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    Class GPUCommandBufferOps

    Reference GPU compute IOps implementation

    Inheritance
    Object
    CPUOps
    GPUCommandBufferOps
    Inherited Members
    CPUOps.Sum(TensorInt[])
    CPUOps.Cast(TensorInt)
    CPUOps.Cast(TensorFloat)
    CPUOps.PostLayerCleanup()
    CPUOps.NewTensorFloat(TensorShape, AllocScope)
    CPUOps.NewTensorInt(TensorShape, AllocScope)
    CPUOps.NewOutputTensorFloat(TensorShape)
    CPUOps.NewOutputTensorInt(TensorShape)
    CPUOps.NewOutputTensor(TensorShape, DataType)
    CPUOps.NewTempTensorFloat(TensorShape)
    CPUOps.NewTempTensorInt(TensorShape)
    CPUOps.ResetAllocator(Boolean)
    CPUOps.Dispose()
    CPUOps.Compress(Tensor, TensorInt, Int32)
    CPUOps.NonMaxSuppression(TensorFloat, TensorFloat, Int32, Single, Single, CenterPointBox)
    CPUOps.LRN(TensorFloat, Single, Single, Single, Int32)
    CPUOps.Multinomial(TensorFloat, Int32, Nullable<Single>)
    CPUOps.NonZero(TensorFloat)
    CPUOps.NonZero(TensorInt)
    CPUOps.Shape(Tensor, Int32, Int32)
    CPUOps.Size(TensorShape)
    CPUOps.Copy(Tensor)
    CPUOps.Reshape(Tensor, TensorShape)
    CPUOps.LSTM(TensorFloat, TensorFloat, TensorFloat, TensorFloat, TensorInt, TensorFloat, TensorFloat, TensorFloat, RnnDirection, RnnActivation[], Single[], Single[], Boolean, Single, RnnLayout)
    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 GPUCommandBufferOps : CPUOps, IOps, IDisposable

    Constructors

    GPUCommandBufferOps(ITensorAllocator)

    Create GPUCommandBufferOps

    Declaration
    public GPUCommandBufferOps(ITensorAllocator allocator = null)
    Parameters
    Type Name Description
    ITensorAllocator allocator

    allocator

    GPUCommandBufferOps(CommandBuffer, ITensorAllocator)

    Create GPUCommandBufferOps

    Declaration
    public GPUCommandBufferOps(CommandBuffer cb, ITensorAllocator allocator = null)
    Parameters
    Type Name Description
    CommandBuffer cb

    commandbuffer

    ITensorAllocator allocator

    allocator

    Fields

    cb

    Declaration
    public CommandBuffer cb
    Field Value
    Type Description
    CommandBuffer

    Properties

    deviceType

    Returns the DeviceType for the ops.

    Declaration
    public override DeviceType deviceType { get; }
    Property Value
    Type Description
    DeviceType
    Overrides
    CPUOps.deviceType

    Methods

    Abs(TensorFloat)

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

    Declaration
    public override TensorFloat Abs(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X
    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.Abs(TensorFloat)

    Abs(TensorInt)

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

    Declaration
    public override TensorInt Abs(TensorInt X)
    Parameters
    Type Name Description
    TensorInt X
    Returns
    Type Description
    TensorInt

    The computed output tensor.

    Overrides
    CPUOps.Abs(TensorInt)

    Acos(TensorFloat)

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

    Declaration
    public override TensorFloat Acos(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X
    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.Acos(TensorFloat)

    Acosh(TensorFloat)

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

    Declaration
    public override TensorFloat Acosh(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X
    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.Add(TensorInt, TensorInt)

    And(TensorInt, TensorInt)

    Declaration
    public override TensorInt And(TensorInt A, TensorInt B)
    Parameters
    Type Name Description
    TensorInt A
    TensorInt B
    Returns
    Type Description
    TensorInt
    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override TensorFloat Asin(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X
    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.Asin(TensorFloat)

    Asinh(TensorFloat)

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

    Declaration
    public override TensorFloat Asinh(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X
    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.Asinh(TensorFloat)

    Atan(TensorFloat)

