The computed output tensor.

Implements

IOps.Concat(Tensor[], Int32)

ConstantOfShape(TensorShape, Int32)

Generates a tensor with a given shape filled with a given value.

Declaration

public virtual TensorInt ConstantOfShape(TensorShape X, int value)

Parameters

Type	Name	Description
TensorShape	X	The input tensor shape.
Int32	value	The fill value.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.ConstantOfShape(TensorShape, Int32)

ConstantOfShape(TensorShape, Single)

Generates a tensor with a given shape filled with a given value.

Declaration

public virtual TensorFloat ConstantOfShape(TensorShape X, float value)

Parameters

Type	Name	Description
TensorShape	X	The input tensor shape.
Single	value	The fill value.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.ConstantOfShape(TensorShape, Single)

Conv(TensorFloat, TensorFloat, TensorFloat, Int32, Int32[], Int32[], Int32[], FusableActivation)

Applies a convolution filter to an input tensor.

Declaration

public virtual TensorFloat Conv(TensorFloat X, TensorFloat K, TensorFloat B, int groups, int[] stride, int[] pad, int[] dilation, FusableActivation fusedActivation)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
TensorFloat	K	The filter tensor.
TensorFloat	B	The optional bias tensor.
Int32	groups	The number of groups that input channels and output channels are divided into.
Int32[]	stride	The optional stride value for each spatial dimension of the filter.
Int32[]	pad	The optional lower and upper padding values for each spatial dimension of the filter.
Int32[]	dilation	The optional dilation value of each spatial dimension of the filter.
FusableActivation	fusedActivation	The fused activation type to apply after the convolution.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Conv(TensorFloat, TensorFloat, TensorFloat, Int32, Int32[], Int32[], Int32[], FusableActivation)

Conv2DTrans(TensorFloat, TensorFloat, TensorFloat, Int32[], Int32[], Int32[], FusableActivation)

Applies a transpose convolution filter to an input tensor.

Declaration

public virtual TensorFloat Conv2DTrans(TensorFloat X, TensorFloat K, TensorFloat B, int[] stride, int[] pad, int[] outputAdjustment, FusableActivation fusedActivation)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
TensorFloat	K	The filter tensor.
TensorFloat	B	The optional bias tensor.
Int32[]	stride	The optional stride value for each spatial dimension of the filter.
Int32[]	pad	The optional lower and upper padding values for each spatial dimension of the filter.
Int32[]	outputAdjustment	The output padding value for each spatial dimension in the filter.
FusableActivation	fusedActivation	The fused activation type to apply after the convolution.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Conv2DTrans(TensorFloat, TensorFloat, TensorFloat, Int32[], Int32[], Int32[], FusableActivation)

Copy(Tensor)

Creates a copy of a given input tensor with the same shape and values.

Declaration

public virtual Tensor Copy(Tensor X)

Parameters

Type	Name	Description
Tensor	X

Returns

Type	Description
Tensor	The computed output tensor.

Implements

IOps.Copy(Tensor)

Cos(TensorFloat)

Computes an output tensor by applying the element-wise Cos trigonometric function: f(x) = cos(x).

Declaration

public virtual TensorFloat Cos(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Cos(TensorFloat)

Cosh(TensorFloat)

Computes an output tensor by applying the element-wise Cosh trigonometric function: f(x) = cosh(x).

Declaration

public virtual TensorFloat Cosh(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Cosh(TensorFloat)

CumSum(TensorFloat, Int32, Boolean, Boolean)

Performs the cumulative sum along a given axis.

Declaration

public virtual TensorFloat CumSum(TensorFloat X, int axis, bool reverse, bool exclusive)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32	axis	The axis along which to apply the cumulative sum.
Boolean	reverse	Whether to perform the cumulative sum from the end of the axis.
Boolean	exclusive	Whether to include the respective input element in the cumulative sum.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.CumSum(TensorFloat, Int32, Boolean, Boolean)

CumSum(TensorInt, Int32, Boolean, Boolean)

Performs the cumulative sum along a given axis.

Declaration

public virtual TensorInt CumSum(TensorInt X, int axis, bool reverse, bool exclusive)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.
Int32	axis	The axis along which to apply the cumulative sum.
Boolean	reverse	Whether to perform the cumulative sum from the end of the axis.
Boolean	exclusive	Whether to include the respective input element in the cumulative sum.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.CumSum(TensorInt, Int32, Boolean, Boolean)

Dense(TensorFloat, TensorFloat, TensorFloat, FusableActivation)

Performs a matrix multiplication operation: f(x, w, b) = X x W + B.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorFloat Dense(TensorFloat X, TensorFloat W, TensorFloat B, FusableActivation fusedActivation)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
TensorFloat	W	The weights tensor.
TensorFloat	B	The bias tensor.
FusableActivation	fusedActivation	The fused activation to apply to the output tensor after the dense operation.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Dense(TensorFloat, TensorFloat, TensorFloat, FusableActivation)

DepthToSpace(TensorFloat, Int32, DepthToSpaceMode)

Computes the output tensor by permuting data from depth into blocks of spatial data.

Declaration

public virtual TensorFloat DepthToSpace(TensorFloat X, int blocksize, DepthToSpaceMode mode)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32	blocksize	The size of the blocks to move the depth data into.
DepthToSpaceMode	mode	The ordering of the data in the output tensor as a DepthToSpaceMode.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.DepthToSpace(TensorFloat, Int32, DepthToSpaceMode)

Dispose()

Disposes of the ops and any associated memory.

Declaration

public virtual void Dispose()

Implements

IDisposable.Dispose()

Div(TensorFloat, TensorFloat)

Performs an element-wise Div math operation: f(a, b) = a / b.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorFloat Div(TensorFloat A, TensorFloat B)

Parameters

Type	Name	Description
TensorFloat	A	The first input tensor.
TensorFloat	B	The second input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Div(TensorFloat, TensorFloat)

Div(TensorInt, TensorInt)

Performs an element-wise Div math operation: f(a, b) = a / b.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorInt Div(TensorInt A, TensorInt B)

Parameters

Type	Name	Description
TensorInt	A	The first input tensor.
TensorInt	B	The second input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Div(TensorInt, TensorInt)

Einsum(String, TensorFloat[])

Performs an Einsum math operation.

Declaration

public virtual TensorFloat Einsum(string equation, params TensorFloat[] operands)

Parameters

Type	Name	Description
String	equation	The equation of the Einstein summation as a comma-separated list of subscript labels.
TensorFloat[]	operands	The input tensors of the Einsum.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Einsum(String, TensorFloat[])

Elu(TensorFloat, Single)

Computes an output tensor by applying the element-wise Elu activation function: f(x) = x if x >= 0, otherwise f(x) = alpha * (e^x - 1).

Declaration

public virtual TensorFloat Elu(TensorFloat X, float alpha)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Single	alpha	The alpha value to use for the Elu activation function.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Elu(TensorFloat, Single)

Equal(TensorFloat, TensorFloat)

Performs an element-wise Equal logical comparison operation: f(a, b) = 1 if a == b, otherwise f(x) = 0.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorInt Equal(TensorFloat A, TensorFloat B)

Parameters

Type	Name	Description
TensorFloat	A	The first input tensor.
TensorFloat	B	The second input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Equal(TensorFloat, TensorFloat)

Equal(TensorInt, TensorInt)

Performs an element-wise Equal logical comparison operation: f(a, b) = 1 if a == b, otherwise f(x) = 0.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorInt Equal(TensorInt A, TensorInt B)

Parameters

Type	Name	Description
TensorInt	A	The first input tensor.
TensorInt	B	The second input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Equal(TensorInt, TensorInt)

Erf(TensorFloat)

Computes an output tensor by applying the element-wise Erf activation function: f(x) = erf(x).

