Class Ops

Inheritance

Object

Ops

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 abstract class Ops : IDisposable

Constructors

Ops(BackendType, ITensorAllocator)

Declaration

protected Ops(BackendType backendType, ITensorAllocator allocator)

Parameters

Type	Name	Description
BackendType	backendType
ITensorAllocator	allocator

Properties

backendType

Declaration

public BackendType backendType { get; }

Property Value

Type	Description
BackendType

Methods

Abs(TensorFloat)

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

Declaration

public TensorFloat Abs(TensorFloat x)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Abs(TensorInt)

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

Declaration

public TensorInt Abs(TensorInt x)

Parameters

Type	Name	Description
TensorInt	x	The input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Acos(TensorFloat)

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

Declaration

public TensorFloat Acos(TensorFloat x)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Acosh(TensorFloat)

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

Declaration

public TensorFloat Acosh(TensorFloat x)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Add(Single, TensorFloat)

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

Declaration

public TensorFloat Add(float a, TensorFloat B)

Parameters

Type	Name	Description
Single	a	The first argument as a float.
TensorFloat	B	The second argument as a tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Add(TensorFloat, Single)

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

Declaration

public TensorFloat Add(TensorFloat A, float b)

Parameters

Type	Name	Description
TensorFloat	A	The first argument as a tensor.
Single	b	The second argument as a float.

Returns

Type	Description
TensorFloat	The computed output tensor.

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 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.

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 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.

And(TensorInt, TensorInt)

Declaration

public TensorInt And(TensorInt A, TensorInt B)

Parameters

Type	Name	Description
TensorInt	A
TensorInt	B

Returns

Type	Description
TensorInt

ArgMax(TensorFloat, Int32, Boolean, Boolean)

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

Declaration

public 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.

ArgMax(TensorInt, Int32, Boolean, Boolean)

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

Declaration

public 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.

ArgMin(TensorFloat, Int32, Boolean, Boolean)

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

Declaration

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

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.

ArgMin(TensorInt, Int32, Boolean, Boolean)

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

Declaration

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

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.

Asin(TensorFloat)

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

Declaration

public TensorFloat Asin(TensorFloat x)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Asinh(TensorFloat)

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

Declaration

public TensorFloat Asinh(TensorFloat x)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Atan(TensorFloat)

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

Declaration

public TensorFloat Atan(TensorFloat x)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Atanh(TensorFloat)

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

Declaration

public TensorFloat Atanh(TensorFloat x)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

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 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.

AxisNormalization(TensorFloat, TensorFloat, TensorFloat, Single)

Computes the mean variance on the last dimension of the input tensor and normalizes it according to scale and bias tensors.

Declaration

public 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.

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 Tensor Bernoulli(TensorFloat x, DataType dataType, float? seed)

Parameters

Type	Name	Description
TensorFloat	x	The probabilities input tensor.
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.

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 Tensor Cast(Tensor x, DataType toType)

Parameters

Type	Name	Description
Tensor	x	The input tensor.
DataType	toType	The data type to cast to as a DataType.

Returns

Type	Description
Tensor	The computed output tensor.

Ceil(TensorFloat)

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

Declaration

public TensorFloat Ceil(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

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 TensorFloat Celu(TensorFloat x, float alpha)

Parameters

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

Returns

Type	Description
TensorFloat	The computed output tensor.

Clip(TensorFloat, Single, Single)

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

Declaration

public TensorFloat Clip(TensorFloat x, float min, float max)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.
Single	min	The lower clip value.
Single	max	The upper clip value.

Returns

Type	Description
TensorFloat	The computed output tensor.

Compress(Tensor, TensorInt, Int32)

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

Declaration

public Tensor Compress(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 compress.

Returns

Type	Description
Tensor	The computed output tensor.

Concat(Tensor[], Int32)

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

Declaration

public 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.

ConstantOfShape(TensorShape, Int32)

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

Declaration

public 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.

ConstantOfShape(TensorShape, Single)

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

Declaration

public 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.

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

Applies a convolution filter to an input tensor.

Declaration

public 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.

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

Applies a transpose convolution filter to an input tensor.

Declaration

public 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.

Copy(Tensor)

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

Declaration

public Tensor Copy(Tensor x)

Parameters

Type	Name	Description
Tensor	x	The input tensor.

Returns

Type	Description
Tensor	The computed output tensor.

