Elu

Elu - 22

Version

  • name: Elu (GitHub)

  • domain: main

  • since_version: 22

  • function: True

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 22.

Summary

Elu takes one input data (Tensor) and produces one output data (Tensor) where the function f(x) = alpha * (exp(x) - 1.) for x < 0, f(x) = x for x >= 0., is applied to the tensor elementwise.

Function Body

The function definition for this operator.

<
  domain: "",
  opset_import: ["" : 18]
>
Elu <alpha>(X) => (Y)
{
   Alpha = Constant <value_float: float = @alpha> ()
   AlphaCast = CastLike (Alpha, X)
   Zero = Constant <value: tensor = float {0}> ()
   ZeroCast = CastLike (Zero, X)
   One = Constant <value: tensor = float {1}> ()
   OneCast = CastLike (One, X)
   XLessThanZero = Less (X, ZeroCast)
   ExpX = Exp (X)
   ExpXSubOne = Sub (ExpX, OneCast)
   AlphaMulExpXSubOne = Mul (AlphaCast, ExpXSubOne)
   Y = Where (XLessThanZero, AlphaMulExpXSubOne, X)
}

Attributes

  • alpha - FLOAT (default is '1.0'):

    Coefficient of ELU.

Inputs

  • X (heterogeneous) - T:

    1D input tensor

Outputs

  • Y (heterogeneous) - T:

    1D output tensor

Type Constraints

  • T in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16) ):

    Constrain input and output types to float tensors.

Elu - 6

Version

  • name: Elu (GitHub)

  • domain: main

  • since_version: 6

  • function: True

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 6.

Summary

Elu takes one input data (Tensor) and produces one output data (Tensor) where the function f(x) = alpha * (exp(x) - 1.) for x < 0, f(x) = x for x >= 0., is applied to the tensor elementwise.

Function Body

The function definition for this operator.

<
  domain: "",
  opset_import: ["" : 18]
>
Elu <alpha>(X) => (Y)
{
   Alpha = Constant <value_float: float = @alpha> ()
   AlphaCast = CastLike (Alpha, X)
   Zero = Constant <value: tensor = float {0}> ()
   ZeroCast = CastLike (Zero, X)
   One = Constant <value: tensor = float {1}> ()
   OneCast = CastLike (One, X)
   XLessThanZero = Less (X, ZeroCast)
   ExpX = Exp (X)
   ExpXSubOne = Sub (ExpX, OneCast)
   AlphaMulExpXSubOne = Mul (AlphaCast, ExpXSubOne)
   Y = Where (XLessThanZero, AlphaMulExpXSubOne, X)
}

Attributes

  • alpha - FLOAT (default is '1.0'):

    Coefficient of ELU.

Inputs

  • X (heterogeneous) - T:

    1D input tensor

Outputs

  • Y (heterogeneous) - T:

    1D output tensor

Type Constraints

  • T in ( tensor(double), tensor(float), tensor(float16) ):

    Constrain input and output types to float tensors.

Elu - 1

Version

  • name: Elu (GitHub)

  • domain: main

  • since_version: 1

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: False

This version of the operator has been available since version 1.

Summary

Elu takes one input data (Tensor) and produces one output data (Tensor) where the function f(x) = alpha * (exp(x) - 1.) for x < 0, f(x) = x for x >= 0., is applied to the tensor elementwise.

Attributes

  • alpha - FLOAT (default is '1.0'):

    Coefficient of ELU default to 1.0.

  • consumed_inputs - INTS :

    legacy optimization attribute.

Inputs

  • X (heterogeneous) - T:

    1D input tensor

Outputs

  • Y (heterogeneous) - T:

    1D input tensor

Type Constraints

  • T in ( tensor(double), tensor(float), tensor(float16) ):

    Constrain input and output types to float tensors.