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

    Declaration
    public override TensorFloat Atan(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X
    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.Atan(TensorFloat)

    Atanh(TensorFloat)

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

    Declaration
    public override TensorFloat Atanh(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X
    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.AveragePool(TensorFloat, Int32[], Int32[], Int32[])

    AxisNormalization(TensorFloat, TensorFloat, TensorFloat, Single)

    Declaration
    public override TensorFloat AxisNormalization(TensorFloat X, TensorFloat S, TensorFloat B, float epsilon)
    Parameters
    Type Name Description
    TensorFloat X
    TensorFloat S
    TensorFloat B
    Single epsilon
    Returns
    Type Description
    TensorFloat
    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.Cast(Tensor, DataType)

    Ceil(TensorFloat)

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

    Declaration
    public override TensorFloat Ceil(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X

    The input tensor.

    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.Clip(TensorFloat, Single, Single)

    CompressWithIndices(Tensor, TensorInt, Int32, Int32)

    Declaration
    protected override 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
    Overrides
    CPUOps.CompressWithIndices(Tensor, TensorInt, Int32, Int32)

    Concat(Tensor[], Int32)

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

    Declaration
    public override 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.

    Overrides
    CPUOps.Concat(Tensor[], Int32)

    ConstantOfShape(TensorShape, Int32)

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

    Declaration
    public override 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.

    Overrides
    CPUOps.ConstantOfShape(TensorShape, Int32)

    ConstantOfShape(TensorShape, Single)

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

    Declaration
    public override 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.

    Overrides
    CPUOps.ConstantOfShape(TensorShape, Single)

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

    Applies a convolution filter to an input tensor.

    Declaration
    public override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.Conv2DTrans(TensorFloat, TensorFloat, TensorFloat, Int32[], Int32[], Int32[], FusableActivation)

    Cos(TensorFloat)

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

    Declaration
    public override TensorFloat Cos(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X
    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.Cos(TensorFloat)

    Cosh(TensorFloat)

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

    Declaration
    public override TensorFloat Cosh(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X
    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.Cosh(TensorFloat)

    CumSum(TensorFloat, Int32, Boolean, Boolean)

    Performs the cumulative sum along a given axis.

    Declaration
    public override 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.

    Overrides
    CPUOps.CumSum(TensorFloat, Int32, Boolean, Boolean)

    CumSum(TensorInt, Int32, Boolean, Boolean)

    Performs the cumulative sum along a given axis.

    Declaration
    public override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.DepthToSpace(TensorFloat, Int32, DepthToSpaceMode)

    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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.Div(TensorInt, TensorInt)

    Einsum(String, TensorFloat[])

    Performs an Einsum math operation.

    Declaration
    public override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.Equal(TensorInt, TensorInt)

    Erf(TensorFloat)

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

    Declaration
    public override TensorFloat Erf(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X
    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.Erf(TensorFloat)

    Exp(TensorFloat)

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

    Declaration
    public override TensorFloat Exp(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X
    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.Exp(TensorFloat)

    Expand(Tensor, TensorShape)

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

    Declaration
    public override 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.

    Overrides
    CPUOps.Expand(Tensor, TensorShape)

    Floor(TensorFloat)

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

    Declaration
    public override TensorFloat Floor(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X

    The input tensor.

    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.GatherElements(Tensor, TensorInt, Int32)

    GatherND(Tensor, TensorInt, Int32)

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

    Declaration
    public override 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.

    Overrides
    CPUOps.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 override TensorFloat Gelu(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X

    The input tensor.

    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.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 override TensorFloat GlobalAveragePool(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X

    The input tensor.

    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.GlobalAveragePool(TensorFloat)

    GlobalAverageVariancePool(TensorFloat, TensorFloat, Int32)

    Declaration
    public override void GlobalAverageVariancePool(TensorFloat O, TensorFloat X, int axis)
    Parameters
    Type Name Description
    TensorFloat O
    TensorFloat X
    Int32 axis
    Overrides
    CPUOps.GlobalAverageVariancePool(TensorFloat, TensorFloat, Int32)

    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 override TensorFloat GlobalMaxPool(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X

    The input tensor.