Cos(TensorFloat)

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

Declaration

public TensorFloat Cos(TensorFloat x)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Cosh(TensorFloat)

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

Declaration

public TensorFloat Cosh(TensorFloat x)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

CumSum(TensorFloat, Int32, Boolean, Boolean)

Performs the cumulative sum along a given axis.

Declaration

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

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.

CumSum(TensorInt, Int32, Boolean, Boolean)

Performs the cumulative sum along a given axis.

Declaration

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

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.

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 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.

DepthToSpace(TensorFloat, Int32, DepthToSpaceMode)

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

Declaration

public 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.

Dispose()

Declaration

public void Dispose()

Implements

IDisposable.Dispose()

Div(TensorFloat, Single)

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

Declaration

public TensorFloat Div(TensorFloat A, float b)

Parameters

Type	Name	Description
TensorFloat	A	The first argument as a tensor.
Single	b	The second argument as a float.

Returns

Type	Description
TensorFloat	The computed output tensor.

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 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.

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 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.

Einsum(String, TensorFloat[])

Performs an Einsum math operation.

Declaration

public 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.

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 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.

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 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.

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 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.

Erf(TensorFloat)

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

Declaration

public TensorFloat Erf(TensorFloat x)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Exp(TensorFloat)

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

Declaration

public TensorFloat Exp(TensorFloat x)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Expand(Tensor, TensorShape)

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

Declaration

public Tensor Expand(Tensor X, TensorShape shape)

Parameters

Type	Name	Description
Tensor	X	The input tensor.
TensorShape	shape	The shape to broadcast the input shape together with to calculate the output tensor.

Returns

Type	Description
Tensor	The computed output tensor.

Floor(TensorFloat)

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

Declaration

public TensorFloat Floor(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

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 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.

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 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.

Gather(Tensor, TensorInt, Int32)

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

Declaration

public 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.

GatherElements(Tensor, TensorInt, Int32)

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

Declaration

public 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.

GatherND(Tensor, TensorInt, Int32)

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

Declaration

public 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.

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 TensorFloat Gelu(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

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 TensorFloat GlobalAveragePool(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

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 TensorFloat GlobalMaxPool(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

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 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.

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 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.

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 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.

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 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.

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 TensorFloat Hardmax(TensorFloat X, int axis = -1)

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.

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 TensorFloat HardSigmoid(TensorFloat x, float alpha, float beta)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.
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.

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 TensorFloat HardSwish(TensorFloat x)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

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 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.

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 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.

IsNaN(TensorFloat)

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

Declaration

public TensorInt IsNaN(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

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 TensorFloat LeakyRelu(TensorFloat x, float alpha)

Parameters

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

Returns

Type	Description
TensorFloat	The computed output tensor.

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 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.

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 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.

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 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.

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 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.

Log(TensorFloat)

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

Declaration

public TensorFloat Log(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

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 TensorFloat LogSoftmax(TensorFloat X, int axis = -1)

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.

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

Normalizes the input tensor over local input regions.

Declaration

public 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.

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 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, RnnLayout layout)

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.

MatMul(TensorFloat, TensorFloat)

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

Declaration

public 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.

MatMul2D(TensorFloat, Boolean, TensorFloat, Boolean)

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

Declaration

public 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	The second input tensor.
Boolean	yTranspose	Whether to transpose the second input tensor before performing the matrix multiplication.

Returns

Type	Description
TensorFloat	The computed output tensor.

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 TensorFloat Max(TensorFloat[] tensors)

Parameters

Type	Name	Description
TensorFloat[]	tensors	The input tensors.

Returns

Type	Description
TensorFloat	The computed output tensor.

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 TensorInt Max(TensorInt[] tensors)

Parameters

Type	Name	Description
TensorInt[]	tensors	The input tensors.

Returns

Type	Description
TensorInt	The computed output tensor.

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 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.

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 TensorFloat Mean(TensorFloat[] tensors)

Parameters

Type	Name	Description
TensorFloat[]	tensors	The input tensors.

Returns

Type	Description
TensorFloat	The computed output tensor.

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 TensorFloat Min(TensorFloat[] tensors)

Parameters

Type	Name	Description
TensorFloat[]	tensors	The input tensors.

Returns

Type	Description
TensorFloat	The computed output tensor.