Declaration

public virtual TensorFloat Erf(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Erf(TensorFloat)

Exp(TensorFloat)

Computes an output tensor by applying the element-wise Exp math function: f(x) = exp(x).

Declaration

public virtual TensorFloat Exp(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Exp(TensorFloat)

Expand(Tensor, TensorShape)

Calculates an output tensor by broadcasting the input tensor into a given shape.

Declaration

public virtual Tensor Expand(Tensor X, TensorShape newShape)

Parameters

Type	Name	Description
Tensor	X	The input tensor.
TensorShape	newShape

Returns

Type	Description
Tensor	The computed output tensor.

Implements

IOps.Expand(Tensor, TensorShape)

Floor(TensorFloat)

Computes an output tensor by applying the element-wise Floor math function: f(x) = floor(x).

Declaration

public virtual TensorFloat Floor(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Floor(TensorFloat)

FMod(TensorFloat, TensorFloat)

Performs an element-wise Mod math operation: f(a, b) = a % b.

The sign of the remainder is the same as the sign of the dividend, as in C#.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorFloat FMod(TensorFloat A, TensorFloat B)

Parameters

Type	Name	Description
TensorFloat	A	The first input tensor.
TensorFloat	B	The second input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.FMod(TensorFloat, TensorFloat)

FMod(TensorInt, TensorInt)

Performs an element-wise Mod math operation: f(a, b) = a % b.

The sign of the remainder is the same as the sign of the dividend, as in C#.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorInt FMod(TensorInt A, TensorInt B)

Parameters

Type	Name	Description
TensorInt	A	The first input tensor.
TensorInt	B	The second input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.FMod(TensorInt, TensorInt)

Gather(Tensor, TensorInt, Int32)

Takes values from the input tensor indexed by the indices tensor along a given axis and concatenates them.

Declaration

public virtual Tensor Gather(Tensor X, TensorInt indices, int axis)

Parameters

Type	Name	Description
Tensor	X	The input tensor.
TensorInt	indices	The indices tensor.
Int32	axis	The axis along which to gather.

Returns

Type	Description
Tensor	The computed output tensor.

Implements

IOps.Gather(Tensor, TensorInt, Int32)

GatherElements(Tensor, TensorInt, Int32)

Takes values from the input tensor indexed by the indices tensor along a given axis and concatenates them.

Declaration

public virtual Tensor GatherElements(Tensor X, TensorInt indices, int axis)

Parameters

Type	Name	Description
Tensor	X	The input tensor.
TensorInt	indices	The indices tensor.
Int32	axis	The axis along which to gather.

Returns

Type	Description
Tensor	The computed output tensor.

Implements

IOps.GatherElements(Tensor, TensorInt, Int32)

GatherND(Tensor, TensorInt, Int32)

Takes slices of values from the batched input tensor indexed by the indices tensor.

Declaration

public virtual Tensor GatherND(Tensor X, TensorInt indices, int batchDims)

Parameters

Type	Name	Description
Tensor	X	The input tensor.
TensorInt	indices	The indices tensor.
Int32	batchDims	The number of batch dimensions of the input tensor, the gather begins at the next dimension.

Returns

Type	Description
Tensor	The computed output tensor.

Implements

IOps.GatherND(Tensor, TensorInt, Int32)

Gelu(TensorFloat)

Computes an output tensor by applying the element-wise Gelu activation function: f(x) = x / 2 * (1 + erf(x / sqrt(2))).

Declaration

public virtual TensorFloat Gelu(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Gelu(TensorFloat)

GlobalAveragePool(TensorFloat)

Calculates an output tensor by pooling the mean values of the input tensor across all of its spatial dimensions. The spatial dimensions of the output are size 1.

Declaration

public virtual TensorFloat GlobalAveragePool(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.GlobalAveragePool(TensorFloat)

GlobalAverageVariancePool(TensorFloat, TensorFloat, Int32)

Declaration

public virtual void GlobalAverageVariancePool(TensorFloat O, TensorFloat X, int axis)

Parameters

Type	Name	Description
TensorFloat	O
TensorFloat	X
Int32	axis

GlobalMaxPool(TensorFloat)

Calculates an output tensor by pooling the maximum values of the input tensor across all of its spatial dimensions. The spatial dimensions of the output are size 1.

Declaration

public virtual TensorFloat GlobalMaxPool(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.GlobalMaxPool(TensorFloat)

Greater(TensorFloat, TensorFloat)

Performs an element-wise Greater logical comparison operation: f(a, b) = 1 if a > b, otherwise f(x) = 0.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorInt Greater(TensorFloat A, TensorFloat B)

Parameters

Type	Name	Description
TensorFloat	A	The first input tensor.
TensorFloat	B	The second input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Greater(TensorFloat, TensorFloat)

Greater(TensorInt, TensorInt)

Performs an element-wise Greater logical comparison operation: f(a, b) = 1 if a > b, otherwise f(x) = 0.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorInt Greater(TensorInt A, TensorInt B)

Parameters

Type	Name	Description
TensorInt	A	The first input tensor.
TensorInt	B	The second input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Greater(TensorInt, TensorInt)

GreaterOrEqual(TensorFloat, TensorFloat)

Performs an element-wise GreaterOrEqual logical comparison operation: f(a, b) = 1 if a >= b, otherwise f(x) = 0.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorInt GreaterOrEqual(TensorFloat A, TensorFloat B)

Parameters

Type	Name	Description
TensorFloat	A	The first input tensor.
TensorFloat	B	The second input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.GreaterOrEqual(TensorFloat, TensorFloat)

GreaterOrEqual(TensorInt, TensorInt)

Performs an element-wise GreaterOrEqual logical comparison operation: f(a, b) = 1 if a >= b, otherwise f(x) = 0.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorInt GreaterOrEqual(TensorInt A, TensorInt B)

Parameters

Type	Name	Description
TensorInt	A	The first input tensor.
TensorInt	B	The second input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.GreaterOrEqual(TensorInt, TensorInt)

Hardmax(TensorFloat, Int32)

Computes an output tensor by applying the Hardmax activation function along an axis: f(x, axis) = 1 if x is the first maximum value along the specified axis, otherwise f(x) = 0.

Declaration

public virtual TensorFloat Hardmax(TensorFloat X, int axis)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32	axis	The axis along which to apply the Hardmax activation function. The default value is -1.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Hardmax(TensorFloat, Int32)

HardSigmoid(TensorFloat, Single, Single)

Computes an output tensor by applying the element-wise HardSigmoid activation function: f(x) = clamp(alpha * x + beta, 0, 1).

Declaration

public virtual TensorFloat HardSigmoid(TensorFloat X, float alpha, float beta)

Parameters

Type	Name	Description
TensorFloat	X
Single	alpha	The alpha value to use for the HardSigmoid activation function.
Single	beta	The beta value to use for the HardSigmoid activation function.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.HardSigmoid(TensorFloat, Single, Single)

HardSwish(TensorFloat)

Computes an output tensor by applying the element-wise HardSwish activation function: f(x) = x * max(0, min(1, 1/6 * x + 0.5)).

Declaration

public virtual TensorFloat HardSwish(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.HardSwish(TensorFloat)

InstanceNormalization(TensorFloat, TensorFloat, TensorFloat, Single)

Computes the mean variance on the spatial dimensions of the input tensor and normalizes them according to scale and bias tensors.

Declaration

public virtual TensorFloat InstanceNormalization(TensorFloat X, TensorFloat S, TensorFloat B, float epsilon)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
TensorFloat	S	The scale tensor.
TensorFloat	B	The bias tensor.
Single	epsilon	The epsilon value the layer uses to avoid division by zero.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.InstanceNormalization(TensorFloat, TensorFloat, TensorFloat, Single)

IsInf(TensorFloat, Boolean, Boolean)

Performs an element-wise IsInf logical operation: f(x) = 1 elementwise if x is +Inf and detectPositive is true, or x is -Inf and detectNegative is true. Otherwise f(x) = 0.