    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override TensorFloat HardSwish(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X
    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override TensorInt IsNaN(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X

    The input tensor.

    Returns
    Type Description
    TensorInt

    The computed output tensor.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.LessOrEqual(TensorInt, TensorInt)

    Log(TensorFloat)

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

    Declaration
    public override TensorFloat Log(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X

    The input tensor.

    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.LogSoftmax(TensorFloat, Int32)

    MatMul(TensorFloat, TensorFloat)

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

    Declaration
    public override 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.

    Overrides
    CPUOps.MatMul(TensorFloat, TensorFloat)

    MatMul2D(TensorFloat, Boolean, TensorFloat, Boolean)

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

    Declaration
    public override 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.

    Overrides
    CPUOps.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 override TensorFloat Max(TensorFloat[] tensors)
    Parameters
    Type Name Description
    TensorFloat[] tensors

    The input tensors.

    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.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 override TensorInt Max(TensorInt[] tensors)
    Parameters
    Type Name Description
    TensorInt[] tensors

    The input tensors.

    Returns
    Type Description
    TensorInt

    The computed output tensor.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override TensorFloat Mean(TensorFloat[] tensors)
    Parameters
    Type Name Description
    TensorFloat[] tensors

    The input tensors.

    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.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 override 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
    Overrides
    CPUOps.MemCopy(Tensor, Tensor, Int32, Int32, Int32)

    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 override 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
    Overrides
    CPUOps.MemCopyStride(Tensor, Tensor, Int32, Int32, Int32, Int32, Int32, Int32)

    MemSet(Tensor, Int32, Int32, Int32)

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

    Declaration
    protected override void MemSet(Tensor O, int value, int length = -1, int offsetO = 0)
    Parameters
    Type Name Description
    Tensor O
    Int32 value
    Int32 length
    Int32 offsetO
    Overrides
    CPUOps.MemSet(Tensor, Int32, Int32, Int32)

    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 override TensorFloat Min(TensorFloat[] tensors)
    Parameters
    Type Name Description
    TensorFloat[] tensors

    The input tensors.

    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.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 override TensorInt Min(TensorInt[] tensors)
    Parameters
    Type Name Description
    TensorInt[] tensors

    The input tensors.

    Returns
    Type Description
    TensorInt

    The computed output tensor.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.Mul(TensorInt, TensorInt)

    Neg(TensorFloat)

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

    Declaration
    public override TensorFloat Neg(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X

    The input tensor.

    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.Neg(TensorFloat)

    Neg(TensorInt)

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

    Declaration
    public override TensorInt Neg(TensorInt X)
    Parameters
    Type Name Description
    TensorInt X

    The input tensor.

    Returns
    Type Description
    TensorInt

    The computed output tensor.

    Overrides
    CPUOps.Neg(TensorInt)

    NewTensor(TensorShape, DataType, AllocScope)

    Allocates a new tensor with the internal allocator.

    Declaration
    public override Tensor NewTensor(TensorShape shape, DataType dataType, AllocScope scope)
    Parameters
    Type Name Description
    TensorShape shape
    DataType dataType
    AllocScope scope
    Returns
    Type Description
    Tensor
    Overrides
    CPUOps.NewTensor(TensorShape, DataType, AllocScope)

    Not(TensorInt)

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

    Declaration
    public override TensorInt Not(TensorInt X)
    Parameters
    Type Name Description
    TensorInt X

    The input tensor.

    Returns
    Type Description
    TensorInt

    The computed output tensor.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.Pad(TensorFloat, Int32[], PadMode, Single)

    PinToDevice(Tensor, Boolean)

    Prepares Tensor for use with CPU backend.

    Declaration
    public override 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.

    Overrides
    CPUOps.PinToDevice(Tensor, Boolean)

    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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override TensorFloat PRelu(TensorFloat X, TensorFloat S)
    Parameters
    Type Name Description
    TensorFloat X
    TensorFloat S
    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.Range(Single, Single, Single)

    Reciprocal(TensorFloat)

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

    Declaration
    public override TensorFloat Reciprocal(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X
    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override TensorFloat Relu(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X
    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.Relu(TensorFloat)

    Relu6(TensorFloat)

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

    Declaration
    public override TensorFloat Relu6(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X

    The input tensor.