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 TensorInt Min(TensorInt[] tensors)

Parameters

Type	Name	Description
TensorInt[]	tensors	The input tensors.

Returns

Type	Description
TensorInt	The computed output tensor.

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 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.

Mul(Single, TensorFloat)

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

Declaration

public TensorFloat Mul(float a, TensorFloat B)

Parameters

Type	Name	Description
Single	a	The first argument as a float.
TensorFloat	B	The second argument as a tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Mul(TensorFloat, Single)

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

Declaration

public TensorFloat Mul(TensorFloat A, float b)

Parameters

Type	Name	Description
TensorFloat	A	The first argument as a tensor.
Single	b	The second argument as a float.

Returns

Type	Description
TensorFloat	The computed output tensor.

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 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.

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 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.

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 TensorInt Multinomial(TensorFloat x, int count, float? seed)

Parameters

Type	Name	Description
TensorFloat	x	The probabilities input tensor.
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.

Neg(TensorFloat)

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

Declaration

public TensorFloat Neg(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Neg(TensorInt)

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

Declaration

public TensorInt Neg(TensorInt X)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

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 TensorInt NonMaxSuppression(TensorFloat boxes, TensorFloat scores, int maxOutputBoxesPerClass, float iouThreshold, float scoreThreshold, CenterPointBox centerPointBox)

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.

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.

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.

Not(TensorInt)

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

Declaration

public TensorInt Not(TensorInt X)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

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

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

Declaration

public TensorInt OneHot(TensorInt indices, int axis, int depth, int offValue, int onValue)

Parameters

Type	Name	Description
TensorInt	indices	The indices input tensor.
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.

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 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.

Pad(TensorFloat, ReadOnlySpan<Int32>, PadMode, Single)

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

Declaration

public TensorFloat Pad(TensorFloat X, ReadOnlySpan<int> pad, PadMode padMode = PadMode.Constant, float constant = 0F)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
ReadOnlySpan<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.

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 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.

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 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.

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 TensorFloat PRelu(TensorFloat x, TensorFloat slope)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.
TensorFloat	slope	The slope tensor, must be unidirectional broadcastable to x.

Returns

Type	Description
TensorFloat	The computed output tensor.

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 TensorFloat RandomNormal(TensorShape S, float mean, float scale, float? seed)

Parameters

Type	Name	Description
TensorShape	S	The shape to use for the output tensor.
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.

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 TensorFloat RandomUniform(TensorShape S, float low, float high, float? seed)

Parameters

Type	Name	Description
TensorShape	S	The shape to use for the output tensor.
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.

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 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.

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 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.

Reciprocal(TensorFloat)

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

Declaration

public TensorFloat Reciprocal(TensorFloat x)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

ReduceL1(TensorFloat, ReadOnlySpan<Int32>, Boolean)

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

Declaration

public TensorFloat ReduceL1(TensorFloat X, ReadOnlySpan<int> axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
ReadOnlySpan<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.

ReduceL1(TensorInt, ReadOnlySpan<Int32>, Boolean)

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

Declaration

public TensorInt ReduceL1(TensorInt X, ReadOnlySpan<int> axes, bool keepdim)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.
ReadOnlySpan<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.

ReduceL2(TensorFloat, ReadOnlySpan<Int32>, Boolean)

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

Declaration

public TensorFloat ReduceL2(TensorFloat X, ReadOnlySpan<int> axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
ReadOnlySpan<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.

ReduceLogSum(TensorFloat, ReadOnlySpan<Int32>, Boolean)

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

Declaration

public TensorFloat ReduceLogSum(TensorFloat X, ReadOnlySpan<int> axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
ReadOnlySpan<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.

ReduceLogSumExp(TensorFloat, ReadOnlySpan<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 TensorFloat ReduceLogSumExp(TensorFloat X, ReadOnlySpan<int> axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
ReadOnlySpan<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.

ReduceMax(TensorFloat, ReadOnlySpan<Int32>, Boolean)

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

Declaration

public TensorFloat ReduceMax(TensorFloat X, ReadOnlySpan<int> axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
ReadOnlySpan<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.

ReduceMax(TensorInt, ReadOnlySpan<Int32>, Boolean)

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

Declaration

public TensorInt ReduceMax(TensorInt X, ReadOnlySpan<int> axes, bool keepdim)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.
ReadOnlySpan<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.