Declaration

public virtual TensorInt IsInf(TensorFloat X, bool detectNegative, bool detectPositive)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Boolean	detectNegative	Whether to detect negative infinities in the IsInf function.
Boolean	detectPositive	Whether to detect positive infinities in the IsInf function.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.IsInf(TensorFloat, Boolean, Boolean)

IsNaN(TensorFloat)

Performs an element-wise IsNaN logical operation: f(x) = 1 if x is NaN, otherwise f(x) = 0.

Declaration

public virtual TensorInt IsNaN(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.IsNaN(TensorFloat)

LeakyRelu(TensorFloat, Single)

Computes an output tensor by applying the element-wise LeakyRelu activation function: f(x) = x if x >= 0, otherwise f(x) = alpha * x.

Declaration

public virtual TensorFloat LeakyRelu(TensorFloat X, float alpha)

Parameters

Type	Name	Description
TensorFloat	X
Single	alpha	The alpha value to use for the LeakyRelu activation function.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.LeakyRelu(TensorFloat, Single)

Less(TensorFloat, TensorFloat)

Performs an element-wise Less logical comparison operation: f(a, b) = 1 if a < b, otherwise f(x) = 0.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorInt Less(TensorFloat A, TensorFloat B)

Parameters

Type	Name	Description
TensorFloat	A	The first input tensor.
TensorFloat	B	The second input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Less(TensorFloat, TensorFloat)

Less(TensorInt, TensorInt)

Performs an element-wise Less logical comparison operation: f(a, b) = 1 if a < b, otherwise f(x) = 0.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorInt Less(TensorInt A, TensorInt B)

Parameters

Type	Name	Description
TensorInt	A	The first input tensor.
TensorInt	B	The second input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Less(TensorInt, TensorInt)

LessOrEqual(TensorFloat, TensorFloat)

Performs an element-wise LessOrEqual logical comparison operation: f(a, b) = 1 if a <= b, otherwise f(x) = 0.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorInt LessOrEqual(TensorFloat A, TensorFloat B)

Parameters

Type	Name	Description
TensorFloat	A	The first input tensor.
TensorFloat	B	The second input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.LessOrEqual(TensorFloat, TensorFloat)

LessOrEqual(TensorInt, TensorInt)

Performs an element-wise LessOrEqual logical comparison operation: f(a, b) = 1 if a <= b, otherwise f(x) = 0.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorInt LessOrEqual(TensorInt A, TensorInt B)

Parameters

Type	Name	Description
TensorInt	A	The first input tensor.
TensorInt	B	The second input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.LessOrEqual(TensorInt, TensorInt)

Log(TensorFloat)

Computes an output tensor by applying the element-wise Log math function: f(x) = log(x).

Declaration

public virtual TensorFloat Log(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Log(TensorFloat)

LogSoftmax(TensorFloat, Int32)

Computes an output tensor by applying the LogSoftmax activation function along an axis: f(x, axis) = log(Softmax(x, axis)).

Declaration

public virtual TensorFloat LogSoftmax(TensorFloat X, int axis)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32	axis	The axis along which to apply the LogSoftmax activation function. The default value is -1.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.LogSoftmax(TensorFloat, Int32)

LRN(TensorFloat, Single, Single, Single, Int32)

Normalizes the input tensor over local input regions.

Declaration

public virtual TensorFloat LRN(TensorFloat X, float alpha, float beta, float bias, int size)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Single	alpha	The scaling parameter to use for the normalization.
Single	beta	The exponent to use for the normalization.
Single	bias	The bias value to use for the normalization.
Int32	size	The number of channels to sum over.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.LRN(TensorFloat, Single, Single, Single, Int32)

LSTM(TensorFloat, TensorFloat, TensorFloat, TensorFloat, TensorInt, TensorFloat, TensorFloat, TensorFloat, RnnDirection, RnnActivation[], Single[], Single[], Boolean, Single, RnnLayout)

Generates an output tensor by computing a one-layer long short-term memory (LSTM) on an input tensor.

Declaration

public virtual TensorFloat[] LSTM(TensorFloat X, TensorFloat W, TensorFloat R, TensorFloat B, TensorInt sequenceLens, TensorFloat initialH, TensorFloat initialC, TensorFloat P, RnnDirection direction, RnnActivation[] activations, float[] activationAlpha, float[] activationBeta, bool inputForget, float clip = 3.40282347E+38F, RnnLayout layout = RnnLayout.SequenceFirst)

Parameters

Type	Name	Description
TensorFloat	X	The input sequences tensor.
TensorFloat	W	The weights tensor for the gates of the LSTM.
TensorFloat	R	The recurrent weights tensor for the gates of the LSTM.
TensorFloat	B	The optional bias tensor for the input gate of the LSTM.
TensorInt	sequenceLens	The optional 1D tensor specifying the lengths of the sequences in a batch.
TensorFloat	initialH	The optional initial values tensor of the hidden neurons of the LSTM. If this is null, the layer uses 0.
TensorFloat	initialC	The optional initial values tensor of the cells of the LSTM. If this is null, the layer uses 0.
TensorFloat	P	The optional weight tensor for the peepholes of the LSTM. If this is null, the layer uses 0.
RnnDirection	direction	The direction of the LSTM as an RnnDirection.
RnnActivation[]	activations	The activation functions of the LSTM as an array of RnnActivation.
Single[]	activationAlpha	The alpha values of the activation functions of the LSTM.
Single[]	activationBeta	The beta values of the activation functions of the LSTM.
Boolean	inputForget	Whether to forget the input values in the LSTM. If this is false, the layer couples the input and forget gates.
Single	clip	The cell clip threshold of the LSTM.
RnnLayout	layout	The layout of the tensors as an RnnLayout.

Returns

Type	Description
TensorFloat[]	The computed output tensor.

Implements

IOps.LSTM(TensorFloat, TensorFloat, TensorFloat, TensorFloat, TensorInt, TensorFloat, TensorFloat, TensorFloat, RnnDirection, RnnActivation[], Single[], Single[], Boolean, Single, RnnLayout)

MatMul(TensorFloat, TensorFloat)

Performs a multi-dimensional matrix multiplication operation: f(a, b) = a x b.

Declaration

public virtual TensorFloat MatMul(TensorFloat X, TensorFloat Y)

Parameters

Type	Name	Description
TensorFloat	X	The first input tensor.
TensorFloat	Y	The second input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.MatMul(TensorFloat, TensorFloat)

MatMul2D(TensorFloat, Boolean, TensorFloat, Boolean)

Performs a matrix multiplication operation with optional transposes: f(a, b) = a' x b'.

Declaration

public virtual TensorFloat MatMul2D(TensorFloat X, bool xTranspose, TensorFloat Y, bool yTranspose)

Parameters

Type	Name	Description
TensorFloat	X	The first input tensor.
Boolean	xTranspose	Whether to transpose the first input tensor before performing the matrix multiplication.
TensorFloat	Y
Boolean	yTranspose	Whether to transpose the second input tensor before performing the matrix multiplication.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.MatMul2D(TensorFloat, Boolean, TensorFloat, Boolean)

Max(TensorFloat[])

Performs an element-wise Max math operation: f(x1, x2 ... xn) = max(x1, x2 ... xn).

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorFloat Max(TensorFloat[] tensors)

Parameters

Type	Name	Description
TensorFloat[]	tensors	The input tensors.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Max(TensorFloat[])

Max(TensorInt[])

Performs an element-wise Max math operation: f(x1, x2 ... xn) = max(x1, x2 ... xn).

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorInt Max(TensorInt[] tensors)

Parameters

Type	Name	Description
TensorInt[]	tensors	The input tensors.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Max(TensorInt[])

MaxPool(TensorFloat, Int32[], Int32[], Int32[])

Calculates an output tensor by pooling the maximum values of the input tensor across its spatial dimensions according to the given pool and stride values.