    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.Relu6(TensorFloat)

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

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

    Declaration
    public override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override TensorFloat Round(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X

    The input tensor.

    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.Selu(TensorFloat, Single, Single)

    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 override 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.

    Overrides
    CPUOps.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 override TensorFloat Sigmoid(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X

    The input tensor.

    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.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 override TensorFloat Sign(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X

    The input tensor.

    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.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 override TensorInt Sign(TensorInt X)
    Parameters
    Type Name Description
    TensorInt X

    The input tensor.

    Returns
    Type Description
    TensorInt

    The computed output tensor.

    Overrides
    CPUOps.Sign(TensorInt)

    Sin(TensorFloat)

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

    Declaration
    public override TensorFloat Sin(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X
    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.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 override 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
    Overrides
    CPUOps.SinglePassLSTM(TensorFloat, TensorFloat, TensorFloat, TensorFloat, TensorInt, TensorFloat, TensorFloat, TensorFloat, TensorFloat, RnnActivation[], Single[], Single[], Boolean, Single, Boolean, Int32, RnnLayout)

    Sinh(TensorFloat)

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

    Declaration
    public override TensorFloat Sinh(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X
    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.Sinh(TensorFloat)

    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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override TensorFloat Softplus(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X

    The input tensor.

    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.Softplus(TensorFloat)

    Softsign(TensorFloat)

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

    Declaration
    public override TensorFloat Softsign(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X
    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.Softsign(TensorFloat)

    SpaceToDepth(TensorFloat, Int32)

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

    Declaration
    public override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override TensorFloat Sqrt(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X
    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.Sqrt(TensorFloat)

    Square(TensorFloat)

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

    Declaration
    public override TensorFloat Square(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X
    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override TensorFloat Sum(TensorFloat[] tensors)
    Parameters
    Type Name Description
    TensorFloat[] tensors

    The input tensors.

    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.Sum(TensorFloat[])

    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 override TensorFloat Swish(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X
    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.Swish(TensorFloat)

    Tan(TensorFloat)

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

    Declaration
    public override TensorFloat Tan(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X
    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.Tan(TensorFloat)

    Tanh(TensorFloat)

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

    Declaration
    public override TensorFloat Tanh(TensorFloat X)
    Parameters
    Type Name Description
    TensorFloat X

    The input tensor.

    Returns
    Type Description
    TensorFloat

    The computed output tensor.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.TopK(TensorFloat, Int32, Int32, Boolean, Boolean)

    Transpose(Tensor)

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

    Declaration
    public override Tensor Transpose(Tensor X)
    Parameters
    Type Name Description
    Tensor X
    Returns
    Type Description
    Tensor

    The computed output tensor.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.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 override 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.

    Overrides
    CPUOps.Where(TensorInt, Tensor, Tensor)

    Xor(TensorInt, TensorInt)

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

    Declaration
    public override 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.

    Overrides
    CPUOps.Xor(TensorInt, TensorInt)