ReduceMean(TensorFloat, ReadOnlySpan<Int32>, Boolean)

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

Declaration

public TensorFloat ReduceMean(TensorFloat X, ReadOnlySpan<int> axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
ReadOnlySpan<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.

ReduceMin(TensorFloat, ReadOnlySpan<Int32>, Boolean)

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

Declaration

public TensorFloat ReduceMin(TensorFloat X, ReadOnlySpan<int> axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
ReadOnlySpan<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.

ReduceMin(TensorInt, ReadOnlySpan<Int32>, Boolean)

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

Declaration

public TensorInt ReduceMin(TensorInt X, ReadOnlySpan<int> axes, bool keepdim)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.
ReadOnlySpan<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.

ReduceProd(TensorFloat, ReadOnlySpan<Int32>, Boolean)

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

Declaration

public TensorFloat ReduceProd(TensorFloat X, ReadOnlySpan<int> axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
ReadOnlySpan<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.

ReduceProd(TensorInt, ReadOnlySpan<Int32>, Boolean)

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

Declaration

public TensorInt ReduceProd(TensorInt X, ReadOnlySpan<int> axes, bool keepdim)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.
ReadOnlySpan<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.

ReduceSum(TensorFloat, ReadOnlySpan<Int32>, Boolean)

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

Declaration

public TensorFloat ReduceSum(TensorFloat X, ReadOnlySpan<int> axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
ReadOnlySpan<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.

ReduceSum(TensorInt, ReadOnlySpan<Int32>, Boolean)

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

Declaration

public TensorInt ReduceSum(TensorInt X, ReadOnlySpan<int> axes, bool keepdim)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.
ReadOnlySpan<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.

ReduceSumSquare(TensorFloat, ReadOnlySpan<Int32>, Boolean)

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

Declaration

public TensorFloat ReduceSumSquare(TensorFloat X, ReadOnlySpan<int> axes, bool keepdim)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
ReadOnlySpan<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.

ReduceSumSquare(TensorInt, ReadOnlySpan<Int32>, Boolean)

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

Declaration

public TensorInt ReduceSumSquare(TensorInt X, ReadOnlySpan<int> axes, bool keepdim)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.
ReadOnlySpan<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.

Relu(TensorFloat)

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

Declaration

public TensorFloat Relu(TensorFloat x)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Relu6(TensorFloat)

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

Declaration

public TensorFloat Relu6(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

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 Tensor Reshape(Tensor X, TensorShape shape)

Parameters

Type	Name	Description
Tensor	X	The input tensor.
TensorShape	shape	The shape of the output tensor.

Returns

Type	Description
Tensor	The computed output tensor.

Resize(TensorFloat, ReadOnlySpan<Single>, InterpolationMode, NearestMode, CoordTransformMode)

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

Declaration

public TensorFloat Resize(TensorFloat X, ReadOnlySpan<float> scale, InterpolationMode interpolationMode, NearestMode nearestMode = NearestMode.RoundPreferFloor, CoordTransformMode coordTransformMode = CoordTransformMode.HalfPixel)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.
ReadOnlySpan<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.

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 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.

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 TensorFloat Round(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

ScaleBias(TensorFloat, TensorFloat, TensorFloat)

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

Declaration

public 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.

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 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.

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 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.

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 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.

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 TensorFloat Selu(TensorFloat x, float alpha, float gamma)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.
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.

Set(Tensor, Tensor, Int32, Int32, Int32)

Updates values of A with values from B similar to setting a slice in numpy. A[..., start:end, ....] = B

This returns a new tensor rather than working on A in-place.

This supports numpy-style one-directional broadcasting of B into A.

Declaration

public Tensor Set(Tensor A, Tensor B, int axis, int start, int end)

Parameters

Type	Name	Description
Tensor	A	The first argument as a tensor.
Tensor	B	The second argument as a tensor.
Int32	axis
Int32	start
Int32	end

Returns

Type	Description
Tensor	The computed output tensor.

Shape(Tensor, Int32, Int32)

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

Declaration

public 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.

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 TensorFloat Shrink(TensorFloat x, float bias, float lambd)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.
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.

Sigmoid(TensorFloat)

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

Declaration

public TensorFloat Sigmoid(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

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 TensorFloat Sign(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

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 TensorInt Sign(TensorInt X)

Parameters

Type	Name	Description
TensorInt	X	The input tensor.