Declaration

public virtual TensorFloat MaxPool(TensorFloat X, int[] pool, int[] stride, int[] pad)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	pool	The size of the kernel along each spatial axis.
Int32[]	stride	The stride along each spatial axis.
Int32[]	pad	The lower and upper padding values for each spatial dimension. For example, [pad_left, pad_right] for 1D, or [pad_top, pad_bottom, pad_left, pad_right] for 2D.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.MaxPool(TensorFloat, Int32[], Int32[], Int32[])

Mean(TensorFloat[])

Performs an element-wise Mean math operation: f(x1, x2 ... xn) = (x1 + x2 ... xn) / n.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorFloat Mean(TensorFloat[] tensors)

Parameters

Type	Name	Description
TensorFloat[]	tensors	The input tensors.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Mean(TensorFloat[])

MemCopy(Tensor, Tensor, Int32, Int32, Int32)

Copy blocks of values from X to O, we copy 'count' blocks each of length 'length' values with initial offsets given by 'offsetX', 'offsetO' and with strides given by 'strideX', 'strideO'

Declaration

protected virtual void MemCopy(Tensor X, Tensor O, int length = -1, int offsetX = 0, int offsetO = 0)

Parameters

Type	Name	Description
Tensor	X
Tensor	O
Int32	length
Int32	offsetX
Int32	offsetO

MemCopyStride(Tensor, Tensor, Int32, Int32, Int32, Int32, Int32, Int32)

Copy blocks of values from X to O, we copy 'count' blocks each of length 'length' values with initial offsets given by 'offsetX', 'offsetO' and with strides given by 'strideX', 'strideO'

Declaration

protected virtual void MemCopyStride(Tensor X, Tensor O, int strideX, int strideO, int length, int count, int offsetX = 0, int offsetO = 0)

Parameters

Type	Name	Description
Tensor	X
Tensor	O
Int32	strideX
Int32	strideO
Int32	length
Int32	count
Int32	offsetX
Int32	offsetO

MemSet(Tensor, Int32, Int32, Int32)

Set values of O in indices [offset0, offsetO + length] to value

Declaration

protected virtual void MemSet(Tensor O, int value, int length = -1, int offsetO = 0)

Parameters

Type	Name	Description
Tensor	O
Int32	value
Int32	length
Int32	offsetO

Min(TensorFloat[])

Performs an element-wise Min math operation: f(x1, x2 ... xn) = min(x1, x2 ... xn).

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorFloat Min(TensorFloat[] tensors)

Parameters

Type	Name	Description
TensorFloat[]	tensors	The input tensors.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Min(TensorFloat[])

Min(TensorInt[])

Performs an element-wise Min math operation: f(x1, x2 ... xn) = min(x1, x2 ... xn).

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorInt Min(TensorInt[] tensors)

Parameters

Type	Name	Description
TensorInt[]	tensors	The input tensors.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Min(TensorInt[])

Mod(TensorInt, TensorInt)

Performs an element-wise Mod math operation: f(a, b) = a % b.

The sign of the remainder is the same as the sign of the divisor, as in Python.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorInt Mod(TensorInt A, TensorInt B)

Parameters

Type	Name	Description
TensorInt	A	The first input tensor.
TensorInt	B	The second input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Mod(TensorInt, TensorInt)

Mul(TensorFloat, TensorFloat)

Performs an element-wise Mul math operation: f(a, b) = a * b.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorFloat Mul(TensorFloat A, TensorFloat B)

Parameters

Type	Name	Description
TensorFloat	A	The first input tensor.
TensorFloat	B	The second input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Mul(TensorFloat, TensorFloat)

Mul(TensorInt, TensorInt)

Performs an element-wise Mul math operation: f(a, b) = a * b.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorInt Mul(TensorInt A, TensorInt B)

Parameters

Type	Name	Description
TensorInt	A	The first input tensor.
TensorInt	B	The second input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Mul(TensorInt, TensorInt)

Multinomial(TensorFloat, Int32, Nullable<Single>)

Generates an output tensor with values from a multinomial distribution according to the probabilities given by the input tensor.

Declaration

public virtual TensorInt Multinomial(TensorFloat X, int count, float? seed)

Parameters

Type	Name	Description
TensorFloat	X
Int32	count	The number of times to sample the input.
Nullable<Single>	seed	The optional seed to use for the random number generation. If this is null the operation generates a seed using System.Random().

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Multinomial(TensorFloat, Int32, Nullable<Single>)

Neg(TensorFloat)

Computes an output tensor by applying the element-wise Neg math function: f(x) = -x.

Declaration

public virtual TensorFloat Neg(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Neg(TensorFloat)

Neg(TensorInt)

Computes an output tensor by applying the element-wise Neg math function: f(x) = -x.

Declaration

public virtual TensorInt Neg(TensorInt X)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Neg(TensorInt)

NewOutputTensor(TensorShape, DataType)

Allocate a new Tensor of a given shape and data type using the AllocScope.LayerOutput scope.

Declaration

protected Tensor NewOutputTensor(TensorShape shape, DataType dataType)

Parameters

Type	Name	Description
TensorShape	shape
DataType	dataType

Returns

Type	Description
Tensor

NewOutputTensorFloat(TensorShape)

Allocate a new TensorFloat of a given shape using the AllocScope.LayerOutput scope.

Declaration

protected TensorFloat NewOutputTensorFloat(TensorShape shape)

Parameters

Type	Name	Description
TensorShape	shape

Returns

Type	Description
TensorFloat

NewOutputTensorInt(TensorShape)

Allocate a new TensorInt of a given shape using the AllocScope.LayerOutput scope.

Declaration

protected TensorInt NewOutputTensorInt(TensorShape shape)

Parameters

Type	Name	Description
TensorShape	shape

Returns

Type	Description
TensorInt

NewTempTensorFloat(TensorShape)

Allocate a new TensorFloat of a given shape using the AllocScope.InternalToLayer scope.

Declaration

protected TensorFloat NewTempTensorFloat(TensorShape shape)

Parameters

Type	Name	Description
TensorShape	shape

Returns

Type	Description
TensorFloat

NewTempTensorInt(TensorShape)

Allocate a new TensorInt of a given shape using the AllocScope.InternalToLayer scope.

Declaration

protected TensorInt NewTempTensorInt(TensorShape shape)

Parameters

Type	Name	Description
TensorShape	shape

Returns

Type	Description
TensorInt

NewTensor(TensorShape, DataType, AllocScope)

Allocates a new tensor with the internal allocator.

Declaration

public virtual Tensor NewTensor(TensorShape shape, DataType dataType, AllocScope scope)

Parameters

Type	Name	Description
TensorShape	shape
DataType	dataType
AllocScope	scope

Returns

Type	Description
Tensor

Implements

IOps.NewTensor(TensorShape, DataType, AllocScope)

NewTensorFloat(TensorShape, AllocScope)

Allocate a new TensorFloat of a given shape.

Declaration

protected TensorFloat NewTensorFloat(TensorShape shape, AllocScope scope)

Parameters

Type	Name	Description
TensorShape	shape
AllocScope	scope

Returns

Type	Description
TensorFloat

NewTensorInt(TensorShape, AllocScope)

Allocate a new TensorInt of a given shape.

Declaration

protected TensorInt NewTensorInt(TensorShape shape, AllocScope scope)

Parameters

Type	Name	Description
TensorShape	shape
AllocScope	scope

Returns

Type	Description
TensorInt

NonMaxSuppression(TensorFloat, TensorFloat, Int32, Single, Single, CenterPointBox)

Calculates an output tensor of selected indices of boxes from input boxes and scores tensors where the indices are based on the scores and amount of intersection with previously selected boxes.