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    In This Article
    • Constructors
      • GPUCommandBufferOps(ITensorAllocator)
      • GPUCommandBufferOps(CommandBuffer, ITensorAllocator)
    • Fields
      • cb
    • Properties
      • deviceType
    • Methods
      • Abs(TensorFloat)
      • Abs(TensorInt)
      • Acos(TensorFloat)
      • Acosh(TensorFloat)
      • Add(TensorFloat, TensorFloat)
      • Add(TensorInt, TensorInt)
      • And(TensorInt, TensorInt)
      • ArgMax(TensorFloat, Int32, Boolean, Boolean)
      • ArgMax(TensorInt, Int32, Boolean, Boolean)
      • ArgMin(TensorFloat, Int32, Boolean, Boolean)
      • ArgMin(TensorInt, Int32, Boolean, Boolean)
      • Asin(TensorFloat)
      • Asinh(TensorFloat)
      • Atan(TensorFloat)
      • Atanh(TensorFloat)
      • AveragePool(TensorFloat, Int32[], Int32[], Int32[])
      • AxisNormalization(TensorFloat, TensorFloat, TensorFloat, Single)
      • Bernoulli(TensorFloat, DataType, Nullable<Single>)
      • Cast(Tensor, DataType)
      • Ceil(TensorFloat)
      • Celu(TensorFloat, Single)
      • Clip(TensorFloat, Single, Single)
      • CompressWithIndices(Tensor, TensorInt, Int32, Int32)
      • Concat(Tensor[], Int32)
      • ConstantOfShape(TensorShape, Int32)
      • ConstantOfShape(TensorShape, Single)
      • Conv(TensorFloat, TensorFloat, TensorFloat, Int32, Int32[], Int32[], Int32[], FusableActivation)
      • Conv2DTrans(TensorFloat, TensorFloat, TensorFloat, Int32[], Int32[], Int32[], FusableActivation)
      • Cos(TensorFloat)
      • Cosh(TensorFloat)
      • CumSum(TensorFloat, Int32, Boolean, Boolean)
      • CumSum(TensorInt, Int32, Boolean, Boolean)
      • Dense(TensorFloat, TensorFloat, TensorFloat, FusableActivation)
      • DepthToSpace(TensorFloat, Int32, DepthToSpaceMode)
      • Div(TensorFloat, TensorFloat)
      • Div(TensorInt, TensorInt)
      • Einsum(String, TensorFloat[])
      • Elu(TensorFloat, Single)
      • Equal(TensorFloat, TensorFloat)
      • Equal(TensorInt, TensorInt)
      • Erf(TensorFloat)
      • Exp(TensorFloat)
      • Expand(Tensor, TensorShape)
      • Floor(TensorFloat)
      • FMod(TensorFloat, TensorFloat)
      • FMod(TensorInt, TensorInt)
      • Gather(Tensor, TensorInt, Int32)
      • GatherElements(Tensor, TensorInt, Int32)
      • GatherND(Tensor, TensorInt, Int32)
      • Gelu(TensorFloat)
      • GlobalAveragePool(TensorFloat)
      • GlobalAverageVariancePool(TensorFloat, TensorFloat, Int32)
      • GlobalMaxPool(TensorFloat)
      • Greater(TensorFloat, TensorFloat)
      • Greater(TensorInt, TensorInt)
      • GreaterOrEqual(TensorFloat, TensorFloat)
      • GreaterOrEqual(TensorInt, TensorInt)
      • Hardmax(TensorFloat, Int32)
      • HardSigmoid(TensorFloat, Single, Single)
      • HardSwish(TensorFloat)
      • InstanceNormalization(TensorFloat, TensorFloat, TensorFloat, Single)
      • IsInf(TensorFloat, Boolean, Boolean)
      • IsNaN(TensorFloat)
      • LeakyRelu(TensorFloat, Single)
      • Less(TensorFloat, TensorFloat)
      • Less(TensorInt, TensorInt)
      • LessOrEqual(TensorFloat, TensorFloat)
      • LessOrEqual(TensorInt, TensorInt)
      • Log(TensorFloat)
      • LogSoftmax(TensorFloat, Int32)
      • MatMul(TensorFloat, TensorFloat)
      • MatMul2D(TensorFloat, Boolean, TensorFloat, Boolean)
      • Max(TensorFloat[])
      • Max(TensorInt[])
      • MaxPool(TensorFloat, Int32[], Int32[], Int32[])
      • Mean(TensorFloat[])
      • MemCopy(Tensor, Tensor, Int32, Int32, Int32)