Returns

Type	Description
TensorInt	The computed output tensor.

Sin(TensorFloat)

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

Declaration

public TensorFloat Sin(TensorFloat x)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Sinh(TensorFloat)

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

Declaration

public TensorFloat Sinh(TensorFloat x)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Size(TensorShape)

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

Declaration

public TensorInt Size(TensorShape shape)

Parameters

Type	Name	Description
TensorShape	shape	The input tensor shape.

Returns

Type	Description
TensorInt	The computed output tensor.

Slice(Tensor, ReadOnlySpan<Int32>, ReadOnlySpan<Int32>, ReadOnlySpan<Int32>, ReadOnlySpan<Int32>)

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

Declaration

public Tensor Slice(Tensor X, ReadOnlySpan<int> starts, ReadOnlySpan<int> ends, ReadOnlySpan<int> axes, ReadOnlySpan<int> steps)

Parameters

Type	Name	Description
Tensor	X	The input tensor.
ReadOnlySpan<Int32>	starts	The start index along each axis.
ReadOnlySpan<Int32>	ends	The end index along each axis.
ReadOnlySpan<Int32>	axes	The axes along which to slice. If this is null, the layer slices all axes.
ReadOnlySpan<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.

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 TensorFloat Softmax(TensorFloat X, int axis = -1)

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.

Softplus(TensorFloat)

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

Declaration

public TensorFloat Softplus(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Softsign(TensorFloat)

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

Declaration

public TensorFloat Softsign(TensorFloat x)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

SpaceToDepth(TensorFloat, Int32)

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

Declaration

public TensorFloat SpaceToDepth(TensorFloat x, int blocksize)

Parameters

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

Returns

Type	Description
TensorFloat	The computed output tensor.

Split(Tensor, Int32, Int32, Int32)

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

Declaration

public Tensor Split(Tensor X, int axis, int start = 0, int end = 2147483647)

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.

Sqrt(TensorFloat)

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

Declaration

public TensorFloat Sqrt(TensorFloat x)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Square(TensorFloat)

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

Declaration

public TensorFloat Square(TensorFloat x)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Sub(Single, TensorFloat)

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

Declaration

public TensorFloat Sub(float a, TensorFloat B)

Parameters

Type	Name	Description
Single	a	The first argument as a float.
TensorFloat	B	The second argument as a tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Sub(TensorFloat, Single)

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

Declaration

public TensorFloat Sub(TensorFloat A, float b)

Parameters

Type	Name	Description
TensorFloat	A	The first argument as a tensor.
Single	b	The second argument as a float.

Returns

Type	Description
TensorFloat	The computed output tensor.

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 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.

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 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.

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 TensorFloat Sum(TensorFloat[] tensors)

Parameters

Type	Name	Description
TensorFloat[]	tensors	The input tensors.

Returns

Type	Description
TensorFloat	The computed output tensor.

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 TensorFloat Swish(TensorFloat x)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Tan(TensorFloat)

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

Declaration

public TensorFloat Tan(TensorFloat x)

Parameters

Type	Name	Description
TensorFloat	x	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

Tanh(TensorFloat)

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

Declaration

public TensorFloat Tanh(TensorFloat X)

Parameters

Type	Name	Description
TensorFloat	X	The input tensor.

Returns

Type	Description
TensorFloat	The computed output tensor.

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 TensorFloat ThresholdedRelu(TensorFloat x, float alpha)

Parameters

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

Returns

Type	Description
TensorFloat	The computed output tensor.

Tile(Tensor, ReadOnlySpan<Int32>)

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

Declaration

public Tensor Tile(Tensor X, ReadOnlySpan<int> repeats)

Parameters

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

Returns

Type	Description
Tensor	The computed output tensor.

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

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

Declaration

public 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.

Transpose(Tensor)

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

Declaration

public Tensor Transpose(Tensor x)

Parameters

Type	Name	Description
Tensor	x	The input tensor.

Returns

Type	Description
Tensor	The computed output 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 Tensor Transpose(Tensor x, int[] permutations)

Parameters

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

Returns

Type	Description
Tensor	The computed output tensor.

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 Tensor Tril(Tensor X, int k = 0)

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.

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 Tensor Triu(Tensor X, int k = 0)

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.

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 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.

Xor(TensorInt, TensorInt)

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

Declaration

public 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.