Declaration

public virtual TensorInt NonMaxSuppression(TensorFloat boxes, TensorFloat scores, int maxOutputBoxesPerClass = 0, float iouThreshold = 0F, float scoreThreshold = 0F, CenterPointBox centerPointBox = CenterPointBox.Corners)

Parameters

Type	Name	Description
TensorFloat	boxes	The boxes input tensor.
TensorFloat	scores	The scores input tensor.
Int32	maxOutputBoxesPerClass	The maximum number of boxes to return for each class.
Single	iouThreshold	The threshold above which the intersect-over-union rejects a box.
Single	scoreThreshold	The threshold below which the box score filters a box from the output.
CenterPointBox	centerPointBox	The format the boxes tensor uses to store the box data as a CenterPointBox.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.NonMaxSuppression(TensorFloat, TensorFloat, Int32, Single, Single, CenterPointBox)

NonZero(TensorFloat)

Returns the indices of the elements of the input tensor that are not zero.

Declaration

public TensorInt NonZero(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.NonZero(TensorFloat)

NonZero(TensorInt)

Returns the indices of the elements of the input tensor that are not zero.

Declaration

public TensorInt NonZero(TensorInt X)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.NonZero(TensorInt)

Not(TensorInt)

Performs an element-wise Not logical operation: f(x) = ~x.

Declaration

public virtual TensorInt Not(TensorInt X)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Not(TensorInt)

OneHot(TensorInt, Int32, Int32, Int32, Int32)

Generates a one-hot tensor with a given depth, indices and on and off values.

Declaration

public virtual TensorInt OneHot(TensorInt X, int axis, int depth, int offValue, int onValue)

Parameters

Type	Name	Description
TensorInt	X
Int32	axis	The axis along which the operation adds the one-hot representation.
Int32	depth	The depth of the one-hot tensor.
Int32	offValue	The value to use for an off element.
Int32	onValue	The value to use for an on element.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.OneHot(TensorInt, Int32, Int32, Int32, Int32)

Or(TensorInt, TensorInt)

Performs an element-wise Or logical operation: f(a, b) = a | b.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorInt Or(TensorInt A, TensorInt B)

Parameters

Type	Name	Description
TensorInt	A	The first input tensor.
TensorInt	B	The second input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Or(TensorInt, TensorInt)

Pad(TensorFloat, Int32[], PadMode, Single)

Calculates the output tensor by adding padding to the input tensor according to the given padding values and mode.

Declaration

public virtual TensorFloat Pad(TensorFloat X, int[] pad, PadMode padMode, float constant)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	pad	The lower and upper padding values for each dimension.
PadMode	padMode	The PadMode to use when padding. The default value is PadMode.Constant.
Single	constant	The constant value to fill with when using PadMode.Constant. The default value is 0.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Pad(TensorFloat, Int32[], PadMode, Single)

PinToDevice(Tensor, Boolean)

Prepares Tensor for use with CPU backend.

Declaration

public virtual Tensor PinToDevice(Tensor X, bool uploadCache = true)

Parameters

Type	Name	Description
Tensor	X	Tensor to prepare for CPU backend.
Boolean	uploadCache	Whether to copy tensor data to CPU backend.

Returns

Type	Description
Tensor	Tensor once prepared for CPU backend.

Implements

IOps.PinToDevice(Tensor, Boolean)

PostLayerCleanup()

Called after every layer execution. It allows IOps to run cleanup operations such as clearing temporary buffers only used in the scope of the last layer executed.

Declaration

public virtual void PostLayerCleanup()

Implements

IOps.PostLayerCleanup()

Pow(TensorFloat, TensorFloat)

Performs an element-wise Pow math operation: f(a, b) = pow(a, b).

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorFloat Pow(TensorFloat A, TensorFloat B)

Parameters

Type	Name	Description
TensorFloat	A	The first input tensor.
TensorFloat	B	The second input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Pow(TensorFloat, TensorFloat)

Pow(TensorFloat, TensorInt)

Performs an element-wise Pow math operation: f(a, b) = pow(a, b).

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorFloat Pow(TensorFloat A, TensorInt B)

Parameters

Type	Name	Description
TensorFloat	A	The first input tensor.
TensorInt	B	The second input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Pow(TensorFloat, TensorInt)

PRelu(TensorFloat, TensorFloat)

Computes an output tensor by applying the element-wise PRelu activation function: f(x) = x if x >= 0, otherwise f(x) = slope * x.

Declaration

public virtual TensorFloat PRelu(TensorFloat X, TensorFloat S)

Parameters

Type	Name	Description
TensorFloat	X
TensorFloat	S

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.PRelu(TensorFloat, TensorFloat)

RandomNormal(TensorShape, Single, Single, Nullable<Single>)

Generates an output tensor of a given shape with random values in a normal distribution with given mean and scale, and an optional seed value.

Declaration

public virtual TensorFloat RandomNormal(TensorShape s, float mean, float scale, float? seed)

Parameters

Type	Name	Description
TensorShape	s
Single	mean	The mean of the normal distribution to use to generate the output.
Single	scale	The standard deviation of the normal distribution to use to generate the output.
Nullable<Single>	seed	The optional seed to use for the random number generation. If this is null the operation generates a seed using System.Random().

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.RandomNormal(TensorShape, Single, Single, Nullable<Single>)

RandomUniform(TensorShape, Single, Single, Nullable<Single>)

Generates an output tensor of a given shape with random values in a uniform distribution between a given low and high, and an optional seed value.

Declaration

public virtual TensorFloat RandomUniform(TensorShape s, float low, float high, float? seed)

Parameters

Type	Name	Description
TensorShape	s
Single	low	The lower end of the interval of the uniform distribution to use to generate the output.
Single	high	The upper end of the interval of the uniform distribution to use to generate the output.
Nullable<Single>	seed	The optional seed to use for the random number generation. If this is null the operation generates a seed using System.Random().

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.RandomUniform(TensorShape, Single, Single, Nullable<Single>)

Range(Int32, Int32, Int32)

Generates a 1D output tensor where the values form an arithmetic progression defined by the start, limit, and delta values.

Declaration

public virtual TensorInt Range(int start, int limit, int delta)

Parameters

Type	Name	Description
Int32	start	The first value in the range.
Int32	limit	The limit of the range.
Int32	delta	The delta between subsequent values in the range.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Range(Int32, Int32, Int32)

Range(Single, Single, Single)

Generates a 1D output tensor where the values form an arithmetic progression defined by the start, limit, and delta values.

Declaration

public virtual TensorFloat Range(float start, float limit, float delta)

Parameters

Type	Name	Description
Single	start	The first value in the range.
Single	limit	The limit of the range.
Single	delta	The delta between subsequent values in the range.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Range(Single, Single, Single)

Reciprocal(TensorFloat)

Computes an output tensor by applying the element-wise Reciprocal math function: f(x) = 1 / x.

Declaration

public virtual TensorFloat Reciprocal(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Reciprocal(TensorFloat)

ReduceL1(TensorFloat, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceL1 operation: f(x1, x2 ... xn) = |x1| + |x2| + ... + |xn|.

Declaration

public virtual TensorFloat ReduceL1(TensorFloat X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.ReduceL1(TensorFloat, Int32[], Boolean)

ReduceL1(TensorInt, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceL1 operation: f(x1, x2 ... xn) = |x1| + |x2| + ... + |xn|.

Declaration

public virtual TensorInt ReduceL1(TensorInt X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.ReduceL1(TensorInt, Int32[], Boolean)

ReduceL2(TensorFloat, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceL2 operation: f(x1, x2 ... xn) = sqrt(x1² + x2² + ... + xn²).

Declaration

public virtual TensorFloat ReduceL2(TensorFloat X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.ReduceL2(TensorFloat, Int32[], Boolean)

ReduceLogSum(TensorFloat, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceLogSum operation: f(x1, x2 ... xn) = log(x1 + x2 + ... + xn).