      • MemCopyStride(Tensor, Tensor, Int32, Int32, Int32, Int32, Int32, Int32)
      • MemSet(Tensor, Int32, Int32, Int32)
      • Min(TensorFloat[])
      • Min(TensorInt[])
      • Mod(TensorInt, TensorInt)
      • Mul(TensorFloat, TensorFloat)
      • Mul(TensorInt, TensorInt)
      • Neg(TensorFloat)
      • Neg(TensorInt)
      • NewTensor(TensorShape, DataType, AllocScope)
      • Not(TensorInt)
      • OneHot(TensorInt, Int32, Int32, Int32, Int32)
      • Or(TensorInt, TensorInt)
      • Pad(TensorFloat, Int32[], PadMode, Single)
      • PinToDevice(Tensor, Boolean)
      • Pow(TensorFloat, TensorFloat)
      • Pow(TensorFloat, TensorInt)
      • PRelu(TensorFloat, TensorFloat)
      • RandomNormal(TensorShape, Single, Single, Nullable<Single>)
      • RandomUniform(TensorShape, Single, Single, Nullable<Single>)
      • Range(Int32, Int32, Int32)
      • Range(Single, Single, Single)
      • Reciprocal(TensorFloat)
      • ReduceL1(TensorFloat, Int32[], Boolean)
      • ReduceL1(TensorInt, Int32[], Boolean)
      • ReduceL2(TensorFloat, Int32[], Boolean)
      • ReduceLogSum(TensorFloat, Int32[], Boolean)
      • ReduceLogSumExp(TensorFloat, Int32[], Boolean)
      • ReduceMax(TensorFloat, Int32[], Boolean)
      • ReduceMax(TensorInt, Int32[], Boolean)
      • ReduceMean(TensorFloat, Int32[], Boolean)
      • ReduceMin(TensorFloat, Int32[], Boolean)
      • ReduceMin(TensorInt, Int32[], Boolean)
      • ReduceProd(TensorFloat, Int32[], Boolean)
      • ReduceProd(TensorInt, Int32[], Boolean)
      • ReduceSum(TensorFloat, Int32[], Boolean)
      • ReduceSum(TensorInt, Int32[], Boolean)
      • ReduceSumSquare(TensorFloat, Int32[], Boolean)
      • ReduceSumSquare(TensorInt, Int32[], Boolean)
      • Relu(TensorFloat)
      • Relu6(TensorFloat)
      • Resize(TensorFloat, Single[], InterpolationMode, NearestMode, CoordTransformMode)
      • RoiAlign(TensorFloat, TensorFloat, TensorInt, RoiPoolingMode, Int32, Int32, Int32, Single)
      • Round(TensorFloat)
      • ScaleBias(TensorFloat, TensorFloat, TensorFloat)
      • ScatterElements(Tensor, TensorInt, Tensor, Int32, ScatterReductionMode)
      • ScatterND(TensorFloat, TensorInt, TensorFloat, ScatterReductionMode)
      • ScatterND(TensorInt, TensorInt, TensorInt, ScatterReductionMode)
      • Selu(TensorFloat, Single, Single)
      • Shrink(TensorFloat, Single, Single)
      • Sigmoid(TensorFloat)
      • Sign(TensorFloat)
      • Sign(TensorInt)
      • Sin(TensorFloat)
      • SinglePassLSTM(TensorFloat, TensorFloat, TensorFloat, TensorFloat, TensorInt, TensorFloat, TensorFloat, TensorFloat, TensorFloat, RnnActivation[], Single[], Single[], Boolean, Single, Boolean, Int32, RnnLayout)
      • Sinh(TensorFloat)
      • Slice(Tensor, Int32[], Int32[], Int32[], Int32[])
      • Softmax(TensorFloat, Int32)
      • Softplus(TensorFloat)
      • Softsign(TensorFloat)
      • SpaceToDepth(TensorFloat, Int32)
      • Split(Tensor, Int32, Int32, Int32)
      • Sqrt(TensorFloat)
      • Square(TensorFloat)
      • Sub(TensorFloat, TensorFloat)
      • Sub(TensorInt, TensorInt)
      • Sum(TensorFloat[])
      • Swish(TensorFloat)
      • Tan(TensorFloat)
      • Tanh(TensorFloat)
      • ThresholdedRelu(TensorFloat, Single)
      • Tile(Tensor, Int32[])
      • TopK(TensorFloat, Int32, Int32, Boolean, Boolean)
      • Transpose(Tensor)
      • Transpose(Tensor, Int32[])
      • Tril(Tensor, Int32)
      • Triu(Tensor, Int32)
      • Where(TensorInt, Tensor, Tensor)
      • Xor(TensorInt, TensorInt)
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