Declaration

public virtual TensorFloat ReduceLogSum(TensorFloat X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.ReduceLogSum(TensorFloat, Int32[], Boolean)

ReduceLogSumExp(TensorFloat, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceLogSumExp operation: f(x1, x2 ... xn) = log(e^x1 + e^x2 + ... + e^xn).

Declaration

public virtual TensorFloat ReduceLogSumExp(TensorFloat X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.ReduceLogSumExp(TensorFloat, Int32[], Boolean)

ReduceMax(TensorFloat, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceMax operation: f(x1, x2 ... xn) = max(x1, x2, ... , xn).

Declaration

public virtual TensorFloat ReduceMax(TensorFloat X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.ReduceMax(TensorFloat, Int32[], Boolean)

ReduceMax(TensorInt, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceMean operation: f(x1, x2 ... xn) = max(x1, x2, ... , xn).

Declaration

public virtual TensorInt ReduceMax(TensorInt X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.ReduceMax(TensorInt, Int32[], Boolean)

ReduceMean(TensorFloat, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceMean operation: f(x1, x2 ... xn) = (x1 + x2 + ... + xn) / n.

Declaration

public virtual TensorFloat ReduceMean(TensorFloat X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.ReduceMean(TensorFloat, Int32[], Boolean)

ReduceMin(TensorFloat, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceMin operation: f(x1, x2 ... xn) = min(x1, x2, ... , xn).

Declaration

public virtual TensorFloat ReduceMin(TensorFloat X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.ReduceMin(TensorFloat, Int32[], Boolean)

ReduceMin(TensorInt, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceMin operation: f(x1, x2 ... xn) = min(x1, x2, ... , xn).

Declaration

public virtual TensorInt ReduceMin(TensorInt X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.ReduceMin(TensorInt, Int32[], Boolean)

ReduceProd(TensorFloat, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceProd operation: f(x1, x2 ... xn) = x1 * x2 * ... * xn.

Declaration

public virtual TensorFloat ReduceProd(TensorFloat X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.ReduceProd(TensorFloat, Int32[], Boolean)

ReduceProd(TensorInt, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceProd operation: f(x1, x2 ... xn) = x1 * x2 * ... * xn.

Declaration

public virtual TensorInt ReduceProd(TensorInt X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.ReduceProd(TensorInt, Int32[], Boolean)

ReduceSum(TensorFloat, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceSum operation: f(x1, x2 ... xn) = x1 + x2 + ... + xn.

Declaration

public virtual TensorFloat ReduceSum(TensorFloat X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.ReduceSum(TensorFloat, Int32[], Boolean)

ReduceSum(TensorInt, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceSum operation: f(x1, x2 ... xn) = x1 + x2 + ... + xn.

Declaration

public virtual TensorInt ReduceSum(TensorInt X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.ReduceSum(TensorInt, Int32[], Boolean)

ReduceSumSquare(TensorFloat, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceSumSquare operation: f(x1, x2 ... xn) = x1² + x2² + ... + xn².

Declaration

public virtual TensorFloat ReduceSumSquare(TensorFloat X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.ReduceSumSquare(TensorFloat, Int32[], Boolean)

ReduceSumSquare(TensorInt, Int32[], Boolean)

Reduces an input tensor along the given axes using the ReduceSumSquare operation: f(x1, x2 ... xn) = x1² + x2² + ... + xn².

Declaration

public virtual TensorInt ReduceSumSquare(TensorInt X, int[] axes, bool keepdim)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.
Int32[]	axes	The axes along which to reduce.
Boolean	keepdim	Whether to keep the reduced axes in the output tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.ReduceSumSquare(TensorInt, Int32[], Boolean)

Relu(TensorFloat)

Computes an output tensor by applying the element-wise Relu activation function: f(x) = max(0, x).

Declaration

public virtual TensorFloat Relu(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Relu(TensorFloat)

Relu6(TensorFloat)

Computes an output tensor by applying the element-wise Relu6 activation function: f(x) = clamp(x, 0, 6).

Declaration

public virtual TensorFloat Relu6(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Relu6(TensorFloat)

ResetAllocator(Boolean)

Resets the internal allocator.

Declaration

public virtual void ResetAllocator(bool keepCachedMemory = true)

Parameters

Type	Name	Description
Boolean	keepCachedMemory

Implements

IOps.ResetAllocator(Boolean)

Reshape(Tensor, TensorShape)

Calculates an output tensor by copying the data from the input tensor and using a given shape. The data from the input tensor is unchanged.

Declaration

public virtual Tensor Reshape(Tensor X, TensorShape newShape)

Parameters

Type	Name	Description
Tensor	X	The input tensor.
TensorShape	newShape

Returns

Type	Description
Tensor	The computed output tensor.

Implements

IOps.Reshape(Tensor, TensorShape)

Resize(TensorFloat, Single[], InterpolationMode, NearestMode, CoordTransformMode)

Calculates an output tensor by resampling the input tensor along the spatial dimensions with given scales.

Declaration

public virtual TensorFloat Resize(TensorFloat X, float[] scale, InterpolationMode interpolationMode, NearestMode nearestMode = NearestMode.RoundPreferFloor, CoordTransformMode coordTransformMode = CoordTransformMode.HalfPixel)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Single[]	scale	The factor to scale each dimension by.
InterpolationMode	interpolationMode	The InterpolationMode to use for the operation.
NearestMode	nearestMode	The NearestMode to use for the operation when using InterpolationMode.NearestMode. The default is NearestMode.RoundPreferFloor.
CoordTransformMode	coordTransformMode	The CoordTransformMode to use for the operation. The default is CoordTransformMode.HalfPixel.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Resize(TensorFloat, Single[], InterpolationMode, NearestMode, CoordTransformMode)

RoiAlign(TensorFloat, TensorFloat, TensorInt, RoiPoolingMode, Int32, Int32, Int32, Single)

Calculates an output tensor by pooling the input tensor across each region of interest given by the rois tensor.

Declaration

public virtual TensorFloat RoiAlign(TensorFloat X, TensorFloat Rois, TensorInt Indices, RoiPoolingMode mode, int outputHeight, int outputWidth, int samplingRatio, float spatialScale)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
TensorFloat	Rois	The region of interest input tensor.
TensorInt	Indices	The indices input tensor.
RoiPoolingMode	mode	The pooling mode of the operation as an RoiPoolingMode.
Int32	outputHeight	The height of the output tensor.
Int32	outputWidth	The width of the output tensor.
Int32	samplingRatio	The number of sampling points in the interpolation grid used to compute the output value of each pooled output bin.
Single	spatialScale	The multiplicative spatial scale factor used to translate coordinates from their input spatial scale to the scale used when pooling.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.RoiAlign(TensorFloat, TensorFloat, TensorInt, RoiPoolingMode, Int32, Int32, Int32, Single)

Round(TensorFloat)

Computes an output tensor by applying the element-wise Round math function: f(x) = round(x).

If the fractional part is equal to 0.5, rounds to the nearest even integer.

Declaration

public virtual TensorFloat Round(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Round(TensorFloat)

ScaleBias(TensorFloat, TensorFloat, TensorFloat)

Computes the output tensor with an element-wise ScaleBias function: f(x, s, b) = x * s + b.

Declaration

public virtual TensorFloat ScaleBias(TensorFloat X, TensorFloat S, TensorFloat B)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
TensorFloat	S	The scale tensor.
TensorFloat	B	The bias tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.ScaleBias(TensorFloat, TensorFloat, TensorFloat)

ScatterElements(Tensor, TensorInt, Tensor, Int32, ScatterReductionMode)

Copies the input tensor and updates values at indexes specified by the indices tensor with values specified by the updates tensor along a given axis.

ScatterElements updates the values depending on the reduction mode used.

Declaration

public virtual Tensor ScatterElements(Tensor X, TensorInt indices, Tensor updates, int axis, ScatterReductionMode reduction)

Parameters

Type	Name	Description
Tensor	X	The input tensor.
TensorInt	indices	The indices tensor.
Tensor	updates	The updates tensor.
Int32	axis	The axis on which to perform the scatter.
ScatterReductionMode	reduction	The reduction mode used to update the values as a ScatterReductionMode.

Returns

Type	Description
Tensor	The computed output tensor.

Implements

IOps.ScatterElements(Tensor, TensorInt, Tensor, Int32, ScatterReductionMode)

ScatterND(TensorFloat, TensorInt, TensorFloat, ScatterReductionMode)

Copies the input tensor and updates values at indexes specified by the indices tensor with values specified by the updates tensor.

ScatterND updates the values depending on the reduction mode used.

Declaration

public virtual TensorFloat ScatterND(TensorFloat X, TensorInt indices, TensorFloat updates, ScatterReductionMode reduction)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
TensorInt	indices	The indices tensor.
TensorFloat	updates	The updates tensor.
ScatterReductionMode	reduction	The reduction mode used to update the values as a ScatterReductionMode.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.ScatterND(TensorFloat, TensorInt, TensorFloat, ScatterReductionMode)

ScatterND(TensorInt, TensorInt, TensorInt, ScatterReductionMode)

Copies the input tensor and updates values at indexes specified by the indices tensor with values specified by the updates tensor.

ScatterND updates the values depending on the reduction mode used.

Declaration

public virtual TensorInt ScatterND(TensorInt X, TensorInt indices, TensorInt updates, ScatterReductionMode reduction)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.
TensorInt	indices	The indices tensor.
TensorInt	updates	The updates tensor.
ScatterReductionMode	reduction	The reduction mode used to update the values as a ScatterReductionMode.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.ScatterND(TensorInt, TensorInt, TensorInt, ScatterReductionMode)

Selu(TensorFloat, Single, Single)

Computes an output tensor by applying the element-wise Selu activation function: f(x) = gamma * x if x >= 0, otherwise f(x) = (alpha * e^x - alpha).

Declaration

public virtual TensorFloat Selu(TensorFloat X, float alpha, float gamma)

Parameters

Type	Name	Description
TensorFloat	X
Single	alpha	The alpha value to use for the Selu activation function.
Single	gamma	The alpha value to use for the Selu activation function.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Selu(TensorFloat, Single, Single)

Shape(Tensor, Int32, Int32)

Calculates the shape of an input tensor as a 1D TensorInt.

Declaration

public virtual TensorInt Shape(Tensor X, int start = 0, int end = 8)

Parameters

Type	Name	Description
Tensor	X	The input tensor.
Int32	start	The inclusive start axis for slicing the shape of the input tensor. The default value is 0.
Int32	end	The exclusive end axis for slicing the shape of the input tensor. The default value is 8.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Shape(Tensor, Int32, Int32)

Shrink(TensorFloat, Single, Single)

Computes an output tensor by applying the element-wise Shrink activation function: f(x) = x + bias if x < lambd. f(x) = x - bias if x > lambd. Otherwise f(x) = 0.

Declaration

public virtual TensorFloat Shrink(TensorFloat X, float bias, float lambd)

Parameters

Type	Name	Description
TensorFloat	X
Single	bias	The bias value to use for the Shrink activation function.
Single	lambd	The lambda value to use for the Shrink activation function.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Shrink(TensorFloat, Single, Single)

Sigmoid(TensorFloat)

Computes an output tensor by applying the element-wise Sigmoid activation function: f(x) = 1/(1 + e^(-x)).

Declaration

public virtual TensorFloat Sigmoid(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Sigmoid(TensorFloat)

Sign(TensorFloat)

Performs an element-wise Sign math operation: f(x) = 1 if x > 0. f(x) = -1 if x < 0. Otherwise f(x) = 0.

Declaration

public virtual TensorFloat Sign(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Sign(TensorFloat)

Sign(TensorInt)

Performs an element-wise Sign math operation: f(x) = 1 if x > 0. f(x) = -1 if x < 0. Otherwise f(x) = 0.

Declaration

public virtual TensorInt Sign(TensorInt X)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Sign(TensorInt)

Sin(TensorFloat)

Computes an output tensor by applying the element-wise Sin trigonometric function: f(x) = sin(x).

Declaration

public virtual TensorFloat Sin(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Sin(TensorFloat)

SinglePassLSTM(TensorFloat, TensorFloat, TensorFloat, TensorFloat, TensorInt, TensorFloat, TensorFloat, TensorFloat, TensorFloat, RnnActivation[], Single[], Single[], Boolean, Single, Boolean, Int32, RnnLayout)

Computes a single pass LSTM either forward or reverse dirIndex and layout are used to calculate where to index the various tensors in bidirectional and batch first layout passes X has given layout W, R are cropped to single direction P, B are full number of directions Y has given layout and full number of directions (matches output of Layer) Y_h, Y_c are SequenceFirst layout and cropped to single direction HtxRT and XsixWT are temp vectors of the correct dimension for the intermediate results of the matmuls activations, activationAlpha and activationBeta have full number of dimensions

Declaration

protected virtual void SinglePassLSTM(TensorFloat X, TensorFloat W, TensorFloat R, TensorFloat B, TensorInt sequenceLens, TensorFloat P, TensorFloat Y, TensorFloat Y_h, TensorFloat Y_c, RnnActivation[] activations, float[] activationAlpha, float[] activationBeta, bool inputForget, float clip, bool isReverse, int dirIndex, RnnLayout layout)

Parameters

Type	Name	Description
TensorFloat	X
TensorFloat	W
TensorFloat	R
TensorFloat	B
TensorInt	sequenceLens
TensorFloat	P
TensorFloat	Y
TensorFloat	Y_h
TensorFloat	Y_c
RnnActivation[]	activations
Single[]	activationAlpha
Single[]	activationBeta
Boolean	inputForget
Single	clip
Boolean	isReverse
Int32	dirIndex
RnnLayout	layout

Sinh(TensorFloat)

Computes an output tensor by applying the element-wise Sinh trigonometric function: f(x) = sinh(x).

Declaration

public virtual TensorFloat Sinh(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Sinh(TensorFloat)

Size(TensorShape)

Calculates the number of elements of an input tensor shape as a scalar TensorInt.

Declaration

public virtual TensorInt Size(TensorShape shape)

Parameters

Type	Name	Description
TensorShape	shape	The input tensor shape.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Size(TensorShape)

Slice(Tensor, Int32[], Int32[], Int32[], Int32[])

Calculates an output tensor by slicing the input tensor along given axes with given starts, ends, and steps.

Declaration

public virtual Tensor Slice(Tensor X, int[] starts, int[] ends, int[] axes, int[] steps)

Parameters

Type	Name	Description
Tensor	X	The input tensor.
Int32[]	starts	The start index along each axis.
Int32[]	ends	The end index along each axis.
Int32[]	axes	The axes along which to slice. If this is null, the layer slices all axes.
Int32[]	steps	The step values for slicing. If this is null, the layer uses step size 1 throughout.

Returns

Type	Description
Tensor	The computed output tensor.

Implements

IOps.Slice(Tensor, Int32[], Int32[], Int32[], Int32[])

Softmax(TensorFloat, Int32)

Computes an output tensor by applying the Softmax activation function along an axis: f(x, axis) = exp(X) / ReduceSum(exp(X), axis).

Declaration

public virtual TensorFloat Softmax(TensorFloat X, int axis)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32	axis	The axis along which to apply the Softmax activation function. The default value is -1.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Softmax(TensorFloat, Int32)

Softplus(TensorFloat)

Computes an output tensor by applying the element-wise Softplus activation function: f(x) = ln(e^x + 1).

Declaration

public virtual TensorFloat Softplus(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Softplus(TensorFloat)

Softsign(TensorFloat)

Computes an output tensor by applying the element-wise Softsign activation function: f(x) = x/(|x| + 1).

Declaration

public virtual TensorFloat Softsign(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Softsign(TensorFloat)

SpaceToDepth(TensorFloat, Int32)

Computes the output tensor by permuting data from blocks of spatial data into depth.

Declaration

public virtual TensorFloat SpaceToDepth(TensorFloat X, int blocksize)

Parameters

Type	Name	Description
TensorFloat	X
Int32	blocksize	The size of the blocks to move the depth data into.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.SpaceToDepth(TensorFloat, Int32)

Split(Tensor, Int32, Int32, Int32)

Calculates an output tensor by splitting the input tensor along a given axis between start and end.

Declaration

public virtual Tensor Split(Tensor X, int axis, int start, int end)

Parameters

Type	Name	Description
Tensor	X	The input tensor.
Int32	axis	The axis along which to split the input tensor.
Int32	start	The inclusive start value for the split.
Int32	end	The exclusive end value for the split.

Returns

Type	Description
Tensor	The computed output tensor.

Implements

IOps.Split(Tensor, Int32, Int32, Int32)

Sqrt(TensorFloat)

Computes an output tensor by applying the element-wise Sqrt math function: f(x) = sqrt(x).

Declaration

public virtual TensorFloat Sqrt(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Sqrt(TensorFloat)

Square(TensorFloat)

Computes an output tensor by applying the element-wise Square math function: f(x) = x * x.

Declaration

public virtual TensorFloat Square(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Square(TensorFloat)

Sub(TensorFloat, TensorFloat)

Performs an element-wise Sub math operation: f(a, b) = a - b.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorFloat Sub(TensorFloat A, TensorFloat B)

Parameters

Type	Name	Description
TensorFloat	A	The first input tensor.
TensorFloat	B	The second input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Sub(TensorFloat, TensorFloat)

Sub(TensorInt, TensorInt)

Performs an element-wise Sub math operation: f(a, b) = a - b.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorInt Sub(TensorInt A, TensorInt B)

Parameters

Type	Name	Description
TensorInt	A	The first input tensor.
TensorInt	B	The second input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Sub(TensorInt, TensorInt)

Sum(TensorFloat[])

Performs an element-wise Sum math operation: f(x1, x2 ... xn) = x1 + x2 ... xn.

This supports numpy-style broadcasting of input tensors.

Declaration

public virtual TensorFloat Sum(TensorFloat[] tensors)

Parameters

Type	Name	Description
TensorFloat[]	tensors	The input tensors.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Sum(TensorFloat[])

Sum(TensorInt[])

Declaration

public virtual TensorInt Sum(TensorInt[] tensors)

Parameters

Type	Name	Description
TensorInt[]	tensors

Returns

Type	Description
TensorInt

Swish(TensorFloat)

Computes an output tensor by applying the element-wise Swish activation function: f(x) = sigmoid(x) * x = x / (1 + e^{-x}).

Declaration

public virtual TensorFloat Swish(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Swish(TensorFloat)

Tan(TensorFloat)

Computes an output tensor by applying the element-wise Tan trigonometric function: f(x) = tan(x).

Declaration

public virtual TensorFloat Tan(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Tan(TensorFloat)

Tanh(TensorFloat)

Computes an output tensor by applying the element-wise Tanh activation function: f(x) = tanh(x).

Declaration

public virtual TensorFloat Tanh(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.Tanh(TensorFloat)

ThresholdedRelu(TensorFloat, Single)

Computes an output tensor by applying the element-wise ThresholdedRelu activation function: f(x) = x if x > alpha, otherwise f(x) = 0.

Declaration

public virtual TensorFloat ThresholdedRelu(TensorFloat X, float alpha)

Parameters

Type	Name	Description
TensorFloat	X
Single	alpha	The alpha value to use for the ThresholdedRelu activation function.

Returns

Type	Description
TensorFloat	The computed output tensor.

Implements

IOps.ThresholdedRelu(TensorFloat, Single)

Tile(Tensor, Int32[])

Calculates an output tensor by repeating the input layer a given number of times along each axis.

Declaration

public virtual Tensor Tile(Tensor X, int[] repeats)

Parameters

Type	Name	Description
Tensor	X	The input tensor.
Int32[]	repeats	The number of times to tile the input tensor along each axis.

Returns

Type	Description
Tensor	The computed output tensor.

Implements

IOps.Tile(Tensor, Int32[])

TopK(TensorFloat, Int32, Int32, Boolean, Boolean)

Calculates the top-K largest or smallest elements of an input tensor along a given axis.

Declaration

public virtual Tensor[] TopK(TensorFloat X, int k, int axis, bool largest, bool sorted)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
Int32	k	The number of elements to calculate.
Int32	axis	The axis along which to perform the top-K operation.
Boolean	largest	Whether to calculate the top-K largest elements. If this is false, the layer calculates the top-K smallest elements.
Boolean	sorted	Whether to return the elements in sorted order.

Returns

Type	Description
Tensor[]	The computed output tensor.

Implements

IOps.TopK(TensorFloat, Int32, Int32, Boolean, Boolean)

Transpose(Tensor)

Calculates an output tensor by reversing the dimensions of the input tensor.

Declaration

public virtual Tensor Transpose(Tensor X)

Parameters

Type	Name	Description
Tensor	X

Returns

Type	Description
Tensor	The computed output tensor.

Implements

IOps.Transpose(Tensor)

Transpose(Tensor, Int32[])

Calculates an output tensor by permuting the axes and data of the input tensor according to the given permutations.

Declaration

public virtual Tensor Transpose(Tensor X, int[] permutations)

Parameters

Type	Name	Description
Tensor	X
Int32[]	permutations	The axes to sample the output tensor from in the input tensor.

Returns

Type	Description
Tensor	The computed output tensor.

Implements

IOps.Transpose(Tensor, Int32[])

Tril(Tensor, Int32)

Computes the output tensor by retaining the lower triangular values from an input matrix or matrix batch and setting the other values to zero.

Declaration

public virtual Tensor Tril(Tensor X, int k)

Parameters

Type	Name	Description
Tensor	X	The input tensor.
Int32	k	The offset from the diagonal to keep.

Returns

Type	Description
Tensor	The computed output tensor.

Implements

IOps.Tril(Tensor, Int32)

Triu(Tensor, Int32)

Computes the output tensor by retaining the upper triangular values from an input matrix or matrix batch and setting the other values to zero.

Declaration

public virtual Tensor Triu(Tensor X, int k)

Parameters

Type	Name	Description
Tensor	X	The input tensor.
Int32	k	The offset from the diagonal to exclude.

Returns

Type	Description
Tensor	The computed output tensor.

Implements

IOps.Triu(Tensor, Int32)

Where(TensorInt, Tensor, Tensor)

Performs an element-wise Where logical operation: f(condition, a, b) = a if condition is true, otherwise f(condition, a, b) = b.

Declaration

public virtual Tensor Where(TensorInt C, Tensor A, Tensor B)

Parameters

Type	Name	Description
TensorInt	C	The condition tensor.
Tensor	A	The first input tensor.
Tensor	B	The second input tensor.

Returns

Type	Description
Tensor	The computed output tensor.

Implements

IOps.Where(TensorInt, Tensor, Tensor)

Xor(TensorInt, TensorInt)

Performs an element-wise Xor logical operation: f(a) = a ^ b.

Declaration

public virtual TensorInt Xor(TensorInt A, TensorInt B)

Parameters

Type	Name	Description
TensorInt	A	The first input tensor.
TensorInt	B	The second input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Implements

IOps.Xor(TensorInt, TensorInt)